Download Challenges of COMESA Monetary Integration

Challenges of COMESA Monetary
Integration
1
COMESA MONETARY INSTITUTE
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system,
or transmitted in any form or by any means, electronic, mechanical, photocopying, recording or
otherwise without prior permission from COMESA
Disclaimer:
The Authors are solely responsible for the opinions expressed herein. These openions do not
necessarily reflect the position of the COMESA Secretariat, or its member countries or its
institutions to which authors are affiliated
Design and Layout:
COMESA Public Relations Unit
Printed by:
-------------------------------------------------------------October 2011
2
TABLE OF CONTENTS
List of contributors
Foreword
Acknowledgements
Executive Summary
1.0
External and Domestic Shocks in COMESA: Nature and implications for a
Monetary Union
Charles Abuka, Jacob Opolot, Lucas Njoroge and Jimmy Apaa-Okello
2.0
Examining the Scope for Inflation Targeting in Selected COMESA Member
Countries
Noah Mutoti
3.0
Scope and Quality of Macroeconomic Data inSelected COMESA Member Countries
Mehesha Getahum
4.0
Choice of Monetary Policy Regimes in Selected COMESA Member Countries
Christopher Kiptoo
3
List of Contributors
Charles Abuka-Director, Financial Stability Department, Bank of Uganda
Jacob Opolot- Director of Research, Bank of Uganda
Lucas K. Njoroge- Researcher in Research Department of Central Bank of Kenya
Jimmy Apaa-Okello- Economist, Research Department of Bank of Uganda
Noah Mutoti-Assistant Director, Research Division, Bank of Zambia
Christopher K. Kiptoo-Director, Economic Policy Coordination, Office of the Prime Minister of
the Republic of Kenya
Meshesha Getahun-Consultant in COMESA Secretariat, National Accounts Expert
4
FOREWARD
THE EVOLUTION AND CHALLENGES OF IMPLEMENTING THE COMESA
MONETARY AND FISCAL POLICY HARMONISATION PROGRAMME
By Sindiso Ndema Ngwenya, COMESA Secretary General
I.
The Evolution of the Programme
The mandate to set up a Monetary Union in COMESA is derived from Article 4 (4) of the
COMESA Treaty signed in Kampala, Uganda on 5 th November, 1993, which states that
the COMESA Member States shall “in the field of monetary affairs and finance, cooperate in monetary and financial matters and gradually establish convertibility of their
currencies and a payments union as a basis for the eventual establishment of a
monetary union”. This mandate is further reinforced in Articles 76-78 which respectively
deal with the: COMESA Monetary and Fiscal Policy Harmonisation Programme
(MFPHP), Establishment of Currency Convertibility and Formation of an Exchange Rate
Union.
The primary objective of this program is to create a common area of monetary and
financial system stability which will facilitate integration of the financial markets in
particular and economic integration in general. Under the program, Member States
committed themselves to a gradual, four-stage process whereby they would implement
policy measures aimed at achieving macroeconomic convergence and introduction of
currency convertibility. The Programme was intended to prepare the ground for a
common market and eventually culminate in the establishment of a Monetary Union by
2025:
To achieve the above objectives, it was considered essential that the member States
should first go through a process of monetary and fiscal policy harmonization with a
view to achieving macro-economic convergence. In this respect, convergence criteria
were formulated to assist in gauging the progress being made by the member States in
the implementation of the COMESA MFPHP.
The following are the programmes that have are being implemented with varying
degrees of success since the launching of the programme:
(i) liberalization interest rates and exchange rates;
(ii) elimination of direct credit controls on bank lending;
(iii) the removal of restrictions on both imports and exports;
5
(iv) the adjustment of fiscal policies and credit provision to both government and
(v)
the private sector; and
adoption of indirect instruments of monetary control such as reserve
requirements, discount rates and open market operations and;
Despite these achievements, a number of challenges continued to affect many
countries during this period. As at end of 2003, more than half of the countries
could not achieve the single digit inflation rate while14 out of 19 countries missed
the fiscal criterion (i.e. fiscal deficit excluding grants as % of GDP ratio) of less
than or equal to 3.0%; and the monetary expansion criteria of more than 10%.
Slow progress had also been made in respect of the introduction of limited
currency convertibility and informal exchange rate union within the stipulated
period of 1997–2000.
In November 2004, the COMESA MFPHP was revised to not only to address the
above challenges but also to make it consistent with the convergence criteria of
the African Monetary Cooperation Programme (AMCP) under the aegis of the
African Union that envisages the creation of an African Monetary Union by 2021
of which COMESA is one of the building blocks.Consequently, the date for
launching the COMESA Monetary Union (CMU) was changed from 2025 to
2018. The new program has the fiscal and monetary discipline as the overriding
principle for achieving macro-economic convergence in the COMESA region and,
hence the tightening of the convergence criteria. The program is to be
implemented in three stages. Stage 1 would be implemented between 20052010; Stage 2 between 2011-2015; and Stage 3 between 2016-2018. The
Roadmap for the implementation of these three stages was also developed. The
COMESA Monetary Institute (CMI) was established in Nairobi, Kenya and
became operational on 7 March 2011 in order to enhance the implementation of
the Road map.
II.
CHALLENGES ON IMPLEMENTATING
CONVERGENCE CRITERIA IN COMESA
THE
MACROECONOMIC
As indicated above, COMESA has adopted formal frameworks to guide the transition
process and to promote the harmonization and progressive convergence of national
economic structures and macroeconomic policies. The harmonization frameworks
define a set of macroeconomic convergence criteria or targets that must be met by
member-States within more or less tight deadlines. The adherence to these criteria
requires that countries agree to submit their domestic policies to the scrutiny of
appropriate monitoring and coordination mechanisms within the integration bloc.
The coordination mechanism[ which is this mechanism] is usually entrusted with the
responsibility for supervising and monitoring countries' performance. Effective
macroeconomic policy convergence also requires harmonization of national
quantitative statistics, standardization of national methodologies and instruments,
6
and information exchange among countries. Incentives and sanctions are tools that
could be considered as part of the convergence package to motivate or enforce
compliance to the agreed targets. Countries are required to present annual or
periodic reports on the steps taken toward aligning their macroeconomic policies
with the agreed convergence criteria.
Progress towards macroeconomic policy convergence by COMESA member’s
countries indicates moderate performance. The dynamic nature of the international
development environment, the debt situation, and strong pressures for reducing
poverty and investment in social sectors has impacted on countries' ability to meet
targets, particularly with respect to budget deficits. However, overall, countries have
made tremendous efforts in reducing inflation, with many countries maintaining
single digit inflation levels. Initiatives towards debt cancellation and rescheduling
have also contributed a great deal to reducing the debt stock, though external debt
ratios as a percentage of GDP still remain as high as 70% in some countries. Budget
deficits also remain a key challenge as governments have had no choice but to
resort to borrowing to cover gaps between revenues and recurrent and capital
expenditures.
More specifically, the following are some of the challenges faced by member
countries in the implementation of macroeconomic convergence criteria in
COMESA.
1. Insufficient coordination between fiscal and monetary policy;
2. Undiversified nature of the financial markets and instruments;
3. Weak monetary transmission mechanisms;
4. Lack of timely , accurate and high frequency data;
5. Disharmony in concept and methodology of compiling macroeconomic statistics;
6. Lack of technical and institutional capacity; Heavy reliance on few export
commodities which greatly expose member countries to economic shocks and
may limit their ability to cope with these shocks;
7. Variation in the degree of external sector openness;
8. A high degree of exposure to external shocks such as drought, increase in oil
prices etc;
9. Iinsufficient national ownership of the programmes; and
7
10. Lack of formal mechanism of enforcement of penalties on poor performers;
In order to enhance the implementation of the Monetary Cooperation Programme of
COMESA, there is therefore need among others to do the following:
i)
Continue with the macroeconomic stabilisation objectives by implementing
the COMESA Multilateral Fiscal Surveillance Framework;
ii)
Diversify exports in order to reduce vulnerability of member countries to
Terms of Trade shocks;
iii)
Enhance harmonisation of concepts, methodologies and statistical framework
for compilation of macroeconomic statistics;
iv)
Enhance national ownership of the programmes by mainstreaming the
macroeconomic convergence benchmarks into the national planning and
decision making framework;
v)
Embark on improving trade linkages by reducing trade barriers to stimulate
regional growth and pursue increased economic cooperation;
vi)
Increase domestic resource mobilization;
vii)
Design effective risk-sharing and compensatory mechanisms to facilitate
implementation of a union wide macroeconomic policies.
viii)
Facilitate free factor (capital and labour) mobility among member countries by
intensifying financial system integration. For example, capital market
integration is expected to both lower the cost of financial capital and foster a
reallocation of capital from capital abundant to capital scarce countries;
ix)
Ensure financial system stability by implementing the COMESA Framework
for Assessing Financial System Stability;
x)
Design innovative means of financing the regional integration agenda of
COMESA; and
xi)
Strong Commitment by member states.
8
I would like to point out that our region faces persistent but not insurmountable
challenges which I have outlined above
to implement the COMESA Monetary
integration agenda. The purpose of this book is to call on policy makers to apply
innovative means which enhance macroeconomic convergence and financial system
development in our region.
I would also like to thank the Monetary and Exchange Rates Policies Sub-Committee for
undertaking the studies which are contained in this book. I would further like to thank
the European Union[ EU] for providing funding for undertaking all the studies contained
in the book through the Regional Integration Support Programme. I hope that the book
creates an opportunity for our policy makers to address the challenges of the future and
enable the COMESA monetary integration agenda to play its rightful role in COMESA
region’s transformation. I also hope that the book will stimulate a rich debate and
contribute for harmonisation of macroeconomic policies in the region.
Sindiso Ndema Ngwenya
Secretary General-COMESA
9
Acknowledgements
The contributors sincerely thank the COMESA Secretariat who on behalf of the Committee of Central
Bank Governors selected us to undertake the study. We especially thank Mr Ibrahim A. Zeidy,
COMESA Senior Monetary Expert, for providing the logistical support for our study. We are also grateful
to a number of staff in some COMESA central banks for assisting in data required. Finally, we would not
have completed the studies without the contributions of the Research Assistant: Chungu Kapembwa
(Bank of Zambia), Nomsa Kachingwe (Bank of Zambia),
The quality of the manuscript was greatly enhanced by the comments and suggestions from COMESA
Finance and Monetary Affaires Committee and competent editorial services from Dr. Noah Mutoti.
10
11
Executive Summary
Abuka, Opolot, Njoroge and Apaa-Okello investigated the nature and extent of shocks,
symmetry/asymmetry, the transmission mechanism, and historical response to shocks in
individual COMESA member countries. The correlation results indicate that
contemporaneous demand and supply shocks among member countries are generally
symmetric, but with a few exceptions, while the impulse response functions show similar
patterns among countries, but for a few countries. The variance decomposition indicate that
the proportion of variability of real output accounted for by supply shocks are similar for all
countries. These findings further suggest that more integration of COMESA member
countries is likely to increase symmetry of shocks, and increase the possibility of formation
of a monetary union in the near future. The estimates of exchange rate shocks indicate that
variability of RER disturbances is low among most of the COMESA countries. In addition,
the symmetry of RER disturbances is quite promising, except for a few countries. Both the
short-run and long-run analyses reveal that there is no tendency of persistence of REER
fluctuations overtime.
Getahum highlights the challenges of macroeconomic data in Egypt, Ethiopia, Zambia,
Malawi and Mauritius. It is concluded that most statistical offices experience serious
constraints in both financial and human resources. All countries compile GDP at both current
and constant prices. Most countries experience difficulties in compiling the expenditure
components of GDP at constant prices primarily due to absence of appropriate
prices/deflators. It is also noted that most counties are not in line with the 1993 SNA
recommendations when it comes to the issue of production, as it is often the case that
informal sector are not adequately captured in the estimation process. Furthermore, most
countries experience difficulties adhering to the principles of valuation in the system as
outlined in the 1993 SNA, partly due to the absence of required price data and inappropriate
methods adopted. Most countries use wholesale prices or the CPI to value production
whereas the international guideline recommends the use of basic price if not available
producers’ prices for the valuation of production at current prices.
In the case of CPI, all countries under study, more or less, are implementing the ILO manual
as the general framework for the compilation of their consumer price indices. However, the
countries widely differ in terms of classifications used to compile and publish CPI data. Some
countries use the base years which are more than five years old. The countries also differ in
terms of area coverage, number of product items included, and treatment of seasonal and
missing items and timeliness of the publication of the data.
Pertaining to government finance statistics, only two countries follow the recommendations
contained in 2001 GFS to compile the statistics. Even in these countries the migration to the
2001 GFS is not complete. The compilation of balance of payments statistics in four of the
12
five countries is broadly in conformity with the guidelines contained in the fifth edition of the
Balance of Payments Manual. Nonetheless, because of the inadequacy in the data input,
shortcomings exist in coverage and level of disaggregation in both the goods and services
accounts. In most cases transfer is not broken down into current and capital as recommended
in BOP 5. Data on FDI flows and stocks are often not adequate in both scope and quality. It is
evident that the nature and availability of the data used in the computation determine the
coverage and reliability of the BOP statistics.
Mutoti explore the scope for the adoption of the inflation targeting policy framework in
Mauritius, Zambia and Uganda. When looking at the inflation experience of both monetary
targeters and inflation targeters, over the last 10 years, it is found that inflation levels in the
South American IT countries, as well as in South Africa, was low and stable, whereas
monetary targeting countries, with the exception of Mauritius, experienced high and volatile
inflation throughout the period. Ghana, on the other hand, had shown consistently higher
inflation than both Monetary targeting countries and IT countries, even after it adopted IT in
2007. It is noted that during the crisis, inflation levels in IT countries peaked in mid-2008 and
started to decline towards the end of 2008, while inflation levels in monetary targeting
countries, particularly Uganda and Zambia, remained volatile in 2008 and early 2009. The
inflation experience of Mauritius during the crisis was very similar to that of the IT countries,
as its inflation levels also started to fall at the end of 2008, and reached levels lower than
South Africa and Brazil at the end of 2009. Ghana had the highest inflation over the 2 year
period, with inflation levels coming down gradually from mid-2009. Brazil was able to
maintain a relatively stable inflation rate during the 2-year period.
Evaluating the selected prerequisites for IT framework, it was observed that out of the
selected COMESA monetary targeters, Mauritius is better positioned to adopt the IT
framework. The policy lessons among the three countries are that Mauritius has the requisites
to adopt IT. Also experience of Mauritius indicates that it is possible to achieve price
stability without an explicit inflation targeting framework by using interest rates as the
monetary policy instrument. IT countries are able to respond to external shocks faster than
the monetary targeters with the exception of Mauritius – which suggest that the use of interest
rates to undertake monetary policy is much more effective than targeting monetary
aggregates.
In analyzing the appropriate monetary policy frameworks for selected COMESA countries
Kiptoo conclude that given the apparent shortcomings of the current monetary targeting
framework, many of the COMESA central banks have to start the journey towards formal
inflation targeting frameworks. There are, however, limitations in respect of technical and
institutional capacity to model and forecast inflation. Therefore, the current practice of
monetary targeting seems to provide a more realistic and pragmatic monetary policy
framework in COMESA Countries. As a way forward the following recommendations,
among others. (i) The monetary policy framework needs to modify the framework to target
price instead of the quantity in preparation for adoption of Inflation Targeting framework. (ii)
There is need to ensure that COMESA Central banks have both instrument and operational
independence enshrined in respective member country constitutions as is currently the case in
13
Uganda; (iii) To promote the effectiveness of the monetary policy framework, COMESA
countries should carry out pension sector and other financial sector reforms that will lead to
deep and efficient financial markets characterized by well-functioning and liquid bond
markets and reliable yield curve. (iv)Technical and institutional capacity to model and
forecast domestic inflation should be developed in each COMESA central bank in addition to
putting in place vehicles for formal reporting of monetary policy decisions and
communications with the public. This should also go hand in hand with harmonization of the
Concepts and Methodologies of Convergence Criteria;
14
1.0
External and Domestic Shocks in COMESA: Nature and
implications for a Monetary Union
Charles Abuka
Jacob Opolot
Lucas Njoroge
Jimmy Apaa Okello
1.1
Introduction
Implementing a monetary union and a single currency implies that individual national
governments lose control over monetary and exchange rate policies as adjustment tools to
domestic and external shocks. This is because the common monetary authority manages the
pool of international reserves and pursues a common monetary policy for the region as a
whole. Accordingly, if individual countries face asymmetric shocks, the only adjustment
mechanisms that would help mitigate the adverse impact of the shocks are factor (capital and
labour) mobility, wage and price flexibility as well as compensating fiscal transfers [see
Mundell (1961); Mckinnon (1963); and Kenen (1969)]. But, if the shocks are similar across
countries, then union-wide policies to cope with shocks could be designed effectively
[Christodaulakis and Kollinzas (1995)).
The characteristics and extent of shock synchronization are thus critical issues for countries
contemplating forming a monetary union, as they provide information on the costs and
desirability of a union-wide monetary policy. Motivated by the desire to provide a clear
perspective of the feasibility of a monetary union in the Common Market for Eastern and
Southern Africa (COMESA) region, this paper examines the nature and extent of domestic
and external shocks, the transmission mechanism, and the responses of individual COMESA
member countries to the shocks. It also examines whether the symmetry/asymmetry of
shocks is increasing/decreasing over time.
Given the paucity of empirical literature on monetary integration in the COMESA region, the
study contributes significantly to the on-going debate on the desirability of a monetary union
in the region. The rest of the paper is organized as follows. Section 1.2 gives a brief
overview of the theory of optimum currency areas and impact of external shocks on
macroeconomic management in countries contemplating a monetary union. Section 1.3
discusses the structure and performance of selected COMESA countries and their recent
adjustment experiences with external and domestic shocks, while section 1.4 discusses
demand and supply shocks. Section 1.5 discusses exchange rate shocks and Section 1.6
presents policy recommendations and conclusion.
15
1.2
1.2.1
Literature Review
Theory of Optimum Currency Areas
The debate on the optimality of monetary integration has centred on the theory of optimum
currency areas developed by Mundell (1961) and extended by McKinnon (1963), Kenen
(1969), Tower and Willet (1970; 1976) and Fleming (1971), among others. The basic tenet of
the theory is that a country should join a monetary union if the savings it will realize from the
demise of transactions costs outweigh the costs induced by foregoing national monetary and
exchange rate policy. This is because a monetary union implies that a country loses control
over its monetary and exchange rate policy as adjustment tools.
The extent to which loss of independent monetary and exchange rate policy is costly,
nonetheless, depends on the nature of exogenous shocks affecting member countries, and the
ability of their economies to adjust to the shocks. If the shocks are symmetry and the
responses to such shocks are similar, the lower the costs associated with giving up monetary
sovereignty and the exchange rate instruments. Hence, a monetary union is only desirable if
countries face similar shocks or similar business cycles, and have flexible adjustment
mechanisms to deal with shocks. A high degree of asymmetry in shocks and business cycles
of member countries thus renders a common monetary and exchange rate policy incompatible
with individual member countries’ economic policy objectives (Opolot (2008)).
The nature of shocks depends on the structure of the economy, extent of commodity
diversification and the underlying country-specific characteristics. Countries with similar
economic and production structures tend to face symmetric shocks, while those with different
production and economic structures tend to be characterized by asymmetric shocks. In
addition, a more diversified production structure tends to reduce the incidence and intensity
of shocks. Kenen (1969) argues that a country with a low degree of product diversification
needs flexible exchange rates to cushion its economy from outside shocks, and that a highly
diversified economy will find it beneficial to form a monetary union with similarly
diversified economies.
Ingram (1969), however, argues that the criteria proposed by Mundell (1961), McKinnon
(1963) and Kenen (1969) do not incorporate money. Because prices are expressed in real
terms of trade and all external adjustment occurs on the current account, it is necessary to
consider the financial characteristics of the economies in order to determine the optimal size
of a currency area. Scitovsky (1957, 1967) and Ingram (1973) also argue that a high degree of
international financial integration is an important criterion for an optimum currency area and
that there is no need for flexible exchange rates if there is a high degree of financial markets
integration, since fractional changes in the interest rate would evoke sufficient equilibrating
capital movements across national frontiers.
Haberler (1970), Ingram (1969) and Tower and Willett (1970) emphasise similarity in policy
attitude as an important criterion for a successful currency area. Haberler (1970) and Fleming
16
(1971) defend for similarity of inflation rates as an important optimum currency areas (OCA)
criterion. Ingram (1969), Machlup (1977) and Goodhart (1995) posit that political
considerations are important factors for successful monetary integration. They hypothesise
that the success of any currency union depends primarily on the political cohesion among
member states. Jonung and Sjöholm (1998) also submit that strong political will by the
leaders in government is necessary, and there has to be strong public support if a monetary
union is to succeed.
Debate has also emerged on whether monetary integration leads to greater shock symmetry
and business cycle synchronization or to increased asymmetry as integration leads to
specialization. Krugman (1993) argues that international trade increases specialization, thus
making shocks more asymmetric. Contrary to this argument, Frankel and Rose (1998)
propose that some of the OCA criteria are endogenous and that monetary integration leads to
greater symmetry of business cycles. From these arguments, it is apparent that, the overall
impact of trade integration on shock symmetry is not unambiguous, at least theoretically.
Modern formal models of optimum currency areas do not seem to offer a unique answer
either (Babetskii( 2005)). Frankel and Rose (1998) suggest further analysis of the linkage
between international trade and shocks synchronization by distinguishing between interindustry and intra-industry trade. They support the idea that inter-industry trade reflects
specialization, and has the potential of causing asymmetries, while intra-industry trade (when
a country simultaneously imports and exports products of the same category) should lead to
business cycle synchronization.
1.2.2
Impact of External Shocks on Macroeconomic Management
Developing countries are vulnerable to an array of external and domestic shocks, including
oil price, commodity, financial, demand and supply, and exchange rate shocks. Most
COMESA member states have limited domestic oil production and reserves, and imports of
oil make up a significant portion of the country’s oil consumption. These countries seem to
be relatively more vulnerable to oil price shocks compared with countries with greater oil
supply and reserves. It is well documented in both the empirical and theoretical literature the
extent to which oil price shocks exert adverse impacts on macroeconomic management
through raising production and operational costs. Large oil price shocks also affect
economies through delay in business investment by increasing uncertainty or by inducing
costly sectoral reallocation of resources.
Bernanke (1983) argues that when firms experience increased uncertainty about the future
price of oil, then it is optimal for them to postpone irreversible investment expenditures.
When a firm is confronted with a choice of whether to add energy efficient or energyinefficient capital, increased uncertainty born by oil price volatility raises the option value
associated with waiting to invest. As the firm waits for more updated information, it forgoes
returns obtained by making an early commitment, but the chances of making the right
investment decision increase. Thus, as the level of oil price volatility increases, the option
value rises and the incentive to investment declines (Ferderer(1996)).
17
The downward trend in investment incentives ultimately transmits to different sectors of the
economy. By constructing a multi-sector model, Hamilton (1988) demonstrates that relative
price shocks can lead to a reduction in aggregate employment by inducing workers of the
affected sectors to remain unemployed, while waiting for the conditions to improve in their
own sectors rather than moving to other positively affected sectors. Lilien (1982) extends
Hamilton’s work by showing that aggregate unemployment rises when relative price shocks
becomes more variable. Oil price changes affect real output on both the supply and demand
side (Jimenez-Rodriguez and Sanchez (2005)). The increase in oil price leads to higher
production costs exerting adverse effects on supply, and in turn lowers the rate of return on
investment. Consumption demand is also affected through higher production cost. The
relationship between employment and oil price holds true for not only industrial production,
but also for agricultural employment (Uri(1995)).
In response to two consecutive oil price shocks in the 1970s, a considerable number of
studies have examined the impact of shocks in oil price on the economy. Pioneering work by
Hamilton (1983) concludes that every U.S recession that happened between the end of World
War II and 1973 was preceded by a dramatic increase in the price of crude petroleum, with a
lag of around three to four years. Hamilton (1988, 1996) finds that there is an important
correlation between oil shocks and recessions. Hamilton (2008) emphasises the importance of
oil price on macroeconomic activities. A number of researchers have since supported and
extended Hamilton’s findings and work (see for instance, Mork (1989), Burbridge and
Harrison (1984), Gisser and Goodwin (1986), Mork and Olsen (1994), Cunado and Gracia
(2003), Cologni and Manera (2008), Jimenez-Rodriguez (2008), Chen (2008)).
Other studies examined whether there is a long-run relationship between oil price shocks and
real exchange rates. Chen and Chen (2007) find a co-integrating relationship between real oil
prices and real exchange rates. Lardic and Mignon (2006) study the long-run equilibrium
relationship between oil prices and gross domestic product (GDP) in twelve European
countries. They find that the relationship between oil price and GDP is asymmetric, that is,
rising oil prices retard aggregate economic activity more than falling oil prices stimulate it.
Their results show that, while the standard co-integration between the variables is rejected,
there is asymmetric co-integration between oil prices and GDP in most of the European
countries.
Shocks to commodity and food prices and international oil prices translate into terms of trade
(TOT) shocks. The effect of TOT shocks have been extensively discussed since the 1950s
(see for instance, Harberger (1950); Lausen and Metzler (1950)). Deterioration in the TOT of
a small open economy raises expenditures out of a given level of income of that country
thereby reducing savings, and given the level of investment, causes a deterioration in the
current account. Subsequent studies emphasise that the validity of the analysis of effects of
TOT shocks on the economy depend upon other aspects of model formulation. Using
continuous-time, representative-agent stochastic optimising model, Turnovsky (1991) finds
that the key element determining the response of the economy to TOT shocks is the effect on
the rate of growth of real wealth. An unanticipated deterioration in TOT will raise the rate of
growth of real wealth, savings, consumption expenditures and stock of trade bonds, all
18
measured in terms of the domestic good. The overall result depends, to a large degree, on the
choice of unit of measurement.
However, Calvo et al. (1993) find that the TOT in Latin America did not play a major role in
the 1990s. In contrast, Alejandro et al. (2007) find that increases in the TOT were associated
with long-run increases in Latin American countries’ GDP. Most of the empirical literature
stresses the need to adopt an appropriate exchange rate regime in order to cushion against
TOT shocks. On developing countries, Broda and Tille (2003) as well as Chandima (2002)
find that the effects of TOT shocks were much pronounced in countries that had restrictive
exchange rate regimes. They therefore hypothesise that a TOT shock will have little impact
on growth under a flexible exchange rate system because the exchange rate’s own
movements will absorb the effects of the shock. Under a fixed exchange rate, however, this
buffer is absent and the adjustment will fall primarily on growth: consequently, a worsening
of the TOT will lead to a contraction in output.
A large body of literature suggests that the main source of the business cycle originated from
external factors. Aiolfi, et al. (2006), considering four Latin American countries, identify the
presence of common regional factors suggesting that the Latin American business cycle was
driven by external factors and common external shocks. They further find that the financial
channel based on international interest rates seems more significant than the trade channel for
the effects of external shocks on domestic business cycle fluctuations.
Allegret and Alain (2008) find that in all Latin American countries fluctuations in GDP were
influenced by foreign variables. Specifically, in Argentina, Brazil, and Chile foreign variables
explained at least 29% of the GDP variance decompositions, while in Mexico and Uruguay,
the shares were 16% and 20%, respectively. Above all, no domestic variables, except GDP,
exerted a greater influence than foreign innovations.
Empirical literature dedicated to the business cycle stresses that fluctuations in business
cycles follow international capital flows. More precisely, these studies suggest that the
behaviour of capital inflows is pro-cyclical, thus they tend to increase when growth in
destination countries improves. Allegret and Alain (2008) find that GDP decreases after a
shock to capital inflows. They also conclude that the monetary policy constraints due to the
currency board, especially in Argentina limit the ability of authorities to react in the face of
shocks to financial flows, inducing strong and ample macroeconomic variability.
Notwithstanding the effects of all the shocks on the economies, these shocks do ultimately
translate to exchange rate, demand and supply shocks. For instance, a rise in oil prices
deteriorates the TOT for oil importing countries (Dohner(1981)). Therefore, given that oil is
directly linked to the production process, it can have a significant impact on inflation,
employment and output. An oil price shock can increase inflation by increasing the cost of
production. It also affects employment, as inflationary pressure may lead to a fall in demand
and this, in turn, a cut in production(Loungani(1986)). The optimum currency area theory
suggests that countries with similar inflation developments are subject to similar shocks and,
19
therefore like in the case of relatively high correlation of growth rates, are more likely to
form an optimum currency area.
The few studies available on the suitability of COMESA monetary union and other
integration processes in Africa portray mixed evidence. Buigut and Valev (2005) find supply
and demand shocks to the five East African Community countries to be generally
asymmetric. However, the speed and magnitude of adjustment to shocks was similar across
the countries. They argue therefore that further integration of the economies might lead to
more favourable conditions for a monetary union. Khamfula and Huizinga (1998) find that
monetary integration would substantially eliminate real exchange rate variation due to
different monetary policies for some members. They conclude that a monetary union that
embraces all Southern Africa Development Community (SADC) members would amass large
costs relative to the benefits and hence would not be desirable. Feilding and Shields (2001)
identify and compare economic shocks to different members of the West African Economic
and Monetary Union (UEMOA) and the region of the Central Bank of Equatorial Africa
(BEAC) monetary unions. Khamfula and Huizinga (2004) examine which countries are
suited to enter a South Africa Monetary Union (SAMU). They find low degrees of symmetry
of the exchange rate shocks across most of the countries suggesting that a monetary union
among these countries would amass high costs relative to benefits.
1.2.3
Adjustment to Terms-Of-Trade Shocks
It’s important to understand how changes in export and import prices affect a country’s TOT
and how countries adjust automatically to these shocks. Most of the sub-Saharan African
countries export primary commodities and import machinery and oil. Increases in the price of
oil for instance would represent a worsening of the TOT since the country will pay more for
the goods it imports, while increases in the price of primary commodities boost the country’s
export earnings and represents an improvement in its terms of trade. All these changes in the
import and export prices have implications for output, inflation and exchange rates.
The TOT shocks have a marked impact on the economies of developing countries than those
of their developed counterparts. Baxter and Kouparitsas (2000) find that TOT changes are
twice as large in developing countries than developed countries. They attribute this pattern to
the heavy reliance of developing countries on commodity exports, whose prices are more
volatile than those of manufactured goods. In addition, developing countries tend to have a
high degree of openness to foreign trade such that sharp swings in the TOT affect a large
share of their economies.
Another factor that explains a high degree of exposure of developing countries to TOT
shocks is the fact that developing countries have little, if any, leverage over their export
prices (Broda (2003)). World markets dictate the prices of the primary products that
developing countries export. By contrast, developed countries and oil exporters exert a
substantial influence on their export prices. Given that TOT shocks in developing countries
are largely exogenous, Mendoza (1995) and Kose (2002) find that TOT movements account
for roughly half of the output volatility in developing countries.
20
The country’s adjustment to TOT shocks depends on the type of exchange rate regimes.
Countries with flexible exchange rate regimes will adjust effectively to TOT shocks than
countries with fixed exchange rate systems. To understand this further, suppose the price of a
country’s exports falls under both types of regimes. At the start, the fall in the TOT will
reduce the income of the country’s exporters, causing a decline in activity and employment in
the export industries. Since exporters are taking in less foreign currency, they will bring
fewer dollars to the foreign exchange market. As dollars become scarce, fewer market
participants will want to sell dollars to buy the domestic currency and as a result, the
domestic currency will weaken. If this country implements a fixed exchange rate regime, it
will intervene in the foreign exchange market to keep the value of the two currencies in line.
The intervention will in turn drain the domestic currency out of the money market, reducing
the credit available for business investment and expansion. Because this is equivalent to a
tightening of monetary policy, the response to the decline in export prices can lead to a costly
contraction in output. But, if wages were flexible then the TOT shocks would have a
moderate impact on activity and employment . On the other hand, if the country implements a
flexible exchange rate system, it will refrain from intervening in the foreign exchange market
and will permit the currency to depreciate. The depreciation makes exports more competitive
in world markets and thereby increases demand. Rising demand stimulates activity in the
export industries, which will cushion the adverse impact of the TOT shock on output.
Broder and Tille (2003) further find that the worsening of the TOT leads to a substantial
depreciation of the domestic currency under a flexible exchange rate, but has virtually no
effect under a fixed exchange rate regime. In addition, the impact of the TOT shock on
consumer prices differs across exchange rate regimes for instance, the currency depreciation
feeds into higher prices of imported goods and leads to an increase in consumer prices.
However, under a fixed exchange rate regime, the contraction in economic activity translates
into lower wages and prices, which leads to a fall in the consumer price index. The
contraction in prices under a fixed exchange rate also leads to a real depreciation, although by
a much smaller magnitude.
All in all, theory suggests that the adjustment costs to TOT shocks is pronounced in a country
with a fixed exchange rate regime through a larger contraction in output than in a country
with a flexible exchange rate regime, since it will adjust through a currency depreciation that
significantly offsets the shocks’ negative effects on output.
1.3
1.3.1
1.3.1.1
Economic Structure and Performance
Structure
Output
The structure of output in the COMESA region portrays a mixed composition with the
majority of countries depicting structural transformation from agriculture to services and
21
industry, while some countries heavily rely on agriculture. Thus, Figure 1.1 indicates that the
COMESA region exhibits differences in output structures.
Figure 1.1: Structure of output: Sectoral value added % of total GDP
Ratio of agriculture to GDP (%)
1970-79
1980-89
1990-99
2000
2001
2002
2003
2004
2005
2006
2007
2004
2005
2006
2007
2004
2005
2006
2007
Ratio of Industry to GDP (%)
1970-79
1980-89
1990-99
2000
2001
2002
2003
Ratio of services to GDP (%)
1970-79
1980-89
1990-99
2000
2001
2002
2003
Source: World Development Indicators, 2008
Although agricultural value-added has been generally declining, it has been higher in
Ethiopia, Democratic Republic of Congo (DRC), Uganda, Comoros, Rwanda, and Burundi.
In Burundi and Rwanda, it declined from about 66% and 54% in the 1970s to about 35% and
42%, respectively in 2005. The services value added, however, has been highest in Mauritius
followed by Madagascar standing at 53% and 54%, respectively in the 1970s and 67% and
56% in 2007. The contribution of industry to total value-added was highest in Swaziland
followed by Egypt, where in 2007, it accounted for 49% and 36% of total value-added,
respectively. In Egypt, industry value-added however, stagnated in the region of 30% since
the 1980s to 2007. In Uganda and Zambia, respectively, industry value added as a percentage
of total GDP rose from 9.0%, 13% and 15% in the 1970s to over 20 % in 2005.
1.3.1.2 Exports
22
The composition of exports is highly skewed in favour of agricultural products. In Kenya,
notwithstanding its robust manufacturing sector, agricultural exports, on average, still
constitute about 50% of total merchandize export earnings. In Uganda and Rwanda,
agricultural exports have since 2000 respectively constituted between 56% and 68% of total
merchandise exports while in Burundi, their share has remained as high as 90% in total
merchandise export (Table 1.1).
Table 1.1: Structure of exports (as a %age of merchandise exports)
1971-1980 1981-1990
Kenya
Agricultural exports
61.70
Manufactured exports
12.90
Mineral exports
1.20
Other
24.20
Uganda
Agricultural exports
96.60
Manufactured exports
0.40
Mineral exports
2.20
Other
0.80
Burundi
Agricultural exports
96.1
Manufactured exports
1.00
Mineral exports
1.22
Other
1.68
Rwanda
Agricultural exports
91.50
Manufactured exports
0.30
Mineral exports
7.80
Other
0.40
Source: World Development Indicators
1991-2000
2001
2002
2003
2004
2005
65.40
13.50
2.70
18.40
62.10
26.00
2.80
9.10
73.00
23.30
3.50
0.20
42.90
24.00
2.20
30.90
53.50
24.20
3.00
19.30
52.00
23.00
4.00
21.00
50.00
22.06
4.22
23.72
95.01
0.84
2.11
2.04
89.90
7.40
1.20
1.50
68.02
6.90
13.25
11.83
67.21
7.80
13.92
11.07
62.77
9.40
8.13
19.71
65.19
5.64
14.91
14.27
61.97
5.69
10.25
22.09
96.4
1.60
1.11
0.89
96.70
2.20
1.00
0.10
88.70
0.80
10.30
0.20
94.60
1.90
3.40
0.10
93.39
6.36
1.28
0.25
94.57
5.13
1.53
0.30
93.44
6.23
2.46
0.33
95.5
0.30
3.90
0.30
76.40
5.80
9.90
7.90
60.20
2.10
35.25
2.45
61.80
2.70
33.30
2.20
59.60
10.30
23.30
6.80
57.8
7.30
23.90
11.00
56.40
8.30
24.50
10.80
The composition of exports therefore remains highly concentrated in a few commodities,
making them highly vulnerable to terms of trade shocks. Furthermore, the structure of exports
suggests that these countries in the region may experience asymmetric TOT shocks. There is
therefore need for the countries within COMESA to diversify their export bases so as to
reduce the likelihood and intensity of the shocks.
1.3.1.3 External Openness and Aid Dependence
There are significant differences in the degree of external openness, measured by the volume
of trade as a ratio of GDP. As Figure 1.2 indicates, Swaziland is the most open economy
followed by Mauritius and Malawi. Countries whose levels of openness are steadily rising are
DRC from 29% in 1970s to 71% in 2005 and Ethiopia from 19% in the 1980s to 56% in
2005. Swaziland, Mauritius and some of the most open economies in the COMESA region
are better placed to achieve a given level of external adjustment by relying more on other
instruments than the exchange rate.
Swaziland and Mauritius being the most open economies are also less dependent on external
grants followed by Kenya. The other countries, which are more dependent on aid such as
23
Madagascar, Uganda, DRC, Egypt and Zambia, are more likely to face a greater need to
adjust their exchange rates in the event of TOT or a decline in external grants.
Figure 1.2: External Openness (External trade as a ratio of GDP - %)
1970-79
1980-89
1990-99
2000
2001
2002
2003
2004
2005
External grants and other revenue (% of total revenue)
Burundi
Congo, Dem. Rep.
Egypt, Arab Rep.
Kenya
Madagascar
Mauritius
Rwanda
Uganda
Zambia
Source: World Development Indicators, 2008
1.3.2 Performance
Table 1.1B (Appendix B) summarizes key economic performance variables for selected
COMESA countries. Macroeconomic performance in most countries has greatly improved.
Starting from a low growth base of about -2.0% in the 1970s, Uganda and Rwanda have
registered growth rates averaging over 6.0% and 7.0% during 1990-2005. In Kenya, the
economy recovered after 1993. In Rwanda, the economy slummed to the abyss during the
1994 genocide but gradually recovered thereafter. In Burundi, the 1990s was a disappointing
decade, with annual growth averaging about –1.6% per annum. The turn of the millennium
saw almost all countries in the COMESA region registering positive growth rates.
Gross domestic product (GDP) per capita at constant 2000 purchasing power parity (PPP)
prices was highest in Mauritius and Egypt. It gradually increased in Kenya, Uganda, Zambia,
Ethiopia and Comoros. In DRC and Burundi, however, it gradually declined due to ethnic
conflict and civil wars. Income per capita measured at constant 2000 United States Dollars,
declined in Burundi, Eritrea and DRC, while it stagnated in Zambia and Ethiopia. Gross
investment rates have increased in Uganda, Rwanda, Madagascar, Zambia and Ethiopia.
Investment appears to have slowed in Egypt and stagnated in Mauritius.
24
Inflation rates though declining have remained erratic in most countries. Inflation was in
double digits in Kenya, Zambia, Burundi, Malawi and Madagascar, while it was in single
digits in Uganda, Rwanda and Mauritius. In some countries, for instance, Ethiopia and Egypt
inflation was not stable during 1990-2005. Fiscal deficits (excluding grants) measured as a
percentage of GDP were rather erratic, and except in Kenya, were well above the -5.0%
targets in almost all other countries in the COMESA region.
The current account deficits (excluding grants) have been relatively low for most countries of
the region. In 2005, the current account deficit was 32% of GDP in Burundi, 14% in Ethiopia
and 38% in Libya. Gross domestic savings rates have also been fairly adequate for some
countries such as Mauritius, Egypt, Zambia, Kenya and Uganda, while for some countries
such as Ethiopia and DR Congo, they have on average remained below 10% of GDP. The
situation was, however, relatively worse for Burundi, Comoros, Eritrea, Malawi and Rwanda
where gross domestic savings were on average negative during 1990-2005.
In general, the structure of the COMESA economies is dissimilar, with agriculture
contributing a significant portion of output. The structure of exports also follows the output
structure, with agricultural exports dominating as the main export commodities. However, a
close examination of the export structure reveals some peculiarities, which may result in
asymmetric shocks. Furthermore, the heavy reliance on a few export commodities greatly
exposes the COMESA countries to economic shocks and may limit their ability to cope with
these shocks.
The degree of external openness also varies across the region, which means that the cost of
foregoing the exchange rate as an adjustment tool varies from country to country. On the
other hand, notwithstanding the economic disparities, economic performance in the region is
generally promising, except in a few countries like Burundi and D.R. Congo, which have
been embroiled in civil conflict and wars. Furthermore, the macroeconomic policy
framework across the region is to a large extent similar. However, the significant differences
in macroeconomic indicators calls for further efforts at policy orientation, co-ordination and
harmonization in the region.
IMF (2008) finds that the upsurge of tea and coffee prices in the world market – positive
TOT shock – led to the bulk of the appreciation of the Kenya shilling. Kenya, Uganda,
Burundi, Rwanda and Tanzania (EAC countries) all implement a near flexible exchange rate
regime, with occasional intervention to smooth wide volatilities in exchange rates. Similarly,
IMF (2009) finds that commodity price shocks explain much of the increases in inflation in
the East African Community (EAC) countries. The contribution of world food and oil prices
to average annual inflation in EAC is about 3.3 percentage points more than twice in 2007.
As with average inflation, the contribution of world oil and food prices to 12-month EAC
inflation increased from 0.9 percentage points in June 2007 to 6% in June 2008, which
explained more than two-thirds of the observed increase in headline inflation. These results
further underscore how the adjustment to shocks occurs through the exchange rate under a
flexible exchange rate regime.
25
1.4
SVAR Analysis of Demand and Supply Shocks
While the Vector autoregressive (VAR) approach has enjoyed significant application on the
study of supply and demand shocks and the responses of economies to such shocks, very
scanty literature exits in this field for trade blocs in Africa. Following Blanchard and Quah
(1989) and Bayoumi and Eichengreen (1992,1993), this study adopts the VAR approach to
identify shocks based on the aggregate demand-aggregate supply framework. Frenkel and
Nickel (2002) also applied the VAR approach to assess the similarity of shocks between Euro
area and CEECs, while Zhang et al.(2004) applied this approach to identify possible
groupings of East Asian economies with potential for a monetary union. Other studies that
have applied this approach include Bayoumi and Taylor (1995), and Ramaswamy and Slok
(1998) for Europe countries.
In this framework, it is assumed that fluctuations in real output ( y t ) and price level ( pt ) are
due to underlying supply and demand shocks. If the variables are unit root, such that the
 y 
vector X t   t  is stationary, then the joint process of two variables ( y t and pt ) can be
pt 
represented by an infinite moving average representation of a vector of the two variables and
an equal number of structural shocks. If is a vector of demand and supply shocks, ( dt ,  st )
t
, then the bivariate moving average of X t can be formally represented by equation (1.1)
 y    a
X t   t    Li  11i
pt  i 0 a21i
a12i   dt   i
  L Ai  t i
a22i   st  i 1
(1.1)
where yt and p t represent changes in the logs of output and prices respectively, L is the
lag operator, Ai represents the impulse response function of the shocks to the elements of the
vector X t and ( dt ,  st ) are independent white noise supply and demand shocks normalized
so that Var( t )  I .
The above framework assumes that while supply shocks have a permanent effect on output,
demand shocks have only temporary effects. Both, demand and supply however, have
permanent effects on prices. Since demand shocks do not have any effect on output in the
long-run, the cumulative effect of demand shocks on the change of the log of output must be

zero, which leads to the restriction  a11i  0 .
i 0
26
The supply and demand shocks are identified by estimating a finite order VAR. The optimal
lag length (p) is chosen such that its residuals approximate a white noise. Each element of
vector X t is regressed on lagged values of all elements of X t as shown in equation (1.2).
X t  K  1 X t 1   2 X t 2  ...   p X t  p   t
(1.2)
(
where K is a vector of constants,  i' s denote the coefficients from the estimating equation and
  yt 
 t is a vector of the residuals   , that is, a combination of demand and supply shocks.
 pt 
If the process is covariance stationary, then expectations of equation (1.2) will yield the mean
(μ) of the process as shown in equation (1.3).
  K  1    2   ...   p 
(1.3)
.
Subtracting equation (1.3) from (1.2) yields a vector of variables X t in terms of deviations
from the mean as shown in equation (1.4).
X t    1 ( X t 1   )   2 ( X t  2   )  ...   p ( X t  p   )  t
(1.4) .
(1.4)
In order to represent the VAR (p) process in equation (1.4) as a VAR (1.1) process, the
following specifications are made:
 Xt   
 X  
 t 1



.
t  
. ,



.


 X t  p 1   

1  2 . . .  p 
I 0 . . . . 
 2



.

,
.




.


 0 . . . I 2 0 
t
 t 
0
 
.
  
.
.
 
 0 
Equation (1.4) can then be written as a VAR (1.1) process of the form:  t   t 1   t . The
recursive substitution of this VAR (1) process yields:
 t  s   t  s   t  s 1   2 t  s  2  . . .   s 1 t 1   s t
(1.5) .
27
From Hamilton (1994), if all the eigenvalues of  lie inside the unit root circle, then
 s  0 as s   and the VAR is covariance stationary. The first two rows of equation (1.5)
give the vector moving average representation of X t as shown in equation (1.6).
X t     t  1  t 1   2  t 2   3  t 3   4  t 4 ,
(1.6)
(1.6)
where  j  11( j ) and 11( j ) denotes the upper left block of  j , which is the matrix  raised
to the jth power. From equations (1.1) and (1.6), the relationship between the structural
shocks (εt) and the estimated residuals (  t ) is given as  t  A0 t .
Bayoumi and Eichengreen (1992) argue that knowledge of the elements of A0 is important
in the calculation of the underlying structural supply and demand shocks. The variancecovariance matrix of residuals E( t  't )  A0 E( t  t' ) A0' and the  i s are found by estimation.
Four restrictions are used to recover the four elements of A0 in a two-by-two case. Two of
these restrictions are simple normalizations, which define the variances of  dt and  st , usually
to one. Since  dt and  st are deemed to be pure shocks, the third restriction is to assume that
demand and supply shocks are orthogonal so that E ( dt  st )  0 .
E ( t  t' )
then drops out as I2,
leaving E( t  't )    A0 A0' , where Ω is the variance-covariance matrix of residuals, which is
a known symmetric matrix. Three other restrictions obtaining from this argument are shown
in equation (1.7).
Var( yt )  a11 (0) 2  a12 (0) 2
Var( pt )  a21 (0) 2  a22 (0) 2
Cov( yt  pt )  E ( yt  pt )  a11 (0)a 21 (0)  a12 (0)a 22 (0)
(1.7)
The final restriction is to impose the condition that demand shocks have no long-term effects
on output. In the VAR specification, this implies the relationship depicted in equation (1.8).

c11i
 c
i 0

21i
c12i   a11 a12  0 *

c22i  a21 a22  * *
(1.8)
These restrictions allow matrix A0 to be uniquely defined, and thus the demand and supply
shocks to be identified. There have been modifications to this identification scheme, for
example the use of a three variable VAR, but the underlying assumptions remain largely
28
consistent with the above framework. In applying this methodology, the Akaike information
criterion is used to determine the optimal lag length.
The identification of shocks reveals important information about the symmetry or asymmetry
of shocks because the optimum currency area theory highlights asymmetry as an important
cost factor of a currency union. It also stresses that symmetric shocks also cause economic
costs in a currency union if the response to the same type of shock is very different. That is, if
countries are hit by the same shock but their key macro variables such as output, and prices
responses are different, then differences in economic performance could induce disequilibria
between member countries of a currency union. In such a case, relative international
competitiveness is affected between the countries and costs arise because countries cannot
use the exchange rate to eliminate the disequilibria. On this account the analysis of the costs
of both external and internal shocks to COMESA member countries is done by comparing the
response of the COMESA economies to the shocks in terms of the magnitude and speed of
adjustment of the impulse response functions. The larger the magnitudes, the more disruptive
its effects are on the economies, while the slower the adjustment after a shock, the larger are
the costs of maintaining a single currency.
The correlations of the underlying demand and supply shocks are then computed. If the
correlations are positive, then the shocks are considered symmetric and if the correlations are
negative, then the shocks are asymmetric.
In addition, the study analyses the transmission mechanism of external and domestic shocks
in COMESA member countries, by carrying out variance decomposition. The forecast error
variance shows the contribution of each shock to the movements in output and inflation. The
forecast error variance gives an indication of shocks that are more predominant in accounting
for the variability in the two variables. This analysis is important because differences in the
cause of variability in the countries could be indicative of underlying differences in
transmission mechanism and policy strategies of the COMESA countries which would be
obstacles to regional monetary integration. That is, variance decomposition is used to identify
the relative importance of the shocks in accounting for the variation of the vector X t .
Using the VAR approach to analyze whether the East African Community (EAC) constitutes
an OCA, Buigut and Valev (2005) find that contemporaneous shocks among the EAC
countries are mostly asymmetric. Only contemporaneous supply shocks for Kenya and
Burundi are positive and significantly correlated. The correlation of demand shocks showed
only a weak symmetry related to trade patterns and the correlations of lagged values were not
any much better. Their correlation results did not provide strong support for a currency union,
but did indicate that more integration may improve the symmetry of shocks.
Buigut and Valev (2005) find that the impulse response functions, on the other hand, showed
some support for a monetary union in the EAC. With the exception of Uganda, the impulse
response functions of the other four EAC countries followed a similar pattern. The bulk of
adjustment of output to a supply shock occurred within the first three to four years and the
long-run magnitudes were close. The adjustment of prices to supply shocks was also within
29
the first three years. The magnitude of the response was however, much larger in Uganda and
the adjustment took relatively longer.
In addition, the variance decomposition produced mixed results. The proportions of
variability of real output accounted for by supply shocks were similar for all the EAC
countries. However, demand shocks contributed markedly to different proportions of the
variation in the price levels. Evidence in favour of linking an East African currency to an
external anchor was weak. Overall, these results did not exhibit strong evidence for the
formation of a currency union in the EAC.
It’s clearly evident that the empirical work on the desirability of a monetary union in
COMESA is scarce given that several parameters of the OCA criteria have not been
examined. There is virtually no empirical investigation of business cycle characteristics and
the extent of their synchronization. In addition, the extent of real exchange rate (RER)
variability, symmetry and persistence in the region has not been examined. This study
therefore contributes significantly to the on-going debate on the desirability of a monetary
union in the COMESA region, as it highlights new evidence relating to the nature of business
cycles and exchange rate shocks.
1.4.1
Data Characteristics
The annual data, over the period 1970-2007 is mainly from the World Development
Indicators which was supplemented with data from the IMF’s International Financial
Statistics. The Real GDP growth is used to measure changes in output, while changes in the
GDP deflator measures price changes with the base for all countries being year 2000. Both
variables are in log and were found to be I(1). Note that only eleven countries of the nineteen
COMESA members had complete data set over the study period from 1970 to 2007. Each of
these country’s data are abbreviated as DLBGDP, DLBDF for Burundi; DLDRGDP, DLDRDF for
DRC; DLEGGDP, DLEGDF for Egypt; DLKEGDP, DLKEDF for Kenya; DLMADGDP, DLMADF for
Madagascar; DLMWGDP, DLMWDF for Malawi; DLRWGDP, DLRWDF for Rwanda; DLSUDGDP,
DLSUDF for Sudan; DLSWZGDP, DLSWZDF for Swaziland; DLSYGDP, DLSYDF for Seychelles, and
DLZAMGDP, DLZAMDF for Zambia.
1.4.2
Identifying the Shocks
Table 1.2, shows the size of the underlying demand and supply shocks across COMESA
countries. Shocks vary across countries in terms of both magnitude and frequency with
demand shocks showing a greater dispersion.
Table 1.2: Descriptive statistics of the shocks
Demand shocks
Country
Maximum
Supply shocks
Minimum
Range
Maximum
Minimum
Range
30
Burundi
0.28
0.017
0.26
0.11
-0.08
0.20
DRC
Egypt
5.59
0.06
5.53
0.09
-0.14
0.22
0.18
0.012
0.16
0.14
0.00
0.14
Kenya
0.35
0.01
0.34
0.20
-0.01
0.21
Madagascar
0.37
0.03
0.35
0.09
-0.14
0.23
Malawi
0.57
0.07
0.50
0.15
-0.11
0.26
Rwanda
0.42
-0.05
0.47
0.30
-0.70
1.00
Sudan
0.97
0.00
0.97
0.15
-0.06
0.23
Swaziland
0.36
0.00
0.36
0.14
-0.02
0.16
Seychelles
0.07
-0.02
0.09
0.19
-0.09
0.28
Zambia
1.02
0.09
0.93
0.09
-0.09
0.18
DRC has experienced the widest swings in demand shocks with Seychelles experiencing the
least swings. Rwanda on the other hand experienced the widest swings in supply shocks and
Egypt the least swings. DRC also experienced the largest positive demand shock with
Rwanda experiencing the most severe demand shock concomitantly with the supply shocks.
Rwanda’s unique experience could be attributed to the large negative effect of the genocide
and on the other hand the large positive effect on the current prudent macroeconomic
management. Similarly the effects of conflicts cannot be ruled out on the nature of shocks in
Burundi, Uganda and the DRC.
Both demand and supply shocks seems to be relatively equally distributed at less than unity
with the exception of demand shocks in the DRC. This suggests that the relatively small size
of shocks means fewer impediments to fixing the exchange rate within monetary union. This
may further suggest a higher likelihood of shocks convergences within COMESA countries.
1.4.3
Correlations of Supply And Demand Shocks
The correlation coefficients of the identified demand and supply shocks are reported in
Tables 1.3 and 1.4. The significant correlations are highlighted using the stars. Positive
correlations represent symmetric shocks and negative correlations represent asymmetric
shocks. The more symmetric the shocks, the more feasible it becomes for countries to
establish a monetary union. However, the symmetry of supply shocks is considered more
critical when establishing a monetary union as they are more likely to be invariant to demand
management policies.
Table 1.3: Correlation analysis of demand shocks
Probability
DLBDF
DLDRDF
DLEGDF
DLKEDF
DLMADF
DLMWDF
DLRWDF
DLSUDF
DLSWZDF
DLBDF
1.00
-0.15
-0.05
0.09
-0.03
0.09
0.19
-0.08
-0.16
DLDRDF
DLEGDF
DLKEDF
DLMADF
DLMWDF
DLRWDF
DLSUDF
DLSWZDF
1.00
0.18
0.53**
0.5**
0.10
0.16
0.76**
0.06
1.00
0.25
0.15
-0.39*
0.37
0.43*
0.43
1.00
0.31
0.18
0.30
0.53**
0.19
1.00
0.5**
0.060***
0.61***
0.21
1.00
0.09
0.05
-0.04
1.00
0.52**
0.41*
1.00
0.61***
1.00
DLSYDF
DLZAMDF
31
DLSYDF
DLZAMDF
-0.28
-0.26
-0.39
0.53**
-0.02
0.68***
-0.47**
0.39
-0.50
0.11
-0.46**
-0.20
-0.29
0.11
-0.23
0.69***
0.16
0.57***
1.00
0.04
(*), (**) and (***) represents 10%, 5%, and 1% levels of significance respectively.
Table 1.3 shows correlation analysis of demand shocks among selected COMESA member
countries. The contemporaneous demand shocks reveal that, Kenya and DRC; Madagascar
and DRC; Malawi and Madagascar; Sudan and DRC; Sudan and Egypt; Sudan and Kenya;
Sudan and Madagascar; Rwanda and Madagascar; Sudan and Rwanda; Swaziland and Egypt;
Swaziland and Rwanda; Swaziland and Sudan; Zambia and DRC; Zambia and Egypt; Zambia
and Swaziland; and, Zambia and Sudan all exhibit positive and statistically significant
demand shocks over the study period. Only Malawi and Egypt; Seychelles and Kenya; and,
Seychelles and Malawi experience negative and statistically significant demand shocks. In
addition, most of the remaining countries exhibit positive coefficients although not
statistically significant. This suggests in general, symmetry of demand shocks among
COMESA member countries.
Table 1.4: Correlation Analysis of Supply Shocks
Probabilit
y
DLBG
DP
1.00
DLDRG
DP
DLEGG
DP
DLKEG
DP
DLMAG
DP
DLMW
GDP
DLBGDP
DLDRG
0.28*
1.00
DP
DLEGGD
0.17
-0.15
1.00
P
DLKEGD
0.05
0.45**
-0.10
1.00
P
DLMAG
-0.24
0.10
-0.04
0.17
1.00
DP
DLMWG
-0.22
0.10
0.12
0.39**
0.27
1.00
DP
DLRWG
0.11
0.13
0.14
0.03
0.04
0.41**
DP
DLSUDG
0.07
-0.04
0.13
-0.30*
-0.02
-0.04
DP
DLSWZ
0.12
0.20
-0.24
0.27*
-0.03
-0.05
GDP
DLSYGD
-0.05
-0.26
0.06
0.22
0.10
0.33**
P
DLZAM
-0.05
0.14
-0.10
0.11
0.03
0.18
GDP
(*), (**) and (***) represents 10%, 5%, and 1% levels of significance respectively.
DLRWG
DP
DLSUD
GDP
DLSWZ
GDP
DLSYG
DP
DLZAM
GDP
1.00
0.16
1.00
-0.09
0.06
1.00
0.15
-0.23
-0.06
1.00
0.39**
0.21
-0.02
-0.02
1.00
Table 1.4 shows correlation analysis of contemporaneous supply shocks for the selected
COMESA member countries. DRC and Burundi; Kenya and DRC; Malawi and Kenya;
Rwanda and Malawi; Swaziland and Kenya; Seychelles and Malawi; and Zambia and
Rwanda all exhibit positive and statistically significant supply shocks. In this case also, a
majority of the other countries within the selected COMESA sample shows positive but not
statistically significant supply shocks. Only Sudan and Kenya experience significant
negative supply shocks over the study period. In general therefore, correlation analysis also
reveals symmetry of supply shocks among COMESA member countries.
Overall, although the sizes of the correlation coefficients are generally low, most coefficients
are positive and hence support symmetry of shocks within COMESA. This may render
1.00
32
support for a monetary union congruent to other studies for the countries in the region1. The
low correlation coefficients may reflect low trade linkages among the countries of COMESA,
although this is currently changing on one hand or paucity of data on the other. A number of
countries such as Egypt and Kenya are currently having significant trade links with
COMESA member countries.
1.4.4 Impulse Responses
In general, the impulse responses to supply shocks as presented in Appendix A Figure 1A can
be summarized by Figure 1.3 to 1.5 below. Figure 1.3 presents the summary of impulse
response functions of the price level to a positive demand shock. The figure indicates that the
effect of demand shocks on prices is generally positive for a majority of COMESA countries.
The main exceptions being Burundi and Malawi, exhibiting negative effects of demand
shocks on prices after period three and five periods respectively. Rwanda and Swaziland
depict an alternating pattern of shocks after every year.
Figure 1.3: Impulse responses of the price level to a positive demand shock:
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
-0.1
1
2
3
4
5
6
7
8
9
Bur
DRC
Egp
Ken
Madg
Rw
Sud
Swz
Sy
Zam
10
Maw
Figure 1.4 presents the summary of impulse response functions of the price level to a positive
supply shock and in general indicate that responses seems to follow more or less similar
pattern except for the DRC. The effect of conflicts and possibly poor data quality could
explain this behaviour for DRC. Congruent with other studies2, the bulk of the adjustment
occurs within the first three to four years for all countries except DRC which takes much
more time. The absolute magnitude of change being less than 0.1 for all countries except for
DRC whose size reaches 0.6.
Figure 1.4: Impulse responses of the price level to a positive supply shock
1
For example see Mkenda (2001).
2
For example, see Bayoumi and Eichengreen (1992).
33
0.1
0
-0.1
1
2
3
4
5
6
7
8
9
10
-0.2
-0.3
-0.4
-0.5
-0.6
-0.7
Bur
DRC
Egp
Ken
Madg
Maw
Sud
Swz
Sy
Zam
Sy
Zam
Rw
Figure 1.5 summarizes the impulse response functions of the output level to a positive supply
shock. In this case, the functions follow more or less similar patterns except for the DRC.
Most of the adjustment of prices to an output shock occurs within the first 4 years, before it
converges with an exception of DRC which takes a much longer time before converging.
Figure 1.5: Summary of Impulse Response functions of the output level to a positive supply shock
0.16
0.14
0.12
0.1
0.08
0.06
0.04
0.02
0
-0.02
-0.04
1
2
3
4
5
6
7
8
Bur
DRC
Egp
Ken
Madg
Rw
Sud
Swz
Sy
Zam
9
10
Maw
Figure 1.5 depicts the impulse response functions of the output to a positive supply shock for
all the selected COMESA countries. In this case, except for the initial impact of the shocks
that depicts a wider variation, the response of output to supply shock also converges after 3 to
4 years.
Overall, the three figures suggests that the impulse responses of prices and output to demand
and supply shocks, the speed of adjustments as well as the long-run effect are similar across
COMESA member countries but with a few exceptions. The countries have similar
magnitudes and speed of adjustments to the shocks suggesting symmetry of shocks. This
finding concurs with the descriptive and correlation analysis discussed above and tentatively
lender support to a possibility of sustaining a monetary union among COMESA member
countries.
1.4.4
Variance Decomposition
Table 1.5 shows the %age of real output and price variability due to supply shocks at one to
ten years’ time horizon. The proportion due to the demand shocks is obtained by subtracting
the given %age from 100 %. The table reveals that supply shocks account for most of the
34
variability of real output, while demand shocks accounts for most of the variability in prices.
With an exception of Seychelles and Zambia, for all the other countries, supply shocks
account for over 75 % of all the variability of output over the ten year horizon.
On the other hand, demand shocks account for over 50 % of all the variability in prices over
the same time horizon but with greater spread across countries. Demand shocks account for a
much higher proportion of the variability in the price level relative to its contribution to the
variability in real output. However, these %ages differ markedly among the COMESA
countries ranging from 100 % in all countries in the first year to around 52 % in DRC in the
ten periods.
Table 1.5: Variance decomposition: Proportion of real output and price variability due to Supply Shocks
Country
Burundi
DRC
Egypt
Kenya
Madagascar
Malawi
Rwandi
Sudan
Swaziland
Seychelles
Zambia
Horizon
Output
Price
Output
Price
Output
Price
Output
Price
Output
Price
Output
Price
Output
Price
Output
Price
Output
Price
Output
Price
Output
Price
1
80.99
0.00
90.29
0.00
96.44
0.00
97.47
0.00
90.26
0.00
99.17
0.00
97.35
0.00
90.56
0.00
96.25
0.00
56.10
0.00
86.48
0.00
2
79.55
2.68
85.65
31.73
95.79
3.64
95.28
0.20
90.39
0.47
99.20
11.77
95.16
16.64
88.55
0.25
96.40
2.07
57.36
31.66
86.92
0.11
3
79.86
6.28
85.54
36.98
96.00
9.28
95.22
0.52
89.91
0.48
98.64
12.69
95.01
17.13
87.01
2.17
93.59
8.65
56.56
34.64
69.40
0.27
4
78.66
7.21
85.18
43.08
96.02
10.16
94.89
0.71
89.87
0.51
98.61
13.43
95.01
17.09
85.45
2.20
93.59
8.83
56.07
35.79
62.89
0.28
5
78.41
7.51
85.13
45.25
95.98
12.44
94.78
0.77
89.87
0.52
98.57
13.47
95.01
17.11
84.06
2.15
93.25
9.32
56.10
35.38
63.11
0.28
6
78.34
7.52
85.08
46.68
95.97
13.17
94.77
0.77
89.87
0.52
98.57
13.48
95.01
17.11
82.98
2.23
93.24
9.25
56.17
35.71
63.20
0.28
7
78.33
7.52
85.07
47.31
95.95
13.85
94.77
0.77
89.86
0.52
98.57
13.48
95.01
17.11
82.09
2.29
93.11
939
56.19
35.94
63.05
0.28
8
78.33
7.52
85.06
47.66
95.94
14.15
94.77
0.77
89.86
0.52
98.57
13.48
95.01
17.11
81.36
2.32
93.10
9.35
56.17
36.11
63.06
0.28
9
78.33
7.52
85.06
47.82
95.93
14.35
94.77
0.77
89.86
0.52
98.57
13.48
95.01
17.11
80.75
2.33
93.06
9.37
56.17
36.17
63.05
0.28
10
78.33
7.53
85.05
47.90
95.93
14.45
94.77
0.77
89.86
0.52
98.57
13.48
95.01
17.11
80.25
2.35
93.06
9.35
56.17
36.21
63.05
0.28
Overall this suggests that supply shocks contribute to output changes in COMESA countries
in a similar way. However, the same cannot be deduced on the contribution of supply shocks
to price changes.
1.5
Exchange Rate Shocks
As already noted, the main macroeconomic costs of a monetary union originate from loss of
nominal exchange rate flexibility as an instrument for adjustment to shocks (Vaubel, 1976;
1978; Marston, 1984; and Von Hagen and Fratianni, 1991). With sticky goods and factor
prices, real exchange rate adjustment is achieved more swiftly and easily if nominal exchange
rates rather than regional price levels change (Von Hagen and Neumann, 1994).
Consequently, the more often nominal exchange rate changes are required, the higher the
costs of joining a monetary union. The importance of flexible exchange rates is highest in the
presence of strong asymmetric shocks or if there is pronounced differences in shockabsorbing mechanisms of member countries. This part of the paper assesses the viability of a
35
monetary union in COMESA by analysing the conditional variances, extent of
symmetry/asymmetry and persistence of real exchange rates shocks.
1.5.1
Methodology
The methodology for analysing exchange rates variability, symmetry and persistence follows
the framework developed by Von Hagen and Neumann (1994). Hagen and Neumann (1994)
methodology has already been applied by Khanfula and Huizinga (2004) and Ogunkola
(2005) in analyzing the viability of a monetary union in SADC and ECOWAS, respectively.
Following the traditional approach, the real exchange rate (RER) between the reference
country3 and any other country i is defined as:
RERik ,t  pi ,t  ei ,t  p k ,t ,
(1.9)
where p i ,t is the logarithm of the consumer price index (CPI) for country i in period t, ei ,t is
the logarithm of the nominal exchange rate4 between the currency of country i and the
reference country’s currency and p k ,t is the logarithm of the CPI of the reference country in
period t.
Given that the monthly consumer price indexes exhibit seasonal patterns, the effect of
seasonality on the monthly variations of the RER as computed in equation (11) is removed by
regressing the observed changes in the RER on a set of 12 monthly dummies, Dm , as shown
in equation (1.10).
RERik ,t 

m 1,12
m
Dm   ikm,t
(1.10) ,
m
where βm are the parameters to be estimated, and  ik,
t are resulting residuals, which are the
seasonally adjusted RER changes. The quarterly deseasonalised REER changes, denoted by
q
 ik,
t are obtained by consecutively aggregating over the seasonally adjusted monthly RER
changes to obtain a typical four quarters in each year. The monthly deseasonalised RER
changes and quarterly deseasonalised RER changes represent short run and long run cases,
respectively. In order to minimise the possibility of correlation between the residuals and
their lagged values - a common phenomenon in time series analysis - we regress them on
their own lags as suggested by Khanfula and Huizinga (2004). We used seven lags for the
monthly series and four lags for the quarterly series.
3 In this study, Kenya and Egypt are taken to be the reference countries since it is the country, which trades most with the other countries in the region. The subscript k, for
the reference country thus stands for either Kenya or Egypt.
Nominal exchange rates calculated on the basis of monthly averages, are defined as units of country i’s currency per
unit of the Kenya, Mauritius and Egypt currency.
4
36
The residuals with no autocorrelation denoted by Wik ,t and Z ik ,t , are RER disturbances for
short-run and long-run cases, respectively and their variances are the conditional RER
variances, which are computed as in equations (1.11) and (1.12).
Vikm,t  var(Wik ,t )
(1.11)
Vikq,t  var( Z ik ,t )
(1.12)
The symmetry of the underlying disturbances is measured by taking correlations of the
respective RER shocks across the member countries, while the persistence of RER
fluctuations is measured by the first-order autocorrelation coefficients of the deseasonalised
RER changes. The persistence of real exchange rate variations implies that countries are not
well integrated economically. If countries are well integrated economically, then there will be
little persistence in Real exchange rate variations over time (Von Hagen and Neumann
(1994). This is because commodity arbitrage by inter-region trade and factor mobility would
quickly eliminate differences in relative prices. Sustained persistence of real exchange rate
variations would depict that there is low degree of economic integration (Khanfula and
Huizinga (2004)).
1.5.2 Data Characteristics
Monthly seasonally unadjusted data series of prices and nominal exchange rates for the
period 1990M01 – 2008M12 were obtained from the International Financial Statistics (IFS)
database of the International Monetary Fund. Prices are consumer price indexes. It is
important to note that since the 1990s; most of the COMESA countries have more or less
operated varying degrees of market determined exchange rate regimes, so there are no
fundamental regime changes that will have a profound effect on real exchange rate variability
during this period.
1.5.3 Variability and Symmetry of Real Exchange Rate Shocks
Short-run analysis
Tables 1.6a and 1.6b present the short-run variability (measured by the standard deviations)
in the real exchange rate (RER) shocks between Kenya and Egypt and some of the COMESA
members, respectively. The standard deviations among some of the COMESA countries
indicate that in the short-run, Kenya, Burundi, Egypt, Madagascar, Mauritius, Malawi,
Swaziland, Uganda and Zambia could start a monetary union. DRC and Rwanda could join
the monetary union if they achieved some degree of real exchange rate variability that is
manageable. Based on this analysis, DRC and Rwanda have extremely high RER variability,
which could expose the monetary union to much higher variation of relative prices.
37
Table 1.6a: Short-run variability (Kenya as a reference country)
Country
Standard deviation ( x 1000)
Burundi
63.89
DRC
248.80
Egypt
43.50
Madagascar
72.41
Mauritius
40.47
Malawi
67.59
Rwanda
172.61
Swaziland
52.44
Uganda
41.59
Zambia
71.89
Table 1.6b: Short-run variability (Egypt as a reference country)
Country
Standard deviation (x1000)
Burundi
64.32
DRC
308.82
Kenya
49.87
Madagascar
62.57
Mauritius
36.11
Malawi
63.04
Rwanda
39.68
Swaziland
49.32
Uganda
28.89
Zambia
72.89
In order to make a better assertion for the feasibility of forming a monetary union, the study
analyses the extent of symmetry of RER shocks across countries by examining the
correlations of RER shocks. Table 1.7a and 1.7b indicate that the correlations for bilateral
real exchange rate shocks are relatively high across most of the countries. Correlations
between Burundi and most of the countries, (except Mauritius, Rwanda and Uganda), DRC
and all the countries, Malawi and Swaziland, Malawi and Zambia, and Zambia and all the
countries are relatively low. This implies that, in the short run, real exchange rate shocks are
asymmetric between the economies in the COMESA region. Thus, a realistic, but somewhat
weak initial monetary union should include a few member countries such as Kenya,
Mauritius, Rwanda, Egypt, Madagascar, Rwanda, Uganda, and Swaziland.
Table 1.7a: correlations of short-run RER shocks (Kenya as a reference country)
Burundi
Burundi
DRC
Egypt
Madagascar
Mauritius
1.00
DRC
0.15
1.00
Egypt
0.35**
0.13
1.00
Madagascar
0.36**
0.04
0.51**
1.00
Mauritius
0.48*
0.09
0.75**
0.65**
1.00
Malawi
Rwanda
Swaziland
Uganda
Zambia
38
Malawi
0.35**
0.06
0.42**
0.83**
0.53**
1.00
Rwanda
0.45**
0.25**
0.70**
0.43**
0.61**
0.43**
1.00
Swaziland
0.30**
0.08
0.50**
0.41**
0.57**
0.33**
0.40**
1.00
Uganda
0.51**
0.13
0.74**
0.48**
0.75**
0.41**
0.65**
0.56**
1.00
Zambia
0.30**
0.25**
0.20**
0.22**
0.28**
0.19**
0.20**
0.29**
0.23**
1.00
Table 1.7b: correlations of short-run RER shocks (Egypt as a reference country)
Burundi
DRC
Madagascar
Kenya
Mauritius
Malawi
Rwanda
Swaziland
Burundi
1.00
DRC
0.13
1.00
Madagascar
0.39**
0.03
1.00
Kenya
0.37**
0.00
0.34**
Mauritius
0.52**
0.05
0.52**
0.44*
1.00
Malawi
0.33**
0.05
0.28**
0.22**
0.30**
1.00
Rwanda
0.56**
0.05
0.44*
0.40**
0.53**
0.36**
1.00
Swaziland
0.27**
0.01
0.33**
0.34**
0.43**
0.17
0.39
1.00
Uganda
1.00
Uganda
0.49*
0.06
0.30**
0.37**
0.49**
0.23**
0.57*
0.36**
1.00
Zambia
0.40*
0.26**
0.25**
0.32**
0.28**
0.15
0.31**
0.29**
0.27**
(**) represents 5% levels of significance
Long-run analysis
In the long run, the real exchange variability is larger than in the short-run. As shown in
Table 1.8a and 1.8b, the standard deviations of real exchange rate shocks between Kenya and
some COMESA countries turned out to be considerably larger than those of the short-run.
Thus, the real exchange rate between Kenya and the other members of COMESA was more
volatile in the long run than in the short run. Notwithstanding the large variability, the longrun analysis implies that Kenya, Burundi, Egypt, Madagascar, Mauritius, Malawi, Swaziland
and Uganda could join the COMESA monetary union. The analysis however shows that,
absorbing DRC, Rwanda and Zambia in the union at early stages would be economically
disruptive as their real exchange rates are more volatile in relative terms.
Table 1.8a: Long-run variability of the RER (Kenya as a reference country)
Country
Standard deviation ( x 1000)
Burundi
101.08
DRC
430.97
Egypt
93.97
Madagascar
Mauritius
136.14
85.18
Malawi
122.16
Rwanda
273.51
Swaziland
114.30
Uganda
Zambia
91.24
1.00
39
Zambia
151.79
Table 1.8b: Long-run variability of the RER (Egypt as a reference country)
Country
Standard deviation (x 1000)
Burundi
82.52
DRC
716.37
Madagascar
116.20
Kenya
94.90
Mauritius
57.69
Malawi
143.85
Rwanda
56.53
Swaziland
84.94
Uganda
59.01
Zambia
160.92
Tables 1.9a and 1.9b indicate that in the long run, symmetry of real exchange rate shocks
exists across most of the COMESA countries. In the long-run, some degree of symmetry of
RER shocks exists among countries such as Kenya, Burundi, Mauritius, Rwanda, Swaziland,
Uganda, Egypt, Madagascar, and Malawi. Thus, the formation of a monetary union among
most of the countries in the sample is possible, except for Zambia and DRC.
Table 1.9a: correlations of long-run RER shocks (Kenya as a reference country)
Burundi
DRC
Egypt
Madagascar
Mauritius
Malawi
Rwanda
Swaziland
Uganda
Burundi
1.00
DRC
0.10
1.00
Egypt
0.61**
-0.03
1.00
Madagascar
0.39**
-0.03
0.58**
1.00
Mauritius
0.66**
-0.12
0.78**
0.65**
1.00
Malawi
0.34**
0.07
0.48**
0.87**
0.56**
Rwanda
0.53**
0.02
0.56**
0.21
0.54**
0.21
1.00
Swaziland
0.51**
0.00
0.66**
0.44**
0.71**
0.39**
0.34**
1.00
Uganda
0.68**
-0.01
0.74**
0.37
0.70**
0.37**
0.66**
0.44**
1.00
Zambia
0.10
0.33
-0.05
-0.06
-0.02
0.02
-0.06
0.09
-0.05
1.00
Table 9b: correlations of long-run RER shocks across COMESA countries (Egypt as a reference country)
Burund
i
Burundi
DRC
Madagasca
r
Kenya
Mauritiu
s
Malaw
i
Rwand
a
Swazilan
d
1.00
DRC
Madagasca
r
0.27**
1.00
0.27**
0.14
1.00
Kenya
0.27**
0.03
0.27**
Mauritius
0.39**
0.07
0.56**
Malawi
0.30**
0.09
0.25**
Rwanda
0.67**
0.11
0.39**
Swaziland
0.36**
0.12
0.32**
1.00
0.46*
*
0.18
0.40*
*
0.33*
*
Zambia
1.00
0.36**
1.00
0.59**
0.47**
1.00
0.48**
0.37**
0.56**
1.00
Ugand
a
Zambi
a
1.00
40
Uganda
0.51***
0.13
0.30**
Zambia
0.36**
0.12
0.29**
0.40*
*
0.34*
*
0.54**
0.19
0.57**
0.36**
1.00
0.35**
0.36**
0.39**
0.29**
0.36**
1.00
(**) represents 5% levels of significance
1.5.4 Persistence of Real Exchange Rate Shocks
The persistence of RER changes over time is measured by obtaining the first-order
autocorrelation coefficients of the deseasonalised short-run and long-run RER changes. The
short-run autocorrelation coefficients are shown in Table 10a and 10b. The negative
autocorrelation coefficients for Burundi and Uganda indicate that the real exchange rate
deviations from their equilibrium levels tend to reverse overtime. At 5% significance level,
the coefficients were positive and significant for DRC, Madagascar, Mauritius, Malawi,
Swaziland and Zambia. The rest of the countries with positive coefficients are not significant
at the 5% level. Thus, except for four countries namely Burundi, Egypt, Rwanda and Uganda,
there is no tendency of persistence of RER fluctuations overtime.
Table 1.10a: First-order autocorrelations of short-run RER changes (Kenya)
Country
Burundi
DRC
Egypt
Madagascar
Mauritius
Malawi
Rwanda
Swaziland
Uganda
Zambia
AR(1) coefficient
t-statistics
-0.09
-1.27
0.21**
2.77
0.14**
1.96
0.25**
3.57
0.20**
2.83
0.22**
3.15
0.11
1.62
0.26**
3.79
-0.004
-0.06
0.35**
5.24
Significant at 5%
No
Yes
No
Yes
Yes
Yes
No
Yes
No
Yes
Table 1.10b: First-order autocorrelations of short-run RER changes (Egypt)
Country
Burundi
DRC
Kenya
Madagascar
Mauritius
Malawi
Rwanda
AR(1) coefficient
t-statistics
-0.069
-1.035
0.584**
10.470
No
Yes
0.209**
3.202
Yes
0.120
1.809
No
0.125
1.894
No
0.336**
5.326
Yes
0.085
1.230
No
Significant at 5%
0.028
0.416
No
0.181**
2.810
Yes
0.344**
Zambia
Critical value at 5% level of significance is 1.96
5.510
Yes
Swaziland
Uganda
41
In the long run, however, all countries have insignificant positive coefficients at the 5% level
as shown in Table 11a and 11b. Thus, in the long-run, there is no tendency of persistence of
the bilateral RER fluctuations overtime.
Table 1.11a: First-order autocorrelations of long-run RER changes (Kenya)
Country
AR(1) coefficient
t-statistics
0.12
0.89
-0.14
-1.13
0.02
0.16
0.21
1.67
0.05
0.38
0.15
1.18
-0.06
-0.45
0.08
0.64
-0.04
-0.28
0.25
1.95
Burundi
DRC
Egypt
Madagascar
Mauritius
Malawi
Rwanda
Swaziland
Uganda
Zambia
Significant at 5%
No
No
No
No
No
No
No
No
No
No
Table 1.11b: First-order autocorrelations of long-run RER changes (Egypt)
Country
AR(1) coefficient
t-statistics
0.17
1.49
0.65
7.09
0.29
2.61
0.32
2.89
0.18
1.58
0.30
2.67
-0.39
-3.65
0.11
0.91
0.12
1.09
0.40
0.11
Burundi
DRC
Kenya
Madagascar
Mauritius
Malawi
Rwanda
Swaziland
Uganda
Zambia
Critical value at 5% level of significance is 1.96
Significant at 5%
No
Yes
Yes
Yes
No
Yes
Yes
No
No
No
Overall, it is apparent that the variability of RER disturbances is low among most of the
COMESA countries. This is somewhat explained by the relative macroeconomic stability
experienced in those countries during the 1990s. The symmetry of RER disturbances is also
quite promising, except for a few countries such as DRC and Burundi. Further, both the
short-run (save for a few countries such as DRC, Madagascar, Mauritius, Malawi, Swaziland
and Zambia) and long-run cases reveal no tendency of persistence of RER fluctuations
overtime.
1.6
Conclusion and Policy Implications
The structure of the COMESA economies is dissimilar, with agriculture contributing a
significant portion of output. The structure of exports also follows the output structure, with
agricultural exports dominating as the main export commodities. However, a close
examination of the export structure reveals some peculiarities, which may result in
42
asymmetric shocks. Furthermore, the heavy reliance on a few export commodities greatly
exposes the COMESA countries to economic shocks and may limit their ability to cope with
these shocks.
The degree of external openness also varies across the region, which means that the cost of
foregoing the exchange rate as an adjustment tool varies from country to country. On the
other hand, notwithstanding the economic disparities, economic performance in the region is
generally promising, except in a few countries like Burundi and D.R. Congo, which have
been embroiled in civil conflict and wars. Furthermore, the macroeconomic policy
framework across the region is to a large extent similar. However, the significant differences
in macroeconomic indicators calls for further efforts at policy orientation, co-ordination and
harmonization in the region.
The correlation results indicate that contemporaneous shocks among the COMESA member
countries are generally symmetric, but with a few exceptions. This suggests that the trade
linkages are increasing among COMESA member countries giving tentative support for a
monetary union among the COMESA member countries.
The impulse response functions generally show similar patterns among COMESA member
countries, but with a few exceptions, further supporting the possibility of a monetary union.
The countries in the exception may be reflecting the effects of conflicts or poor data quality.
The much adjustment of output to supply and demand shock occurs within the first 3 to 4
years, while the sizes of the shocks are relatively small.
The variance decomposition results show that the proportion of variability of real output
accounted for by supply shocks are similar for all COMESA countries. These findings further
suggest that more integration of COMESA member countries is likely to increase symmetry
of shocks, and increase the possibility of formation of a monetary union in the near future.
The study also endeavored to analyze conditional short-run and long-run RER disturbances to
assess the desirability of a currency union in COMESA. Both the short-run and long-run
empirical evidence suggest that conditional variances of the real exchange rate disturbances
between Kenya and most of the members of the COMESA are comparable. Thus, the
following countries would be suited to form a monetary union: Kenya, Burundi, Egypt,
Madagascar, Mauritius, Malawi, Swaziland and Uganda. On the contrary, substantial
reduction of exchange rate variability will be required between the countries mentioned
above and DRC, Rwanda and Zambia.
In addition, there is some degree of symmetry of real exchange rate shocks across some of
the economies in the COMESA. This indicates that a monetary union formed among all the
countries would result in high costs relative to the benefits. Further, both the short-run and
long-run cases reveal little tendency of persistence of RER fluctuations overtime. Thus, there
is no tendency for RER changes to persist over time in most of COMESA countries. In terms
of RER movements, the study indicates that there are positive signals of monetary integration
among a few COMESA countries provided they take the required measures before
convergence.
43
Although the empirical evidence favours a monetary union in the COMESA region,
hastening a monetary union before sequential implementation of intensive integrating
measures may prove costly in terms of excessive requirements for large variations in regional
price levels to facilitate RER adjustment after the shocks. Furthermore, with less than optimal
factor mobility and sticky prices, and in the absence of a feasible compensating mechanism, a
monetary union in the COMESA might expose individual countries into considerable strains.
This may limit the potential benefits of a monetary union in the region. These results
therefore have a number of policy implications for the COMESA countries.
First, COMESA countries should continue with the macroeconomic stabilization objective so
as to enhance macroeconomic stability, which will reduce the volatility of macroeconomic
fluctuations. Second, there is need for the COMESA countries to diversify their exports in
order to reduce their vulnerability to terms of trade shocks. Third, there is need to strengthen
macroeconomic policy convergence and harmonization of statistical definitions and
frameworks. In particular, there is need to mainstream the macroeconomic convergence
benchmarks into the national planning and decision-making frameworks. Fourth, COMESA
countries should design effective risk-sharing and compensatory mechanisms for the union as
a whole. It is important to note that some of the successful monetary unions do experience
asymmetric shocks, but have instituted risk sharing and compensation mechanisms to
facilitate a union-wide monetary policy.
Fifth, given that at present, the costs of a monetary a union appear to be much higher than the
benefits, the COMESA member countries should embark on improving trade linkages by
reducing trade barriers to stimulate regional growth and pursue increased economic
cooperation. The best strategy in this regard would be engaging the free trade area created
among COMESA economies in trade with developed markets such as the European Union,
North America and emerging markets in Asia. Finally, if individual countries face
asymmetric shocks, then one of the only adjustment mechanisms that would help mitigate the
adverse impact of these shocks are factor (capital and labour) mobility. The COMESA region
should facilitate free factor (capital and labour) mobility by intensifying capital market
integration, among other measures. Greater capital market integration is expected to both
lower the cost of financial capital and foster a reallocation of capital from capital abundant to
capital scarce countries. In addition, greater capital market integration may promote
technological progress (e.g., venture capital) that can offset decreasing returns to physical
capital and hence generate endogenous growth.
1.7
References
Aiolfi M., Catão, L., & Timmermann, A. 2006. Common Factors in Latin America’s
Business Cycles, IMF Working Paper, WP/06/49, February).
Alejandro I., R. Randall R., and Talvi E. 2007. Business cycles in Latin America: The role of
external factors, Inter-American Development Bank, Research Department.
44
Allegret J., and Alain S. 2008. Does a Monetary Union protect against external shocks? An
assessment of Latin American integration, Journal of Policy Modeling 31 (2009) 102–118
Babetskii, I., 2005. Trade Integration and Synchronization of Shocks: Implications for the EU
Enlargement, Economics of Transition, 13 (1): 105 – 138.
Baxter, Marianne, and Michael Kouparitsas. 2000. What Causes Fluctuations in the Terms of
Trade? NBER Working Paper no. 7462.
Bayoumi, T. and Eichengreen, B., 1992. Shocking Aspects of European Monetary
Unification, National Bureau of Economic Research Working Paper No. 3949, Cambridge,
Massachusetts.
Bayoumi, T. and Eichengreen, B., 1993. Shocking Aspects of European Monetary
Integration, in Torres, F. and Giavazzi, F. (eds.), Adjustment and Growth in the European
Monetary Union, Cambridge University Press, New York.
Bayoumi, T and Taylor, M.P., 1995, Macro-economic shocks, the ERM, and tri-polarity, The
Review of Economics and Statistics, 77(2), 321-331.
Bernanke, B.S., 1983. Irreversibility, uncertainty, and cyclical investment, Quarterly Journal
of Economics 98 (1), 85.
Blanchard, O. J. and Quah, D., 1989. The Dynamic Effects of Aggregate Demand and Supply
Disturbances, American Economic Review, 79(4): 655-673.
Broda, C., 2003. Terms of Trade and Exchange Rate Regimes in Developing Countries.
Federal Reserve Bank of New YorkStaff Reports, no. 148.
Broda C., and Tille C. 2003. Coping with terms-of-trade shocks in developing countries,
Current Issues in Economics and Finance, Federal Reserve Bank of New York Volume 9,
Number 11 November 2003.
Buigut S. K., and Valev N. T., 2005. Is the Proposed East African Monetary Union an
Optimal Currency Area? A Structural Vector Autoregression Analysis, World Development
Vol. 33, No. 12, pp. 2119–2133, 2005.
Burbridge, J., and Harrison, A., 1984. Testing for the effects of oil price rises using vector
autoregressions. International Economic Review 25 (2), 459–484
Calvo, G., Leiderman, L., and Reinhart C. 1993. Capital inflows and real exchange rate
appreciation in Latin America: The role of external factors, IMF Staff papers, Vol. 40 No. 1,
108-151
Chandima M., 2002. External shocks and banking crises in developing countries: Does the
exchange rate regime matter? Cesifo working paper no. 759 Category 6: Monetary Policy and
International Finance.
45
Chen Shiu-Sheng, 2008. Oil price pass through into inflation, Energy Economics 31 (2009)
126-133,
Chen, S.-S., and Chen, H.-C., 2007. Oil prices and real exchange rates, Energy Economics 29
(3), 390–404.
Chenery and Taylor, (1968)
Christodaulakis, D., & Kollinzas, 1995. Comparison of business cycles in the EC:
Idiosyncrasies and irregularities, Economic 62(245), pp.1-27
Cologni, A., and Manera, M., 2008. Oil prices, inflation and interest rates in a structural
cointegrated VAR model for the G-7 countries, Energy Economics 30 (3), 856–888.
Cunado, J., and Gracia, F.P.d., 2003. Oil prices, economic activity and inflation: evidence for
some Asian countries, Quarterly Review of Economics and Finance 45 (1), 65–83.
Dohner, R.S., 1981. Energy prices, economic activity and inflation: survey of issues and
results. In: Mork, K.A. (Ed.), Energy Prices, Inflation and Economic Activity. Ballinger,
Cambridge, MA.
Ferderer, J.P., 1996. Oil price volatility and the macroeconomy, Journal of Macroeconomics
18 (1), 1–26.
Fielding D. and Shields, K., 2001. Modelling Macroeconomic Shocks in the CFA Franc
Zone, Journal of Development Economics 66, 199-223.
Fleming, M. J., 1971. On Exchange Rate Unification, Economic journal, 81(323): 467-488.
Frankel, J. A. and Rose, A. K., 1998. The Endogeneity of the Optimum Currency Area
Criteria. The Economic Journal, Vol. 108 (449): 1009-1025.
Frenkel, M and Nickel, C., 2002. How Symmetric are the Shocks and the Shock adjustment
Dynamics between the Euro Area and Central and Eastern European Countries? IMF
Working Paper, WP 02/222, IMF, Washington, DC.
Gisser, M., and Goodwin, T.H., 1986. Crude oil and the macroeconomy: tests of some
popular notions, Journal of Money, Credit and Banking 18 (1), 95–103.
Goodhart, C. A. E., 1995. The Political Economy of Monetary Union, Chapter 12 in P.
Kenen, ed., Understanding Interdependence. The Macroeconomics of the Open Economy,
Princeton University Press, Princeton.
Haberler, G., 1970. The International Monetary System: Some recent Developments and
Discussions, in Money in the international Economy, Halm (ed.).
Harberger, A.C., 1950, Currency depreciation, income and the balance of trade, Journal of
political economy 58, 1950, 47-60.
Hamilton, J.D., 1983. Oil and the macroeconomy since World War II, Journal of Political
Economy 91 (2), 228–248.
46
Hamilton, J.D., 1988. A neoclassical model of unemployment and the business cycle, Journal
of Political Economy 96 (3), 593–617.
Hamilton, J.D., 2008. Oil and the macroeconomy. In: Durlauf, S., Blume, L. (Eds.), New
Palgrave Directory of Economics, second ed. Palgrave McMillan Ltd.
Hamilton, J.D., 1996. This is what happened to oil price–macroeconomy relation- ship,
Journal of Monetary Economics 38, 215–220.
IMF., 2008. Kenya: Selected Issues. IMF Country Report No. 08/337. International Monetary
Fund, Washington, D.C.
IMF., 2009. Uganda and Rwanda: Selected Issues. IMF Country Report No. 09/36.
International Monetary Fund Washington, D.C.
Ingram, J., 1969. COMMENT: The Currency AREA problem, in: R. Mundell/A. Swoboda
(Hrsg.), Monetary of problem of OF the internationally Economy, Chicago London, P. 95100
Ingram, J. C., 1973. Comments, in Krause and Salant (eds.), European Monetary Unification
and its Meaning for the United States, Washington, Brookings, 184-191
Jimenez-Rodriguez, R., and Sanchez, M., 2005. Oil price shocks and real GDP growth:
empirical evidence for some OECD countries, Applied Economics 37 (2), 201–228.
Jimenez-Rodriguez R., 2008. The impact of oil price shocks: evidence from the industries of
six OECD countries, Elsevier: www.elsevier.com/locate/eneco
Jonung, L., and SjÖholm, F., 1998. Should Finland and Sweden form a Monetary Union,
Working Paper Series in Economics and Finance No. 224.
Kenen, P., 1969, Theory of Optimum Currency Areas: An Eclectic View, in Mundel and
Swoboda (eds.), Monetary problems International Economy, University of Chicago Press.
Khamfulaa Y., and H. Huizinga 1998. The Southern African Development Community:
suitable for a monetary union? Journal of Development Economics 73 (2004) 699– 714.
Khamfula, Y. and Huizinga, H., 2004. The Southern African Development Community:
Suitable for a Monetary Union? Journal of Development Economics, 73(2): 699-714.
Kose, M.Ayhan. 2002. Explaining Business Cycles in Small Open Economies: How Much
Do World Prices Matter? Journal of International Economics 56, no. 2: 299-327.
Krugman, P. 1993. Lessons of Massachusetts for EMU, in Torres, F. and Giavazzi, F. (eds),
Adjustment and Growth in the European Monetary Union, Cambridge University Press,
Cambridge, UK.
Lardic, S., and Mignon, V., 2006. The impact of oil prices on GDP in European countries: an
empirical investigation based on asymmetric cointegration, Energy Policy 34 (18), 3910–
3915.
47
Lausen, S. and L.A. Metzler, 1950, Flexible exchange rates and the theory of employment,
Review of Economics and Statitics 32, 1950, 281-299
Lilien, D.M., 1982. Sectoral shifts and cyclical unemployment. Journal of Political Economy
90 (4), 777–793.
Loungani, P., 1986. On price shocks and the dispersion hypothesis. Review of Economics and
Statistics 68 (3), 536–539.
Machlup, F., 1977. A History of Thought in Economic Integration, Columbia University
Press.
Marston, R. C. 1984: Exchange rate unions as an alternative to flexible rates: The effects of
real and monetary disturbances, in Marston, R. C. and Bilson, J. F. O. (eds.): Exchange rate
theory and practice, The University of Chicago Press, Chicago, 407-442.
McKinnon, R. 1963. Optimum Currency Areas, American Economic Review, 53(4): 717-725
Mendoza, Enrique G. 1995. The Terms of Trade, the Real Exchange Rate, and Economic
Fluctuations. International Economic Review 36, no. 1 (February): 101-37.
Mkenda, B. 2001. Is East Africa an Optimum Currency Area? Scandinavian Working Papers
in Economics, Goteborg University.
Mork, K.A., 1989. Oil and the macroeconomy when prices go up and down: an extension of
Hamilton’s results. Journal of Political Economy 97 (3), 740–744.
Mork, K.A., and Olsen, O., 1994. Macroeconomic responses to oil price increases and
decreases in seven OECD countries. Energy Journal 15 (4), 19–35.
Mundell, R. A. 1961. A Theory of Optimum Currency Areas, The American Economic
Review, 51(4): 657-665.
Opolot J. 2008, Real exchange rate shocks in the East African Community: Nature and
Implications for monetary union, The Bank of Uganda Staff papers Journal, Vol. 2, No. 1,
2009 pp 67-86.
Ogunkola O., 2005. An Evaluation of the Viability of a Single Monetary Zone in ECOWAS,
African Economic Research Consortium, Research Paper 147.
Ramaswamy, R.,and Slok, T., 1998. The Real Effects of Monetary Policy in the European
Union: What are the differences? IMF staff Papers, 45(2), 374-396.
Scitovsky, T., 1957. The Theory of the Balance of Payments and the Problem of a Common
European currency, Kyklos, X, (1) : 18-44.
Scitovsky (1957)
Scitovsky T., 1967. The Theory of Balance-of-Payments Adjustment, Journal ofPolitica1
Econonzy, Aug., 523-531.
48
Scitovsky T., 1958. Economic Theory and Western European Integration, (London).
Tower, E., and Willett, T. D., 1970. The Concept of Optimum Currency Areas and the
Choice Between Fixed and Flexible Exchange Rates, in Approaches to Greater Flexibility of
Exchange Rates, G. N. Halm ed, Princeton, Princeton University Press.
Tower, E., and Willett, T. D., 1976. The Theory of Optimum Currency Areas and Exchangerate Flexibility: A more General framework, Special Papers in International Economics 11,
Princeton University.
Turnovsky, S. and Sen P. (1991) Fiscal policy, capital accumulation, and debt in an open
economy. Oxford Economic Papers 43, 1-24.
Uri, N.D., 1995. The impact of crude oil price volatility on agricultural employment in the
US. The Journal of Energy and Development 20 (Spring), 269–288.
Vaubel, R., 1976. Real Exchange Rate Changes in the European Community: The Empirical
Evidence and its Implications for European Currency Unification, Weltwirtschaftliches
Archiv 112: 429-470.
Vaubel, R., 1978. Real Exchange Rate Changes in the European Community: A New
Approach to the Determination of Optimum Currency Areas, Journal of International
Economics, 8(2): 319-339.
Von Hagen, J., and Fratianni, M., 1991. German Dominance in the EMS: A Correction,
Journal of International Money and Finance, 10(4): 594-594.
Von Hagen, J., and Neumann, M. J. M., 1994. Real Exchange Rates Within and
Between Currency Areas: How far away is the EMU?, Review of Economics and Statistics,
76 (2): 236 – 244
World DevelopmentIndicators
Zhang, Z., Sato, K., and McAleer, M., 2004. Asian Monetary Integration: A structural VAR
approach, Mathematics and Computers in Simulation, 64, 447-458.
Haberler, G., 1970. The International Monetary System: Some Developments and
Discussions, in Approaches to Greater Flexibility of Exchange Rates (ed.) George N. Halm,
Princeton University Press,1970, p.115-23.
Hagen von, Jürgen and Manfred J.M. Neumann, 1994. Real Exchange Rates within and
betweenCurrency Areas: How Far Away Is EMU? Review of Economic Statistics, 1994
August, 236-244.
Hamilton, J.D., 1994. Time Series Analysis, Princeton University Press, Princeton.
49
Appendix A
Figure 1.1A Impulse responses for each country to demand and supply shocks
Response to Cholesky One S.D. Innovations ± 2 S.E.
Response of DLEGDF to DLEGDF
Response to Cholesky One S.D. Innovations ± 2 S.E.
Response of DLEGDF to DLEGGDP
Response of DLDRDF to DLDRDF
Response of DLDRDF to DLDRGDP
.06
.06
1.5
1.5
.04
.04
1.0
1.0
.02
.02
0.5
0.5
.00
.00
0.0
0.0
-.02
-.02
-0.5
-0.5
-.04
-.04
1
2
3
4
5
6
7
8
9
10
-1.0
1
Response of DLEGGDP to DLEGDF
2
3
4
5
6
7
8
9
10
Response of DLEGGDP to DLEGGDP
.04
.04
.03
.03
.02
.02
.01
.01
.00
.00
-.01
-.01
-.02
2
3
4
5
6
7
8
9
10
2
3
4
2
Response of DLBDF to Shock1
.04
.00
.00
-.04
3
4
5
6
7
8
9
2
3
4
5
6
7
8
9
10
9
10
Response of DLBGDP to Shock1
2
3
4
5
6
.04
.02
.02
.00
.00
-.02
-.02
2
3
4
5
6
7
8
9
10
1
.08
.08
.04
.04
.00
.00
7
8
9
2
3
4
5
6
7
8
9
10
1
Response of DLKEGDP to DLKEDF
.02
.02
.02
.02
.01
.01
.00
.00
.00
.00
-.02
-.02
-.01
-.01
-.04
6
7
8
9
10
-.02
1
2
3
4
5
6
7
8
9
10
3
4
5
6
7
8
9
10
7
8
9
10
2
3
4
5
6
7
8
9
10
Response of DLKEGDP to DLKEGDP
.04
5
6
-.04
1
10
.04
4
5
Response of DLKEDF to DLKEGDP
.06
3
4
Response to Cholesky One S.D. Innovations ± 2 S.E.
.06
2
2
Response of DLKEDF to DLKEDF
.03
1
3
-.04
1
Response of DLBGDP to Shock2
-.04
2
Response of DLDRGDP to DLDRGDP
-.04
1
1
.04
10
-.04
1
8
.06
Response of DLBDF to Shock2
.04
7
.06
Response to Structural One S.D. Innovations ± 2 S.E.
.08
6
-.04
1
.08
5
Response of DLDRGDP to DLDRDF
-.02
1
-1.0
1
.03
-.02
1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
9
10
50
Response to Cholesky One S.D. Innovations ± 2 S.E.
Response to Cholesky One S.D. Innovations ± 2 S.E.
Response of DLMADF to DLMADF
Response of DLMWDF to DLMWDF
Response of DLMADF to DLMADGDP
.08
.08
.04
.04
.00
Response of DLMWDF to DLMWGDP
.15
.15
.10
.10
.05
.05
.00
.00
-.05
-.05
.00
-.04
-.10
-.04
1
2
3
4
5
6
7
8
9
10
1
Response of DLMADGDP to DLMADF
2
3
4
5
6
7
8
9
.06
.04
.04
.02
.02
.00
.00
-.02
-.02
-.04
3
4
5
6
7
8
9
10
3
4
5
6
7
8
9
10
1
1
2
3
4
5
6
7
8
9
.08
.06
.06
.04
.04
.02
.02
.00
.00
-.02
-.02
2
3
4
5
Response to Cholesky One S.D. Innovations ± 2 S.E.
Response of DLRWDF to DLRWDF
.2
.1
.1
.0
.0
-.1
-.1
6
7
8
9
10
1
2
3
4
5
6
7
8
9
10
1
Response of DLRWGDP to DLRWDF
2
3
4
5
6
.2
.2
.1
.1
.0
.0
-.1
-.1
7
8
9
10
Response of DLRWGDP to DLRWGDP
2
3
4
5
6
7
8
9
10
1
Response of DLSUDGDP to DLSUDF
.04
.16
.16
.03
.03
.12
.12
.02
.02
.08
.08
.01
.01
.04
.04
.00
.00
.00
.00
-.01
-.01
-.04
-.04
-.02
-.02
-.08
-.08
-.03
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
9
10
3
4
5
6
7
8
9
10
7
8
9
10
2
3
4
5
6
7
8
9
10
Response of DLSUDGDP to DLSUDGDP
.04
3
6
-.2
1
.20
2
5
Response of DLSUDF to DLSUDGDP
.3
.20
1
2
Response of DLSUDF to DLSUDF
.3
-.2
1
4
Response to Cholesky One S.D. Innovations ± 2 S.E.
Response of DLRWDF to DLRWGDP
.2
3
-.04
1
10
2
Response of DLMWGDP to DLMWGDP
.08
-.04
-.04
2
2
Response of DLMWGDP to DLMWDF
Response of DLMADGDP to DLMADGDP
.06
1
-.10
1
10
-.03
1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
9
10
51
Response to Cholesky One S.D. Innovations ± 2 S.E.
Response of DLZAMDF to DLZAMDF
Response of DLZAMDF to DLZAMGDP
.4
.4
.3
.3
.2
.2
.1
.1
.0
.0
-.1
-.1
-.2
-.2
1
2
3
4
5
6
7
8
9
10
1
2
Response of DLZAMGDP to DLZAMDF
3
4
5
6
7
8
9
10
Response of DLZAMGDP to DLZAMGDP
.06
.06
.04
.04
.02
.02
.00
.00
-.02
-.02
-.04
-.04
1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
9
10
Response to Cholesky One S.D. Innovations ± 2 S.E.
Response of DLSWZDF to DLSWZDF
Response to Cholesky One S.D. Innovations ± 2 S.E.
Response of DLSWZDF to DLSWZGDP
.08
Response of DLSYDF to DLSYDF
.08
.06
.06
.04
.04
.02
.02
.00
.00
-.02
-.02
-.04
-.04
1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
9
10
.08
.06
.06
.04
.04
.02
.02
.00
.00
-.02
-.02
-.04
-.04
1
Response of DLSWZGDP to DLSWZDF
Response of DLSWZGDP to DLSWZGDP
.06
Response of DLSYDF to DLSYGDP
.08
2
3
4
5
6
7
8
9
10
1
Response of DLSYGDP to DLSYDF
2
3
4
5
6
7
8
9
10
Response of DLSYGDP to DLSYGDP
.06
.04
.04
.02
.02
.00
.00
-.02
-.02
-.04
-.04
1
2
3
4
5
6
7
8
9
10
.08
.08
.04
.04
.00
.00
-.04
-.04
-.08
1
2
3
4
5
6
7
8
9
10
-.08
1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
9
10
52
Appendix B
Table 1.1B: Performance of some macroeconomic variables for selected COMESA countries
19951990-94 99
2000
2001
2002
2003
2004
2005
2006
Kenya
Aid (% of central government expenditures)
63.2
26.4
23.9
19.2
15.7
18.5
19.8
Aid per capita (current US$)
Current account balance (% of GDP)
36.9
-1.7
17.6
-2.5
16.6
-1.6
14.7
-2.5
12.2
-0.9
15.9
1.0
19.9
-2.2
22.4
-2.6
..
..
GDP growth (annual %)
GDP per capita (constant 2000 US$)
1.6
429.2
2.9
420.9
0.6
414.0
3.8
420.5
0.6
413.9
3.0
417.2
4.9
427.9
5.8
442.3
..
..
-1.6
0.4
-1.6
1.6
-1.6
0.8
2.6
3.4
..
GDP per capita growth (annual %)
GDP per capita, PPP (constant 2000 international
$)
..
..
1,070.3
1,049.7
1,032.5
1,048.7
1,032.3
1,040.5
1,067.1
1,103.1
..
Gross domestic savings (% of GDP)
Gross fixed capital formation (% of GDP)
19.8
18.4
11.4
16.8
9.4
16.7
11.3
18.2
13.1
17.5
13.3
16.1
12.3
16.2
9.3
18.6
..
..
Inflation, consumer prices (annual %)
Fiscal deficit, excluding grants % of GDP
28.0
…
6.8
…
10.0
-2.16
5.7
-4.8
2.0
-3.2
9.8
-4.5
11.6
-1.6
10.3
-1.0
..
35.9
70.6
32.2
92.4
33.6
62.5
31.5
51.6
27.3
71.3
36.3
-4.9
6.0
-4.8
7.7
-6.1
5.6
-6.5
4.9
-6.2
6.3
180.6
2.5
222.8
4.5
243.8
2.4
247.7
1.6
864.5
1.4
1,066.0
7.2
1,166.8
8.1
14.7
25.9
17.1
5.9
Uganda
Aid (% of central government expenditures)
Aid per capita (current US$)
Current account balance (% of GDP)
GDP growth (annual %)
GDP per capita (constant 2000 US$)
GDP per capita growth (annual %)
GDP per capita, PPP (constant 2000 international
$)
Gross domestic savings (% of GDP)
Gross fixed capital formation (% of GDP)
Inflation, consumer prices (annual %)
…
Fiscal deficit, excluding grants % of GDP
Rwanda
Aid (% of central government expenditures)
Aid per capita (current US$)
118.2
67.3
Current account balance (% of GDP)
GDP growth (annual %)
GDP per capita (constant 2000 US$)
GDP per capita growth (annual %)
GDP per capita, PPP (constant 2000 international
$)
Gross domestic savings (% of GDP)
Gross fixed capital formation (% of GDP)
Inflation, consumer prices (annual %)
…
41.6
..
..
-5.7
4.7
-2.8
5.5
-3.0
6.6
..
..
254.7
2.8
257.8
1.2
262.6
1.9
270.2
2.9
..
..
1,185.3
6.5
1,219.0
4.7
1,233.6
6.3
1,256.8
8.4
1,293.1
7.1
..
..
19.6
2.8
18.2
2.0
19.0
-0.3
20.1
7.8
22.1
3.3
20.9
8.2
..
..
-8.04
-11.0
-10.6
-10.6
-9.1
-8.8
..
40.1
35.7
41.1
38.2
55.0
63.7
..
..
-5.0
-11.5
-2.2
15.7
-5.2
6.0
-6.0
6.7
-7.3
9.4
-5.8
0.9
-1.9
4.0
-2.4
6.0
..
..
246.3
-7.0
226.2
9.3
225.7
-1.0
230.5
2.2
245.4
6.5
243.7
-0.7
249.7
2.5
260.1
4.2
..
..
1,016.0
-6.9
933.0
-4.0
930.9
1.3
950.9
2.6
1,012.3
0.0
1,005.1
-0.8
1,030.2
2.4
1,073.1
2.0
..
..
14.2
11.4
14.7
5.8
17.5
4.3
18.4
3.0
16.9
2.3
18.4
7.1
20.5
12.0
22.4
9.1
..
..
-11.8
-9.76
-10.8
-7.0
-8.0
-9.8
..
..
..
..
103.3
64.0
46.0
-3.9
19.1
-2.2
14.3
-7.2
20.7
-5.7
25.2
-0.7
32.3
-4.2
49.7
-25.7
48.4
-32.0
..
..
GDP growth (annual %)
GDP per capita (constant 2000 US$)
-0.1
148.8
-2.8
114.7
-0.9
109.3
2.1
109.1
4.4
110.9
-1.2
106.1
4.8
107.5
0.9
104.6
..
..
-2.0
-3.7
-2.5
-0.2
1.6
-4.3
1.3
-2.6
..
883.9
681.3
649.6
648.2
658.7
630.4
638.6
621.8
..
-7.4
-3.1
-7.3
-9.0
-11.4
-8.2
-8.4
-15.8
..
Gross domestic savings (% of GDP)
..
..
Aid per capita (current US$)
Current account balance (% of GDP)
GDP per capita growth (annual %)
GDP per capita, PPP (constant 2000 international
$)
..
..
..
69.6
Fiscal deficit, excluding grants % of GDP
Burundi
Aid (% of central government expenditures)
43.0
..
..
..
..
..
2007
2008
53
Gross fixed capital formation (% of GDP)
11.7
6.4
6.1
6.2
6.1
11.3
13.4
11.8
..
Inflation, consumer prices (annual %)
Fiscal deficit, excluding grants % of GDP
8.5
18.5
24.3
-9.4
9.2
-8.7
-1.3
-12.1
10.7
-15.5
8.3
-16.7
13.0
-22.7
..
89.4
82.2
85.7
Current account balance (% of GDP)
GDP growth (annual %)
-13.6
-0.8
-11.5
1.6
-17.1
3.6
GDP per capita (constant 2000 US$)
GDP per capita growth (annual %)
GDP per capita, PPP (constant 2000 international
$)
341.8
-3.5
301.9
-0.8
Zambia
Aid (% of central government expenditures)
Aid per capita (current US$)
…
..
..
74.3
..
32.0
..
..
57.6
..
..
52.2
..
..
98.0
..
81.0
..
..
4.9
3.3
5.1
5.4
..
5.2
..
..
302.5
1.5
311.4
2.9
316.0
1.5
326.6
3.4
338.7
3.7
350.5
3.5
..
..
887.3
783.8
785.4
808.4
820.4
847.9
879.2
909.9
..
Gross domestic savings (% of GDP)
Gross fixed capital formation (% of GDP)
8.2
11.6
5.9
13.5
8.3
17.2
17.3
18.7
17.7
21.6
18.7
24.8
18.2
24.6
17.0
24.7
..
..
Inflation, consumer prices (annual %)
Fiscal deficit, excluding grants % of GDP
121.7
30.7
26.0
21.4
22.2
21.4
18.0
18.3
..
44.7
..
..
Malawi
Aid (% of central government expenditures)
Aid per capita (current US$)
…
…
..
..
..
..
..
40.6
38.8
34.2
31.2
Current account balance (% of GDP)
GDP growth (annual %)
-10.8
1.3
-6.3
7.0
-4.2
1.6
-3.5
-5.0
-10.4
2.9
6.1
7.1
2.6
..
..
GDP per capita (constant 2000 US$)
GDP per capita growth (annual %)
GDP per capita, PPP (constant 2000 international
$)
Gross domestic savings (% of GDP)
132.2
-0.6
149.9
4.5
151.5
-1.1
140.5
-7.3
141.2
0.5
146.5
3.8
153.6
4.8
154.1
0.4
..
..
509.0
4.9
577.1
2.0
583.1
3.8
540.8
3.8
543.6
-10.1
564.1
-10.7
591.3
-9.1
593.5
-11.6
..
..
18.8
21.1
11.5
40.9
12.3
29.6
13.8
22.7
10.4
14.7
10.8
9.6
14.4
11.4
13.7
15.4
..
..
Gross fixed capital formation (% of GDP)
Inflation, consumer prices (annual %)
41.9
..
53.1
..
39.8
..
..
Fiscal deficit, excluding grants % of GDP
Ethiopia
Aid (% of central government expenditures)
Aid per capita (current US$)
…
Current account balance (% of GDP)
GDP growth (annual %)
GDP per capita (constant 2000 US$)
GDP per capita growth (annual %)
GDP per capita, PPP (constant 2000 international
$)
Gross domestic savings (% of GDP)
Gross fixed capital formation (% of GDP)
Inflation, consumer prices (annual %)
…
20.2
12.0
..
10.7
..
16.8
80.8
19.3
-0.3
-0.2
-1.9
4.8
0.2
5.4
-4.7
7.9
107.9
-2.1
117.3
2.0
122.0
2.9
720.1
7.8
782.5
11.3
13.5
12.5
19.4
3.6
..
23.2
..
26.0
..
27.2
..
..
-1.9
0.0
-1.7
-3.1
-6.9
12.3
-14.0
8.7
..
..
128.7
5.5
126.0
-2.2
119.6
-5.1
131.7
10.1
140.6
6.8
..
..
814.3
8.0
859.1
8.8
840.6
8.7
798.0
7.5
878.9
4.1
938.3
3.6
..
..
20.5
0.7
21.0
-8.2
23.6
1.7
22.7
17.8
21.3
3.3
26.3
11.6
..
..
Fiscal deficit, excluding grants % of GDP
Eritrea
Aid (% of central government expenditures)
…
…
..
..
..
75.7
..
Aid per capita (current US$)
Current account balance (% of GDP)
36.0
28.5
46.0
-16.6
49.4
-16.5
GDP growth (annual %)
GDP per capita (constant 2000 US$)
17.3
163.9
4.4
212.5
-13.1
178.2
9.2
186.7
0.7
179.8
6.1
182.3
..
59.4
..
..
78.0
..
..
62.2
..
80.7
..
..
1.9
177.9
0.5
171.9
..
..
..
..
GDP per capita growth (annual %)
GDP per capita, PPP (constant 2000 international
$)
17.3
2.1
-16.3
4.8
-3.7
1.4
-2.4
-3.4
..
940.2
1,219.4
1,022.1
1,071.2
1,031.5
1,046.1
1,020.7
986.2
..
Gross domestic savings (% of GDP)
Gross fixed capital formation (% of GDP)
-25.4
16.6
-33.5
29.9
-34.7
31.9
-27.0
28.7
-33.7
26.0
-59.7
25.4
-61.4
22.8
-26.8
20.1
..
..
54
Inflation, consumer prices (annual %)
…
…
..
..
..
..
..
..
..
Fiscal deficit, excluding grants % of GDP
Egypt, Arab Rep.
Aid (% of central government expenditures)
33.2
12.1
6.6
6.2
6.5
Aid per capita (current US$)
Current account balance (% of GDP)
66.5
5.6
30.7
-1.3
19.7
-1.0
18.3
-0.4
17.7
0.7
13.8
4.5
20.0
5.0
12.5
2.4
..
..
GDP growth (annual %)
GDP per capita (constant 2000 US$)
3.6
1,195.5
5.1
1,346.4
5.4
1,483.8
3.5
1,507.0
3.2
1,525.5
3.1
1,543.0
4.2
1,577.0
4.9
1,623.8
..
..
1.6
3.1
3.4
1.6
1.2
1.1
2.2
3.0
..
2,840.8
15.4
3,199.2
12.9
3,525.7
12.9
3,580.8
13.4
3,624.7
13.9
3,666.3
14.3
3,747.2
15.6
3,858.4
15.8
..
..
21.4
14.1
19.3
6.9
18.9
2.7
17.7
2.3
17.8
2.7
16.3
4.5
16.4
11.3
18.0
4.9
..
..
41.2
24.5
15.2
44.0
GDP per capita growth (annual %)
GDP per capita, PPP (constant 2000 international
$)
Gross domestic savings (% of GDP)
Gross fixed capital formation (% of GDP)
Inflation, consumer prices (annual %)
..
..
..
..
Fiscal deficit, excluding grants % of GDP
Congo, Dem. Rep.
Aid (% of central government expenditures)
Aid per capita (current US$)
Current account balance (% of GDP)
10.5
…
3.3
…
3.5
..
..
4.7
..
..
..
22.3
..
..
99.9
..
..
32.7
..
31.8
..
..
..
GDP growth (annual %)
GDP per capita (constant 2000 US$)
-8.6
157.1
-2.4
105.9
-6.9
86.0
-2.1
82.2
3.5
82.8
5.7
85.0
6.6
88.0
6.5
91.0
..
..
GDP per capita growth (annual %)
GDP per capita, PPP (constant 2000 international
$)
-11.8
-4.6
-8.9
-4.5
0.8
2.7
3.5
3.4
..
1,097.2
739.8
600.8
573.8
578.1
593.9
614.7
635.4
..
Gross domestic savings (% of GDP)
Gross fixed capital formation (% of GDP)
6.4
7.2
11.2
8.9
4.5
3.5
3.4
5.4
4.0
8.9
5.0
12.2
3.9
12.7
6.5
..
..
Inflation, consumer prices (annual %)
Fiscal deficit, excluding grants % of GDP
6,425.0
309.4
550.0
313.7
38.1
12.9
4.0
21.3
..
Libya
Aid (% of central government expenditures)
…
Aid per capita (current US$)
Current account balance (% of GDP)
…
1.2
1.3
..
..
..
..
..
..
1.0
4.5
2.6
22.4
1.3
12.3
1.2
0.6
1.4
15.7
2.2
11.6
4.2
38.6
..
..
1.1
6,500.8
4.5
6,662.0
3.3
6,745.4
9.1
7,218.2
4.6
7,403.2
3.5
7,516.6
..
..
-0.8
2.5
1.3
7.0
2.6
1.5
..
GDP growth (annual %)
GDP per capita (constant 2000 US$)
…
…
…
6,554.9
GDP per capita growth (annual %)
GDP per capita, PPP (constant 2000 international
$)
…
…
…
..
..
…
..
..
..
..
..
..
..
..
..
..
..
..
..
..
..
..
..
Gross domestic savings (% of GDP)
Gross fixed capital formation (% of GDP)
18.2
13.7
17.0
11.8
32.9
12.9
23.5
11.9
25.4
13.8
Inflation, consumer prices (annual %)
Fiscal deficit, excluding grants % of GDP
9.2
4.2
-2.9
-8.8
-9.9
-2.1
-2.2
Mauritius
Aid (% of central government expenditures)
Aid per capita (current US$)
8.8
44.9
4.0
29.3
2.2
17.2
2.3
17.8
2.5
19.7
-1.3
-11.9
2.9
30.7
2.5
25.7
..
..
Current account balance (% of GDP)
GDP growth (annual %)
-3.1
5.5
-0.9
5.4
-0.8
4.0
6.1
5.6
5.5
2.7
1.8
3.2
-1.8
4.7
-5.4
4.6
..
..
2,761.4
4.2
3,348.9
4.2
3,765.6
3.0
3,931.8
4.4
4,003.9
1.8
4,089.1
2.1
4,244.7
3.8
4,403.5
3.7
..
..
7,093.5
24.3
8,602.9
23.9
9,673.4
23.9
…
…
…
…
28.5
8.6
25.6
6.6
25.3
4.2
GDP per capita (constant 2000 US$)
GDP per capita growth (annual %)
GDP per capita, PPP (constant 2000 international
$)
Gross domestic savings (% of GDP)
Gross fixed capital formation (% of GDP)
Inflation, consumer prices (annual %)
26.0
10,286.0
25.2
23.1
0.0
22.3
6.4
24.8
23.4
18.9
..
..
22.2
3.9
22.1
4.7
21.3
4.9
..
..
55
Fiscal deficit, excluding grants % of GDP
Comoros
Aid (% of central government expenditures)
Aid per capita (current US$)
…
Current account balance (% of GDP)
GDP growth (annual %)
GDP per capita (constant 2000 US$)
GDP per capita growth (annual %)
GDP per capita, PPP (constant 2000 international
$)
Madagascar
Aid (% of central government expenditures)
Aid per capita (current US$)
Current account balance (% of GDP)
GDP growth (annual %)
GDP per capita (constant 2000 US$)
GDP per capita growth (annual %)
GDP per capita, PPP (constant 2000 international
$)
Gross domestic savings (% of GDP)
Gross fixed capital formation (% of GDP)
Inflation, consumer prices (annual %)
..
107.0
65.1
-2.8
1.2
-8.2
2.0
399.4
-1.1
..
34.6
49.7
..
57.5
42.5
43.3
4.2
..
..
378.7
-0.1
373.7
-1.2
378.0
1.2
385.5
2.0
386.8
0.3
377.8
-2.3
385.6
2.1
..
..
1,836.9
1,741.7
1,718.5
1,738.6
1,772.9
1,778.8
1,737.5
1,773.1
..
-3.3
15.5
-5.7
13.8
-2.6
10.4
-2.7
10.1
-2.1
11.0
-4.0
10.3
-8.8
9.4
-12.9
9.3
..
..
…
…
..
..
42.0
-0.2
..
..
..
..
2.5
..
..
..
4.1
…
..
..
3.3
…
..
..
0.9
Gross domestic savings (% of GDP)
Gross fixed capital formation (% of GDP)
Inflation, consumer prices (annual %)
Fiscal deficit, excluding grants % of GDP
…
..
..
..
..
29.4
31.3
15.6
19.9
16.0
22.4
17.8
21.5
17.5
30.6
45.4
68.9
-8.2
0.0
-6.4
3.2
-6.7
4.8
-3.1
6.0
-6.1
-12.7
-3.4
9.8
246.9
-2.9
231.1
0.2
239.4
1.7
246.7
3.0
209.4
-15.1
869.4
2.7
813.9
5.7
843.2
7.7
868.6
15.3
11.8
16.8
13.0
17.9
15.0
12.0
18.5
6.9
..
49.9
..
..
-3.6
5.3
-3.7
4.6
..
..
223.6
6.8
229.1
2.4
233.3
1.8
..
..
737.5
7.7
787.5
8.9
806.7
7.8
821.4
7.7
..
..
14.3
15.9
17.9
-1.2
24.3
13.8
22.4
18.5
..
..
Fiscal deficit, excluding grants % of GDP
Source: World Development Indicators Database and International Financial Statistics
..
56
2.0
Examining the Scope for Inflation Targeting in Selected COMESA
Member Countries
Noah Mutoti
2.1
Introduction
In the early 1990s, due to the poor performance of exchange rate and monetary targeting to
achieve price stability (i.e., low and stable inflation), several industrialized countries opted to
adopt the Inflation Targeting (IT) framework (see Appendix I). The IT policy framework is
simply characterized by the public announcement of the inflation target, coupled with a
credible and accountable commitment on the part of the monetary authority to the
achievement of the target, which is usually in the low single digits. This calls for the central
bank to make public the target inflation rate and then attempt to steer actual inflation towards
the target through the use of policy instrument, typically the short-term interest rate. To
achieve the inflation target therefore requires a stable and predictable relationship between
the policy instruments and inflation rate as well as institutional capacity to model and forecast
inflation, inter alia (Christoffersen et al. (2001).
After the successful results of IT in developed countries, some developing nations including
two Africa countries South Africa and Ghana started to implement IT. Nonetheless, virtually
all COMESA countries have been implementing monetary targeting. Is there a scope for IT in
COMESA members? We address this central question, and many others, by first highlighting
the monetary policy framework COMESA members have been implementing and the
inflation performance. Section 2.3 then reviews the IT conceptual framework. We then assess
the scope for IT in Zambia, Uganda and Mauritius in Sections 2.4 and 2.5. In particularly
Section 2.5 employs a structural vector autoregressive (SVAR) frame to examine the link
between the policy rate and inflation. The concluding remarks are offered in Section 2.6.
2.2
Monetary Policy Regimes in COMESA
It is well understood that to achieve price stability, a central bank needs to provide the
economy with a nominal anchor for which it is responsible. There are three basic ways of
fulfilling this task: exchange rate targeting, monetary targeting and IT. This section reviews
the experience of COMESA countries under exchange rate and monetary targeting.
2.2.1 Exchange Rate Targeting
As the name implies, the exchange rate serves as the nominal anchor or intermediate target of
monetary policy in an exchange rate regime. Its implementation thus entails the monetary
authority either buying or selling foreign exchange at given quoted rates to maintain the
exchange rate at its preannounced level or range. Exchange rate regimes include currency
57
board arrangements, fixed pegs with and without bands as well as crawling pegs with and
without bands. In COMESA, only Comoros, Eritrea and Swaziland are under this regime.
While exchange-rate targeting is easily understood by the public and has been particularly
useful in stabilising inflation quickly after bouts of very high rates of inflation, it has a
number of disadvantages. The first one is that it prevents a central bank from conducting
independent monetary policy. The central bank is thus unable to effectively respond to all the
shocks that hit the economy. Second, shocks to the anchor country are directly transmitted to
the targeting country. Another shortcoming of exchange rate targeting is that it weakens the
accountability of the policymakers. This is because the daily fluctuations of the exchange
rates could provide an early warning signal that monetary policy is overly expansionary.
Targeting the exchange rate thus removes this early warning signal.
2.2.2 Monetary Targeting
Almost all COMESA countries have been implementing monetary targeting. Under this
framework, the nominal anchor considered to tie down inflation expectations is monetary
aggregates. Specifically, the monetary authority uses its instruments to achieve a target
growth rate for a monetary aggregate, such as reserve money or broad money and the targeted
aggregate is the intermediate target of monetary policy. Advantages of this regime include the
following. First, in contrast to exchange rate targeting, the monetary authorities are able to
respond to shocks to the domestic economy. Thus, monetary targeting enables a policy-maker
to take account of domestic developments in setting policy. Second, it is easy to determine
relatively quickly whether the target is being achieved or not.
The success of monetary targeting, nonetheless, relies on the controllability of the monetary
aggregate and the predictability of the ultimate target, inflation. In all, it depends on a strong
and reliable relationship between inflation and the monetary aggregates chosen as the targets.
Controllability simply implies a stable money multiplier. That is, the money multiplier must
be stable for the monetary authority to have a direct control of money. Predictability entails
the existence of a stable demand for money function.
Undoubtedly, in an environment of financial liberalization the relationship between monetary
aggregates and inflation remaining stable is unlikely with serious consequences. A weak and
unstable relationship between money and inflation gives rise to situations where: (i)
achieving the monetary aggregate does not produce the desired inflation outcome; (ii)
monetary aggregates fail to provide reliable signals of the stance of monetary policy; and (iii)
there is no effective anchor for inflation expectations. Furthermore, a weak relationship
between the targeted monetary aggregate and inflation makes it difficult for the central bank
to be transparent and accountable to the public. Although this does not necessarily imply that
monetary policy is irresponsible, it complicates the central bank’s communications with the
public and markets, and thereby impairs its credibility.
By the early 1990s, when it was understood that the relationship between monetary
aggregates and inflation and nominal income had broken down most developed countries,
like the United States and the United Kingdom, abandoned monetary targeting.
58
2.3
Inflation Experiences
In this sub-section, we compare the inflation experience of selected COMESA members
under monetary targeting to that of IT countries in Africa and South America (Chile and
Brazil). Table 2.1 suggests the following: (i) Mauritius‘s inflation performance is within the
context of price stability (i.e., low single digits), at par with two emerging markets
implementing IT in South American, Chile and Brazil and better than Africa‘s IT countries
Ghana and South Africa (ii) the selected COMESA members’ inflation rates have been
generally lower than Ghana over the past 10 years.(iii) Uganda has generally experience low
inflation until 2006.
What has been the inflation experience of these countries during the 2008/09 global and
financial crisis? We note the following from Figure 2.1: (i) IT countries inflation started to
decline earlier than monetary targeters. For example, inflation in Chile and South Africa
started to come down during the third quarter of 2008 whereas Uganda and Zambia’s
inflation remained volatile. (ii) Although Ghana had consistently higher inflation during the
crisis, the inflation rate came down gradually in the second quarter of 2009. (iii) Though
Mauritius is a monetary targeter her inflation level started to decline almost the same time as
South Africa. (iv)Reflecting monetary easing towards the end of 2009, in response to the
crisis, inflation had been observed to increase slightly but within the targets in South
American IT countries, South Africa and Mauritius.
Table 2.1: Inflation Rates in Selected COMESA and IT countries
2000 2001 2002
2003
2004
2005
Monetary Targeting
Zambia
30.1 18.7 26.7
17.2
17.5
15.9
Uganda
4.2
-4.0
5.8
5.9
8.0
3.5
Mauritius
4.7
5.3
5.7
3.8
5.7
3.9
Sudan
3.7
7.4
8.0
8.1
7.5
5.6
Inflation Targeting
Ghana
40.5 21.3 15.2
23.6
11.8
14.8
South Africa
7.0
4.6
12.4
0.4
3.3
3.6
Chile
4.5
2.6
2.8
1.1
2.4
3.7
Brazil
6.0
7.7
12.5
9.3
7.6
5.7
2006
2007
2008
2009
8.2
11.3
11.9
15.8
8.9
4.9
8.6
8.4
16.6
14.2
6.7
14.9
9.9
10.9
1.5
13.4
11.7
5.8
2.6
3.1
12.8
9.0
7.8
4.4
18.3
9.5
7.1
5.9
16.0
6.8
-1.4
4.3
Source: Various Central banks
Note: December annual headline inflation rates
Ghana adopted IT in May 2007 and South Africa in February 2000
Chile and Brazil adopted in September 1999 and June 1999, respectively
Figure 2.1: Inflation during the Crisis, 2008-2009
59
25
Ghana
20
15
Sudan
(%)
Uganda
10
Zambia
South Africa
5
Brazil
Mauritius
0
Chile
-5
IV
I
II
III
2008
2.4
IV
I
II
III
IV
2009
Inflation Targeting—Conceptual Issues
Mishkin and Bernanke (1997), and many others, including Svensson (1999), Lowe (1997)
and Macklem (1998), summarize IT to comprise six main features: (i) an institutional
commitment to price stability as the primary goal of monetary policy, to which other policy
objectives are subordinated; (ii) the public announcement of medium term numerical targets
for inflation ; (iii) an information-inclusive strategy , encompassing the use of several
variables including monetary aggregates and exchange rate in setting policy; (iv) a
transparent monetary policy strategy that ascribes a central role to communicating to the
public the plans, objectives and the rationale of the central bank decisions; (v) mechanisms
for making monetary authorities accountable for achieving the inflation targets; and (vi) a
framework for policy decisions that can be described as “inflation-forecast targeting”, which
means, the use of inflation forecast is the intermediate target. In what follows, some of these
features of IT are described.
2.4.1
Price Stability
In an IT regime, the overriding objective of monetary policy is to achieve the specified
inflation target at the end of the policy horizon, defined as the period by which the central
bank expects inflation to return to its target following the combination of shocks and
monetary policy responses. It is worth noting that the goals other than inflation are not
pursued in the inflation-targeting regime when they are inconsistent with the inflation
60
objective. A fixed exchange rate arrangement, for instance, is inconsistent with IT regime.
However, Freedman and Otker-Robe(2010) argues that while the rate of inflation is the
primary objective, this does not mean that the central bank is indifferent to developments in
other economic variables that are consistent with the inflation target. For example, a full
employment goal is not inconsistent with the inflation target. Although there is a short-run
trade-off between these two objectives, in the long run the contribution of low and stable
inflation to growth is certain. In the end, the response of the central bank to a shock will
depend on the country’s preference for inflation stability relative to output stability, the type
of shock to which the central bank is reacting (demand or supply), whether the economy is
already at its target of inflation and the credibility of the central bank (Freedman and
Laxton(2009b).
This brings in the issue of flexible versus strict inflation targeting. Flexible inflation targeting
means that monetary policy aims at stabilizing both inflation around the inflation target and
the real economy. Stabilizing the real economy implies stabilizing resource utilization around
a normal level, keeping in mind that monetary policy cannot affect the long-term level of
resource utilization. On the other hand, strict inflation targeting aims at stabilizing inflation
only without regard to the stability of the real economy (King (1997)). Because of the time
lags between monetary-policy actions and their effect on inflation and the real economy,
flexible inflation targeting is more effective as it relies on forecasts of inflation and the real
economy (Mishkin and Schmidt-Hebbel(2001).
In support of flexible IT, Svensson(2010) concluded that flexible inflation targeting, applied
in the right way and using all the information about financial factors that is relevant for the
forecast of inflation and resource utilization at any horizon, remains the best-practice
monetary policy before, during and after the financial crisis.
2.4.2
Medium Term Target
What should the medium-term target be? Which measure of inflation should be used? In
developed economies, the price stability target is around 3% while in developing countries it
is 5%. For the transparency of an IT regime, the public should be familiar with the price
index to be targeted, either core inflation or headline inflation. The reason why central
bankers may be interested in choosing a core index is that it excludes the effects of nonmonetary determinants of inflation. Further, it is recommended to target core inflation, which
gives information on underlying pressures, if it constitutes 90% of the consumer price index
(CPI). There is, however, generally consensus that the public better understands headline
inflation than core inflation. Accordingly, as shown in Table 2.2, most IT countries target
headline inflation.
Another question in this arena is whether the inflation target should be a point or range
target? Since monetary policy impact inflation with lags and it is not possible to predict the
future inflation rate exactly, it is difficult to reach to the point target. Thus, the adoption of
the range is recommended. And as the positive results of the IT regime are realized by the
public, the bandwidth should be narrowed. Also shown in Table 2.2 most countries target the
CPI inflation within a band.
61
Table 2.2: Target Variable and Range
Target Variable
CPI
CPIX
CPI
CPI
CPI
CPI(HICPI)
Underlying CPI
Ghana
South Africa
Chile
Brazil
New Zealand
UK
Republic of Korea
2.4.3
Inflation Target
7-9
3-6
2-4
4.5  2
1-3
2.0
3 1
Transparency and Accountability
Transparency has become one of the main attributes that characterize best-practice monetary
policy. Transparency could be understood as a property of the communication strategy of the
monetary authority. Advocates of IT argue that this framework enhances transparency in all
these dimensions. This happens because of the existence of an explicit target for inflation,
which makes it clear the goal of monetary policy, and also provides a benchmark that can be
used to monitor policy outcomes.
One way to enhance transparency is increased communication with the public about the
monetary policy decision-making process which not only keeps the public well informed but
also anchors their inflation expectations. Various approaches have been adopted including the
advance publication of monetary policy meeting dates, publication of minutes of the MPC,
press conferences or press releases and the publication of inflation forecasts in the monetary
policy reports. Table 2.3 suggests that IT improves accountability by reporting to Parliament
and writing letters to the Government when the inflation target is missed.
Table 2.3: Accountability
Hearing in Parliament
Ghana
South Africa
Chile
Brazil
New Zealand
UK
Republic of Korea
Source: Truman(2003)
2.4.4
No
No
Yes
No
Yes
Yes
Yes
Open Letter or report when
target is missed
No
No
No
Yes
No
Yes
No
Inflation forecast Intermediate Target
Why is forecast inflation the intermediate target? Bernanke and Mishkin (1997) argue that the
use of conditional forecasts as the intermediate-target variables provides the solution to the
problem of imperfect control of inflation. Inflation control is imperfect due to, inter alia, lags
in the transmission mechanism and uncertainty about the transmission mechanism, the
current state of the economy and future shocks to the economy as well as influences of
62
factors other than monetary policy on inflation. Sources of the imperfect control of inflation
include aggregate demand and supply shocks, instability of intermediate targets (i.e., velocity
shocks), information asymmetries and uncertainty about the relative strength of policy
instruments.
2.5
Prerequisite Features of Inflation Targeting
Certain prerequisites need to be met for the effective implementation of the IT regime,
namely: (i) instrumental independence of the central bank in pursuing monetary policy; (ii)
absence of fiscal dominance; (iii) a firm commitments by the authorities not to target other
nominal variables that might conflict with the inflation objective; (iv) the choice target for
monetary policy should be forecast inflation to provide a nominal anchor for inflation and
inflation expectations;(v) existence of sufficiently developed financial markets and their
infrastructure so that the effective transmission of monetary policy measures can be ensured;
and (v) inflation forecasting models.
2.5.1
Central Bank Independence
A country considering adopting IT should have the central bank capable of conducting
monetary policy with a degree of independence. Central bank independence simply refers to
the freedom of monetary policymakers from direct political or governmental influence in the
conduct of policy. This, however, does not necessarily mean the central bank must be fully
independent , but more modestly, that the monetary authorities must be able to gear (more or
less) freely the instruments of monetary policy toward the attainment of some nominal
objective—i.e., there should exist some reasonable degree of instrument independence, but
not necessarily goal independence.
According to Freedman and Otker-Robe (2010) as well as Roger (2009), central bank
independence involves instrument independence, the freedom to set the policy interest rate
needed to achieve the desired goal of policy without government interference, rather than
goal independence---the freedom to choose the objective of policy. Given the importance of
instrument independence, a number of IT countries have passed legislation giving the central
bank authority to set interest rates.
2.5.2
Fiscal Discipline
Without fiscal disciple, it is quite difficult to have successful monetary policy under any
framework. So as a requirement for sustained inflation targeting, a country has to exhibit no
significant symptom of fiscal dominance so that the conduct of domestic monetary policy
will not be dictated or severely constrained by developments of a fiscal nature. In general
terms, this implies that public sector borrowing from the central bank (and the banking
system as a whole) needs to be low or non-existent, that the government need to have a
broader revenue base and therefore will not rely significantly on the revenue from seignorage,
that domestic financial markets will need to have enough depth to absorb placements of
public (or private) debt instruments. Failure to comply with these conditions will make the
country vulnerable to inflationary pressures of a fiscal origin. A fiscally driven inflation
63
process will undermine gradually the effectiveness of monetary policy to attain any nominal
target and oblige the central bank to follow an accommodative monetary policy.
Adding to the importance of the absence of fiscal dominance, Freedman and OtkerRobe(2010) argue that if the central bank is required to finance the deficit by lending directly
to the government or by purchasing all new issues of government bonds that the public is
unwilling to purchase, it will not be able to target the preannounce rate of inflation. That is,
if the central bank tries to use its single policy instrument to aim at two goals, one involving
financing the government deficit and the other being the achievement of an inflation target, it
will not be able to achieve both goals with one instrument. This is because if the central bank
has to finance the budget deficit, it will not have control over the size of its own balance
sheet. Hence, it will not be able to exert much influence over the policy rate to set in motion
the effects on the transmission needed to respond to an orderly high or low rate of inflation.
One way to deal with this problem is by prohibiting direct financing of government deficits
by the central bank.
2.5.3
Developed Financial Markets
The instruments of policy, especially market-based instruments, can only be effective in
influencing liquidity conditions, and ultimately developments in prices, under a sound and
well developed financial sector. A relatively developed financial sector thus plays a crucial
role in the effective transmission of monetary policy impulses.
2.5.4 Central Bank Has Control Over Interest Rates
To control inflation, under any monetary policy regime, there must be a reasonable stable
relationship between policy instruments and inflation. Thus, besides instrument
independence, the central bank should have the ability to strongly control or influence the
policy instrument that affects the interest rates feeding into aggregate demand. This implies
that mechanism must exist or be established to enable the central bank initiate a series of
developments flowing from its action to aggregate demand and ultimately to inflation. So
when the central bank sets its policy rate, other interest rates in the market should adjust. If
not, the goal of IT will not be achieved.
2.5.5 Target choice
As already noted, with the primary objective of proving a nominal anchor for inflation and
inflation expectations, the choice target for monetary policy should be forecast inflation.
Accordingly, absence of any firm commitment to target the level or path of any other nominal
variable, other than forecast inflation is a necessary requirement under IT.
2.5.6 Inflation Forecasting Model
Effective monetary policy implementation requires a comprehensive understanding of the
channels through which adjustments in interest rates affects business and household
decisions. As IT is a forward-looking policy strategy, there is need for forecasting model.
64
That is the central bank should have the capacity to model the economy and forecast
inflation. Thus IT motivates central bank to build macroeconomic models vital for policy
analysis and inflation forecasting.
However, since developing and emerging economies have been undergoing major economic
reforms, the ability of the central bank to have a good empirical model of the transmission
mechanism is limited. Further, while a sophisticated methodology of producing forecast can
be developed and improved over time, is not essential at the outset of IT. What is essential for
an IT country is a reasonable framework for forecasting inflation. The model underlying this
framework can initially be simple. The framework should employ whatever formal empirical
relationships have proved useful in the past, whatever variables and indicators containing
information on the future rate of inflation are available, survey evidence, and the judgement
of staff and members of the monetary policy committee (MPC) of the central bank. That is it
should not be based solely on the econometric model, but rather should incorporate the
judgement of specialists and whatever else is available that can contribute to the quality of
forecasts (Laxton, Rose and Scott (2010).
2.6 Assessing Selected COMESA Members for IT
Here we assess the extent to which Zambia, Uganda and Mauritius has met or not met the
fiscal discipline and development of the financial sector conditions to implement the IT
framework. Fiscal disciple is described by the fiscal budget/GDP ratio and the extent to
which the central bank finances the budget deficits. The ratio of private credit to GDP
measures the level of financial development.
2.6.1 Zambia
Table 2.4 shows that Zambia’s fiscal discipline in 2009 was comparable to that of Brazil
when it adopted IT and better than the fiscal position in Ghana in 2007. However, while the
Bank of Zambia Act (see Box 1) states the limit of how much credit the central bank can give
to the Government, it allows for the limit to be exceeded. This suggests that there may be
difficulties of maintaining consistent fiscal discipline. Table 2.4 suggests that Zambia’s
financial sector needs to develop further, if IT is to be effective.
Table 2.4 : Key Financial Ratios for Inflation Targeters
Emerging
Time of
Fiscal Balance/GDP
Market
Adopting IT
Brazil
June 1999
-5.7
Chile
September
-3.0
Private Sector
Credit/GDP
24.4
66.2
65
1999
Sub-Saharan
Ghana
South Africa
COMESA 2009
Zambia
Uganda
Mauritius
Source: IFS
May 2007
February 2000
-8.1
-1.9
23.6
73.0
-5.5
-12.2
-3.0
12.0
16.5
84.3
Box I
THE BANK OF ZAMBIA ACT of 1996
The Bank shall formulate and implement monetary and Bank supervisory policies that will ensure the
maintenance of price and financial systems stability so to promote balanced macro-economic
development.
Advances to Government
Section 49. The Bank shall not advance funds to the Government except in special circumstances and on such terms and conditions
as may be agreed upon between the Bank and the Minister
Limitation of lending to Government
1)
2)
Except as provided for in Section 49, the bank shall not directly or indirectly , at any time, give credit to the Government
by way of short tern advances , purchases or securities in a primary use, or any other form or extension of credit that
exceeds fifteen % of ordinary revenue of the Government in the previous year.
If in the opinion of the Bank the limitation provided for in Sub-section((1), is likely to be exceeded , the Bank shall submit
to the Minister stating
a) The details of the amounts then outstanding of the funds advanced and credit facilities extended by the Bank
and the Bank’s holdings of securities referred to in sub section (1);
b) The causes which are likely to lead to such limitation being exceeded; and
c) Its recommendation to forestall or otherwise remedy the situation.
3) The Bank shall continue to make further reports and recommendations on the matters referred to
in subsection(2), at intervals of not more than six months until such time, as , in its opinion, the
situation has been rectified.
4) Where the limitation provided for in subsection (1) is exceeded, the Bank shall forthwith advise the
Minister of that fact and shall not allow any further increase , whether directly or indirectly , in the
aggregate amount of the funds advanced and credit facility extended by the Bank and the Bank’s
holding of securities referred to in subsection(1)
2.6.2 Uganda
Weak fiscal discipline had been observed in Uganda as the budget deficit/GDP ratio was in
double digits (Table 4). Similar to Zambia, while the Act states the limit of how much credit
the central bank can give to the Government, it allows for the limit to be exceeded (Box II).
This suggests that they may also be difficulties in maintaining consistent fiscal discipline in
66
Uganda. Compared to Zambia, however, Uganda’s financial sector is relatively developed
though it falls below Ghana and other IT countries.
Box II
Bank of Uganda Statute 1993
The Bank of Uganda Act, CAP 51 (Laws of Uganda, 2005) states in Section 5(i) the function of the Bank
"shall be to formulate and implement monetary policy directed to economic objectives of achieving and
maintaining economic stability".
PART VI—BANK RELATIONSHIP WITH THE GOVERNMENT.
34.
Temporary advances.
1)
The bank may make temporary advances to the Government and local governments in respect of temporary
deficiencies of recurrent revenue.
2)
The Treasury shall, at the beginning of each financial year, identify and submit to the bank all its
requirements for temporary advances for that year; and the bank shall, subject to subsection (3), operate within that
requirement.
3)
The total amount of advances made under subsection (1) shall not at any time exceed 18% of the recurrent
revenue of the Government.
40
The bank shall charge market rates of interest on any advance to the Government or local government unless
the board determines otherwise.
35. Report on advances.
(1) Where in the opinion of the bank the limitations on bank credit prescribed under section 33(3) or the holding of
securities is exceeded, the bank shall make a report on the bank’s outstanding advances or holding of securities in
terms of those sections and the causes that have led to the breach
of the limitations, together with any recommendation or remedy; and the bank shall make further reports and
recommendations to the Minister at intervals not exceeding six months until the situation has been rectified.
(2) At any time when the limitations on bank credits or the submitted requirement is exceeded, the powers of the
bank to grant additional financing shall cease until the situation has been rectified.
67
2.6.3 Mauritius
We observe that Mauritius is fiscally disciplined as reflected in both the fiscal deficit/GDP
ratio (Table 4) and the Bank Act in relation to Government financing by the central bank
(Box III). In particular, it compares favourably with Brazil and Ghana when these countries
adopted the IT framework. We conclude that the financial system, as reflected in private
sector credit/GDP ratio of about 84% (Table 4), is well developed and therefore adequate for
an IT framework.
Box III
Bank of Mauritius of 2004
Objects of the Bank
(1) The primary object of the Bank shall be to maintain price stability and to promote orderly and
balanced economic development.
Advances to and deposits from the Government
(1) Notwithstanding section 6(1)(a), the Bank may grant advances to the Government to cover negative
net cash flows of the Government at such rate as may be agreed with the Government.
(2) The total amount of such advances outstanding, together with the amount of Government securities
in the ownership of the Bank, other than under repurchase agreements and those held under section
6(1)(j), shall not at any time exceed 10 per cent of the Government’s revenue excluding grants and
receipts of a capital nature for the current financial year.
(3) Any advances under subsection (1) shall be repaid as soon as possible and shall, in any event, be
repayable not later than 4 months from time to time the advances are granted.
(4) Where any advances have not been repaid within the time specified under subsection (3), any
advances outstanding shall be converted into Government securities at market rates.
(5) Any Government deposits with the Bank shall be compensated at such marketrates as may be
determined by the Bank in accordance with section 56(3).
2.7 SVAR Model Analysis
Already noted, one of the preconditions of a successful IT framework is the existence of a
stable and predictable relationship between the monetary policy instruments, interest rate and
inflation. The transmission of policy impulses to inflation is conceptually modelled as
postulated in Figure 2.2. It is predicted that an increase in policy rate is followed by a rise in
commercial banks’ lending interest rates and in line with the uncovered interest parity (UIP),
the strengthening of the domestic currency. Consequently demand and import prices, in
domestic currency terms, will decline and ultimately, lower inflation rates will be recorded.
68
Figure 2.2:Transmission of Policy Impulses To Inflation
Demand
Lending Rate
export demand
POLICY RATE
Exchange Rate
INFLATION
import prices
(Exchange rate appreciation)
What Figure 2.2 depicts is that the central bank stands at the beginning of the transmission
mechanism of monetary policy, and its actions result in changes in a series of economic
variables that constitute the linkages from its actions to the rate of inflation.
The statistical approximation of Figure 2.2 is guided by the structural vector autoregressive
(SVAR) model, in line with many including Gottschalk and Moore (2001). The model was
identified by means of contemporaneous restrictions as depicted in equation (2.1) and
summarized as follows;
(i)
(ii)
The policy rate only responds contemporaneous to an exchange rate shock (  3 ), on the
premise that its only exchange rate information readily available to the monetary
authority.
The exchange rate responds contemporaneously to the policy shock (  1 ) and price
shock(  5 )
(iii) The CPI responds immediately to exchange rate shock as well as to supply shock (  4 )
(iv) The shock to lending rate (  2 ) has no immediate impact on any other variable
 Policy  1
 Lend  0

 
 Excrt   a31

 
GDP  0
CPI  0
0
a13
0
1
0
0
0
1
0
0
0
1
0
a53
a54
0   1 
 
0   2 
a35   3 
 
0   4 
1   5 
(2.1)
In what follows, we present the empirical analysis, that is, the dynamic responses of the
variables to a policy shock and the sources of variations in the variables.
69
2.7.1 Zambia
The data is monthly over the period January 1995 - December 2009, summarized as follows.





Policy rate (Policy) proxied by the interbank rate.
Lending rate is the commercial banks’ lending interest rates
Exchange rate is the nominal Zambian Kwacha/US Dollar exchange rate
Output is the interpolated real gross domestic product (GDP)
Price is the consumer price index (CPI).
It is worth noting that currently Bank of Zambia does not have an explicit policy rate.
However, the use of interbank rate is justified as the Bank is in the process of coming up with
a policy rate which may mirror the interbank rate. The data sources were various Bank of
Zambia publications. All the variables are in logs, except interest rates. In line with many
studies (e.g. Mutoti and Kihangire (2010), it was assumed that the series are non-stationary.
With few exceptions, the impulse responses to a positive shock to the policy rate are in line
with the priori and the identification strategy. However, the lending rates, exchange rate,
output and consumer prices barely move in response to a monetary policy shock (Figure 2.3).
Figure 2.3: Impulse Responses to a Policy Shock
The variance decompositions (Table 2.5) provides the following insights: (i) What mainly
drive adjustment in the policy rate is its own innovation and modestly exchange rate shocks
(ii) Adjustments in the policy rate barely affect movements in commercial bank interest rates
70
(iii) the policy rate has a modest impact on the exchange rate accounting for around 30% .
(iv) Growth in output is manly driven by supply shocks. (v) CPI inflation is driven by its own
innovation and exchange rate shocks in the short-run. In the medium to long-term, it is
mainly driven by supply shocks, exchange rate and its own innovations.
Table 2.5: Variance Decompositions Zambia
Period/shock
1
6
12
36
60
Policy Rate
90.2
64.7
61.2
60.5
62.4
Lend
0.0
2.4
3.4
4.2
4.5
Period/shock
1
6
12
36
60
Policy Rate
0.0
1.0
0.7
0.7
0.8
Lend
100.0
95.2
94.0
93.2
93.1
Period/shock
1
6
12
36
60
Period/shock
Policy Rate
2.1
27.4
28.3
28.5
29.7
Lend
0.0
0.3
0.5
0.5
0.6
1
6
12
36
60
Policy Rate
0.0
0.3
0.1
0.1
0.2
Lend
0.0
0.4
0.6
0.7
0.6
Period/shock
1
6
12
36
60
Policy Rate
0.2
1.3
0.5
0.4
0.6
Lend
0.0
0.8
1.2
2.3
2.5
Policy Rate
Excrt
9.8
25.0
25.3
24.8
23.9
Lending rate
Excrt
0.0
2.1
2.4
2.5
2.2
Exchange Rate
Excrt
97.7
64.7
62.9
60.6
56.0
GDP
Excrt
0.0
2.7
3.7
3.0
2.2
CPI
Excrt
8.4
30.7
31.5
28.5
25.5
GDP
0.0
3.3
4.8
5.5
5.2
CPI
0.02
4.5
5.3
5.0
4.1
GDP
0.0
0.9
1.8
2.8
3.4
CPI
0.0
0.9
1.2
0.9
0.6
GDP
0.0
0.7
0.9
1.1
1.9
CPI
0.2
6.8
7.3
9.3
11.8
GDP
100.0
91.6
89.2
86.6
84.2
CPI
0.0
4.9
6.3
9.7
12.8
GDP
0.9
13.9
20.2
32.4
36.6
CPI
90.5
53.4
46.6
36.4
34.8
2.7.2 Uganda
The data is monthly over the period Augusts 1998 - December 2009, summarized as follows.

Policy rate (Policy) proxied by the bank of Uganda policy rate.
71




Lending rate is the commercial banks’ lending interest rates
Exchange rate is the Uganda Shillings/US Dollar exchange rate
Output is the interpolated real gross domestic product (GDP)
Price is the consumer price index (CPI).
The data sources were various Bank of Uganda publications. In line with many studies (e.g.
Mutoti and Kihangire (2010), Mutoti and Eti-Ego (2010), it was assumed the series are nonstationary.
Unlike in Zambia, there is a noticeable impact of a policy shock on the lending rates. However,
despite the increase in the policy and lending rates, the exchange rate and output barely
moves. With little movement in the exchange rate and output, the impact of the policy shock
on consumer price is insignificant (Figure 2.4).
Figure 2.4: Impulse Responses to a Policy Shock
We deduce the following from the variance decomposition (Table 2.6).
(i)
(ii)
(iii)
(iv)
(v)
Developments in the policy rate are also modestly induced by the output shocks in the medium
to long-term.
In line with impulse responses, adjustments in the policy rate drive the lending rates,
accounting for slightly over 20% of the innovations in the medium to long-term.
Exchange rate shocks are the main determinants of exchange rate movements.
Growth in output is largely dependent on supply shocks.
Exchange rate and output shocks contribute significantly and modestly to the CPI movements.
72
Table 2.6: Variance Decompositions Uganda
Period/shock
1
6
12
36
60
Policy Rate
99.9
91.6
84.3
75.3
73.6
Lend
0.0
0.6
0.4
0.2
0.2
Period/shock
1
6
12
36
60
Policy Rate
0.0
15.1
24.7
23.9
23.5
Lend
100.0
81.5
70.6
66.1
58.3
Period/shock
1
6
12
36
60
Period/shock
Policy Rate
10.2
2.7
1.3
0.5
0.3
Lend
0.0
1.6
1.8
1.8
1.9
1
6
12
36
60
Policy Rate
0.0
0.1
0.1
0.1
0.1
Lend
0.0
0.1
0.2
0.4
0.7
Period/shock
1
6
12
36
60
Policy Rate
0.3
1.5
1.5
1.2
1.0
Lend
0.0
2.3
1.9
1.7
2.2
Policy Rate
Excrt
0.003
0.3
0.2
0.2
0.4
Lending Rate
Excrt
0.0
1.5
1.0
1.2
6.2
Exchange rate
Excrt
88.9
91.2
91.1
91.8
93.5
GDP
Excrt
0.0
0.1
0.2
0.3
0.3
CPI
Excrt
2.3
3.2
3.3
7.8
18.1
GDP
0.0
7.5
15.1
24.3
25.8
CPI
0.0
0.1
0.1
0.02
0.02
GDP
0.0
1.3
3.3
8.5
11.8
CPI
0.0
0.7
0.4
0.3
0.3
GDP
0.01
4.1
5.5
5.7
4.1
CPI
0.9
0.3
0.3
0.2
0.2
GDP
100.0
98.9
98.1
97.6
97.3
CPI
0.0
0.8
1.4
1.6
1.6
GDP
1.00
9.7
18.8
25.9
21.8
CPI
96.4
83.2
74.4
63.3
53.9
2.7.3 Mauritius
The data is monthly from January 1999 to December 2009, summarized as follows.





Policy rate is represented by the repo rate.
Lending rate is the commercial banks’ lending interest rates
Exchange rate is the Mauritian Rupees/US Dollar exchange rate
Output is the interpolated real gross domestic product (GDP)
Price is the consumer price index (CPI).
73
The data sources were various Bank of Mauritius publications. It was assumed the series are
non-stationary.
Following a positive shock to the repo rate, with some lag a pronounced increase in the
lending rate and a slight appreciation in the rupee is observed. While the output response is
quite insignificant some marginal reduction in the CPI is recorded (Figure 2.5).
Figure 2.5: Impulse Responses to a Policy Shock
The variance decomposition (Table 2.7) suggests the following:
(i)
(ii)
An adjustment in the policy rate is modestly in response to shocks to the exchange rate
shocks and CPI over a twelve month period. In the medium to long-term, adjustments
in the repo rate follows shocks to output and the CPI.
About 30% of lending rate movements are accounted for by the policy shock.
(iii) About 15- 25% movements in the rupee/US dollar exchange rate are attributable to the
policy rate.
(iv)
Through the 60 month horizon, much of growth is accounted for by supply shocks, contributing
over 50%. Money demand shocks, captured by innovations to the CPI, however, make a
modest contribution (13-26%) in the short-term.
(v)
Sources of inflation are mainly exchange rate shocks.
74
Table 2.7: Variance Decompositions for Policy Rate Mauritius
Policy Rate
Period/shock
Policy Rate
Lend
Excrt
1
84.1
0.00
11.2
6
41.8
6.1
11.5
12
33.6
5.5
12.6
36
24.6
3.7
9.8
60
21.5
3.3
8.6
Lending rate
Period/shock
Policy Rate
Lend
Excrt
1
0.0
100.0
0.00
6
30.1
68.6
0.7
12
30.2
66.5
1.7
36
30.6
60.4
3.6
60
29.7
60.1
4.0
Exchange Rate
Period/shock
Policy Rate
Lend
Excrt
1
15.5
0.0
58.8
6
16.8
0.2
57.1
12
18.7
0.5
55.3
36
25.4
2.6
52.7
60
27.1
3.2
52.1
Period/shock
GDP
Policy Rate
Lend
Excrt
1
0.0
0.0
0.0
6
6.5
6.3
1.2
12
9.4
4.8
3.3
36
11.5
2.4
5.8
60
11.8
1.9
6.3
CPI
Period/shock
Policy Rate
Lend
Excrt
1
3.5
0.0
13.2
6
3.8
0.4
11.8
12
3.0
0.7
21.3
36
2.1
0.5
20.9
60
1.9
0.4
20.8
GDP
0.0
0.7
7.8
38.4
49.6
CPI
4.7
40.0
40.5
23.5
17.0
GDP
0.0
0.5
0.3
0.2
0.2
CPI
0.0
0.1
1.3
5.2
6.1
GDP
1.0
1.1
0.9
2.1
2.5
CPI
24.8
4.9
24.6
17.1
15.2
GDP
100.0
55.5
53.2
82.0
69.5
CPI
0.0
24.7
26.8
4.0
13.0
GDP
3.3
1.6
1.1
0.4
0.3
CPI
80.1
82.5
74.0
76.1
76.6
2.8 Concluding Remarks
This undertaking explores the scope for the adoption of the inflation targeting policy
framework in selected COMESA member countries. The following are the key questions
addressed: What has been the experience of selected COMESA countries implementing
monetary targeting compared to emerging countries IT? .What has been the inflation
experience of these countries during the recent global and financial crisis? To what extent has
Zambia, Uganda and Mauritius met or not met the conditions of fiscal discipline and financial
development vital for effective implementation of IT? What is the strength of transmission
75
channel of monetary policy using the interest rate as the policy variable, a vital requirement
for IT?
When looking at the inflation experience of both monetary targeters and inflation targeters,
over the last 10 years, we found that inflation levels in the South American IT countries, as
well as in South Africa, were low and stable, whereas Monetary targeting countries, with the
exception of Mauritius, experienced high and volatile inflation throughout the period. Ghana,
on the other hand, has shown consistently higher inflation than both Monetary targeting
countries and IT countries, even after it adopted IT in 2007.
We noted that during the crisis, inflation levels in IT countries peaked in mid-2008 and
started to decline towards the end of 2008, while inflation levels in monetary targeting
countries – particularly Uganda and Zambia, remained volatile in 2008 and early 2009.
Interestingly, the inflation experience of Mauritius during the crisis was very similar to that
of the IT countries, as its inflation levels also started to fall at the end of 2008, and reached
levels lower than South Africa and Brazil at the end of 2009. Ghana had the highest inflation
over the 2 year period, with inflation levels coming down gradually from mid-2009. Brazil
was able to maintain a relatively stable inflation rate during the 2-year period.
In addressing the third question, we measured fiscal discipline using the fiscal deficit/GDP
ratio, as well as by analysing the Central Bank Act in relation to Government financing.
Financial development was measured using the private sector credit/GDP ratio. When
looking at Zambia, we found that although its level of fiscal discipline was similar to that of
Brazil when it adopted IT, there was still room for improvement, as the Bank Act made
provision for the central bank to exceed the limit placed on Government financing. In terms
of financial development, the financial system is still in need of further development if
Zambia is to adopt the IT framework. With regards to Uganda, it appears to exhibit relatively
weak fiscal discipline. Its fiscal deficit/GDP was the highest among all selected countries,
and its Bank Act, similar to Zambia’s allows for financing limits to be exceeded. While it is
relatively more financially developed than Zambia, there is also need to improve on the
financial system in Uganda before it can effectively undertake IT. Finally, the fiscal
discipline and financial development shown by Mauritius is comparable, if not better than
some IT countries at the time that they adopted the IT framework. Not only is its fiscal deficit
low, the Bank Act does not allow for the limit on government financing to be breached. It
also stipulates the repayment period and method, thus ensuring accountability and discipline
on the part of the central bank and government. We find that out of the selected COMESA
monetary targeters, Mauritius is better positioned to adopt the IT framework.
We address the last questions by means of a SVAR analysis. Except in Zambia, there is
evidence of policy rate influencing the lending rate in Uganda and Mauritius. There is no
strong evidence of the policy rate influencing the exchange rate in both Zambia and Uganda.
Despite the increase in lending rates following a positive shock to the policy rate, output
barely moves in Uganda. Consequently, with little movement in the exchange rate and output,
the impact of the policy shock on consumer price is insignificant in Uganda. In Mauritius,
while output response is quite not significant in response to an increase in the policy rate,
76
some marginal reduction in the CPI is recorded. We deduce using interest rate as the indicator
of the monetary stance is relatively stronger in Mauritius than Uganda and is very weak in
Zambia.
The policy lessons among the three countries are that Mauritius has the requisites to adopt IT.
Also experience of Mauritius indicates that it is possible to achieve price stability without an
explicit inflation targeting framework by using interest rates as the monetary policy
instrument. IT countries were able to respond to external shocks faster than the monetary
targeters with the exception of Mauritius – which suggest that the use of interest rates to
undertake monetary policy is much more effective than targeting monetary aggregates.
There is a saying that if it not broken, do not fix it. Though among COMESA members,
Mauritius is a good candidate for IT, the current monetary policy framework has been able to
deliver price stability. Thus a cautious approach is recommended for Mauritius to shift to IT.
One major issue to be considered in this regard is an examination of the stability of the
money demand.
2.9 References
Carare, A. and Stone. M. (2003), “Inflation Targeting Regimes”, IMF Working Paper,
WP/03/9. International Monetary Fund.
Christoffersen, P., T. Slok and R. Wescott (2001). "Is Inflation Targeting Feasible in
Poland?," Economics of Transition Vol. 9
King, M. (1997), “Changes in UK Monetary Policy: Rules and Discretion in Practice”,
Journal of Monetary Economics 39, 81–97.
Freedman,C. and I.Otker-Robe (2010), “Important Features for Inflation Targeting for
Emerging Economies, IMF Working Paper WP/10/113, Washington DC.
Freedman,C. and Laxton, D(2009b), “IT Framework Design Parameters”, IMF Working
Paper WP/09/86, Washington DC
Gottschalk, J. and D. Moore(2001),”Implementing Inflation Targeting: The Case of
Poland,” Journal of Comparative Economics, Vol 29, No.1.
Laxton, D.Rose,D. And Stott,A(2009),”Forecasting and Policy Analysis System,” IMF
Working Paper WP/09/65, Washington DC.
Lowe, P. (1997), “Monetary Policy and Inflation targeting”, Reserve Bank of Australia.
Macklem, T. (1998), “Price stability, Inflation Targets and Monetary Policy”, Bank of
Canada.
MishkinS.F. and K.Schmidt-Hebbel(2001) “One decade of Inflation Targeting in the
World: What do We Know and What Do We Need to Know? NBER No. 8397
77
Mishkin S.F. and Bernanke S. B. (1997), “Inflation targeting: A new framework for
monetary policy”, Journal of Economic Perspective, Vol. 11, Issue 2 pp. 97-116.
Mutoti, N. and Eti-Ego. M.(2010) “Comparative Study on the Monetary Transmission in
Selected COMESA Member States “ In Issues of COMESA Monetary Harmonization
Programme, COMESA
Mutoti, N. and Kihangire. D (2010) “Sources of Inflation in Selected COMESA Member
States” In Issues of COMESA Monetary Harmonization Programme, COMESA
Roger, S. (2009), “Inflation Targeting at 20: Achievements and Challenges,” IMF
Working Paper WP/09/236, Washington DC.
Svensson, Lars E.O. (2009), “Flexible Inflation Targeting: Lessons from the Crises
”, Sveriges Riks Bank.
Svensson, Lars E.O. (1999), “Inflation Targeting: Some Extensions”, Scandinavian
Journal ofEconomics 101, 337–361.
Truman, E.M(2003), “Inflation Targeting in the World Economy” Washington, DC,
Institute of International Economics
Appendix B
Table 1B: Inflation targeting Counties
Period of IT
Introduction
Australia
April 1993
Brazil
June 1999
Canada
February 1991
Chile
September 1999
Colombia
September 1999
Czech Republic
January 1998
Ghana
May 2007
Hungary
June 2001
Iceland
March 2001
Israel
January 1997
Korea
April 2000
78
Mexico
January 2001
New Zealand
March 1990
Norway
March 2001
Philippines
January 2002
Poland
October 1998
Peru
January 2002
South Africa
February 2000
Sweden
January 1993
Switzerland
January 2000
Thailand
May 2000
Turkey
January 2002
United Kingdom
October 1992
3.0
Scope and Quality of Macroeconomic Data in
Selected COMESA Member Countries
Mehesha Getahum
3.1
Introduction
The countries of the sub-region are at different stages in the scope and quality of their
national accounts statistics. Among those implementing the 1993 SNA the status differs
widely from country to country. Few countries have made good progress towards fulfilling
the minimum requirements set for the implementation of 1993 SNA while the others are on
the initial stage of transition. There are also few member states which are still in the 1968
SNA.
The objective of the study is therefore to investigate the current practices in the concept and
methodology of compilation of national accounts. This is addition to the compilation of
consumer prices, government finance, balance of payments and monetary statistics. The
review is made largely from the point of view of compliance to international standards,
79
recommendations and best practices. The countries covered by the study are Egypt, Ethiopia,
Zambia, Mauritius and Malawi.
The rest of the paper is organized as follows. Sections 3.2 and 3.2 discuss national accounts
and consumer price index. Section 3.3 dwells on Government finance statistics. We end this
paper with a discussion of balance of payments and monetary statistics.
3.2
National Accounts Statistics
3.2.1 Ethiopia
Ethiopia with a population of 76 million (2005) is the second populace country in Africa after
Nigeria. The per capita gross domestic product (GDP) for 2005 was about US dollar 140.
The Central Statistical Agency (CSA) is the central organ of the federal government
responsible for the collection, analysis and dissemination of official statistics. The Ministry of
Finance and Economic Development (MOFED) is the supervising ministry of the CSA.
3.2.1.1 Conceptual Framework, Scope, and Classifications
To a large extent, Ethiopia follows the 1993 SNA as the general framework for compiling the
national accounts statistics. However, due to the nature and availability of data there still
persist some aspects of the 1968 SNA. The scope of the national accounts statistics in terms
of accounts and tables compiled regularly is limited and is far short of the internationally
determined minimum requirement for the implementation of the 1993 SNA Currently, annual
value added and GDP by economic activity at constant prices as well as expenditures on GDP
at current prices are compiled on a regular basis. Provisional estimates of these aggregates are
made available within 10 months after the end of reference year. However, annual value
added and GDP by activity at current prices are compiled and presented with a considerable
time lag of about four years. On the other hand, data on expenditures of GDP at constant
prices are not compiled. Annual value added components at current prices by activity are not
either available. Further, sequence of accounts even for the total economy and rest of the
world account are not compiled.
The base year for the series of national accounts statistics is 1999/2000. The concepts and
methodologies adopted in the compilation of the data are documented and are readily
available in print as well as in soft copy. The document titled “National Accounts of Ethiopia:
Sources and Methods, 1999/2000 Series” discusses the coverage, the source data and the
compilation methods used for each activity group. The production boundary is largely in line
with recommendations of the 1993 SNA. It includes, among others, all own account
production of goods; estimates for gathering of fuel wood; estimates for drawing and carrying
of water by rural households from rivers, streams and ponds; and estimates for own account
80
construction of rural dwellings. Regarding valuation of transactions, output is valued at basic
prices as recommended in SNA 93.
The classification of economic activities strictly follows the International Standard Industrial
Classification (ISIC, Rev.3). Data on household consumption expenditures are compiled and
presented according to the classification of individual consumption by purpose (COICOP) at
the two-digit level. MOFED has not as yet adopted GFS 2001 and hence does not report data
on government operations according to classification of the functions of government
(COFOG) which is the internationally accepted standard classification. Crude estimates of
consumption expenditures by NPISHs are provided at aggregate level.
3.2.1.2 The Nature and Availability of Source Data
The Central Statistical Agency is the major sources of data. In addition to the CSA data are
also collected from various government offices, private producers and NPISHs. The Central
Statistical Agency is receiving adequate budgetary allocations from the government to
conduct various statistical inquiries (censuses, periodic and annual surveys). The major
surveys conducted by the CSA since 1995 include: (i) Censuses such as agricultural sample
enumeration (2001/2001) and population and housing censuses (1994). (ii) Periodic surveys:
(a) household, income, consumption, and expenditure surveys (1995 and 2003); (b)
distributive trade and services surveys (1997 and 2001; (c) small-scale manufacturing surveys
(1995 & 2003); (d) urban informal sector Surveys (1995 and 2003); (e) small-scale
manufacturing surveys; (f) cottage and handicrafts manufacturing surveys (1997 and 2001);
and (g) labour force survey (1999). (iii) Annual surveys, which are the crop and livestock
surveys. Three surveys are conducted annually pertaining to crop production (crop forecast
survey, crop cutting survey for the main season; and small rain production survey). Others are
the large and medium-scale manufacturing surveys, retail price surveys and agricultural
producers’ price surveys.
3.2.1.3 Methods of Estimation
The bench mark data on crop and livestock production is derived from the
2001/02Agricultural Sample Enumeration. In very few cases the 2000 Household, Income,
Consumption and Expenditure Survey results are also used to establish bench mark estimates.
The estimation covers 90 items of crop. Annual estimates are based on the results of annual
crop and livestock surveys. The CSA monthly producer prices of crops are processed and
annualized weighted average prices are compiled and used for the estimation purpose. Major
inputs like fertilizer, improved seed and chemicalsare obtained from the CSA and/or the
Ministry of Agriculture and rural development.
The manufacturing activity is broken down into large and medium-scale, small-scale and
cottage/handicrafts industries. The CSA collects data on large manufacturing establishments
81
(employing 10 or more) through an annual census. Two periodic sample surveys are
conducted so far pertaining to the activities of small scale and cottage industries. Various
input indicators are used to estimate the output and value added for the other non-survey
years in the case of small-scale and cottage industries. The problem with the manufacturing
activity so far has been the absence of producer price index to compile estimates at constant
prices. The CSA has now begun collecting data on producer prices of the manufacturing
activity. The construction of the producers’ price index is believed to bring about significant
improvements in the quality of the data and especially the constant price estimates.
Ministry of Mines and Energy provides data on quantity and value of mineral production.
Crude estimates of alluvial gold production by artisan miners are also available. There are no
reliable and comprehensive data on quarrying activities. In the absence of such data, number
of persons engaged in this activity obtained from the labour force survey and an estimated
income per worker has been used.
The data source for electricity is the Ethiopian Electric Power Corporation. As regards water,
estimates are made separately for (i) Addis Ababa, (ii) other urban water supply and (iii) rural
water supply. The information contained in the population and housing census, household
budget survey, and the various welfare monitoring as well as health and demographic surveys
have been used to make estimates in the latter two cases.
For the construction activity the major source of data are the government budgetary
expenditure accounts for public works, the Ethiopian Investment Authority for private
construction projects and the various CSA surveys and censuses. The one time survey on
construction (Survey of Contractors) by the CSA was used partially to establish the cost
structure for the different types of construction components or activities. The National
Accounts Department has made effort to include in the estimation all construction activities
undertakenby the public bodies, private businesses and NPISHs. All own account
construction activities including rural housing are covered. The input data on the private
sector, however, is weak and unreliable. The absence of construction materials index has also
remained a serious challenge to the quality of the data.
The 2001 report on Distributive and Service trade conducted by the CSA provided the
benchmark information on production, intermediate cost and value added by the trade
services activities. Annual estimates are computed by extrapolating the benchmark value
added with a combined volume index of tradable output of goods producing sectors and of
imports that pass through the trading channel. CPI is applied to inflate the constant price
estimates to obtain the value added in current prices.
For the purpose of computation transport and communications, industrial activity is split as
follows: road, animal transport, railway, water, air, telecommunications, and posts. The
required data for the estimation purpose are collected from the concerned public institutions
by NAD through an annual mail survey. In addition, the income and expenditure statements
of these institutions are also used. The Road Transport Authority and/or the Ministry of
Transport provide data on the number and capacity by type of commercial road motor
82
vehicles. Estimates of transport services provided by pack animals and animal drawn cart are
estimated based on information contained in HICES.
The financial services sector includes the National Bank of Ethiopia (NBE), commercial
banks, insurance companies and micro finance institutions. The required data on the
operations of financial institutions are collected through the annual survey of financial
institutions. Additional data are also obtained from the NBE. FISIM is allocated to interindustry and final use as recommended by the 93 SNA. The part allocated to inter-industry is
not distributed to the industries but is deducted from a nominal industry.
The output and value added of public administration and defence are estimated based on the
annual revenue and expenditure accounts of Ministry of Finance and Economic
Development. The estimates on municipalities are however relied on rather incomplete data
collected from some municipalities. Estimates of cost of fixed assets are prepared annually
and used in the computation of value added as recommended in 93 SNA.
Education and health services are divided into public and private. The output and value added
of such services provided by the government are based on government expenditure accounts.
The source data for the private education is the Ministry of Education (MOE) annual
education statistics. Private health is based on data from Inland Revenue Authority. Output of
the traditional health practitioners is also estimated and included. The constant price
estimation in the case of education is based on combined volume indicators of number of
teachers and students by category which is extracted from the MOE bulletin on education
statistics.
For other community, social, and personal services the major source data is the Distributive
and Service Trade Survey. Additional information is also collected from the relevant
concerned institutions. Information on the activities of non-religious NGO is obtained from
DPPC which is the supervisory authority. Activities of religious organizations are also
estimated though the scope and quality of the input data are inadequate.
In addition, the CSA conducts a variety of periodic inquiries (every five years, on distributive
trades, hotels and restaurants and other service trades and household income, consumption
and expenditure (HICES), and labour force surveys to yield, inter alia, data used in the
estimation of national accounts).
3.2.1.4 Methods of Estimating Expenditure
Household consumption expenditure was independently estimated for the bench mark year of
1999/00 based on the results of the 1999/00 Household Income, Consumption, and
Expenditure Survey (HICES). This household budget survey is actually the only one
currently available for the work of national accounts. The second survey which was
conducted in 2004/05 was, at the time of the mission, not published or made available for the
83
work on national accounts. Consequently, commodity flow method was used to estimate the
consumption expenditure for the subsequent non bench mark years. The classification scheme
adopted for the household consumption expenditure is the classification of individual
consumption according to purpose (COICOP) at the two-digit level.
Comprehensive survey covering all the activities of NPISHs has not been conducted so far.
The bench mark estimates on the activities of NPISHs are based on a one time survey
conducted jointly by the Disaster Prevention and Preparedness Commission and Christian
Relief and Development Agency (CRDA). All NPISHs dispensing food aid and services in
kind to households are required to furnish the relevant data as well as details of their
operational expenditures. These account for a significant portion of the operations of the
NPISHs. In addition, the NAD makes an estimate of the components of operational
expenditures (including a breakdown between employee compensation, intermediate
consumption, and consumption of fixed capital).
The NAD also makes a coverage adjustment for the activities of other NPISHs operating in
Ethiopia, in particular churches and other religious organizations. The data currently
available on the production consumption and capital formation activities of NPISHs are
inadequate in terms of quality, coverage and continuity.
Government consumption expenditure covers that of general government encompassing the
federal government, 9 state or regional governments, Addis Ababa and Dire Dawa (both
designated as federal territories), other municipalities and Kebele administrations. The
principal sources of data are the consolidated statements of the federal and state or regional
governments (prepared by the Central Accounts Department of the MOFED). These
statements are analyzed in detail to yield estimates of government consumption expenditure
and capital formation. For other municipalities, the NAD prepares estimates on the basis of
an analysis of a sample of accounting statements and population indicators.
In the Ethiopian national accounts, gross fixed capital formation is presented classified by
type of assets, industry and ownership. Assets are classified into residential construction,
non-residential construction, other construction, transport equipment, other machinery and
equipment, and cultivated assets (encompassing the net increase in dairy animals and
breeding stock). Estimates of investment are prepared separately for the private sector and the
public sector (covering general government and public enterprises).
Private investment in housing is estimated via a data model entailing benchmark data from
the decennial census of population and housing, estimates of construction costs pertaining to
different structures, and a sample survey of construction enterprises. The housing census
provides data on the stock of housing units in terms of material used in the construction. The
trend implied in the inter census period is extrapolated for subsequent periods, adjusted by a
factor representing the replacement of dwellings.
84
Construction costs in terms of such characteristics as roof (corrugated iron sheets, wood and
thatch and other), wall (wood and mud, wood and thatch, cement and brick and other), and
floor (cement, wood and tiles, and other) are estimated. These costs are utilized in
conjunction with the annual increase in stock of dwellings by type to yield estimates of
private investment in dwellings. The resultant estimates are cross checked with results
obtained from the sample survey of construction enterprises that provide input-output
relationships and the available supply of building materials.
Private investment in non-residential construction is estimated from information obtained
from the Ethiopian Investment Authority on the total costs of projects implemented (with
separate identification of buildings and machinery and equipment). For the public sector,
investment by type of assets is based on an analysis of government expenditures. The other
data source for machinery and equipment include import data from the Ethiopian Customs
Authority.
Pertaining to change in inventories the principal source of data utilized are the financial
statements of public enterprises, the various CSA surveys, and changes in government
stockpile of petroleum and food reserves. A fixed ratio is applied to gross output to estimate
the value in subsequent years. This approach will introduce an element of holding gains in
change in inventories. In the Ethiopia national accounts no data are provided with respect to
acquisition less disposals of valuables primarily because of the fact that this item is
insignificant and partly because of absence of data.
3.2.1.5 Recommendations
The following recommendations were provided. (i) Improve the quality and coverage of data
on small scale businesses and informal sector, quarrying activities, urban
as well as and
rural water supply. (ii) Conduct periodic surveys on construction activities and compile
construction materials price indices. (iii) Improve the coverage, quality and periodicity of the
surveys on hotels and transport as well as the trading activities. Conduct annual surveys of
economic indicators that could be used for the estimation of value added in distributive and
service trade sectors. (iv) Improve the methodology and quality of input data in respect of
road transport services. (v) Improve the data source and methodology of the constant price
estimation of the financial intermediation services. (vi) The current data on real estate,
renting, and business activities are not very reliable in both coverage and quality. Therefore,
it is important that adequate and timely data are made available through regular surveys as
well as from administrative records for compiling reliable estimates of output and value
added from these important and dynamic activities.
3.2.2 Zambia
85
National statistics are compiled by theCentral Statistics Office(CSO) which is a department
of the Ministry of Finance and National Planning. According to CSO data, the population of
Zambia was 11.44 Million in 2005 with a land area of 752,614 square kilometres. The GDP
per capita for the same year was estimated at US dollar 635.5. On the structure of production,
agriculture contributed 21% to GDP followed by trade 18%, construction 12% and
manufacturing 11% in 2005. During the period 2002 to 2005 copper, on the average,
accounted for about 82% of export earnings of the country.
3.2.2.1 Conceptual Framework, Scope and Classification
The 1968 SNA is the general framework largely followed in compiling the national accounts
statistics. As a result, the current practice in Zambia in terms of concepts, definitions and
scope is not fully in conformity with internationally accepted standards and guidelines
contained in the 1993 SNA. The CSO is considering implementing the 1993 SNA.
The CSO compiles on a regular basis value added and GDP at current and constant prices by
economic activity. The Office also compiles GDP by expenditure components at both current
and constant prices. The data are made available to users with a time lag of about a year.
There are no other accounts and tables presently being compiled and hence falls short of
meeting the minimum requirements set by ISWGNA.
The International Standard Industrial Classification of all Activities, Rev.2 is used for the
compilation of GDP by kind of economic activity. Currently the activity classification
recommended is ISIC 3. Household consumption expenditure is computed as a residual and
hence Classification of Individual Consumption by Purpose (COICOP) has not been used to
classify household consumption expenditure. The household budget survey which was
conducted in 2002/3 follows COICOP to classify household consumption expenditure.
However, the result of this important survey has not been used, to date, for the compilation of
national accounts statistics. CSO is currently compiling government finance statistics, more
or less, according to the internationally recommended GFS 2001system, and, hence
government consumption expenditure classified according to COFOG is readily available. As
there are no independent estimations on the activities of NPISH on both production and
consumption sides COPNI classification is not in use.
3.2.2.2 The Nature and Availability of Source Data
The base year for national accounts data is 1994. This base year is undoubtedly too old and
might not reflect the structural changes that the economy has undergone over the last 13
years. It is probably worth mentioning here that the international recommendation is to
change base year every five years.
The major sources of data for the compilation of the national accounts statistics are: (i)
agricultural census; (ii) crop forecast survey (CFS); (iii) post-harvest survey (PHS); (iv)
census of industrial production (CIP); (v) industrial production index (IPI); (vi) national
86
income inquiry (NII); (vii) household budget survey; (viii) consumer price index (CPI); (ix)
wholesale price index (WPI); (x) business register; and (xi) administrative records.
The agricultural census was conducted in Zambia during the period 1990 and 1991/92. The
first phase of the census was conducted in conjunction with the Population and Housing
Census in 1990. The second part carried out in 1991/92 was actually a sample survey. The
Census covered 12 items of crop and included data on area cultivated, crop harvested and
intermediate inputs used up in the process of production. Agricultural census has not been
conducted since then.
CFS is conducted annually to provide forecasts on area and production of major crops. Only
8 items of crop are covered by the Crop Forecast Survey. Also PHS is conducted annually to
collect information on the production and sales of crops, livestock and livestock products;
agricultural inputs utilized and capital formation. The problem with this survey is that there is
a considerable time lag of one year before the results are released.
The CIP census was conducted in 1995 and contains data for the years 1993 and 1994. The
1995 CIP served as the benchmark for the computation of output and value added for
subsequent years. The Census covered about one-third of the large establishments that existed
at the time of the survey in the mining, manufacturing, and electricity and water industrial
activities. The next CIP was conducted in 2005 but the result has not so far been used for the
purpose of compiling national accounts statistics.
The IIP covers the activities of mining, manufacturing and electricity and includes
establishments that employ twenty or more persons. The index measures, at regular intervals,
volume changes in industrial production. The index is applied to the benchmark estimate
based on the results of the 1994 CIIP to estimate the value added and output originating from
the industries. The coverage of the IIP, in recent years, has been reduced to only
manufacturing activities.
NII is an enterprise-based survey that covers the activities of trade, transport and other
services. The register of businesses by the CSA provided the sampling frame for the NII. The
survey covered all large enterprises employing 100 and more employees (about 2000) and a
10% sample from the others. The data collected included incomes, expenditures, employment
and earnings, stock and fixed assets. The National Income Inquiry which was conducted in
1995 generated benchmark data for the years 1993 and 1994. The next and most recent NII
survey was conducted in 2005. The result of this survey which contains data for the year
2003 has also not been utilized for work on national accounts.
The HBS was conducted in 2003 almost ten years after the one in 1993/94. The results of the
2003 HBS are also not used for compilation of national accounts statistics awaiting the
planned change of the base year and subsequent total revision of source data and
methodology.
CPI surveys are conducted regularly and the results are published on a monthly basis. The
WPI, which was used as a proxy to basic / producers’ prices in the1990s, has been
87
discontinued since 1998. This price data has not, unfortunately, been replaced by any other
appropriate price data that could be used for the valuation of output at basic prices.
The business register was last updated for 2005 data.In addition to the above CSO surveys,
some data from administrative records are collected and used for the estimation purpose.
These include external trade and balance of payments, government finance statistics, mineral
production and the like. This source of data, however, does not seem to be fully explored and
utilized.
3.2.2.3 Methods of estimating production
Agriculture, Hunting and Forestry
The surveys CFS and PHS provide the necessary production data for only 10 items of crop.
The benchmark estimates of fruit and vegetable are based on the 1993/94 HBS and annual
estimates are made by analyzing the production performance in the other crops. The value of
intermediate consumption is extracted from the PHS. The valuation of the crop produced
presents a challenge, as there are no basic or producer prices. Annual estimates of crop
production are computed by extrapolating the base year by the index of relative change in
production in the current year. On the other hand, the value added at constant price is inflated
by the food component of the CPI to arrive at the value added at current price. This method
of estimation has some drawbacks and is not largely in conformity with internationally
recommended valuation practices. Secondly, all the crops produced in the country are not
included in the estimation. Thirdly, crop by-products are omitted from the valuation. The
production and price data on livestock, forestry, and fishery activities are also not firm and
reliable. The present estimates of production from these activities exclude firewood and
charcoal used by both rural and urban households as well as wood used for housing
construction in rural areas.
Mining and Quarrying
Data on production and prices on copper mining which is the single most important mining
activity are collected from all companies involved in this activity. The data source for the
other mining activities including coal and quarrying is the IIP which is used to estimate the
value added at constant prices. The constant price estimate thus obtained is inflated using the
change in consumer prices to obtain the value added at current prices. It should be
emphasized here that the continued use of components of the CPI as a deflator/inflator could
produce misleading results. Small scale and other precious stone mining are not properly
covered in the estimation.
Manufacturing
88
The respective component of the IIP is applied to the benchmark estimate of 1994 (CIP) to
arrive at the value added at constant prices. The CIP benchmark estimate, which is ten years
old, might not represent the currently prevailing structure of production. In addition, the price
index (CPI) used to derive the current price estimates is not recommended. Due to the nature
of available data the current methodology used to estimate value added and output at both
current and constant prices in the manufacturing industry does not follow internationally
accepted standards and guidelines.
Electricity and Water Supply
The electricity produced is obtained from the Zambia Electricity Supply Corporation
(ZESCO), the major producer and distributor of electricity in the country and used to estimate
the value added at constant prices. In the case of water supply the 1994 benchmark estimate
of value added is brought forward using index of growth of urban population. The respective
components of CPI are then applied to estimate the value added in current prices. Rural water
supply is not included in the estimation.
Construction
A composite index of volume of sales of cement on the domestic market and quantity of
stone quarry is constructed and used to extrapolate the 1994 benchmark (CIP) value added
estimate. The use of these indictors might not produce the desired result because cement and
quarry materials make up, on the average, less than half of the total construction materials
and might not even be appropriate for those construction activities that use less or none of
these input materials. Rural housing and other rural construction activities are not accounted
for in the estimation.
Wholesale and Retail Trade, Repair of Motor Vehicles
The benchmark estimates of value added from these activities are established using data
derived from the NII. A combined index of agriculture, manufacturing and imports that pass
through the trading channel is constructed and used for subsequent years. CPI is used to
derive current price estimates.
Hotels and Restaurants
For restaurants, the Index of Industrial Production (the food, beverages, and tobacco
component) is employed to compute the value added at constant prices. CPI is used to inflate
the value added at constant prices to obtain current price estimates. In the case of hotels bed
occupancy rate is used for constant price estimation and CPI is applied to arrive at current
price estimates.
89
Transport and Communications
For the organized transport activities like air, rail, communications and posts the required
data are obtained regularly from the respective agencies. In the case of road transport tax data
are used to estimate the volume of activities. The transport component of the CPI is again
used to estimate the volume of activities in current prices.
Real Estate, Renting and Business Services
The benchmark estimate for real estate activity is computed using data from HBS and
population census and that of business services is based on NII. Subsequent annual volume
estimates use growth rates of population and the rent component of the CPI are applied for
current price estimations. The growth rates of the trade sector are used to compile the value
added estimates of business services at both current and constant prices. The services of rural
owner occupied housing units have not been imputed.
Financial Intermediation
For the banking activity employment index is used to bring forward the 1994 base year value
added. In the case of insurance activity, index of the number of policies issued is applied on
the base year value added to obtain the current year value added at constant prices. In both
cases the CPI is used for the current price estimation. The whole of FISIM is deducted from
the inter-industry as recommended in the 1968 SNA.
Public Administration and Defence
The data for the estimation of output and value added is derived from government revenue
and expenditure accounts obtained from the government finance branch of the CSO. There is
no provision for the consumption of fixed asset in the government finance statistics and hence
value added originating from this activity is reported only in the net concept. The national
accounts branch collects the required data on a regular basis to compute the contributions of
local governments to value added. Employment index is used to derive the estimates at
constant prices.
Education, Health and Social Work
The sources and methods are the same as in 2.10 above for the non-market output. Private
health and education activities are not included in the estimation.
90
3.2.2.4Methods of Estimating Expenditure
Private Final Consumption Expenditure (PFCE)
Private final consumption expenditure is estimated as a residual. Hence, break down of
private final consumption expenditure according to COICOP is not available on a regular
basis. Though the 2003 HBS contains adequate information for the compilation of PFCE by
COICOP classification the result has not been used for compiling private final consumption
expenditure. There is no independent estimation of the activities of NPISHs and hence the
consumption expenditures of this sector are lumped under PFCE.
Government Final Consumption Expenditure (GFCE)
The public finance branch of the CSA compiles government revenue and expenditure data
according to the GFS 2001 system. The effective utilization of the data available would allow
NAB to compile government consumption expenditure at a more disaggregated level.
Gross Fixed Capital Formation(GFCF)
The machinery and equipment component of gross fixed capital formation is estimated using
data on merchandise imports. The use of this single indicator of merchandise imports for the
estimation of GFCF makes it difficult to compile the estimates by type of asset or industry.
The construction component of gross investment is as described above. The only item
considered under the category of inventory is the change in the stocks of the mining sector
and mainly copper. The change in stocks of material and supplies, finished products, goods
for sale, or livestock are not estimated. There are no estimates of acquisitions or disposals of
valuables available.
3.2.2.5 Recommendations
The statistical base of the country is weak. The data situation has actually deteriorated
overtime both in quality and availability. The national accounts estimates of most of the
activities are based on the rather out-dated 1994 benchmark figures. It is observed that single
indicator is widely used for the extrapolation of bench mark estimates which is generally not
recommended. In addition valuation of output has also remained a serious problem as there
are no appropriate price indices. Because of the existence of a serious gap in data some
important economic activities like the non-observed activities are also not properly accounted
for in the estimation. Regular production surveys for most of the activities are not conducted.
At the same time the limited data already available within the CSO are also not fully and
effectively utilized. As indicated above the 2003 HBS, the enterprise surveys conducted in
91
2005 (Census of Industrial Production, Census of Construction, National Income Inquiry, and
the Index of Industrial Production) are yet to be used for the purpose. It is also noted that
there is less emphasis to data source from administrative records. The case of data on the
operations of financial institutions serves as good example. Data currently used for the
purpose are inadequate and unreliable whereas the data on financial intermediation services
could easily be obtained from the individual concerned institutions or from the government
agency supervising their operations.
The methods employed to estimate both the production and expenditure components of GDP
are, in most cases, not in line with recommended guidelines and hence need to be reviewed
and improved. The current base year which is 1994 is too old against the normally
recommended five years. For most activities estimates of benchmark value added are brought
forward using single indicators. Direct estimates of output and intermediate consumption are
rare. The 1994 base year CPI which is also too old is often used to inflate volume measures to
arrive at current price value added and in some instances CPI is also applied to compile
constant price value added by deflating current price estimates.
The problem associated with the quality, availability and timeliness of input data is the major
challenge of the work on national accounts in Zambia. The other challenge faced by the Unit
is the shortage of staff and training opportunities on national accounts. NAB staff identified
the following as priority for training areas: (i) Basic course in national accounting. (ii)
Financial intermediation including FISIM. (iii) Price and volume measures. (iv) Supply and
use table and input output table. (v) In addition to the training opportunities, NAB also seeks
technical assistance to change the out-dated base year to most recent one.
In order to produce sound and comparable national accounts estimates it is recommended
that: (i) Recruit and train more professional staff for NAB. (ii) Improve the data set required
for the work on national accounts statistics. (iii) Conduct more regular establishment,
distributive and trade services, labour force, and informal (non-observed economy) sector
surveys. (iv) Expand the item coverage of crops in the annual agricultural surveys and
improve the timeliness of the result of the Post-Harvest Survey. (v) Compile producers’ price
statistics for the output of major agricultural and other products to replace the discontinued
Whole Sale Price Index. (vi) Conduct survey on the production, consumption and capital
formation activities of non-profit institutions serving households. (vii) Strengthen the data
collection efforts from administrative records. (viii) Make good use of the already
existing/available survey and other data within the CSO. (ix) Implement the
recommendations of the 1993 SNA. Zambia is currently implementing the 1968 SNA. In
order to produce sound, reliable and comparable national accounts estimates the gradual
migration to the 1993 SNA is inevitable. (x) Update the base year of the current GDP and
CPI series. Update the current 1994 out-dated base year to a more recent year and avoid
excessive use of bench marking. The NAB is considering updating the base year but there is
no concrete programme or time table for the exercise. The base year of the current CPI series
should also be updated to more recent year based on the results of the 2002/3 HBS as
weights. The work to change the base year of CPI to 2003 is underway and is expected to be
92
completed within a reasonably short period. (xi) Compile supply and use table on a regular
basis.
3.2.3 Malawi
The population of Malawi was estimated at 12.8 million in 2006. The total area of the country
is 11.85 million hectares of which about 21% is under water cover. According to the revised
national accounts estimates the per capita GDP of the country reached US dollar 247.2 in
2006. In 2004 (base year) agriculture contributed 31% to GDP followed by trade 15%,
private social and community services 9% and manufacturing 9%. Tobacco is the single
largest export of the country accounting for 54% of the total exports in 2005. For the same
year the shares of sugar and tea to total exports were both at 9%.
In Malawi, the national accounts statistics are compiled by the National statistical Office
(NSO). The NSO is a department in the Office of the President and Cabinet.
Malawi has a long tradition of compiling national accounts statistics. The first estimates were
prepared for the year 1938 and the result was published in 1948 while the country was under
the British protectorate. After independence in 1964, NSO produced the first national
accounts estimates for the period 1964-1970 which was published in 1972. The last national
accounts estimates before the current revised series were compiled using 1994 as the base
year and contained data for the period 1990-1994. These estimates were later updated
including the years up to 2001 and preliminary figures were also provided for the years 2002
to 2005. The previous series of national accounts data were compiled based on the
recommendations of the 1968 SNA.
The revised series uses 2004 as the bench mark estimates. The revision has been compiled on
a bottom up approach using the supply and use framework. The national accounts and BOP
Division in cooperation with Statistics Norway produced in early 2007 for the first time in
Malawi Supply and Use tables (SUT) for the years 2002, 2003, and 2004. Based on the
supply and use tables thus elaborated national accounts estimates were compiled for the years
mentioned and the results were released in March, 2007. Preliminary estimates for the years
2005 and 2006 and projection for 2006/07 were also made available at the same time.
The level of the revised GDP in current prices has increased by 37.5% compared to the
previous series. Private social and community services increased by 578.4% and that of
ownership of dwellings, construction, electricity and water, transport and communications,
and manufacturing increased by 343.1%, 123.3%, 89.9%, 63.1, and 26.4%, respectively. The
dominant activity agriculture has been revised upwards by 28.4%. The main reasons
attributed for this very significant upward revision are improved coverage of small and
medium scale businesses as well as production for own use and the inclusion of the
contribution of NPISHs in the estimates. The volume changes from year to year in the revised
93
series, however, showed minor deviations from the old estimates and no systematic bias has
also been observed.
3.2.3.1 Conceptual Framework, Scope and Classification
The revised series is compiled largely based on the recommendations of the 1993 SNA.
However, the transition to the 1993 SNA is yet in process. At the time of this mission only
aggregate value added and GDP estimates by activity classified according to ISIC rev.2 were
available. The revised series used ISIC rev 2 classifications only for the purpose of making
comparisons between the estimates of the two series. Except this comparison table no other
tables were released at the time. It was reported that the compilation of the tables and
accounts according to the 1993 SNA are still work in progress. In this regard the necessary
input data for the compilation of value added and GDP by activity as well as by expenditure
components at both current and constant prices are readily available and it is believed that the
tables will be produced and released for users soon. NSO is also planning to undertake the
compilation of the rest of the world account, institutional sector accounts, sequence of
accounts up to capital account and the input output table. Annual SUT tables are already
available for the years 2002, 2003, and 2004.
3.2.3.2 The Nature and Availability of Source Data
Business Information Register (BIR)
The NSO compiles a Business Information Register which contains a list of all economic
establishments in the country. The BIR covers all establishments registered by the Registrar
General of Companies. Included in the business registers are enterprises in agriculture,
mining, manufacturing, construction, trade, hotels and restaurants, transport and business
services. The BIR provides the information categorized into large, medium and small scale
businesses based on the size of the employment of the establishments. In the case of
agriculture the categorization is only into two: large and small scale. Government services are
categorized as large scale. In 2005, the NSO has a register of about 500 large establishments.
The BIR is updated annually but with a considerable time lag. The last partial update
available was for the year 2001. It was partial because the revision did not cover the entire
country. In the BIR ISIC rev 2 was used to classify economic activities. The BIR served as
the sampling frame for most of the economic surveys conducted by the NSO.
Annual Economic Survey (AES)
The Annual Economic Survey has remained for a long time the most important source of
information for the compilation of national accounts statistics. The AES encompasses large
scale enterprises employing 20 and above that are listed in the BIR. The AES is supposed to
be a complete enumeration of large establishments, though in practice this is often not
94
achieved. In addition, the response rate is not satisfactory. The response rate which was 50%
in 1998 increased to only 75%. The AES provides data on output, intermediate consumption,
income, employment, and capital formation. The first such survey was conducted for the year
1973 and the last published AES pertains to the year 2001. Annual Economic Surveys were
also conducted for the years 2002, 2003 and 2004 but the survey results have not been
published. The 2002 survey covered 327 enterprises of which the results of 216 of them were
used for compilation of SUT and national accounts figures.
Medium Business Economic Survey (MBES)
This survey includes establishments which employ 5 to 20 persons. The survey was planned
to be conducted every five years but that did not happen. The last such survey was conducted
back in 1998.
Integrated Household Survey (IHS)
Integrated Household Survey is conducted periodically every five to seven years. The survey
is intended to generate data on employment, income and expenditures of households
including household businesses. The last IHS was conducted in 2004/05 and the one before
this was under taken in 1998/99. The 1987 Population and Housing Survey served as the
sampling frame for the IHS.
Survey of Household Expenditures and Small Scale Economic Activities (SHESSEA)
This survey was conducted in 1990/91 and includes data on output, intermediate consumption
and change in inventory.
SHESSEA was under taken in order to supplement the 1998/99 IHS which was found in
adequate in terms of coverage and poor quality data especially for non-agricultural activities.
This survey was not repeated and actually the IHS 2004/5 result was used for the purpose.
Index of Industrial Production (IIP)
Manufacturing and utilities production surveys are conducted monthly and the results are
used to compile monthly indices of industrial production. The surveys usually cover about 50
relatively bigger manufacturing and utility establishments which represent about 75% and
100%, of total production, respectively. The IIP was used in the previous series to extrapolate
manufacturing value added when the results of the AES were not available or not
satisfactory. The base year for the IIP is 1984 (1984=100).
Crop Estimation Survey (CES)
95
The Crop Estimation Survey is conducted annually in three rounds (crop forecast in
November-December, revised forecast in January-February and actual production in AprilMay) by the Ministry of Agriculture, Food Security and Irrigation (MAFSI). Because of
dissatisfaction with the quality of the data produced by MAFSI, the responsibility of
conducting the survey has recently been transferred to the NSO. The CES provides data on
the area cultivated and volume of crop production by type of holding (large and
smallholding). The coverage of the survey is about 90 % in terms of cereal, fruit and
vegetable.
National Census of Agriculture and Livestock (NCAL)
The National Census of Agriculture and Livestock is currently being conducted with support
from the Norwegian Government and is planned to be completed in October, 2007. Similar
census was conducted 14 years back in 1992/93. The 2007 NCAL is expected to generate
reliable and comprehensive data on a wide range of agricultural activities of all kinds and size
in the country.
Population and Housing Census (PHC) and Demographic Health Survey (DHS)
The last PHC was conducted in 1998. The DHS was conducted in 1995 and repeated in the
year 2000.
Administrative Records
In addition to the above enumerated surveys the compilation of national accounts statistics
uses several other administrative data sources. These include government finance statistics
from the Ministry of Finance, external trade statistics from the Revenue Authority, data on
financial intermediation services from the Reserve Bank of Malawi, forestry products
statistics from the Department of Forestry, fishing data from Department of Fisheries and
MALDECO, livestock data from Department of Animal Health, Tea Association of Malawi,
Illovo Sugar Company, Tobacco Control Commission, Electric Supply Commission of
Malawi for electric energy, Water Boards (five) including Lilongwe and Blantyre for urban
water supply.
3.2.3.3 Methods of Estimation of Production
The revised national accounts data for the years 2002, 2003 and 2004 are based on the SUT
tables compiled for the respective years. The starting point is the balancing of products which
ensure that the supply or the output and imports of the products are balanced with the use or
final domestic expenditures, intermediate consumption and exports of the products. The
products chosen for balancing were initially drawn from activity classification (ISIC rev.3)
96
and later linked (converted) to Central Product Classification (CPC). Since SUTs are not yet
compiled for 2005 and 2006 preliminary national accounts figures for these years are arrived
at by super imposing the volume indictors (growth rates) from the old series on to the 2004
revised value added estimates.
Agriculture, Forestry and Fishing
Data on large scale agriculture are obtained largely from the AES and also from Auction
Holdings for tobacco, Tea Association, Illovo Company for sugar. The AES included about
37 enterprises in 2002 and 31 during 2003 to 2004. In the case of small holders the results of
crop estimation surveys and data obtained from Departments of Animal Health, Fishery, and
Forestry provide input and output data. Input prices are also collected by MOA through the
Agro economic Survey. The 2004 IHS is used to determine the proportion of production
marketed and meant for own use. The Ministry of Agriculture collects farm gate prices for
few products. In the absence of appropriate price data, consumer prices are used for products
not covered by MOA price survey. The currently available data on output, input, prices, and
capital formation require improvements in both scope and quality. It is hoped that the existing
data gap would be mitigated when the on-going agricultural sample census is completed and
the results made available.
Mining and Quarrying
Mining is not an important activity in Malawi. Data on large scale mining and quarrying
activities are obtained from the AES. The AES covered 4 enterprises engaged in the mining
of hard coal and quarrying of stones, sands and clay. The 1990 HESSEA was used to
establish the bench mark estimates. The combined indices of volume growth in the large
scale and population growth are used to bring forward the bench mark estimates.
Manufacturing
The AES is the major source of data for the large scale manufacturing activity. The survey
covered 89 enterprises during 1999-2002 and 92 during 2003-2004. The survey provides the
necessary data on output, intermediate input and investment. There are cases, however, when
the result of the survey are found inadequate. In such cases series of adjustments are made
during the process of compiling the product balances (SUT). For medium scale
manufacturing, the bench mark estimates established from the 1998 Medium Business
Economic Survey results are brought forward using the growth rate observed in the large
scale sector. In the case of small scale manufacturing activity the bench mark from the 1990
HESSEA is extrapolated by the population growth. The bench mark estimates for cottage and
handicrafts industries are based on the 2004 IHS and bought forward and backward using
growth in population. The paucity of data on the activities of medium and small scale
97
manufacturing as well as cottage and handicrafts industries seriously undermines the
reliability of the estimations.
Electricity and Water
The Electricity Supply Commission of Malawi provides the necessary information on the
activity. Data on urban water supply are obtained from the five Water Boards representing
urban areas in the country. Rural water is not covered in the estimation.
Construction
The AES provides the necessary data for large scale construction. The survey conducted up
to 2004 included 26 large scale manufacturing companies. The medium and small scale
construction activities are estimated based on commodity flow analysis where domestic
production and imports of construction materials like cement, timber, iron, etc are used. The
estimation in the case of medium and small scale construction activity is not reliable.
Omissions are also noted especially in the case of rural housing.
Trade and Hotels and Restaurants
For large scale operators in the area of trade and hotel and restaurant activities the AES again
is the major source of data. The survey provides data annually on 66 trading enterprises.
Regarding hotels and restaurants the number of such enterprises covered by the survey which
was only 7 during 1999-2002 increased to18 during the years 2003-2004. In the case of other
activities not covered by the survey including the informal sector activities the source data are
scanty and not reliable making the estimation rather speculative. In the absence of reliable
data different indicators drawn from the various small scale and household based surveys
were used to make crude estimates.
Transport and Communications
Data on large scale operators of the transport and communications industry are extracted
largely from the AES. The survey covers 31 enterprises engaged in the provision of such
services. In addition, in the case of air, rail and lake transportation respective responsible
agencies are contacted for information. For posts and telecommunications as the companies
providing the services are small in number data directly collected from the concerned
institutions could have produced better results instead of relying on the AES which is a
general survey. Regarding medium and small scale haulers adequate and reliable information
on their operations are not available. In the absence of such data indirect estimations are
made using the import and export quantity indices together with indices from the domestic
sales of cement and sugar.
98
Financial Intermediation Services
The AES is the major source of data for the computation of FISIM and value added from this
industry. During the period 2000 to 2004 the survey sampled 27 to 34 enterprises engaged in
financial intermediation services. Information from the MBES and HESSEA are used in the
case of medium and small scale activities. Given the unique nature of the industry and the
demand for detailed input data for the compilation, administering specialized questionnaires
or the use of business accounts/ financial statements/ of the enterprises could produce more
reliable estimates.
Other market Services
Like the other sectors these services also use AES for large scale and MBES, IHS or
HESSEA for medium and small scale activities
Public Administration and defence, Non-market Health, and Education and NPISHs
The data sources for public administration and defence as well as non-market health and
education are the audited revenue and expenditure accounts of the Office of the Accountant
General, Ministry of Finance. The currently compiled data from the MOF does not strictly
comply with the GFS system and hence several adjustments have to be made before using the
data for compiling the value added originating from this activity. In addition, the compilation
of supply and use tables requires more disaggregated data than the published accounts of
MOF. As a result, proportional allocations are made to lower level products to complete the
product balances at that level.
The currently published data of MOF does not include local governments (municipalities)
and extra-budgetary units except the University of Malawi. Furthermore, data on cost of fixed
asset is not included in the MOF data neither has NSO made attempts to make such
estimations. Hence, the gross value added originating from the general government as
reported by the NSO is less by the amount of the cost of fixed asset.
In the case of NPISHs, no nation-wide survey has so far been conducted. The current bench
mark estimates on NPISHs are extracted from limited and rather quick sample survey
conducted in the capital Lilongwe. Estimates for the other non-survey years are approximated
using data on population and inflation. Given the importance of the activities of NPISHs in
the country the need for more reliable source data is crucial to improve the quality of the
estimates.
3.2.3.4 Method of Estimating Expenditure
Household Consumption Expenditure
99
The IHS conducted in 2004/05 has been used to estimate the bench mark estimates. For the
other years the bench mark estimates are brought forward and backward using population and
inflation figures. The inclusion of relevant indicators on production could have improved the
estimation for non-survey years.
Government Consumption Expenditure
The major sources of data are the revenue and expenditure accounts of the Ministry of
Finance. The problems and limitations indicated above in terms of coverage of the data also
hold here.
Gross Capital Formation
Gross capital formation by type of asset is compiled separately for the public sector, large
scale enterprises, medium scale enterprises, and small enterprises. Change in inventory is also
made available in the revised series. The government audited accounts and the AES are the
data sources for investment by government and large scale enterprises, respectively. On the
other hand, investment data on medium and small scale enterprises are extracted from the
1994 and 1998 MBES and the 1990 HESSEA. The data sources in the case of the later are
not firm and reliable and needs to be improved.
3.2.3.5Recommendations
The national accounts figures in Malawi are compiled from supply and use tables.
Constructing supply and use tables require even more detailed data on, among others,
production, intermediate consumption, final consumption expenditures, investment
expenditures and net exports of goods and services. The data are required at sufficiently
disaggregated product level. In Malawi the supplies and uses of over 350 products are
balanced annually. This is a big step forward in terms of improvements in the methodology
employed. However, there are no parallel improvements in the source data which could have
bought about substantial impact on the quality and coverage of the estimates. Due to the
serious gaps in the source data, series of adjustments and revisions were made on the existing
data including on survey results to ensure product balances for the 2002-2004 SUTs.
To produce timely, comprehensive, and reliable national accounts statistics, Malawi has to
improve its existing data base. The Business Information Register (BIR) is not regularly
updated. The last such update which was actually only partial update was done for 2001.The
BIR needs to be updated annually incorporating all the changes and also expanding the
coverage for it to serve as useful data source and as a sampling frame for the other surveys.
The data source on the activities of medium and small scale economic activities is fragmented
and not reliable. The Informal sector activity has not been adequately captured. Production of
computer software and data bases as well as work on entertainment and, literary, or artistic
originals are not covered. So called illegal activities like prostitution are also not captured.
The price data available for the purpose of valuation and constant price estimation are often
not adequate. The NSO is not currently producing data on producers’ price statistics and
100
hence there exists a problem of valuation. Basic or producers’ price data are required to value
the output and hence the value added in crop agriculture, forestry, fishing, manufacturing,
and mining. Construction price indices are also crucial for the valuation of construction
activities.
On the expenditure side of GDP, the input data for the compilation of gross capital formation
requires further improvements in both quality and coverage. The data constraint becomes
serious in the case of medium and small scale establishments and own account (household)
capital formation. Rural investment and especially rural housing construction is not properly
addressed. The data on change in inventory is also concern and needs to be improved.
Government consumption expenditure does not include local governments (municipalities)
and much of the extra budgetary government units.
In order to produce reliable and comparable national accounts statistics the following are
recommended: (i) Revision of the 1967 Statistics Act. The Statistics Act of 1967 which is too
old is currently under consideration for revision. It is believed that the new statistical act
being drafted will provide the NSO the necessary legal environment to executive its
responsibilities more competently. (ii) Recruit and train more professional staff for work on
national accounts statistics. (iii) Improve the source data. (iv)Conduct more regular
establishment (large, medium & small scale), household, labour force, and informal sector
surveys and also improve the timeliness of the availability of survey results. (v) Collect and
compile producers’ prices for major agricultural and other product to
replace the
discontinued series. (vi) Conduct nation-wide survey on the production, consumption and
capital formation activities of NPISHs. (vii) Update the Business Information Register
annually. Strengthen the data collection effort from administrative (non-survey) sources.
(viii) Complete the migration to the 1993 SNA. (ix) Compile annual value added and GDP at
constant and current prices by activity according to ISIC rev. 3 classifications. (x) Compile
the expenditure components at current and constant prices according to COICOP for
household, COFOG for government and other recommended classifications. (xi) Allocate
FISIM to both inter-industry and final consumption as recommended in the 1993 SNA. (xiii)
Compile estimates of consumption of fixed asset at least for the non-market producers. (ix)
Update bench mark estimates for both production and prices as recommended in the 1993
SNA. (x) Expand government finance statistics to include local governments and extra
budgetary government units.
3.2.4 Egypt
The Central Agency for Public Mobilization and Statistics (CAPMAS) is the government
department charged with the responsibility of conducting and coordinating the overall
statistical operations in the country. CAPMAS is an autonomous organization attached to the
Ministry of Economic Development (MOED). Earlier CAPMAS and MOED shared the
responsibility of compiling national accounts statistics with the latter preparing estimates of
constant price GDP.CAPMAS was compiling the national accounts statistics according to the
recommendations of the 1968 SNA and this estimate was taken as the official data. Since
101
1999, however, the tasks in the two offices were merged and the full responsibility of
compiling national accounts statistics was entrusted to the National Accounts Unit which is
an integral part of the Ministry of Economic Development. The National Accounts Unit falls
under the office of the Under Secretary for Central Department of National Accounts of
MOED.
3.2.4.1 Conceptual Framework, Scope and Classifications
Egypt officially adopted the 1993 SNA in 1995 and since then the overall conceptual
framework of the compilation of the national accounts statistics is generally in line with the
recommendations of the 1993 SNA with some exceptions. Regarding the scope of accounts
and tables the following are compiled: (i) Annual value added and GDP at current and
constant prices by industrial activity. (ii) Annual expenditure GDP at current and constant
prices. (iii) Annual value added components at current prices. (iv) Annual sequence of
accounts for the total economy up to the capital account. (v) Annual rest of the world
accounts up to net lending. (vi) Quarterly value added and GDP at current and constant
prices by activity.
It is important to note here that the compilation of the constant price estimates do not strictly
follow international guidelines and recommendations. The problems reported for this short
coming is basically the absence of appropriate deflators (data). Quarterly accounts of
expenditures on GDP at both current and constant prices are not compiled. Except for the
experimental table that was attempted in 1995, Egypt does not compile annual supply and use
tables. Input output tables are not produced on a regular basis. The last published table is for
the year 1991.
The classifications adopted in the Egyptian national accounts are in line with international
recommendations. Industrial activities are classified according to ISIC rev.1 while COICOP
is used to classify household final consumption expenditure. Functions of government are
classified, broadly, in line with COFOG.
3.2.4.2 The Nature and Availability of Source Data
Egypt conducts agricultural censuses every 10 years. The last such census was conducted in
1999/2000. The census results serve as bench mark estimates and subsequent annual
estimates are based on data obtained from the Ministry of Agriculture. Data on fishing
activities are obtained from the Ministry of Agriculture.
On manufacturing, the bench mark estimates are based on the results of the census of
manufacturing which is conducted by CAPMAS every five years. The last census was taken
in the year 2000/01. Annual data are obtained from the Bulletin of Statistics published by
CAPMAS.
102
Electricity, gas and water supply data are extracted from the annual statistical bulletin of
CAPMAS.CAPMAS conducts surveys on construction industry every five years. The results
of these surveys are used to establish the bench mark estimates. Annual estimates are based
on data obtained from the statistical publications of CAPMAS.
The CAPMAS also conducts benchmarks surveys on wholesale, retail trade, repair of motor
vehicles every five years. The last one being in 2000/01.Annual estimates are extracted from
the statistical bulletin of CAPMAS.
Surveys on hotels and restaurants are conducted every five years. The last survey was for
2000/01. Annual data are obtained from the CAPMAS.On public administration and defence;
compulsory social security, the sources of data are the annual revenue and expenditure
accounts of the government.
On Transport, storage and communication, CAPMAS conducts bench mark surveys every
five years; the last one being in 2000/01. Annual estimates are also obtained from CAPMAS.
Data on financial intermediation servicesare based on periodic censuses of such
establishments conducted by CAPMAS with the last one in 2000/01. CAPMAS also provides
the required data for the annual estimates.
The household budget surveys which are conducted periodically every five years by
CAPMAS provide the bench mark estimates of the real estate, renting and business services.
Data on education, health and social work; other community, social and personal service
activities are based on surveys are conducted every five years; the last one being in 2000/01.
CAPMAS also provides the data required for the annual estimates. For such services
provided by government data are extracted from the published accounts of the Ministry of
Finance.Estimates on private households with employed personsare computed from the five
yearly Household surveys of CAPMAS. The last HBS was conducted in 2004/05.
The estimations of the activities of the informal sector and small businesses engaging five or
less employees are captured based on the five yearly household budget surveys. However, the
bench mark as well as the annual data available on these activities is not adequate both in
coverage and quality. The data source on services such as transport, personal and business
services, private health, and real estate are also not reliable.
The concept of basic prices has so far not been used in valuation of output due basically to
absence of appropriate price data (producers’ prices). Currently, CAPMAS has started
collecting producer prices for selected products and hence this problem might be solved for
those activities the prices are available. As indicated earlier, the compilation of estimates of
constant price output and value added are not in line with international guidelines and
recommendations. The problem again is the absence of appropriate price deflators especially
for service activities like transport.
103
3.2.4.3 Recommendations
(i)
Conduct surveys on the activities of the informal sector and small businesses.
(ii) Improve the timeliness, quality and coverage of data on the services activities.
(iii) Compile producers’ price and construction price indices.
(iv) Compile constant price GDP as recommended in the 1993 SNA.
3.2.5 Mauritius
Mauritius with a population estimated at 1.2 million (2005) belongs to the group of few
African countries with per capita GDP exceeding 5000 US dollars. The Mauritian economy is
dominated by the manufacturing and the distributive services industries. In 2006, the
manufacturing sector accounted for about 20% of GDP while the data for transport and
communications and trading services were a little over 12 % each. Real estate, renting, and
business activities, financial services, and hotels and restaurants contributed 10.5%, 10.3%
and 8.5%, respectively. The contribution of agriculture was only 5.6% for the year
mentioned.
The national accounts statistics of Mauritius are compiled and disseminated by the Central
Statistics Office (CSO). The CSO is an autonomous organization falling under the overall
supervision of the Ministry of Finance and Economic Development.
Mauritius has a long tradition in the compilation of national accounts data with the first
published national accounts statistics dating back to the year 1948. Mauritius implemented
the 1968 SNA starting 1983 and 1993 SNA since 2001. The bench mark year for the current
estimates is 2005. In addition to the annual estimates, Mauritius started compiling and
publishing Quarterly National Accounts (QNA) as from 2005. The first published QNA
figures included data from the first quarter of 1999.
3.2.5.1 Conceptual Framework, Scope and Classifications
The 1993 SNA is the general framework followed to compile the national accounts statistics.
The CSO compiles and publishes national accounts statistics on a regular basis. The CSO
national accounts publications, in addition to the annual data, also provide brief descriptions
on the classifications used, sources of data and methods of estimation. The annual
publication” National Accounts of Mauritius” includes data on: (a) GDP by industry group at
current prices, (b) real growth rates of GDP by industry group, (c) expenditure on GDP at
current prices by major category, (d) real growth rates of expenditures on GDP by major
category, (e) gross fixed capital formation at current prices by type and use, (f) real growth
rates of gross capital formation by type and use, and (g) national disposable income and gross
national savings. The 2006 Publication also contains data on quarterly GDP by industry
group at current prices and the quarterly expenditure on GDP at current prices. In addition to
104
the above Mauritius compiles and publishes Integrated Economic Account for the total
economy. Pertaining to classifications of economic activities, Mauritius currently uses NSIC
Rev.3 (National Standard Industrial Classification) which is in conformity with the
internationally recommended ISIC, Rev. 3.
3.2.5.2The Nature and Availability of Source Data
The CSO regularly conducts surveys and censuses as well as special enquiries when found
necessary. In addition, the CSO also collects data from administrative records on various
economic activities.(i) Surveys and Censuses. These include housing and population census;
census of Economic Activities; household Budget Survey, annual census of industrial
production; annual survey of large and establishments; annual survey of employment and
earnings; and quarterly employment survey in EPZ and Pioneer Status Enterprises. (ii) .
Administrative records on building permits statistics; register of license holders; road
transport statistics; trade statistics; Mauritius Chamber of Commerce; Mauritius sugar
syndicate; agricultural research and extension; registrar of companies; financial services
commission; Board of Investment; and various public and private organizations. (iii) Special
enquiries. These include special surveys of building contractors and parastatals; special
enquiries from docks and stevedoring and large distributive enterprises; special enquiries
from food crop planters, livestock and poultry breeders and providers of agricultural services;
and special enquiries from real estate agencies, architects and engineers, advertising agencies,
and auditing firms. (iv) Personal interviews. The CSO also conducts personal interviews to
extract the required data such as personal interviews of owners of small manufacturing
industries; and personal interviews of taxi, lorry and vans owners. (v) Price statistics, which
includes producer price indices; consumer price indices; wage/salary indices; construction
price indices; import and export price indices
3.2.5.3 Recommendations
(i) Compile and allocate FISIM as recommended in the 1993 SNA; and
(ii) Improve the data need for the compilation of constant price estimates especially for the
services industries.
3.3
Consumer Price Statistics
This brief report on the scope and quality of the CPI reviews current practices in terms of
coverage, expenditure weights used, classifications adopted, content and number of the
basket of goods and services, computation of the indices, and the periodicity and timeliness
of the release of the data. The conceptual and methodological guidelines provided by the
International Labour Organization are used by the countries visited as the general framework
for compilation of their respective price indices.
The coverage in terms of both geographic and population generally follows the international
recommendations. In Egypt the retail prices are collected from 48000 households drawn from
105
all parts of the country in both urban and rural areas. The price survey in Ethiopia uses 119
representative market outlets covering the entire geographic territory of the country. In
Mauritius prices are collected through the 370 market outlets in the two biggest islands. In
Zambia and Malawi the CPIs also cover all resident households in all parts of the countries.
In Ethiopia and Egypt the expenditure weights for the existing CPIs are based on household
budget surveys conducted in both countries during 1999/2000. In the case of Mauritius, the
expenditure weights of the existing CPI are derived from the 2001/02 Household Budget
Survey (HBS). The expenditure weights of the current CPI of Malawi are computed from the
1997/98 Integrated Household Survey (IHS). In Zambia the weights are based on the 1993/94
Household Budget Survey. In Egypt, Mauritius and Zambia the weight base years
correspond to the price reference periods while in Ethiopia and Zambia the two periods are
different. The base year for the CPI in Malawi is 2000 and in Ethiopia the base period is
December 2000. The current weights used by the countries range from five years in
Mauritius to 14 years in the case of Zambia. Except Mauritius, the expenditure weights
especially in the case of Zambia and Malawi are considered old to represent the current
structure of consumption of the population. As a result, all the four countries concerned have
completed the necessary preparations to launch new series of CPI with updated weights. The
planned expenditure weights are based on the 2002/03 Living Conditions Survey in Zambia,
2004 IHS in Malawi, 2004/05 HICES in Ethiopia, and the 2004/05 Household, Income,
Expenditure and Consumption Survey in Egypt.
The five countries considered here differ widely in the number of items included in the basket
of goods and services. Egypt has the largest number with 746 product items. In Malawi and
Zambia the CPI baskets include 400 and 323 items, respectively. Mauritius CPI includes 194
items in the basket of goods and services. The number of items included in the CPI basket in
Ethiopia ranges from 85 to 175 representing the different regional governments as the
national CPI in Ethiopia is the aggregation of the regional CPIs.
The countries generally follow COICOP (Classification of Individual Consumption by
Purpose) at various levels of aggregations to classify and present their household
consumption expenditures. Mauritius presents its CPI in 12 divisions, 41 groups and 83
classes while Egypt has 12 groups and 45 sub-groups. Ethiopia, Zambia and Malawi, on the
other hand, use two digit level COICOP classifications slightly modified to suit country
specific circumstances. Ethiopia publishes the CPI data in 11 major groups while Zambia
uses 8 major groups. In Malawi the national CPI is published categorized into seven major
groups.
Some products especially the production of fruit and vegetable are subject to seasonal
fluctuations and as a result seasonally disappear from market. Countries use different
approaches to deal with this issue of seasonality of products. In Mauritius, a 12 month
moving average price is computed and used instead of the actual monthly price quotations to
smooth out the seasonal price variations. Egypt assigns the weights of seasonally
disappearing items to the available product within the group. In Malawi and Zambia seasonal
items are reported to be available throughout the year and hence no adjustment.
106
In the case of treatment of missing items, Malawi, Mauritius and Egypt use the carry forward
procedure for a period of three months after which if not available the item would be replaced
by the nearest substitute. In Zambia the missing prices are estimated by taking the average
growth in similar products.
All countries use arithmetic average of price ratios formula to compile the indices.
All the countries compile and publish monthly CPI data. Regarding the timeliness of the
publication it ranges from seven days in Zambia to 30 days in Malawi after the reference
month.
To sum up, in all the five countries the general framework adopted for compiling the price
indices is the conceptual and methodological guideline provided by the ILO. It is believed
that the CPIs in these countries would be more comparable and harmonized in concepts and
methods when the on-going revision works in Egypt, Ethiopia, Zambia and Malawi are
completed and new CPI figures with new basket of goods and services and updated base year
are released.
3.4
Government Finance Statistics
Government Finance Statistics Manual 2001 is the current international guideline for
compiling fiscal data. The previous manual referred to as the Government Finance Statistics
Manual was published in1986. Both manuals are issued by the International Monetary Fund.
The major changes in the revised GFS system include the coverage of units to be recorded in
the system, the timing at which economic events are to be recorded, definitions,
classifications, and balancing items. Moreover, the revised GFS is more harmonized with
other macroeconomic statistical systems like the 1993 SNA than the 1986 GFS system.
Understanding the changes between the two manuals is important because the difference or
comparability of the data between the countries depends on which manual the countries are
implementing.
Egypt officially adopted the 2001 GFS manual in the year 2005/06 as the general framework
for the compilation of government finance statistics. The budget for 2005/06 which was
compiled on the basis of 2001 GFS was presented and adopted by the parliament. The
migration, however, is not yet complete as there still persist some aspects of the 1986 GFS.
The Ministry of Finance is compiling the government finance statistics in Egypt. Mauritius is
currently implementing the 1986 GFS manual. The National Statistical Office (NSO) of
Mauritius is responsible for the compilation of the data. The NSO works in close
collaboration with the Ministry of Finance. The NSO of Mauritius in collaboration with the
South Africa Treasury is making the necessary preparations to migrate to the 2001 GFS.
Mauritius plans to introduce 2001 GFS as of 2008/9 budget year. Malawi is not currently in
the GFS system. The concepts and methodologies adopted for compiling the fiscal data,
however, are broadly in conformity with the 1986 GFS manual. Malawi has also started the
process to implement the 2001 GFS manual. Bridge tables linking the current fiscal table to
107
the 2001 GFS are elaborated and some preliminary (trial) results are already out. The
Ministry of Finance which has the mandate to produce government finance statistics is
determined to shift to the 2001 GFS with in a short time period. Unlike the other three
countries, Zambia and Ethiopia each produce two sets of government finance statistics. In
Zambia, the CSO produces government finance statistics which is broadly in line with the
2001 GFS recommendations but has not been officially adopted and published. The audited
revenue and expenditure accounts compiled and published by the Ministry of Finance of
Zambia is, on the other hand, the official data on government finance statistics though do not
strictly conform to the GFS system. In Ethiopia, both sets of fiscal data are produced with in
the same ministry, i.e., Ministry of Finance and Economic Development. The Central
Accounts Department of MOFED compiles and publishes the official government fiscal data
while the Macro policy and Management Department compiles the data more or less in 2001
GFS format. The latter which has never been official or published is compiled only for the
consumption of the IMF.
The 2001 GFS system covers all units of the general government sector which consists of all
resident government units and all resident non-profit institutions that are controlled and
mainly financed by government. Specifically, the units of general government include the
central government, state governments and local governments. The central government is
classified into budgetary central government, extra budgetary units and social security funds.
The government finance statistics in Egypt covers all the units of general government
including the budgetary central government, extra budgetary entities and the Social Insurance
Fund. Mauritian fiscal data also covers the activities of the central government, local
governments and extra budgetary units. In Ethiopia, Malawi and Zambia the fiscal data are
basically limited to the budgetary central government operations. In these three countries
efforts are being made to expand the coverage of their fiscal data so as to include local
governments and extra budgetary units.
In all of the five countries flows are recorded on a cash basis though the 2001 GFS guideline
recommends the accrual accounting. The countries have found the accrual accounting
recommendation rather difficult to implement. Furthermore, some argue that the recording on
a cash basis is more realistic and practical for planning and budgeting especially in
developing economies.
The classification system adopted in Egypt is generally in line with the revised GFS system.
The Ministry of Finance compiles and publishes the tables on revenue, the economic and
functional classifications of expenses and the statement of government operations as
recommended in the manual with some changes. The functional classification of expense is
based on COFOG. In Mauritius, the CSO produces and disseminates fiscal data following the
classification recommended in the 1986 GFS. The CSO data, in addition to the central
government operations, includes extra budgetary units and social security schemes. The
overall structure and classification of the official and published fiscal data in Malawi, Zambia
and Ethiopia largely follow the 1986 GFS.
108
In conclusion all the five countries are using the cash basis accounting instead of the accrual
system recommended by the revised GFS. None of the five countries have provided estimates
of consumption of fixed asset in their fiscal data implying that operating surplus is only
available on a gross basis. Egypt is the only country currently implementing the 2001 GFS.
Malawi and Mauritius are now at an advanced stage of preparation to migrate to the 2001
GFS and it is expected that they will start implementing the revised system before the end of
the coming year. Ethiopia and Zambia are currently producing two sets of fiscal data. It is
very important for these countries to strengthen the unit responsible for the compilation of
government statistics and produce one set of data consistent with international guidelines and
recommendations.
3.5
Balance of Payments and Monetary Statistics
The compilations of the balance of payments statistics in Egypt, Zambia, Malawi, and
Mauritius generally follow the guidelines and recommendations of the fifth edition of the
Balance of Payments Manual (BPM5). Ethiopia, on the other hand, is basically implementing
BOP manual IV. However, Ethiopia is currently making the necessary preparations to move
to BPM5 and have already started implementing some aspects of BPM5. In all the countries
except Malawi, BOP statistics are compiled by the respective Central Banks and in Malawi
this responsibility lies with the National Statistical Office.
The compilations of the current accounts, in all case, are generally in broad conformity with
the international guideline though some shortcomings are observed. In the goods account the
informal or unregistered cross border trade activities are not fully accounted for. In the case
of services and income the input data are often inadequate and not disaggregated enough as
required. In most cases transfer is not broken down into current and capital and as a result the
practice is that all transfer receipts are registered under current transfer. The treatment of
wages and salaries paid to employees of UN and other similar international organizations is
sometimes not consistent with the recommendation of BPM5. Residency criteria contained in
the BPM5 are not sometimes observed.
Most countries derive data on FDI flows and stocks from the banking system and investment
agencies. Few countries have conducted enterprise surveys on FDI. Malawi conducted
Private Capital Flows Survey in 2003 which provided the required data for the years 2000
and 2001. The Malawi survey included data on both FDI and portfolio investments as well as
investment incomes. In Egypt an enterprise survey on FDI was recently under taken jointly
by the Ministry of Investment, Central Bank of Egypt, Central Agency for Public
Mobilization and Statistics and the IMF. The result is not yet ready.
In some countries the data on FDI flows and stocks are inadequate in both scope and quality.
Though the countries generally follow the same international guideline for the compilation of
the balance of payments statistics, the nature and availability of the input data used for the
computation affect the coverage and reliability and hence the comparability of the BOP
statistics.
109
Zambia, Mauritius, and Egypt generally follow the IMFs Monetary and Financial Statistics
Manual (MFSM) to compile their monetary statistics while Malawi is on transition to the
MFSM. Ethiopia is currently compiling the monetary statistics according to the IMF’s draft
Guide to Money and Banking Statistics issued in 1984. At the time of the mission, the
National Bank of Ethiopia was conducting a study to move to the MFSM.
The monetary and financial statistics are compiled in most of the countries as recommended
by the MFSM with regard to coverage, classification, residency criteria, valuation and
consolidation. In some countries, however the other depository corporations surveys do not
cover all such units as recommended in the international guideline. In order to produce
comparable data the scope of the monetary surveys should be in line with the
recommendations of the 2000 MFSM. Though already implementing largely the MFSM,
Malawi needs to overcome the current limitations in coverage and classification and complete
the migration to MFSM. Ethiopia is the only country implementing the 1984 draft guideline
and hence it is important that Ethiopia adopts the current international guideline in order to
produce comparable monetary and financial statistics.
Annex 1
Major Recommendations to Improve the Scope and Quality of the
Statistics
Shortcomings
Recommendations
A. National Accounts
1. Some countries still in the 1968
Implement the 1993 SNA
SNA.
2. The use of old base year
Regularly update the base year every five
110
years and if possibly implement chapter 16
of the 1993 SNA
3. Delay in the release of the statistics
Improve the timeliness of the data
4. Expenditure side GDP at constant
Compile the required price indices on
prices not computed in most cases
construction and foreign trade
5. Private consumption expenditure in
Compile independent estimates of PFCE.
most cases computed as a residual
6. Limitation in the production
Expand the production boundary as
boundary
recommended.
7. Under coverage in the informal
The available data set should capture such
and/or own account production
activities as much as possible
activities
8. The production and consumption
Compile the required data & produce
expenditure of NPISHs mostly not
estimates of NPISHs production &
available
consumption.
9. Data on gross capital formation
Improve the data needs and methodology of
inadequate
estimation of fixed capital formation &
inventories
10. Data on consumption of fixed
Compile estimates of consumption of fixed
asset not often available
asset at least for the non-market producers.
10. Valuation problem
Use basic /producers’ prices to value output
as recommended
12. Allocation of FISIM not often
Compile and allocate FISIM between inter
done
industry and final consumption as
recommended
11. Limitation in the scope of
Compile accounts and tables determined as
accounts and tables compiled
minimum requirements
B. Consumer Price Statistics
1. The use of old base year in some
Update the base yea of the expenditure
countries
weights every five years
2. Classification problem
Adopt the internationally recommended
COICOP classifications
3. Time lag in the publication
Improve the timeliness of the dissemination
111
dissemination of data
of the data
C. Government Finance
Statistics
1. Some still in the 1968 GFS
Migrate to the 2001 GFS
system.
2. Limitation in coverage
Include local governments/municipalities
and extra budgetary government units as
well as social security funds in the GFS
statistics
3. Considerable time lag in some
Improve the timeliness of the data
countries in the compilation and
dissemination of the statistics.
D. Balance of Payments
1. Few countries still implementing
Migrate to BOPM 5
BOPM 4
2. Problems of under coverage
Capture as much as possible the informal or
unregistered cross border trade activities
3. Data on the services account
Improve the data input and level of
inadequate
disaggregation
4. Classification problem pertaining
Classify transfer into capital and current as
to transfer
recommended in the BOP 5
5. Limitation in the FDI statistics
Conduct enterprise survey
E. Monetary Statistics
1. Countries not yet implementing the
2000 MFSM
2. Limitation in the other corporations
Expand the coverage of the survey to
survey
include all units as recommended in the
MFSM.
112
4.0
Choice of Monetary Policy Regimes in Selected
COMESA Member Countries
Christopher Kiptoo
4.1
Introduction
Monetary policy is one of the key policy tools for guaranteeing a stable macroeconomic
environment. The appropriate nominal anchor, the variable the central bank uses to discipline
its policy decisions, is thus critical in the choice of the monetary policy framework adopted.
Several advanced countries, over the 1990s, decided to make low inflation the main objective
of their monetary policy, and tried to reach this objective with a strategy of inflation targeting
(IT). This was partly a response to various difficulties encountered with alternative strategies
like exchange rate and monetary targeting. The overall success of these countries in bringing
down and stabilizing inflation at low levels caused many emerging and transition countries to
consider the adoption of IT. In Eastern Europe, transition countries like Poland, Hungary, and
the Czech Republic are pursuing such a monetary policy framework successfully. This
international experience has also sparked an active discussion in COMESA countries about a
change in the monetary policy strategy with a view to adopting the IT framework.
Contributing to the preparation towards the achievement of the COMESA Monetary Union in
2018, this paper aims at examining the appropriate monetary policy regime for selected
COMESA members. The specific objectives are as follows: (i) review of the existing
monetary policy framework in selected developing countries, with a focus on COMESA
member countries; (ii) discuss the pros and cons of different monetary policy regimes; (iii)
outline the requirements for implementing different monetary policy regimes. (iv) analyze
performance and challenges of the existing monetary policy regimes in selected COMESA
countries; (v) examine the institutional arrangements for implementing different monetary
policy regimes; (vi) investigate the demand for money function for selected countries, since it
is crucial for the effectiveness of monetary policy; (vii) make recommendations for
COMESA region on the appropriate monetary policy regime.
In what follows, Section 4.2 reviews the monetary policy framework. This is followed in
section 4.3 by an examination the performance of selected COMESA member states with
respect to inflation. Experiences of other countries are discussed in Section 4.4. Section 4.5
then assesses the appropriate monetary policy regime. Conclusions and policy
recommendations are contained in Section 4.6.
113
4.2
Review of Monetary Policy Frameworks
While the choice of monetary policy framework adopted by a country depends on economic,
financial and institutional environment within which policy is operating apart from other
constraints in policy formulation, generally countries have adopted either exchange rate,
monetary or inflation targeting frameworks.
4.2.1 Exchange Rate Targeting
Under this framework, the monetary authority stands ready to buy or sell foreign exchange at
given quoted rates to maintain the exchange rate at its predetermined level or within a range.
Thus, the exchange rate serves as the nominal anchor or intermediate target of monetary
policy. This monetary policy framework is characterized by various arrangements.
4.2.1.1 Exchange Arrangement With No Separate Legal Tender
Some countries have either the US currency or the euro circulating as the sole legal tender.
Those with the US dollar circulating as the sole legal tender are Ecuador, El Salvador,
Marshall Islands, Federal States of Micronesia, Palau, Panama and Timor-Leste. Nations with
the Euro circulating as the sole legal tender are Montenegro, San Marino and Kiribati.
4.2.1.2 Currency Board Arrangement
Countries that have adopted a currency board arrangement are 13. Of these, 8 including one
from COMESA have a monetary regime that is based on an explicit legislative commitment5
to exchange their domestic currency for the US dollar at a fixed exchange rate. The 8
countries are Antigua and Barbuda, Djibouti, Dominica, Grenada, Hong Kong SAR, St. Kitts
and Nevis, St. Lucia, and St. Vincent and the Grenadines. The remaining 5 countries, namely
Bosnia and Herzegovina, Bulgaria, Estonia, Lithuania and Brunei Darussalam have a
monetary regime that is based on an explicit legislative commitment to exchange their
domestic currency for the Euro at a fixed exchange rate.
4.2.1.3 Other Fixed Peg Arrangements
At least 68 countries have formally pegged their currencies at a fixed rate to either the US
dollar or the Euro or a basket of currencies, where the basket is formed from the currencies of
5This
implies that domestic currency will be issued only against foreign exchange and that it remains fully
backed by foreign assets, eliminating traditional central bank functions, such as monetary control and lenderof-last-resort, and leaving little scope for discretionary monetary policy.
114
major trading or financial partners and weights reflect the geographical distribution of trade,
services, or capital flows. Of the 68 countries, 36 and 16 have respectively pegged their
currencies at a fixed rate to the US dollar and the Euro. Those that have pegged their currency
to a basket of currencies are 7(see also appendix II).
Pegged Exchange Rate within Horizontal Bands
Few countries are implementing pegged exchange rates within horizontal bands where the
value of the currency is maintained within certain margins of fluctuation. It also includes
arrangements of countries in the exchange rate mechanism (ERM) of the European Monetary
System (EMS) that was replaced with the ERM II on January 1, 1999. There is a limited
degree of monetary policy discretion, depending on the band width. These countries are
Slovak Rep., Syria and Tonga.
Crawling Peg
One COMESA member and 7 others have adopted crawling pegs where the currency is
adjusted periodically in small amounts at a fixed rate or in response to changes in selective
quantitative indicators, such as past inflation differentials vis-à-vis major trading partners,
differentials between the inflation target and expected inflation in major trading partners.
These nations are Bolivia, China, Ethiopia, Iraq, Nicaragua, Uzbekistan, Botswana and Iran.
Crawling Band
Costa Rica and Azerbaijan are the only two countries that have crawling bands, implying
each currency is maintained within certain fluctuation margins. The degree of exchange rate
flexibility is a function of the band width. The commitment to maintain the exchange rate
within the band imposes constraints on monetary policy, with the degree of policy
independence being a function of the band width. (See also appendix I)
4.2.1.4 Advantages of Fixed Exchange Rate Regime
(i)
(ii)
(iii)
(iv)
(v)
(vi)
(vii)
(viii)
Easily understood by the public.
Over the longer run, if it is fully supported by monetary policy, an unchanged peg will
tend to produce the same rate of inflation as in the country of the currency peg.
Has the benefit of simplicity and transparency.
Provides a visible and easily monitored anchor for price expectations.
The nominal anchor of an exchange rate target fixes the inflation rate for internationally
traded goods, and thus directly contributes to keeping inflation under control.
If the exchange-rate target is credible, it anchors inflation expectations to the inflation
rate in the anchor country to whose currency it is pegged.
Provides an automatic rule for the conduct of monetary policy that avoids the timeinconsistency problem.
It forces a tightening of monetary policy when there is a tendency for the domestic
currency to depreciate or a loosening of policy when there is a tendency for the
domestic currency to appreciate.
115
(ix) Monetary policy no longer has the discretion that can result in the pursuit of
expansionary policy to obtain employment gains which lead to time-inconsistency.
4.2.1.5 Disadvantages of Fixed Exchange Rate Regime
(i) Loss of autonomy in monetary policy so that the policymaker is unable to respond to
developments in the domestic economy that are not present in the country to which the
currency is pegged.
(ii) With open capital markets, an exchange-rate target causes domestic interest rates to be
closely linked to those of the anchor country. The targeting country thus loses the ability
to use monetary policy to respond to domestic shocks that are independent of those
hitting the anchor country.
(iii) An exchange-rate target means that shocks to the anchor country are directly transmitted
to the targeting country because changes in interest rates in the anchor country lead to a
corresponding change in interest rates in the targeting country.
(iv) Exchange-rate targets leave countries open to speculative attacks on their currencies.
Defense of the currencies is very expensive.
(v) An exchange rate peg that is not fully supported by monetary policy and accompanied by
fiscal discipline may present the following drawbacks.
o Excessive monetary expansion or fiscal laxity will increase inflation pressures. Nontradables prices will rise relative to tradables prices, held down by foreign competition.
Eventually, the deterioration in international competitiveness leads to external current
account imbalances. Such a peg becomes less and less credible.
o Individuals and firms will try to shift out of the domestic currency into foreign
currencies, leading to capital outflows and/or a parallel exchange rate that is more
depreciated than the official rate.
o Given limited official foreign exchange reserves, the authorities may resort to rationing
of foreign exchange, opening the door to favoritism in its allocation and corruption, and
inefficiencies as imports of necessary intermediate inputs are curtailed.
(vi) Can weaken the accountability of policymakers because it eliminates an important signal
that can help keep monetary policy from becoming too expansionary.
(vii) For some countries, there is no obvious currency to peg to.
4.2.2 Monetary Targeting
Under this framework, a stable and well understood relationship is assumed between inflation
and a chosen monetary aggregate. To attain the intermediate target and ultimate objectives of
monetary policy, an operational framework is adopted that specifies operational variables of
the monetary policy, such as interest rates or monetary base of the banking system.
In the case of the former, interest rates are used as a policy variable and control of inflation is
the ultimate objective while broad measure of money is the intermediate target. The central
bank affects the level of short-term interest rates by its discount policy, supplemented by
open market operations to influence money supply. In the case of the latter, the monetary
116
aggregates are controlled through making changes to monetary base of the banks. In this case,
the interest rates are not used as policy instruments and are instead allowed to fluctuate
according to market forces. The policy instrument under these conditions is then primarily
Bank reserves requirement. The monetary authority uses its instruments to achieve a target
growth rate for a monetary aggregate, such as reserve money, M1, or M2, and the targeted
aggregate becomes the nominal anchor or intermediate target of monetary policy. Most of the
COMESA countries are implementing this framework. These are Burundi, Malawi, Rwanda,
Kenya, Madagascar, Sudan, Tanzania, Uganda, Zambia and Zimbabwe. Most of these
countries have managed floating exchange rate regimes with no predetermined path for
theexchange rate. This means that the monetary authority attempts to influence the exchange
rate without having a specific exchange rate path or target (see also appendix II).
4.2.2.1 Advantages of Monetary Targeting
(i)
Monetary targets have the benefit of simplicity and transparency i.e. Easy to follow,
does not require sophisticated models or techniques;
(ii) Enables a policy-maker to take account of domestic developments in setting policy;
(iii) Easy to determine relatively quickly whether the target is being achieved;
(iv) Published monetary aggregate vis-à-vis the target gives public signal on the adopted
stance of monetary policy;
(v) Signals help fix inflationary expectations that help produce less inflation;
(vi) Allows for accountability in case of monetary policy mistakes;
(vii) A target for the growth rate of a monetary aggregate provides a nominal anchor that is
fairly easily understood by the public and is easily communicated to the public;
(viii) Enables the central bank to choose goals for inflation that may differ from those of
other countries and allows some response to output fluctuations;
(ix) Like an exchange-rate target, information on whether the central bank is achieving its
target is known almost immediately -- announced figures for monetary aggregates are
typically reported periodically with very short time-lags, within a couple of weeks;
o Thus, monetary targets can send almost immediate signals to both the public and
markets about the stance of monetary policy and the intentions of the policymakers
to keep inflation in check;
(x) Monetary targets also have the advantage of being able to promote almost immediate
accountability for monetary policy to keep inflation low and so constrain the monetary
policymaker from falling into the time-inconsistency trap.
4.2.2.2 Disadvantages of Monetary Targeting
(i)
Successful monetary targeting is reliant on the relationship between the targeted
aggregate and the goal of monetary policy remaining stable, and the aggregate being
controllable by the central bank;
o The failure of these two conditions in a large number of countries has led to the
widespread abandoning of monetary targeting;
(ii) Money demand has proved unstable in many countries, limiting its usefulness as an
indicator of the appropriate stance of monetary policy;
(iii) Requires strong and reliable relationship between the goal variable and the target
variable;
(iv) Other related challenges include:
o Instabilities in the money multiplier;
117
o
o
o
o
Interest rate volatility;
Problems in forecasting liquidity;
Choice of reserve aggregates for targeting; and
Limited and inflexible monetary policy instruments.
4.2.3 Inflation Targeting
This involves the public announcement of medium-term numerical targets for inflation, with
an institutional commitment by the monetary authority to achieve these targets. Additional
key features include increased communication with the public and the markets about the
plans and objectives of monetary policymakers and increased accountability of the central
bank for its inflation objectives. Monetary policy decisions are guided by the deviation of
forecasts of future inflation from the announced inflation target, with the inflation forecast
acting (implicitly or explicitly) as the intermediate target of monetary policy.
Appendix I shows that at least 44 countries use inflation targeting as a framework for
monetary policy. None of these countries is in COMESA. Majority of them fall under the
classification of developed countries or emerging markets. Majority of them have adopted
independently floating exchange rate regimes. The rest have managed floating exchange rate
regimes. South Africa and Ghana are among the few countries in Africa that have adopted
inflation targeting monetary policy framework.
4.2.3.1 Advantages of Inflation Targeting
(i)
(ii)
(iii)
(iv)
(v)
(vi)
(vii)
Enables monetary policy to focus on domestic considerations and to respond to shocks
to the domestic economy;
Has the advantage that velocity shocks are largely irrelevant because the monetary
policy strategy no longer relies on a stable money-inflation relationship;
o Thus, unlike monetary targeting, stability of the relationship between the monetary
aggregates and inflation is not essential as it focuses directly on the final goal –
inflation;
By allowing policy to respond to all available information – and not just to the
monetary aggregates – an inflation target allows discretion at the level of interpreting
information
o I.e. allows the monetary authorities to use all available information, and not just one
variable, to determine the best settings for monetary policy;
It is easily understood by the public and is thus highly transparent.
o In practice, IT has been associated with a significant increase in the transparency and
accountability of monetary policy;
Reduces the likelihood of the central bank of falling into time inconsistency trap since
it raises accountability and transparency;
An inflation target can be regarded as ‘constrained discretion’ where a realistic balance
is struck between simplicity and the ability to respond flexibly to shocks;
Sustained success in the conduct of monetary policy as measured against a preannounced and well-defined inflation target can be instrumental in building public
support for creating an independent central bank.
118
4.2.3.2 Disadvantages of Inflation Targeting
(i)
(ii)
(iii)
(iv)
(v)
(vi)
(vii)
Difficulty of directly controlling inflation;
The long and variable lags in monetary policy and the absence of a simple rule may
make it difficult for the public to monitor the performance of the central bank in a
timely manner;
IT is especially difficult in emerging market economies because inflation is hard to
control and there exist long lags between the adoption of monetary policy instruments
and the inflation outcome;
Is too rigid and it allows for too much discretion;
Has the potential to increase output instability along with lowering of output growth.
The exchange rate flexibility required for success in inflation targeting might cause
financial instability, especially in the context of the emerging market economies;
Finally, high degree of (partial) dollarization is likely to create a potentially serious
problem for inflation targeting.
Table 4.1 summarizes the operating targets under the different monetary policy regimes. The
institutional Arrangements for the success implementation of each regime is presented in
appendix II
Table 4.1: Operating Targets under Alternative Monetary Policy Frameworks
Monetary policy framework
Operating target
Exchange rate targeting
Short-term interest for a country with
developed markets or open capital account
Money base (or NIR) for a country with
undeveloped markets or capital restrictions
Money base for a country with high inflation
or undeveloped markets
Money base for a country with low inflation,
developed markets or unstable multiplier
Short-term interest for a country with low
inflation, developed markets or unstable
multiplier
Short-term interest for a country with
developed markets or open capital account
Monetary targeting
Inflation targeting
4.3
Performance of Monetary Policy Regimes in Selected COMESA members
4.3.1 Uganda
Uganda has been implementing a monetary aggregate targeting framework since 1993.
Monetary policy framework set within the context of macroeconomic objectives of achieving
real economic growth and the maintenance of price stability as defined by Government from
time to time. Monetary targeting framework is based on reserve money program of IMF’s
financial programme. Monetary policy management is hinged on a quantity targeting
119
framework where money supply is the intermediate target and reserve money the operating
target
Prior to markets liberalizations, direct monetary policy control was applied. This involves
interest rate and credit controls. A shift to indirect monetary policy control was part of
financial sector liberalization process. Open market operations through treasury bills and
repurchase agreements are the principle instrument for managing day to day liquidity. Other
instruments include Bank rate, cash reserve requirements and rediscount rate
Since the adoption of the reserve money program, inflation has been brought under control.
Inflation declined from double-digit levels of 66% in June 1992 to single-digit levels for most
of the 1990s to 2008. It rose to 14.2% in 2009 before declining to 9.4% in 2010. The
impressive inflation outturn is largely a result of the continued pursuance of prudent
monetary and fiscal policies. Fiscal restraint, in conjunction with close co-ordination between
the monetary and fiscal authorities contributed significantly to bringing down inflationary
expectations.
4.3.2 Zambia
IMF’s financial programming form the core of the monetary targeting framework. Prior to
1992, Zambia relied on direct instruments of monetary policy such as fixed interest rates and
credit allocations, core liquid assets, statutory reserve requirement and fixed exchange rate
regime. Indirect monetary policy instruments employed currently are the open market
operations (Secured Loans, Term Deposits & Treasury bill auctions), Bank Rate, Rediscount
facility and Core Liquid Asset Ratio.
Since implementation of monetary targeting framework and use of indirect monetary policy
instruments, inflation has fallen remarkably from 3 digits in early 1990s to single digits. It
stood at 8.5% in 2010.
4.3.3 Kenya
Kenya pursues monetary targeting framework to achieve inflation objective. The framework
has remained fairly the same with the Central Bank of Kenya (CBK) continuously refined
monetary policy operations and procedures to enhance efficiency and effectiveness in a
changing financial and economic environment. In formulating monetary programs, the Bank
start with estimating the money demand consistent with the target rate of inflation and GDP
growth. This form the basis for setting desired path for monetary growth to which actual
money supply had to conform during policy implementation stage. However, with the time
lag in obtaining information needed for effective control of broad monetary aggregates, the
CBK formulates its monetary policy implementation strategy on the basis of reserve moneymore readily available as liability of the central bank. The reserve money program design is
consistent with desired money supply expansion.
120
Prior to financial market liberalization, direct monetary policy instruments were used. These
were in form of interest and credit controls. Indirect monetary policy instruments currently
employed include open market operations, bank rate, cash ratio requirements and rediscount
facility.
Monetary targeting framework has served the country well with refinement of operations and
procedures to enhance effectiveness. The surge in inflation in early 1990s to about 70% (the
time of first multiparty elections in 1992) led to amendment of the CBK Act in 1996 to give
CBK more autonomy to manage monetary policy. The inflation has been maintained at
satisfactory level supported by prudent fiscal policy. The inflation rate recorded single digits
in most cases since 1995 and it averaged 10% in 2007. It, however, surged to 16.125% in
2008 owing to a rise in food prices and international oil prices before dropping to 9.2% in
2009 and 3.9% in 2010.
4.3.4 Malawi
Malawi pursues monetary targeting framework. In its pursuit of price stability, the Bank of
Malawi monitors growth in reserve monetary aggregate. To influence growth in monetary
stock the Bank increases or decreases the amount of reserve money by managing both
domestic and foreign sources of reserve money. The Bank’s daily management of monetary
movements involves estimates of banking system liquidity situations.
Malawi initially used direct monetary policy instruments such as interest rates and credit
controls, strict controls on foreign exchange and capital flows. After liberalization, there was
a shift to indirect monetary policy instruments that include open market operations, liquidity
reserve requirements, repurchase and discount window facility.
Since adoption of the monetary targeting framework the pace of inflation decelerated from
double digit (range of 29.6-83.3% between 1994 and 2000) to 6.9% in 2010.
4.3.5 Swaziland
Monetary policy formulation influenced largely by membership to Common Monetary Area
(CMA). The countries facilitate smooth implementation of CMA agreements through CMA
Governors meeting. The tools at the disposal for the Bank include the discount interest rate,
reserve requirements and open market operations. The Bank however utilizes interest rates
that has proved effective to curtail inflation arising from the demand side. With free flow of
capital under the CMA and unitary fixed exchange rate to rand (no exchange rate risk)
implies limited scope for the Swaziland interest rate to deviate substantially from South
Africa’s.
Swaziland has recorded single digit annual inflation rate since 1996. Just like most countries,
the inflation rate surge in 2008 to double digit (13.1%) due to rise in food and oil prices
before dropping to 7.4% and 4.5% in 2009 and 2010 respectively.
121
4.3.6 Seychelles
Prior to enactment of the Central Bank of Seychelles Act of 2004 economic management
relied heavily on fiscal policy with monetary policy relegated to an accommodating role.
Since November 2008, the Monetary Policy Framework is based on monetary targeting. This
transition was to support a liberalized foreign exchange market and floating exchange rate
regime as part of IMF-supported economic reform program adopted by the authorities in
November 2008.
Interventions for managing bank's liquidity are guided by liquidity monitoring framework
maintained by the Bank. This framework identifies the factors which influence bank liquidity
and is used to make forecasts on future liquidity flows.
The policy instruments include Open market operations (OMO), Minimum Reserve
Requirements (MRR) and the lending facility. OMOs involve Treasury bill auction, deposit
auction arrangements and foreign exchange auction. The MRR includes the Local asset Ratio.
This tool requires commercial banks to invest a specific %age of their local currency deposit
liabilities in government securities and other claims on government and has been an
important vehicle for funding government deficits since its inception in 1986.
The lending facility is the Standing Credit Facility (SCF) which is an overnight collateralized
loan facility that provides funds to the commercial banks, so as to cover temporary end-ofday shortfalls that can arise in the daily settlement of payments. The Emergency Lending
facility (ELF) is an emergency liquidity support facility primarily to prevent severe and
persistent short term liquidity problems to lead to insolvency and to avoid bank runs.
Prudent and well balanced monetary and fiscal stances have reduced inflation to low single
digit levels since the 1990s. Inflation, however, rose to 36.9% in 2008 and to 31.9% in 2009
before drooping to -2.4% in 2010
4.3.7 Egypt
Principal monetary targeting in Egypt has remained focused on price stability and
stabilization of the exchange rate. The operational target initially involved nominal interest
rate management and controlling excess bank reserves but was replaced in 2005 by overnight
interest rate on interbank transactions.
Direct instruments such as quantitative and administrative determination of interest rates
using interest rate and credit ceiling were abolished from 1992 and replaced with indirect
market based instruments such as required reserve ratio, reserve money and open market
operations- discount rate and interest rate.
Timely monetary policy responses by the Central Bank manage to contain inflation
expectation particularly emanating from demand pressures. Inflation therefore averaged 7.8%
in the 1990s and 8.6% in the 2000s. The surge in inflation in the latter period was due to
122
supply shocks such as international fuel price hike. Inflation rose to double digit levels of
10.9%, 11.7% and 16.2% in 2007, 2008 and 2009 respectively.
4.3.8 Burundi
The country uses monetary aggregate targets to control inflation. It’s implementing the IMF’s
Poverty Reduction Growth Facility Program. Prior to 1986, direct monetary policy
instruments were used such as credit ceiling and interest rates control. Indirect instruments
adopted after 1986 through the structural adjustment programs. The instruments at the
disposal of the central bank include refinance policy, auction of treasury certificates and
reserve requirements.
Remarkable success has been recorded to reduce inflation to single digit of 8.3 % in 2007.
However, it rose to 24.5 % in 2008 (due mainly to surge in food and oil prices) before
dropping to 10.7% in 2009 and further to 6.4% in 2010.
4.3.9 Zimbabwe
In the 1980s, the instruments were direct control of interest rate, credit ceilings, use of reserve
bank bills and prescribed liquid assets ratio. Following some reforms, the instruments at the
Bank disposal were Bank rate, open market operations and reserve requirements. Good
progress initially made before political instability to contain inflationary pressures. Inflation
has stabilized since the economy became fully dollarized. Inflation was 6.5% in 2009 and
3.0% in 2010.
4.4
Other Country Experiences
4.4.1 Exchange-Rate Targeting
Industrialized Countries
Both France and the United Kingdom successfully used exchange-rate targeting to lower
inflation by tying the value of their currencies to the German mark. In 1987, when France
first pegged their exchange rate to the mark, its inflation rate was 3%, two %age points above
the German inflation rate. By 1992 its inflation rate had fallen to 2%. By 1996, the French
and German inflation rates had converged, to a number slightly below 2%. Similarly, after
pegging to the German mark in 1990, the United Kingdom was able to lower its inflation rate
from 10% to 3% by 1992, when it was forced to abandon the Exchange Rate Mechanism
(ERM).
The biggest cost to exchange-rate targeting in industrialized countries has been the loss of an
independent monetary policy to deal with domestic considerations. If an independent,
domestic monetary policy can be conducted responsibly, this can be a serious cost indeed, as
the comparison between the experience of France and the United Kingdom indicate.
Emerging Market Countries
123
Exchange-rate targeting has also been an effective means of reducing inflation in emerging
market countries. An important recent example has been Argentina, which in 1990
established a currency board arrangement, requiring the central bank to exchange U.S. dollars
for new pesos at a fixed exchange rate of 1 to 1. The currency board is an especially strong
and transparent commitment to an exchange-rate target because it requires that the noteissuing authority, whether the central bank or the government, stands ready to exchange the
domestic currency for foreign currency at the specified fixed exchange rate whenever the
public requests it.
To credibly meet these requests, a currency board typically has more than 100% foreign
reserves backing the domestic currency and allows the monetary authorities absolutely no
discretion. The early years of Argentina's currency board looked stunningly successful.
Inflation which had been running at over a one-thousand % annual rate in 1989 and 1990 fell
to under 5% by the end of 1994, and economic growth was rapid, averaging almost 8% at an
annual rate from 1991 to 1994.
In the case of emerging market countries, exchange-rate targeting has highly been
problematic because it can promote financial fragility. As we have seen recently in Mexico
and East Asia, exchange-rate targeting has been followed by disastrous financial crises which
have devastated their economies. However, in countries whose political and monetary
institutions are particularly weak and therefore have been experiencing continued bouts of
hyperinflation, exchange-rate targeting may be the only way to break inflationary psychology
and stabilize the economy.
In this situation, exchange-rate targeting is the stabilization policy of last resort. However,
targeting the exchange rate with only a weak and non-transparent commitment mechanism, as
most emerging market countries have done, has the potential to be disastrous. If exchangerate targeting is believed to be the only route possible to stabilize the economy, then an
emerging market country is probably best served by going all the way and adopting a
currency board.
4.4.2 Monetary Targeting
Industrialized Countries
Monetary targeting was adopted by several developed countries in the 1970s following the
collapse of the Gold Standard. Two countries which have officially engaged in monetary
targeting for over twenty years starting at the end of 1974 have been Germany and
Switzerland. The success of monetary policy in these two countries in controlling inflation is
the reason that monetary targeting still has strong advocates. The key fact about monetary
targeting regimes in Germany and Switzerland is that the targeting regimes were very far
from a Friedman-type monetary targeting rule in which a monetary aggregate is kept on a
constant-growth-rate path and is the primary focus of monetary policy. Thus, monetary
targeting in Germany and Switzerland should be seen primarily as a method that was
124
employed for communicating the strategy of monetary policy that focuses on long-run
considerations and the control of inflation.
As is emphasized in Neumann and von Hagen (1993), Bernanke and Mishkin (1992) and
Mishkin and Posen (1997), the calculation of monetary target ranges in these two countries
was a public exercise. First and foremost, a numerical inflation goal was prominently
featured in the setting of target ranges. Then with estimates of potential output growth and
velocity trends, a quantity-equation framework was used to back out the target growth rate
for the monetary aggregate. Second, monetary targeting, far from being a rigid policy rule,
was quite flexible in practice. The target ranges for money growth were missed on the order
of fifty % of the time, often because the Bundesbank's and the Swiss National Bank's concern
about other objectives, including output and exchange rates.
Furthermore, the Bundesbank demonstrated its flexibility by allowing its inflation goal to
vary over time and to converge slowly to the long-run inflation goal quite gradually. Another
point to note is that monetary targeting regimes in both Germany and Switzerland
demonstrated a strong commitment to the communication of the strategy to the general
public. The money-growth targets were continually used as a framework for explanation of
the monetary policy strategy and both the Bundesbank and the Swiss National Bank used
remarkable effort, both in their publications and in frequent speeches by central bank
officials, to communicate to the public what the central bank is trying to achieve. Indeed,
given that both central banks frequently miss their money-growth targets by significant
amounts, their monetary-targeting frameworks were best viewed as a mechanism for
transparently communicating how monetary policy is being directed to achieve their inflation
goals and as a means for increasing the accountability of the central bank.
Two key lessons that may be drawn from German and Swiss monetary targeting framework
are as follows.
(i)
A monetary targeting regime can restrain inflation in the longer run, even when the
regime permits substantial target misses. Thus, adherence to a rigid policy rule has not
been found to be necessary to obtain good inflation outcomes; and
(ii)
The key reason why monetary targeting has been reasonably successful in these two
countries, despite frequent target misses is that the objectives of monetary policy are
clearly stated and both the Bundesbank and the Swiss National Bank actively engage in
communicating the strategy of monetary policy to the public, thereby enhancing
transparency of monetary policy and accountability of the central bank. Because of
these key elements of flexibility, transparency and accountability, Germany and
Switzerland have been thought of as "hybrid" inflation targeters and monetary targeters.
Emerging Markets
The monetary policy in India is engaged in managing the transition to a higher growth path
while ensuring that pressures on actual inflation and inflation expectations are contained. The
role of monetary policy by the Reserve Bank of India is to maintain stability and so
contribute to growth. The Reserve Bank continued with the policy of gradual withdrawal of
125
monetary accommodation, using various monetary policy instruments to stabilize inflationary
expectations, while continuing to pursue the medium term goal of maintain inflation at 5.0%.
4.4.3 Inflation Targeting
Fully Fledged Inflation Targeters
New Zealand was the first country to formally adopt inflation targeting in 1990, with Canada
following in 1991, the United Kingdom in 1992, Sweden in 1993, Finland in 1993, Australia
in 1994 and Spain in 1994. Israel and Chile have also adopted a form of inflation targeting.
Experience with monetary targeting in environments of unstable money demand also
prompted countries, such as Indonesia, for example, to adopt IT. Another consideration is the
search for a credible monetary regime in countries where efforts towards disinflation needed
to be complemented by policy initiatives to liberalize prices in the course of structural
reform. This is the case of the Czech Republic, for example.
The Bank of England monetary policy objective is to deliver price stability – low inflation
and, subject to that, to support the Government’s economic objectives including those for
growth and employment. Price stability is defined by the Government’s inflation target of
2%. The remit recognizes the role of price stability in achieving economic stability more
generally, and in providing the right conditions for sustainable growth in output and
employment. The Government's inflation target is announced each year by the Chancellor of
the Exchequer in the annual Budget statement. The Bank operates inflation targeting
framework, and the Bank targets the inflation rate of 2% expressed in terms of an annual rate
of inflation based on the Consumer Prices Index (CPI). The remit is not to achieve the lowest
possible inflation rate. Inflation below the target of 2% is judged to be just as bad as inflation
above the target. If the target is missed by more than 1 %age point on either side – i.e. if the
annual rate of CPI inflation is more than 3% or less than 1% – the Governor of the Bank must
write an open letter to the Chancellor explaining the reasons why inflation has increased or
fallen to such an extent and what the Bank proposes to do to ensure inflation comes back to
the target. A target of 2% does not mean that inflation will be held at this rate constantly.
That would be neither possible nor desirable. Interest rates would be changing all the time,
and by large amounts, causing unnecessary uncertainty and volatility in the economy. Even
then it would not be possible to keep inflation at 2% in each and every month. Instead, the
monetary policy committee’s (MPC’s) aim is to set interest rates so that inflation can be
brought back to target within a reasonable time period without creating undue instability in
the economy.
The goal of Canadian monetary policy is to contribute to rising living standards for all
Canadians through low and stable inflation. Specifically, the Bank aims to keep the rate of
inflation at 2% target midpoint of a target range established jointly with the government,
which has been 1 to 3% since 1995. Inflation-control targeting has been a cornerstone of
monetary policy in Canada since its introduction in 1991. Inflation-control target has helped
to make the Bank's monetary policy actions more readily understandable to financial markets
and the public. One of the most important benefits of a clear inflation target is its role in
126
anchoring expectations of future inflation. This, in turn, leads to the kind of economic
decision making by individuals, businesses, and governments that brings about noninflationary growth in the economy.
The South African Reserve Bank conducts monetary policy within an inflation targeting
framework. The current targets are for average inflation rate to be within the target range of 6
to 3 per cent in 2002, 2003, and 2004. The Reserve Bank of New Zealand uses monetary
policy to maintain price stability as defined in the Policy Targets Agreement (PTA). The PTA
requires the Bank to keep inflation between 1 and 3% on average over the medium term. The
Bank implements monetary policy by setting the Official Cash Rate (OCR), which is
reviewed eight times a year.
Latin American Experience
A set of Latin American countries have established explicit inflation targeting regimes in
recent years. Indeed, different varieties of IT are applied today in five Latin American
countries: Brazil, Chile, Colombia, Mexico, and Peru. Different, looser forms of IT were
experimented with prior to, and often in preparation for, the adoption of fully fledged IT. For
example, Chile only abandoned exchange rate targeting in 1999, having put in place a looser
form of IT, including by granting the central bank operational autonomy and announcing
explicit inflation targets, in the early 1990s. Brazil, however, adopted IT in June 1999, soon
after the collapse of the exchange rate peg in January of the same year, but did not rely on an
intermediate nominal anchor during the transition period. As elsewhere in the world, four
main factors have prompted Latin American countries to adopt IT:
(i)
Public announcement of central bank inflation targets makes targets the economy’s
nominal anchor and the main policy objective of monetary policy.
(ii)
Forward-looking numerical inflation targets complement public information about
monetary policy objectives and implementation to make monetary policy more
effective in a world of forward-looking rational private agents.
(iii) Publicly announced inflation targets are an easy way to make central banks accountable
to society at large and their political representatives – a major prerequisite imposed on
newly independent central banks. The latter factor can explain IT adoption in countries
with long democratic tradition that have recently granted operational independence to
central banks (like many industrial-country inflation targeters) and others that have
embraced democracy only recently (like the Latin Americans in the 1990s).
(iv) Finally, disappointment of alternative monetary and exchange rate regimes – ranging
from true disappointment with money growth targets to abandonment of fixed exchange
rate regimes after full-fledged balance of-payments crises – led many countries to adopt
IT as the only remaining alternative.
As in other regions, early IT adopters in the region started in an evolutionary way, by
announcing public inflation objectives and learning only over time – from other countries’
and their own experience – about the necessary prerequisites and components of what now is
viewed as a full-fledged IT regime (see Bernanke et al. 1999 and Schaechter et al. 2000.
127
4.4.4 Performance of Inflation-Targeting Regimes
Overall, there is fairly compelling evidence for some countries under examination that
inflation has become less volatile and persistent in the post IT period. Further interest rates
have become less volatile and inflation expectations more responsive to monetary policy
moves. In other words, the performance of inflation-targeting regimes has been quite good.
Inflation-targeting countries seem to have significantly reduced both the rate of inflation and
inflation expectations beyond that which would likely have occurred in the absence of
inflation targets. Furthermore, once inflation is down, it has stayed down; following
disinflations, the inflation rate in targeting countries has not bounced back up during
subsequent cyclical expansions of the economy. Also inflation targeting seems to ameliorate
the effects of inflationary shocks.
Shortly after adopting inflation targets in February 1991, the Bank of Canada was faced with
a new goods and services tax (GST) -- an indirect tax similar to a value-added tax -- an
adverse supply shock that in earlier periods might have led to an increase in the rate of
inflation. Instead the tax increase led to only a one-time increase in the price level; it did not
generate second- and third-round rises in wages and prices that would have led to a persistent
rise in the inflation rate. Another example is the experience of the United Kingdom and
Sweden following their departures from the ERM exchange rate pegs in 1992. In both cases,
devaluation would normally have stimulated inflation because of the direct effects on higher
export and import prices and the subsequent effects on wage demands and price-setting
behavior. Again it seems reasonable to attribute the lack of inflationary response in these
episodes to adoption of inflation targeting, which short-circuited the second- and later-round
effects and helped to focus public attention on the temporary nature of the devaluation
shocks.
4.5
Assessment of the Effectiveness of Monetary Policy Regimes
4.5.1 Monetary Targeting
4.5.1.1 The Income Velocity Approach
Most countries in the COMESA region use the quantity theory approach in estimating a
money demand function. This approach involved expressing the quantity of money in terms
of the quantity of goods and services it can buy i.e. in real money terms. In an equation form,
this is shown as:
M
 ky
P
Where:
(4.1)
128
M is stock of money, P is the price level, k is a constant and y is real GDP. This demand
function states that the quantity of real money balances demanded is proportional to real
income. Rearranging equation 4.1 yields the following equation:
i
M    Py
k
(4.2)
which is equivalent to
Mv  Py
(4.3)
Where v=1/k. Equation 4.3 can be rewritten as follows:
Mv  Y
(4.4)
Where Y is the nominal GDP obtained by multiplying the price level P by real GDP (y).
Because velocity (v) is fixed in the short run, any change in the money supply leads to a
proportionate change in nominal GDP as indicated in the following equation.
v
Y
M
(4.5)
Based on data available, the money velocity in most COMESA countries is often assumed to
be relatively stable. Thus, a constant velocity is assumed in the forecasting of the monetary
aggregates. This involved multiplying the velocity with the forecasted nominal GDP to get
the monetary aggregate. In an equation form, this is indicated as follows:
M  Y *v
(4.6)
Velocity and money multiplier are 2 variables that are of primordial importance for any
central bank in its attempts to contain price inflation. The basic nature of the money supply is
analytical. To understand it fully requires understanding of how it is used for purposes of
monetary policy. Understanding the causality between the monetary base, money supply and
price inflation is key to forecast the various items in the monetary survey. It is worth noting
that underestimating the expected velocity tends to overstate the demand for money and this
is therefore likely to result in a higher inflation and perhaps a worsening in the balance of
payments. The reverse is true.
Central banks cannot determine directly the rate of monetary expansion. The rate of monetary
expansion depends on a multitude of factors outside the immediate sphere of influence of a
central bank. These forces the monetary authorities to identify yet another intermediate
target: the interest rate or monetary base.
129
Once the link between the monetary base and the money supply is understood, a strong
positive correlation between the evolution of the money supply and that of prices is
postulated. This correlation is not always straightforward, because it depends on the stability
and predictability of velocity, and, ultimately, on money demand.
Money stock / monetary base = money multiplier i.e.
mm 
M
HH
(4.7)
Because deposits are larger than the sum of bank reserves and cash held in vaults, the mm>1.
The smaller the monetary base is in relation to the money stock, the larger is the money
multiplier. Thus, the value of the mm is determined by two factors:
(i)
The reserve deposit ratio (i.e. of cash reserves -notes and coins in vaults- that banks are
willing or required to hold relative to total deposits); and
(ii)
The currency deposit ratio (i.e. ratio of cash in circulation that the non-banking sectors
wish to hold relative to total bank deposits).
The following factors that can influence the money multiplier:
(i)
Reserve requirement (negative correlation);
(ii)
Interest rates (positive correlation).
(iii) Variation of deposit withdrawals (negative correlation);
(iv) Uncertainty of economic environment (negative correlation);
(v)
Institutional factors in the payment system that reduces the need to hold cash (positive
correlation); and
(vi) Black market activities (negative correlation).
Figures 4.1 to 4.6 show velocity and in some countries money multiplier for selected group of
COMESA Countries.
130
Figure 4.1: Velocity in Egypt
1.400
1.200
1.000
0.800
Velocity
0.600
0.400
0.200
0.000
1998
1999
2000
2001
2002
2003
2004
2005
2006
Figure 4.2: Money Multiplier and Velocity in Malawi
7.000
6.000
5.000
4.000
3.000
2.000
1.000
0.000
Velocity
Money multiplier
2007
131
Figure 4.3: Money Multiplier and Velocity in Uganda
12.000
10.000
8.000
6.000
4.000
2.000
0.000
Velocity
Money Multiplier
Figure 4.4: Money Multiplier and Velocity Mauritius
9
8
7
Axis Title
6
5
4
3
2
1
0
Velocity
Multiplier
Figure 4.5: Money Multiplier and Velocity in Zambia
6.000
5.000
4.000
3.000
2.000
1.000
0.000
Velocity
Multiplier
132
Figure 4.6: Velocity in Rwanda
6.0
5.8
5.6
5.4
5.2
5.0
4.8
4.5.1.2 Econometric Estimation Approach
Econometric techniques can be employed to estimate a money demand function. According
to economic theory, people demand money mainly for three reasons:
(i)
Transactions motive (to smooth out difference between income and expenditure related
to real output and prices)
(ii)
Precautionary motives; and
(iii) Portfolio motive (related to return on money and return on other assets).
Thus, the nominal money demand is a function of income (GDP, personal disposable income
or wealth), inflation and opportunity cost variables such as interest rates:
𝑀𝑑 = 𝑓(𝑌, 𝑃, 𝑖, 𝑒).
(4.8)
Where Md = money demand, Y = income (GDP), P = expected rate of inflation (proxied by
the inflation rate), r = interest rate and e is the nominal exchange rate. The demand for real
cash balances is positively related to income and negatively related to the opportunity cost of
holding money. In an equation form, this is expressed as:
𝑀𝑑
𝑃
= 𝑓(𝑖, 𝑌, 𝑒)
(4.9)
133
Md
Where P is real money demand. Expressing equation 4.9 in natural logarithms yields the
following:
ln
Md
   1 ln( Y )   2 i   3 ln( e)
P
(4.10)
Equation 4.10 shows that the real money demand is a function of the variables representing
the real domestic economic activity and the opportunity cost of holding real money balances.
Real money demand specification implicitly implies the absence of money illusion as well as
price homogeneity. According to Boughton (1981) and Johansen (1992), there may be less
estimation problems if real money balances are used rather than nominal money balances as
the dependent variable. Furthermore, relatively higher number of empirical studies that have
employed real money balances found significant empirical evidence compared to those that
have used nominal money balances. Equation 4.10 was therefore estimated in this study using
latest econometric techniques, in particular the VAR-based tests developed by Johansen
(1995). The Johansen method provides a unified framework for the estimation and testing of
cointegrating relations in the context of VAR error correction models.
4.5.1.3 Empirical Results for Money Demand Estimations
Given that the monetary targeting framework is used by most COMESA countries, the
effectiveness of this framework is assessed by testing for the stability of the demand for
money estimate for a selected group of COMESA countries. Test for stability of demand for
money was important as supply of money is one of the key instruments of monetary policy
conduct by COMESA central banks. If the demand for money is stable, then money supply is
the most suitable monetary policy instrument but if the money demand function is not stable
central banks should use interest rate as the more appropriate instrument for the conduct of
monetary policy
Accordingly, a standard money demand was first estimated using cointegration technique.
Thereafter stability tests were conducted by means of CUSUM and CUSUMSQ stability
tests. The variables are as follows:
Time series data for all the relevant variables was obtained for the period 1993-2003. To
ensure that enough degrees of freedom in the models to be estimated were available, monthly
data covering the entire study period was collected. The method of data collection was
secondary research, which essentially involved reviewing data sources that have been
collected for some other purpose than the study at hand. The choice of this method was
necessitated by the fact that the problem that was being investigated in this study did not
require the researcher to use data collection techniques such as interviews or questionnaires
to extract the data from a group of respondents.
134
All the relevant data for this study were available in secondary form both from local and
international sources. The main sources of international data were the International Financial
Statistics (IFS) and the Direction of Trade Statistics (DTS). The Library Network that serves
the World Bank Group and the International Monetary Fund (IMF) and which is based in
Washington DC, USA was the sole source of data from international sources. The library6
has several electronic databases including, the IFS and the DTS.
4.5.1 Kenya
Equation 4.11 is the estimated long-run money demand.
RM 3t  6.94 0.24 RGDPt  0.01 IRt  1.24 NERt
(8.30)
( 5.15)
( 2.43)
(4.11)
( 14.69)
The estimated model is stable as shown in the Figures in respect of stability tests such as
CUSUM and CUSUM sum of Squares. It appears that the evolution and development of the
financial market together with innovation in information technology in Kenya did not bring
in the expected element of sensitivity in the demand for broad money in Kenya’s economy.
Figure CUSUM Stability Test for Kenya’s Money Demand
Figur e 5.11: Cus um Stability Tes t f or Keny a's Money Demand
1.2
1.0
0.8
2000
2001
2002
2003
2004
CUSUM of Squares
6
2005
2006
5% Signif ic anc e
The library also provides access to books, journal titles, journal articles, working papers, conference
proceedings, technical reports, videos, software, electronic resources, etc.
2007
135
4.5.2 Malawi
The estimated long-run money demand is as presented in equation (4.12).
RM 2t  3.78 0.17 RGDPt  0.12 IRt  0.70 NERt
(1.99)
( 0.17)
( 2.05)
(4.12)
( 2.03)
While the CUSUM test showed stability, the plot of the CUSUM sum of squares showed
instability of the demand for money function during the period 2002 and 2006. The plot of
recursive residual shows presence of money demand instability between 2002 and 2003 as
well as between 2006 and 2007 as it only touches and goes beyond the lower and the upper
band respectively during these periods. Other than these periods, demand for money has
generally been stable in Malawi.
Figure 5.15: Cusum Stability T est for M alawi's M oney Dem and
Figure 5.16: Cusum Squares Stability test for m alawi's M oney Dem and M odel
30
1.2
20
1.0
0.8
10
0.6
0
0.4
-10
0.2
-20
0.0
-30
-0.2
2001
2002
2003
CUSUM
2004
2005
2006
2007
2001
5% Significance
2002
2003
2004
CUSUM of Squares
2005
2006
2007
5% Significance
Figure 5.17: Recus ive Coeffiecients Stability Tes t for Malawi's Money Dem and Model
0.8
.1
0.4
.0
0.0
-.1
-0.4
-.2
-0.8
-1.2
-.3
2002
2003
2004
2005
2006
2007
2002
Recurs ive C(1) Es tim ates
± 2 S.E.
2003
2004
2005
2006
2007
Recurs ive C(2) Es tim ates
± 2 S.E.
.004
16
.003
12
.002
8
.001
4
.000
0
-.001
-.002
-4
2002
2003
2004
2005
2006
Recurs ive C(3) Es tim ates
± 2 S.E.
2007
2002
2003
2004
2005
2006
2007
Recurs ive C(4) Es tim ates
± 2 S.E.
4.5.3 Mauritius
The long-run equilibrium parameters for the money demand function is presented in equation
(4.13)
136
RM 2t  2.17 0.57 RGDPt  0.08 IRt  1.01 NERt
( 6.84)
( 2.07)
( 9.76)
(4.13)
( 10.27)
The model was unstable as shown in the Figures below in respect of stability tests such as
CUSUM and CUSUM sum of Squares. Both tests showed instability of the demand for
money function in Mauritius especially after 2002. The plot of recursive residual also shows
several periods of money demand instability. In general therefore, money demand in
Mauritius has been unstable.
Figure 5.19: Normaility Test for Mauritius Money Demand Model
Figure 5.20: Recursive Residuals stability test for mauritius Money demand Model
.12
35
Series: Residuals
Sample 1993M01 2007M11
Observ ations 179
30
25
Mean
Median
Maximum
Minimum
Std. Dev .
Skewness
Kurtosis
20
15
10
.04
-1.41e-16
-0.005761
0.100894
-0.105387
0.030673
0.264523
4.490167
.00
-.04
-.08
5
Jarque-Bera
Probability
0
-0.10
-0.05
-0.00
0.05
.08
18.64947
0.000089
-.12
94
0.10
95
96
97
98
99
00
01
02
Recursive Residuals
Fi gure 5.21:Cusum Test Mauri ti us Money Demand Model
03
04
05
06
07
± 2 S.E.
Fi gure 5.22: Cusum Squares Tests for Mauri ti us Money Demand Model
100
1.2
80
1.0
60
0.8
40
0.6
20
0.4
0
0.2
-20
0.0
-40
94
95
96
97
98
99
00
CUSUM
01
02
03
04
05
06
07 -0.2
94
5% Si gni fi cance
95
96
97
98
99
00
01
CUSUM of Squares
02
03
04
05
06
07
5% Si gni fi cance
Figure 5.23: Recurs ive Es tim ates for Mauritius Money Dem and Model
.7
.3
.6
.2
.5
.1
.4
.0
.3
-.1
.2
-.2
1996
1998
2000
2002
2004
2006
1996
Recurs ive C(1) Es tim ates
± 2 S.E.
1998
2000
2002
2004
2006
Recurs ive C(2) Es tim ates
± 2 S.E.
-0.4
5
-0.6
4
-0.8
3
-1.0
2
-1.2
1
-1.4
-1.6
0
1996
1998
2000
2002
2004
Recurs ive C(3) Es tim ates
± 2 S.E.
2006
1996
1998
2000
2002
2004
Recurs ive C(4) Es tim ates
± 2 S.E.
2006
137
4.5.4 Uganda
The estimated long-run money demand is presented as equation 4.14.
RM 2t   13.86 11.82 RGDPt  0.03 IRt  0.78 NERt
( 5.14)
( 4.26)
( 7.51)
(4.14)
( 3.56)
Figure 5.39: Cusum Test for Money demand Model for Uganda
Figure 5.40: Cusum Square Test for Money demand Model for Uganda
100
1.2
80
1.0
60
0.8
40
0.6
20
0.4
0
0.2
-20
0.0
-40
1994
1996
1998
2000
CUSUM
2002
2004
2006
-0.2
1994
5% Significance
Figure 5.41: Recurs ive Es tim ates
1996
1998
2000
CUSUM of Squares
2002
2004
2006
5% Significance
for Money dem and Model for Uganda
.0
16
12
-.1
8
-.2
4
-.3
0
-.4
-4
1996
1998
2000
2002
2004
1996
2006
1998
2000
2002
2004
2006
Recurs ive C(2) Es tim ates
± 2 S.E.
Recurs ive C(1) Es tim ates
± 2 S.E.
0.0
20
-0.4
10
-0.8
0
-1.2
-10
-1.6
-2.0
-20
1996
1998
2000
2002
2004
2006
Recurs ive C(3) Es tim ates
± 2 S.E.
1996
1998
2000
2002
2004
2006
Recurs ive C(4) Es tim ates
± 2 S.E.
4.5.5 Egypt
Equation 4.15 below presents estimated money demand for Egypt. The influence of
opportunity cost on demand for money is much stronger in comparison to Uganda and
Kenya, in part suggesting a more developed financial system.
RM 2t  3.66 0.92 RGDPt  0.59 IRt  0.83 NERt
( 4.88)
( 3.37)
( 4.93)
( 4.80)
(4.15)
138
While the CUSUM test shows stability, the plot of the CUSUM sum of squares shows
instability of the demand for money function during the period 2000-2002 and 2003-2004.
The plot of recursive residual shows presence of money demand instability between 2001 and
2002 as well as between 2004 and 2005. Other than these periods, demand for money has
generally been stable in Egypt.
FIgure 5.4: Cusum Stability Test f or Egypt's Money Demand
FIgure 5.5: Cusum squares Stability Test f or Egypt's Money Demand
30
1.2
20
1.0
0.8
10
0.6
0
0.4
-10
0.2
-20
0.0
-30
-0.2
2000
2001
2002
2003
CUSUM
2004
2005
2006
2000
2001
5% Signif icance
2002
2003
CUSUM of Squares
2004
2005
2006
5% Signif icance
Figure 5.6: Recurs ive Es tim ates Stability Tes t for Egypt's Money Dem and
1.6
.15
.10
1.2
.05
0.8
.00
0.4
-.05
0.0
-.10
00
01
02
03
04
05
06
00
01
Recurs ive C(1) Es tim ates
± 2 S.E.
02
03
04
05
06
Recurs ive C(2) Es tim ates
± 2 S.E.
4
10
2
8
0
6
-2
4
-4
2
-6
0
00
01
02
03
04
05
06
00
Recurs ive C(3) Es tim ates
± 2 S.E.
01
02
03
04
05
Recurs ive C(4) Es tim ates
± 2 S.E.
4.5.6 Zambia
Equation (4.16) is the estimated long-run money demand
RM 2t   7.23 1.87 RGDPt  0.01 IRt  0.62 NERt
( 2.81)
( 4.07)
( 1.77)
( 6.08)
(4.16)
06
139
Like the case of Mauritius, the model was unstable as shown in the recursive residual chart
below in which there are several periods of money demand instability evidenced by the
estimates touching and going beyond the lower and the upper band respectively. The
CUSUM and CUSUM sum of Squares tests also supported these results. Both tests showed
instability of the demand for money function in Zambia.
Fi gure 5.27: Cusum T est for Mauri ti us Money demand Model
Fi gure 5.28: Cusum Squares T est for Mauri ti us Money demand Model
1.2
40
1.0
20
0.8
0
0.6
-20
0.4
-40
0.2
-60
0.0
-80
-0.2
97
98
99
00
01
02
CUSUM
03
04
05
97
06
98
99
00
01
02
CUSUM of Squares
5% Si gnifi cance
03
04
05
06
5% Si gnifi cance
Figure 5.29: Recurs ive Es tim ates for Mauritius Money dem and Model
20
.10
16
.05
.00
12
-.05
8
-.10
-.15
4
-.20
0
98
99
00
01
02
03
04
05
06
-.25
98
99
00
Recurs ive C(1) Es tim ates
± 2 S.E.
01
02
03
04
05
06
05
06
Recurs ive C(2) Es tim ates
± 2 S.E.
0.4
40
0.0
0
-0.4
-0.8
-40
-1.2
-80
-1.6
-2.0
-120
-2.4
-2.8
-160
98
99
00
01
02
03
04
05
06
Recurs ive C(3) Es tim ates
± 2 S.E.
98
99
00
01
02
03
04
Recurs ive C(4) Es tim ates
± 2 S.E.
4.5.7 Swaziland
The estimated money demand model (equation 4.17) was generally stable as shown in the
recursive residual chart below in which only the period around 1998 was the money demand
unstable. The CUSUM attest showed stability throughout the study period while the CUSUM
sum of Squares tests showed instability around 2001/2002. In general, therefore money
demand in Swaziland has generally been stable.
RM 2t   4.58 1.09 RGDPt  0.01 IRt  1.13 NERt
( 1.49)
( 4.24)
( 1.85)
( 12.91)
(4.17)
140
Figure 5.32: Recursive Residuals for swaziland Money Demand model
Figure 5.31: Normality Test for Swaziland Money Demand Model
.5
30
Series: Residuals
Sample 1993M01 2006M12
Observations 168
25
20
Mean
Median
Maximum
Minimum
Std. Dev.
Skewness
Kurtosis
15
10
5
Jarque-Bera
Probability
0
-0.2
-0.1
-0.0
0.1
0.2
0.3
0.4
.4
.3
.2
1.53e-15
-0.012562
0.471587
-0.176986
0.079040
1.513712
9.629080
.1
.0
-.1
-.2
-.3
371.7700
0.000000
93
94
95
96
97
98
99
00
01
02
Recursive Residuals
Figure 5.33: Cusum Test for swaziland Money Demand model
40
03
04
05
06
± 2 S.E.
Figure 5.34: Cusum Squares Test for swaziland Money Demand model
1.2
30
1.0
20
0.8
10
0.6
0
0.4
-10
0.2
-20
0.0
-30
-40
93
94
95
96
97
98
99
00
CUSUM
01
02
03
04
05
06
-0.2
93
94
95
5% Significance
96
97
98
99
00
CUSUM of Squares
01
02
03
04
05
06
5% Significance
Figure 5.35: Recurs ive es tim ates for s waziland Money Dem and m odel
16
.8
12
.6
.4
8
.2
4
.0
0
-.2
-4
-.4
-8
1994
1996
1998
2000
2002
2004
2006
-.6
1994
1996
Recurs ive C(1) Es tim ates
± 2 S.E.
1998
2000
2002
2004
2006
Recurs ive C(2) Es tim ates
± 2 S.E.
2
100
1
50
0
0
-1
-50
-2
-100
-3
-4
1994
1996
1998
2000
2002
2004
Recurs ive C(3) Es tim ates
± 2 S.E.
4.6
2006
-150
1994
1996
1998
2000
2002
2004
Recurs ive C(4) Es tim ates
± 2 S.E.
Inflation Targeting
Table 4.2 summarizes the extent of COMESA members meeting the IT conditions.
2006
141
Table 4.2
COMESA Compliance Levels of IT Conditions
Features
Precondition
Institutional
Features
Price Stability as a Primary Goal
Operational
Features
Technical Features
Central Bank Independence
High
Medium
Central bank Transparency and
Accountability
Low
Absence of fiscal dominance
Low
Deep and efficient financial markets
Low
Strong interest rate channel
Low
Availability of data/infrastructure
Low
Measure of inflation and setting process
Medium
Analytical capacity including forecasting
Medium
Publication of inflation reports
4.7
Compliance level
Low
Policy Recommendations
The empirical results of this study found that money demand is relatively stable in some
countries while it is unstable in others. In spite of this relative stability for some countries,
there have been concerns about the appropriateness of the current monetary targeting
framework. These concerns arise from instabilities in the money multiplier; interest rate
volatility; problems in forecasting liquidity; choice of reserve aggregates for targeting as well
as limited and inflexible monetary policy instruments.
As the shortcomings of the current monetary targeting framework have become more
apparent, many of the COMESA central banks have to start the journey towards formal
inflation targeting frameworks. A successful implementation of inflation targeting, as an
alternative to the current monetary policy framework, would require the following conditions
to be in place: A strong fiscal position and entrenched macroeconomic stability; A welldeveloped financial system; Central bank policy and instruments independence and a
mandate to achieve price stability; Reasonably well understood channels between policy
instruments and inflation; A sound methodology for inflation forecasting; and transparent
policies to build accountability and credibility. At the moment, however, the following
factors limit the implementation of an Inflation Targeting framework: Weak monetary policy
transmission mechanisms; Lack of timely, accurate and high frequency data, especially on
real sector activities; lack of technical and institutional capacity to model and forecast
142
inflation (hence Lack of appropriate forecasting models); inability of some of the central
banks to undertake independent monetary policy decisions; Weak fiscal positions; narrow
range of monetary policy instruments.
Given the above factors, it is clear that the COMESA region requires more time to switch to a
regime of inflation targeting. Therefore, the current practice of monetary targeting seems to
provide a more realistic and pragmatic monetary policy framework in COMESA Countries as
the preconditions for implementation of the inflation targeting framework are put in place
within each country in COMESA. The following recommendations are therefore made to
simultaneously keep refining the current monetary targeting framework in addition to putting
in place the preconditions for inflation targeting in the future:
4.7.1 Modified Reserve Money Framework:
Despite the fact that reserve moneyframework has generally served the region well, a number
of challenges have called for its modification. One such modification is to target price instead
of the quantity as COMESA countries prepare for adoption of Inflation Targeting framework;
4.7.2 Central Bank Independence:
The issue of central bank independence is important as it improves the conduct of monetary
policy, especially in the context of Central Banks implementing Inflation Targeting. Central
banks in the COMESA region should therefore have both instrument and operational
independence and this should be in the respective member countries’ constitutions as is
currently the case in Uganda. However, central banks must ensure accountability to the
public and sound corporate governance. To meet the requirement of central bank
independence, a country must not show any of the symptoms of “fiscal dominance” where
the conduct of monetary policy is dictated or constrained by purely fiscal considerations.
Moreover, a timeframe for getting the public debt to GDP ratio to internationally accepted
norms should be worked. In this respect, member countries should work very hard to meet
the current convergence criteria in respect of this item;
4.7.3 Strengthen Fiscal and Monetary Policy Coordination:
There is need to have clear institutional arrangement that will ensure enhanced co-ordination
between monetary and fiscal policies within and among COMESA central banks and
ministries of finance.
4.7.4 Deepening of Financial Markets.
For effectiveness of any monetary policy framework, countries need to have deep and
efficient financial markets characterized by well-functioning and liquid bond markets and
reliable yield curve.
4.7.5 Capacity Building including Forecasting Models:
In order to set up an inflation targeting framework, it is important that the monetary
authorities in COMESA develop the technical and institutional capacity to model and forecast
domestic inflation and to assess the effect of monetary policy instruments such as interest rate
143
changes on the future course of inflation. In particular, the central bank needs to develop its
internal modeling and forecasting capabilities, in addition to putting in place vehicles for
formal reporting of monetary policy decisions and communications with the public. There
must also be a clear understanding of the monetary policy transmission mechanism.
4.7.6 Develop a communication strategy that enhances transparency of monetary
policy:
The central banks through their respective Monetary Policy Committees should develop
effective communication strategies to ensure that the public understands what they do.
Transparency of monetary policy can be enhanced through frequent dissemination of
information to the public.
4.7.7 Availability of Quality Data:
Central banks in the region should build up comprehensive, accurate, high frequency and
high quality data which are important in forecasting and overall conduct of monetary policy.
One of the main preconditions for IT is for the public to believe in the integrity of the
statistical process and statistics used. Therefore timeliness and accuracy of data is critical to
the success of monetary policy. Data collection must be accurate and timely both at country
level and regional level in order to improve forecasting;
144
4.8
References
Ahmed, M (2001), “Demand for money in Bangladesh: An Econometric Investigation into
Some Basic Issues”, Indian Economic Journal, 84-89.
Aliyu, S.U.R. and Englama, A. (21009), “Is Nigeria Ready for Inflation Targeting?”, Journal
of Money, Investment and Banking, ISSN 1450-288X Issue 1,
http://www.eurojournals.com/JMIB.htm
Arif, RR (1996), “Money Demand Stability: Myth or Reality - An Econometric Analysis”,
Development Research Group Study, 13, Mumbai: RBI.
Barro, Robert J., and David Gordon. 1983. "A Positive Theory of Monetary Policy in a
Natural Rate Model." Journal of Political Economy 91, no. 4 (August): 589-610.
Banergee et al., 1993
Bernanke, B.S. (1983), "Non-Monetary Effects of the Financial Crisis in the Propagation of
the Great Depression", American Economic Review, Vol. 73, pp. 257-76.
Bernanke B. & Mishkin, F. (1992), "Central Bank Behavior and the Strategy of Monetary
Policy: Observations from Six Industrialized Countries," NBER Chapters, in: NBER
Macroeconomics Annual 1992, Volume 7, pp. 183-238, National Bureau of Economic
Research, Inc.
Bernanke et al. 1999
Bhattacharya, R (1995), “Cointegrating Relationships in the Demand for Money in India”
The Indian Economic Journal, 43, 69-75.
Boughton, J.M., (1981), 'Recent Instability of the Demand for Money: An International
Perspective' Southern Economic Journal 47, pp. 579-597.
Burundi and IMF (2009) “Letter of Intent, Memorandum of Economic and Financial Policies,
and Technical Memorandum of Understanding”, International Monetary Fund
Calvo, Guillermo, and Carlos Végh. 1992. "Currency Substitution in Developing Countries:
An Introduction." Revista de Análisis Económico, 7:1, pp. 3-27.
Calvo, Guillermo, and Carlos Végh. 1996. "From Currency Substitution to Dollarization:
Analytical and Policy Issues," in Money, Exchange Rates and Output. Guillermo Calvo ed:
MIT Press, pp. 153-75.
Central Bank of Seychelles, Annual Report, Various Issues
Central Bank of Swaziland, Statement of Monetary Policy Committee, Issued by MG.
Dlamini, Governor Central Bank of Swaziland, 2008-06-13.
145
Dickey, D.A. and W.A. Fuller, (1979), 'Distribution of Estimates of Autoregressive Time
Series with Uni Roots', Journal of the American Statistical Association, 74, pp, 27-31
Engle, R and C Granger (1987), “Co-integration and Error Correction: Representation,
Estimation and Testing”, Econometrica, 55, 251-276.
Fundange C. (2008). “The Conduct of Monetary Policy in Zambia” Presentation at the
Philadelphia Reserve Bank on 14th October 2008.
Fry, Maxwell (2000), “Key Issues in the Choice of Monetary Framework” in Lavan
Mahadeva and Gabriel Sterne (eds): Monetary Policy Frameworks in a Global Context
(2000) Routledge, London
Gerald G., R. Lucas and T. Porter (2003), “Monetary Policy in Developing Countries:
Lessons from Kenya”. Forthcoming in the Journal of International Trade and Economic
Development
Goodhart, Charles A. E., and José Viñals. 1994. "Strategy and Tactics of Monetary Policy:
Examples from Europe and the Antipodes." In Jeffrey C. Fuhrer, ed., Goals, Guidelines, and
Constraints Facing Monetary Policymakers. Federal Reserve Bank of Boston Conference
Series 38: 139-87.
Hansen, BE (1992), “Tests for Parameter Instability in Regressions with I(1) Processes”
Journal of Business and Economic Statistics, 10, 321-35.
Johansen, S., (1988), 'Statistical Analysis of Cointegration Vectors,' Journal of Economic
Dynamics and Control, 12, pp. 231-254.
Johansen, S. and K. Juselius, (1990), 'Maximum Likelihood Estimation and Inference on
Cointegration, With Applications to the Demand for Money', Oxford Bulletin of Economies
and Statistics, 52, pp. 169-210
Johansen (1992)
Johansen (1995)
Kalyalya D.H. (2001). “Monetary Policy Framework and Implementation in Zambia,” Paper
Presented at the South African Reserve Bank Conference on Monetary Policy Frameworks in
Africa, September 17-19, 2001, Pretoria, South Africa.
Kinyua J. (2001). “Monetary Policy in Kenya: Evolution and Current Framework,” Paper
Presented at the South African Reserve Bank Conference on Monetary Policy Frameworks in
Africa, September 17-19, 2001, Pretoria, South Africa.
Langa T. (2001). “Monetary Policy in Swaziland”, Paper Presented at the South African
Reserve Bank Conference on Monetary Policy Frameworks in Africa, September 17-19,
2001, Pretoria, South Africa.
Mabika S.E. (2001).”Monetary Policy Framework in Zimbabwe,” Paper Presented at the
South African Reserve Bank Conference on Monetary Policy Frameworks in Africa,
September 17-19, 2001, Pretoria, South Africa.
146
Mishkin, F. S.(1998), “International Experiences with Different Monetary Policy Regimes”,
Graduate School of Business, Columbia University and National Bureau of Economic
Research, Uris Hall 619, Columbia University, New York, New York 10027.
Mishkin F.S. & Posen, A.S.(1997), "Inflation targeting: lessons from four countries,"
Economic Policy Review, Federal Reserve Bank of New York: pp. 9-110. Moursi T.A,
Mosollamy M. and Zakareya E. (2006) “A Draft Review of Contemporary Monetary Policy
In Egypt”, The Cabinet Information and Decision Support Centre.
Mugume A. and E. Kasekende (2009). “Inflation and Inflation Forecasting in Uganda”, Bank
of Uganda.
Musinguzi P. and M. Katarikawe (2001). “Monetary Policy Frameworks in Africa: The Case
of Uganda ”, Paper Presented at the South African Reserve Bank Conference on Monetary
Policy Frameworks in Africa, September 17-19, 2001, Pretoria, South Africa.
Mutoti N. (2006.) “Monetary Policy Transmission in Zambia”, Working Paper No. 6/2006,
Bank of Zambia
Neumann and von Hagen (1993), “Monetary Policy in Other GH-7 Countries: Germany. in:
M. Fratianni & D. Salvatore (eds), Monetary Policy of Developed Economies,Handbook of
Comparative Economies, Vol. 3Obstfeld, Maurice, and Kenneth Rogoff. 1995. "The Mirage
of Fixed Exchange Rates." Journal of Economic Perspectives 9, no. 4 (fall): 73-96.
Perron, P (1989),“The Great Crash, the Oil Price Shock, and the Unit Root Hypothesis”,
Econometrica,57, 1361-1401.
Perron, P (1997),“Further Evidence on Breaking Trend Functions in Macroeconomic
Variables”, Journal of Econometrics, 80, 355-385.
Sata J. (2001). “Monetary Policy Frameworks in Africa: The Case of Malawi,” Paper
Presented at the South African Reserve Bank Conference on Monetary Policy Frameworks in
Africa, September 17-19, 2001, Pretoria, South Africa.
Schaechter et al. 2000
Sota B. (2001). “Monetary Policy Implementation by Central Bank of Burundi” ”, Paper
Presented at the South African Reserve Bank Conference on Monetary Policy Frameworks in
Africa, September 17-19, 2001, Pretoria, South Africa.
147
Appendix C
Table 4.3: Requirements for Implementing Different Monetary Policy Regimes
Requirements
Exchange rate Targeting
Relatively higher level of forex
reserves are required since the
exchange rate is fixed and must
therefore be defended at all cost,
hence need for sufficient forex
reserves
2. Liquid and  The money market has to
deep
operate smoothly so that
money
changes in the central
market
bank’s policy rate can have
an effect on the yield curve.
In this context, it is expected
that the money market is
liquid and deep across
maturities.
 Commercial banks’ liquidity
management and funding
policies must allow a
gradual
adjustment
of
lending rates to money
market conditions.
1. Level
of
Foreign
Exchange
Reserves
Monetary targeting
Inflation targeting
Relatively lower level of forex Relatively lower level of
reserves are required since the forex
reserves are
exchange rate is flexible
required
since
the
exchange rate is flexible




The money market has to
operate smoothly so that
changes in the central
bank’s
policy
rate
(typically a short-term
rate) can have an effect on
the yield curve.
In this context, it is
expected that the money
market is liquid and deep
across maturities.
Commercial
banks’
liquidity management and
funding policies must
allow a gradual adjustment
of lending rates to money
market conditions.
Monetary management has
to ensure that money
market conditions remain
in line with the monetary
policy
stance,
whilst
allowing the market to
develop
the
risk
management instruments
that a more active use of
an interest rate would
warrant.




The money market
has
to
operate
smoothly so that
changes
in
the
central
bank’s
policy rate (typically
a short-term rate)
can have an effect
on the yield curve.
In this context, it is
expected that the
money market is
liquid and deep
across maturities.
Commercial banks’
liquidity
management
and
funding
policies
must
allow
a
gradual adjustment
of lending rates to
money
market
conditions.
Monetary
management has to
ensure that money
market conditions
remain in line with
the monetary policy
stance,
whilst
allowing the market
to develop the risk
management
instruments that a
more active use of
an interest rate
would warrant
148
Table 4.3: Requirements for Implementing Different Monetary Policy Regimes
Requirements
Exchange rate Targeting
Monetary targeting
Inflation targeting
3. Single and
welldefined
inflation
target
Price stability may not be the
primary goal as exchange rate
stability is the ultimate target of
monetary policy. It does not rule
out the possibility of targeting
other
variables
including
exchange rate and monetary
aggregates.
Price stability is the primary
goal and inflation is the
ultimate target of monetary
policy. It rules out the
possibility of targeting other
variables including exchange
rate.
Price stability should be
the primary goal and
inflation should be the
ultimate
target
of
monetary policy. It rules
out the possibility of
targeting other variables
including exchange rate
and
monetary
aggregates.
Robust
econometric
model for forecasting
inflation - Operational
frameworks are required
to model and forecast
inflation and to use
indirect instruments by
the central bank to
achieve the target
Inflation
targeting
regime should operate
under an independent
central bank as fiscal
dominance undermines
the ability of the central
bank to achieve the
inflation target. Overall,
prudence in fiscal and
quasi-fiscal activities is
required in such a way
that fiscal considerations
don’t
constrain
monetary policy
Robust econometric model for Robust econometric model for
4. Robust
inflation
is
econometri forecasting inflation is not forecasting
necessary but not as critical is
c model for critical
in the IT framework
forecasting
inflation
5. Absence of Absence of Fiscal dominance
critical- overall, prudence in
fiscal
dominance fiscal and quasi-fiscal activities
is required in such a way that
fiscal
considerations
don’t
constrain monetary policy
Monetary targeting regime
should operate under an
independent central bank as
fiscal dominance undermines
the ability of the central bank
to achieve the inflation target.
Overall, prudence in fiscal and
quasi-fiscal
activities
is
required in such a way that
fiscal considerations don’t
constrain monetary policy
While IT is inherently oriented
towards achieving domestic
policy objectives, i.e. a low and
stable rate of inflation, the
external position of the country
has to be taken into account.
Therefore, the ideal time for a
shift to IT would be in a period
of external stability, where such
conflicts of interest will not arise
While IT is inherently oriented
towards achieving domestic
policy objectives, i.e. a low
and stable rate of inflation, the
external position of the
country has to be taken into
account. Therefore, the ideal
time for a shift to IT would be
in a period of external
stability, where such conflicts
of interest will not arise
While IT is inherently
oriented
towards
achieving
domestic
policy objectives, i.e. a
low and stable rate of
inflation, the external
position of the country
has to be taken into
account. Therefore, the
ideal time for a shift to
IT would be in a period
of external stability,
where such conflicts of
interest will not arise
7. Stability of While a robust banking system If the banking system is not If the banking system is
and deep financial markets are robust enough and financial not robust enough and
the
6. External
Stability
149
Table 4.3: Requirements for Implementing Different Monetary Policy Regimes
Requirements
Exchange rate Targeting
Monetary targeting
Inflation targeting
Financial
System
necessary, the impact of
monetary policy actions may not
be effective. The establishment
of a well supervised and
regulated, efficient banking
system does not necessary have
to be in place prior to the
introduction of exchange rate
targeting
markets are shallow, the
impact of monetary policy
actions may not be effective.
The establishment of a well
supervised and regulated,
efficient banking system needs
to be in the focus of the
authorities prior to the
introduction of IT
8. Declining
Inflation
The introduction of IT follows a
period of already declining
inflation
rates
so
that
policymakers avoid the problem
of having to set very high targets
or to drastically reduce inflation
over a relatively short period of
time.
The introduction of Monetary
targeting does not necessary
need to follow a period of
already declining inflation
rates so that policymakers
avoid the problem of having to
set very high targets or to
drastically reduce inflation
over a relatively short period
of time.
9. Transpare
ncy and
accountabi
lity
Accountability and transparency
of the central bank is important
to increase financial discipline
and to enhance the credibility of
the central bank in the conduct
of monetary policy
Accountability
and
transparency of the central
bank is important to increase
financial discipline and to
enhance the credibility of the
central bank in the conduct of
monetary policy
financial markets are
shallow, the impact of
monetary policy actions
may not be effective.
The establishment of a
well supervised and
regulated,
efficient
banking system needs to
be in the focus of the
authorities prior to the
introduction of IT
The introduction of IT
follows a period of
already
declining
inflation rates so that
policymakers avoid the
problem of having to set
very high targets or to
drastically
reduce
inflation
over
a
relatively short period of
time.
Accountability
and
transparency of the
central bank is essential
to increase financial
discipline
and
to
enhance the credibility
of the central bank in the
conduct of monetary
policy
Table 4.4The Institutional Arrangements for Implementing Different Monetary Policy Regimes
Aspect of
Institutional
Arrangements
Goal Autonomy
Exchange Rate
Targeting Regime
Monetary Targeting
Regime
Inflation targeting
Regime
 Basic
goals
of  Basic
goals
of  Basic
goals
of
monetary policy are
monetary policy may
monetary policy are
established in central
be established in
established in central
bank
legislation
central
bank
bank
legislation
rather than delegated
legislation
or
rather than delegated
to the central bank.
delegated
to
the
to the central bank.
central bank.
 Monetary policy must
 The more critical
be
planned
to  Price stability is the
practical issues have
maintain a fixed
primary
goal
of
been: (i) how to
exchange rate, so that
monetary policy - the
establish an enduring
the central bank loses
goal is set by the
commitment to price
its opportunity to
government or central
stability
as
the
150
Table 4.3: Requirements for Implementing Different Monetary Policy Regimes
Requirements
Exchange rate Targeting
pursue
independent
monetary policy.
Monetary targeting
an
bank
 Relatively
less
enduring
commitment
to
translate goal into an
effective operational
framework as in the
case of IT.
Inflation targeting
primary goal of
monetary
policy,
regardless of whether
this goal is set by the
government
or
central bank, and (ii)
how to translate the
commitment to a
goal into an effective
operational
framework.
 Credibility of the
adoption of exchange
targeting
neither
depends on whether
the central bank or
government specifies
the goal nor whether  Credibility of the
there is a clear and
adoption of monetary
public commitment
targeting
depends  In practice, adoption
of inflation targeting
by both to take the
more on whether the
generally requires a
actions necessary to
central
bank
or
broad
political
achieve the goal of
government specifies
consensus.
This
price stability.
the goal than on
suggests that the
whether there is a
credibility of the
clear and public
 Targets may be realadoption of inflation
economic
factors
commitment by both
targeting
depends
such as output or
to take the actions
less
on
whether
the
employment
necessary to achieve
central
bank
or
the goal of price
government
specifies
stability.
the goal than on
whether there is a
clear and public
commitment by both
to take the actions
necessary to achieve
the goal of price
stability.

Target autonomy
The specifics of 
inflation targets are
absent since there is
loss of autonomy in
monetary policy so
that the policymaker
is unable to respond
to developments in
the
domestic
economy that are not
present in the country 
to which the currency
is pegged
Allows the central 
bank to specify the
level and details of
price target rather
than inflation target
that
would
be
consistent with the
broadly set goal of
price stability.

As in the case of IT,
the specifics of Price
targets typically are
Allows the central
bank to specify the
level and details of
an inflation target
that
would
be
consistent with the
broadly set goal of
price stability.
The specifics of
inflation
targets
typically are set by
the central bank,
151
Table 4.3: Requirements for Implementing Different Monetary Policy Regimes
Requirements
Exchange rate Targeting


Monetary targeting
Policy maker uses
exchange rate policy
towards maintaining
macroeconomic
stability through the 
linkage
of
the
domestic
currency
with
a
currency
anchor, be it a single
currency or a basket
of currencies.
Policymakers adopt
some
degree
of
management
of
capital inflows and
outflows. In the
absence of capital
controls, they have
had to amass a large
stockpile
of
international reserves
in order to safeguard
their currency against
speculative
attack.
Hence, it is important
that
when
policymakers
are
considering
the
appropriate regime,
they understand the
forces that cause
speculative attacks.
set by the central
bank, either jointly
with the government
or unilaterally.
An
important
consideration
in
involving
the
government
in
deciding on the target
is to ensure that the
fiscal authorities take
the inflation target
seriously in their
planning
and
decisions. For this
reason, even if the
central bank has the
unilateral authority to 
specify the inflation
target, it is still
sensible to pursue a
consultative approach
in
setting
target
parameters and to
have the government
support the target
specification
publicly.

Inflation targeting
either jointly with the
government
or
unilaterally. Of the
24 current inflation
targeters, 4 have
targets specified by
the government, 8 by
the central bank, and
12 are set jointly by
the government and
central bank. In
practice,
close
consultation
is
usually
involved
whichever institution
has
the
formal
authority.
It is in the interest of
both the government
and the central bank
to
achieve
the
maximum common
“ownership” of the
target specification,
and for this to be
understood by the
public.
An
important
consideration
in
involving
the
government
in
deciding on
the
target is to ensure
that
the
fiscal
authorities take the
inflation
target
seriously in their
planning
and
decisions. For this
reason, even if the
central bank has the
unilateral authority to
specify the inflation
target, it is still
sensible to pursue a
consultative
152
Table 4.3: Requirements for Implementing Different Monetary Policy Regimes
Requirements
Exchange rate Targeting

Instrument
autonomy


Monetary targeting
The central bank has  A high degree of 
the mandate and
central bank autonomy
responsibility
to
in the setting of its
safeguard
the
policy instruments is
domestic
currency
essential for credible
against speculative
and
effective
attack. In the absence
Monetary targeting
of capital controls, a
large stockpile of  Once the central bank 
international reserves
has been given the
to safeguard the
mandate
and
currency.
responsibility
to
pursue a Price stability
objective, it needs also
The official interest
rate must be set to
to be given the power
achieve equilibrium
to set its policy
between supply and
instruments to achieve
demand
for
the
that objective as in the
currency
at
the
case of IT.
desired
exchange
rate, so that the
interest rate cannot
be used to pursue
other objectives.
The combination of
one instrument and
several
targets
generally makes the
central bank unable
to effectively fulfill
all its objectives
Inflation targeting
approach in setting
target parameters and
to
have
the
government support
the
target
specification
publicly.
A high degree of
autonomy in the
setting of its policy
instruments
is
essential for credible
and
effective
inflation targeting.
Apart from having
the mandate and
responsibility
to
pursue an inflation
objective, a central
bank is given the
power to set its
policy instruments to
achieve
that
objective.
This
means that:



the operation of
monetary policy
is compromised
by
fiscal
dominance;
Decisions of the
central bank on
policy instrument
settings are not
subject
to
government
approval or veto;
Policy decisionmaking is clearly
free from direct
or
indirect
government
pressure
or
coercion.
153
Table 4.3: Requirements for Implementing Different Monetary Policy Regimes
Requirements
Exchange rate Targeting
Financial
independence

Accountability


The central bank’s
capital
position,
operational financing
arrangements,
and
accounting
and
auditing
practices
should be in place to:
o insulate the
central bank
from political
interference.
o ensure
the
central bank’s
profitability
and
o Improve the
central bank’s
financial
transparency.
An
autonomous
central bank requires
some
form
of
accountability
to
provide
it
with
legitimacy
and
credibility.
The
central
bank/currency board
is
an
especially
strong
and
transparent
commitment to an
exchange-rate target
because it requires
that the note-issuing
authority,
whether
the central bank or
the
government,
stands
ready
to
exchange
the
domestic
currency
for foreign currency
at the specified fixed
exchange
rate
whenever the public
Monetary targeting

The central bank’s 
capital
position,
operational financing
arrangements,
and
accounting
and
auditing
practices
should be in place to:
o insulate
the
central bank from
political
interference.
o ensure the central
bank’s
profitability and
o improve
the
central
bank’s
financial
transparency.

Central
bank 
accountability
for
performance
in
relation to the price
target is important to
provide
it
with
legitimacy
and
credibility.

Accountability

provides incentives to
the central bank to
seek to meet its price
targets
and
to
communicate
its
decisions and actions
transparently.

Inflation targeting
The central bank’s
capital
position,
operational financing
arrangements,
and
accounting
and
auditing
practices
should be in place to:
o insulate the
central bank
from political
interference.
o ensure
the
central bank’s
profitability
and
o improve the
central bank’s
financial
transparency.
Central
bank
accountability
for
performance
in
relation
to
the
inflation target is
critical to provide it
with legitimacy and
credibility.
Further,
accountability
provides incentives
to the central bank to
seek to meet its
targets
and
to
communicate
its
decisions and actions
transparently.
Mechanisms
for
providing
central  Mechanisms
for
bank
policy
providing
central
accountability for its
bank
policy
policy performance
accountability for its
and actions include:
policy performance
(i) Issuing of press
and actions include:
releases
after
(i) Publication of
154
Table 4.3: Requirements for Implementing Different Monetary Policy Regimes
Requirements
Exchange rate Targeting
Monetary targeting
requests it.

In a fixed-exchangerate
regime,
the
general public may
continuously assess
the central bank's
policy by observing
the market exchange
rate. In this respect
monetary
policy
based
on
an
exchange-rate target
shows a very high
degree
of
transparency

Decision-making

structure
not
as
critical as in MT or
IT

Low degree to which
decisions
on
monetary
policy
formulation
and
implementation are
vested
in
the
monetary
policy
committee (MPC)
Decision-Making
Arrangements
monetary policy
committee
meetings
(ii) Publication
of
regular monetary
policy
reports/statements
;
(iii) Publication
of
special reports or
monetary policy
statements and
submitting them
to
parliament
through
the
Ministry
of
Finance
Decision-making

structure
aims
achieving at several
objectives including:
(i) Providing
the
central
bank’s
decision-making
the autonomy;
(ii) bringing
appropriate
knowledge
and
expertise to bear
on
policy
decisions;
Inflation targeting
regular inflation
or
monetary
policy reports;
(ii) Publication of
special reports
or open letters in
the event of
significant
misses of the
target;
(iii) Publishing
minutes
of
policy meetings
within
a
reasonable time
frame
(iv) Monitoring by
the executive or
legislature
(in
the form of a
special report to
executive/parlia
ment
or
a
hearing)
(v) Dismissal of the
decision
maker(s)
(governor, board
members)
in
case
of
unsatisfactory
performance.
Like in the case of
Monetary Targeting
framework,
the
decision-making
structure
in
an
inflation
targeting
central bank aims to
achieve
several
objectives including:
(i) ensuring
the
central
bank’s
decision-making
autonomy;
(ii) bringing
155
Table 4.3: Requirements for Implementing Different Monetary Policy Regimes
Requirements
Exchange rate Targeting

Monetary targeting
(iii)promoting
efficient
and
effective policy
decisions; and
(iv) Ensuring
meaningful
accountability for
policy decisions.
The monitoring and
evaluation of the
central
bank’s
performance
in
relation to targetsare
assigned
to
the
supervisory board



There is no universal
model. The decisionmaking structures of
monetary targeting
central banks vary
considerably but all
of them take into
account the following
issues in designing
the decision-making
structure:
The degree to which
decisions
on
monetary
policy
formulation
and
implementation are
vested
in
the
monetary
policy
committee (MPC)
The monitoring and
evaluation of the
central
bank’s
performance
in
relation to the targets
are assigned to the
supervisory board
Inflation targeting
appropriate
knowledge and
expertise to bear
on
policy
decisions;
(iii)promoting
efficient
and
effective policy
decisions; and
(iv) Ensuring
meaningful
accountability for
policy decisions.
(v) The
decisionmaking structures
of
inflation
targeting central
banks
vary
considerably.
(vii) Two-tier
management structure
involving:
o a
supervisory
board
charged
with
responsibility for
monitoring and
evaluating
the
central
bank’s
performance in
relation to its
assigned
objectives, and
o a
separate
monetary policy
committee (MPC)
responsible
for
the formulation
and
implementation
of
monetary
policy
are
common among
IT practitioners.
156
4.9
Appendix I
4.9.1 Egypt
4.9.1.1 Unit Root Tests
Table 4.5 below shows the result of unit root tests for variables used in the money demand
function for Egypt. The results indicate that all the variables were non-stationary and
integrated of order one.
Table 4.5 Unit Root Tests for Variables Used in the Money Demand Model for Egypt
Variable
ADF
ADF
PP
(Levels)
(1stDifference)
PP (Levels)
(1st Difference)
Order of
Integratio
n
Real Money demand
(Rm3)
-1.054764
-8.416768*
-1.441293
-8.634422*
I(1)
Interest rates (ir)
-1.244659
-9.479075*
-1.844501
-20.91913*
I(1)
Real Domestic Income
(rgdp)
-1.011402
-7.667765*
-3.150653
-20.87708*
I(1)
Nominal Exchange
Rate (NER)
0.126216
-11.02220*
-0.201789
-11.44900*
I(1)
* Shows rejection of the null hypothesis of a unit root at the 1% level. The lag order for the series was
determined by the Akaike Information Criterion (AIC) in the case of ADF and Newey-West and Bandwidth
(NWB) in the case of PP.
Table 4.6 Johansen Cointegration Tests of Money Demand Model for Egypt
Maximal Eigenvalue Test
Trace Test
HO
r 0
r 1
r2
r 3
r 1
r2
r 3
r4
H1
r 1
r2
r 3
r4
r2
r 3
r4
r 5
Statistic
54.72090
19.01004
8.296685
CV(5%)
32.11832
19.01004
19.38704
1.805497
12.51798
83.83312
29.11222
10.10218
63.87610
42.91525
25.87211
1.805497
12.51798
4.9.2 Uganda
Table 4.7 below shows the result of unit root tests for variables used in the money demand
model for Uganda. The results, which are supported by the graphical representation below
indicate that all the variables were non-stationary and integrated of, order one.
157
Table 4.7 Unit Root Tests for Variables Used in the Money Demand Model for Uganda
Variable
ADF
ADF
PP
(Levels)
(1stDifference)
PP (Levels)
(1st Difference)
Order of
Integration
Real Money supply (lnrm3)
-1.624931
-13.05645*
-1.650852
-13.30342*
I(1)
Real Domestic Income
(lnrgdp)
-3.561193
--11.95327*
-3.267467
-11.94643*
I(1)
Nominal Exchange Rate
(lnner)
-0.518566
-11.03633*
-0.725722
-11.48648*
I(1)
Interest rate (ir)
-3.414901
--10.81759*
-2.992989
-10.64243*
I(1)
* Shows rejection of the null hypothesis of a unit root at the 1% level. The lag order for the series was
determined by the AIC in the case of ADF and NWB in the case of PP.
Figure 36: Charts of variables Em ployed in the Money Dem and Model for Uganda
LNRM3
LNGDP
3.2
1.96
1.92
2.8
1.88
2.4
1.84
2.0
1.80
1.6
1.76
94
96
98
00
02
04
06
94
96
98
IR
00
02
04
06
02
04
06
LNNER
25
7.8
20
7.6
15
7.4
10
7.2
5
7.0
0
6.8
94
96
98
00
02
04
06
94
96
98
00
4.9.2.1 The Long Run Results of Money Demand Function for Uganda
Only the Maximal Eigenvalue Test confirmed that there was only one cointegrating
relationship amongst the real money supply, domestic income, nominal exchange rate and
interest rates for Uganda as shown in table 5.23 below.
Table 4.8 Johansen Cointegration Tests of Money Demand Model for Uganda
Maximal Eigenvalue Test
Trace Test
HO
r 0
r 1
r2
r 3
r 1
r2
r 3
r4
H1
r 1
r2
r 3
r4
r2
r 3
r4
r 5
Statistic
27.43016
17.00435
2.757439
1.812185
49.00413
21.57397
4.569623
1.812185
CV(5%)
27.58434
21.13162
8.404448
3.841466
29.79707
15.49471
3.841466
47.85613
158
AppendixII: Monetary Policy Framework
Exchange
rate
arrangeme
nt (Number
of
countries)
Monetary Policy Framework
Exchange rate anchor
U.S. dollar
Euro
Composite
Other
(66 Countries)
(27
Countries)
(15
Countries)
(7
Countries
)
Kiribati
Exchange
arrangeme
nt with no
separate
legal tender
(10)
Ecuador,
Palau
Montenegro
El Salvador,
Panama
San Marino
Marshall
Islands,
TimorLeste
Currency
board
arrangeme
nt (13)
Antigua and
Barbuda2
St.
Lucia2
Bosnia and
Herzegovina
Djibouti
St.
Vincent
and the
Grenadin
es2
Bulgaria
Other
convention
al fixed peg
arrangeme
nt (68)
Monetar
y
aggregat
e target
(22
Countrie
s)
Fed. States of
Micronesia,
Dominica2
Estonia3
Grenada2
Lithuania3
Hong
Kong
SAR
St. Kitts and
Nevis2
Angola
Argentina
Aruba
Bahamas, The
Bahrain
Seychell
es
Sierra
Leone
Solomon
Islands
Sri
Lanka
Suriname
Brunei
Darussala
m
Benin4
Fiji
Bhutan
Burkina
Faso4
Cameroon5
Kuwait
Lesotho
Argentin
a
Malawi
Libya
Namibia
Rwanda
Cape Verde
Morocco
Nepal
Sierra
Leone
Central
African Rep.
Russian
Federation
Swazilan
d
5
Bangladesh
Barbados
Belarus
Belize
Tajikista
n
Trinidad
and
Tobago
Turkmen
istan
United
Arab
Emirates
Chad5
Samoa
Comoros
Tunisia
Congo, Rep.
of5
Côte
d'Ivoire4
Inflation targeting
framework
Other
1
(44 Countries)
(11
Countr
ies)
159
Eritrea
Guyana
Honduras
Jordan
Kazakhstan
Lebanon
Malawi
Venezuel
a, Rep.
Bolivaria
na de
Vietnam
Croatia
Yemen,
Rep. of
Zimbabw
e
Equatorial
Guinea5
Gabon5
Denmark3
GuineaBissau4
Latvia3
Maldives
Macedonia,
FYR
Mali4
Mongolia
Niger4
Netherlands
Antilles
Oman
Senegal4
Togo4
Qatar
Rwanda
Saudi Arabia
Pegged
exchange
rate within
horizontalb
ands (3)
Crawling
peg (8)
Slovak Rep.3
Syria.
Tonga
Bolivia
Botswana
China
Iran,
of.
I.R.
Ethiopia
Iraq
Nicaragua
Uzbekistan
Crawling
band (2)
Managed
floating
with
no
predetermined
path for the
exchange
rate (44)
Costa Rica
Azerbaijan
Cambodia
Algeria
Kyrgyz Rep.
Singapore
Lao P.D.R.
Vanuatu
Afghanis
tan, I.R.
of
Burundi
Armenia6
Colombi
a
Ghana
Liberia
Gambia,
The
Georgia
Mauritania
Guinea
Mauritius
Haiti
Guatema
la
Indonesi
a
Peru
Myanmar
Jamaica
Romania
Domin
ican
Rep.
Egypt
India
Malays
ia
Pakista
n
Paragu
ay
160
Ukraine
Kenya
Serbia6
Madagas
car
Thailand
Moldova
Uruguay
Mozambi
que
Nigeria
Papua
New
Guinea
São
Tomé
and
Príncipe
Sudan
Tanzania
Uganda
Independen
tly floating
(40)
Zambia
Albania
Luxem
bourg7
Congo,
Dem.
Rep. of
Australia
Malta7
Japan
Austria7
Mexico
Belgium7
Netherl
ands7
Somali
a8
Switze
rland
Brazil
New
Zealan
d
Norwa
y
Philippi
nes
Canada
Chile
Cyprus7
Poland
Czech
Rep.
Portuga
l7
Finland7
Sloveni
a7
South
Africa
France7
Germany
Spain7
7
Greece7
Sweden
Hungary
Turkey
United
States
161
Iceland
United
Kingdo
m
Ireland7
Israel
Italy7
Korea,
Rep. of
Source: IMF (2009)
1/ Includes countries that have no explicitly stated nominal anchor, but rather monitor various indicators in
conducting monetary policy
2/ The member participates in the Eastern Caribbean Currency Union
3/ The member participates in the ERM II
4/ The member participates in the West African Economic and Monetary Union
5/ The member participates in the Central African Economic and Monetary Community
6/ The central bank has taken preliminary step toward inflation targeting and is preparing for the transition to
full-fledged inflation targeting
7/ The member participates in the European Economic and Monetary Union
8/ As of end-December 1989