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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. 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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. 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(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 r2 r 3 r 1 r2 r 3 r4 H1 r 1 r2 r 3 r4 r2 r 3 r4 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 r2 r 3 r 1 r2 r 3 r4 H1 r 1 r2 r 3 r4 r2 r 3 r4 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