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Growth and Welfare Effects of Macroeconomic Shocks in Uganda
Abstract
The role of external shocks in explaining output fluctuations is contingent upon several factors
such as policy stability and effectiveness. African countries were relatively resilient to the 2008
global financial and economic crisis due to the strong policy buffers that were established
following sustained periods of prudent policy design and implementation prior to the onset of
the crisis. However, these cushions that relatively minimized the impact of the 2008 global
recession have weakened. Fiscal and external balances are weaker, debt levels are trending up,
foreign exchange reserve levels are lower, and the fiscal space has diminished. This paper
focuses on Uganda and seeks to examine, using a computable general equilibrium model, the
growth and welfare effects of three macroeconomic shocks: changes in terms of trade, changes
in the price of oil, and changes in development assistance inflows. Our analysis reveals three
key findings. First, the opposite and often offsetting effects of the three external shocks
examined in our paper on the agriculture, industry and the services sectors yields only shortterm as opposed to permanent deviations from trend growth in real GDP. Second, the primary
channel through which these three external shocks are transmitted to the domestic economy
and GDP growth is via changes in the real effective exchange rate and the associated effect on
the non-tradable (domestic) and tradable (both import-dependent and/or export-oriented)
sectors. Third, household welfare decreases permanently during the entire simulation period
for two of the three simulations considered by this study.
Keywords:
Sub-Saharan Africa, Uganda, Macroeconomic Shocks, Economic
Growth and Household Welfare, Computable General Equilibrium
JEL classification: D58, E62, I32
1
1. INTRODUCTION
Macroeconomic volatility has varied impacts on growth and business cycles and such
impact is particularly detrimental in developing countries (Loayza and Raddatz 2007).
Some of the more studied causes of macroeconomic volatility include fluctuations in
terms of trade which are estimated to account for up to 50% of all output fluctuations
(Easterly et al. 1993, Hnatkovska and Loayza 2005, Ahmed 2003, and Raddatz 2005).
Domestic conditions including policy buffers, efficiency of factor and product markets,
and quality of institutions have the potential to mitigate or amplify the impact of the
resulting shocks (Caballero and Krishnamurty 2001). Rodrik (1998) argues that social
conflicts and domestic conflict management institutions can determine the severity of
macroeconomic shocks and the ability of countries to respond to these volatilities.
The impact of external shocks on economic output and other outcomes such as poverty
is intuitive, particularly for developing countries including in Africa. For these
countries in particular, commodities which are prone to price fluctuations account for
over 70% of total exports (Raddatz 2008). Between 2007 and 2011, primary commodities
including oil, natural gas and metals accounted for over 80% of intra-Africa exports for
Algeria, Angola, Mali, Mauritania, Niger, and Nigeria (UNCTAD 2013). Thus, swings in
commodity prices can lead to significant changes in fiscal and Balance of Payment
balances, and more broadly on macroeconomic stability.
The role of external shocks in explaining output fluctuations is contingent upon several
factors such as policy stability and effectiveness. African countries were relatively
resilient to the 2008 global financial and economic crisis due to the strong policy buffers
that were established following sustained periods of prudent policy design and
implementation prior to the onset of the crisis. However, these cushions that relatively
minimized the impact of the 2008 global recession have weakened. Fiscal and external
balances are weaker, debt levels are trending up, foreign exchange reserve levels are
lower, and the fiscal space has diminished. For instance, Africa’s overall fiscal balance
including grants has deteriorated from 4.6% of GDP in 2006 to -3.6% in 2011. The
external account balance including grants has mirrored similar trends, worsening from
6.3% of GDP in 2006 to -0.6% in 2011. The high fiscal deficits limit the space for countercyclical measures in case of new external shocks.
This paper focuses on Uganda and seeks to examine the growth and welfare effects of
macroeconomic shocks. We identify at least two reasons for pursuing this line of
inquiry for Uganda.
2
First, identifying and quantifying the key external shocks and their importance in
explaining output volatility are critical for informing policy responses to smooth out the
effects of these shocks as well as structural reforms aimed at reducing Uganda’s
exposure and vulnerability to these shocks.
Second, to examine the potential gains in terms of reduced volatility from implementing
programs aimed at smoothing the impact of these shocks.
It is apparent that getting to the bottom of the sources of volatility is as important for
Uganda as for developing countries especially given the significance of income
fluctuations as well as the limited ability to hedge against these fluctuations. Just like
any other typical developing country, Uganda possesses shallow financial markets and
countercyclical fiscal and monetary policy responses are usually impeded by various
structural bottlenecks. This constraints several of efforts aimed at smoothing out the
impact of external shocks.
The rest of this paper is organized as follows. Section 2 reviews literature and identifies
gaps and Section 3 presents the methodology, data used and simulations. In Section 4
we discuss the findings, while Section 5 concludes with policy recommendations.
2. SOME RELATED LITERATURE
Several studies have been undertaken to analyze the role of external shocks in
explaining macroeconomic volatility more generally and fluctuations in output in
particular. These studies find that negative external shocks have a significant adverse
effects on short- and medium-run growth through their impact on aggregate demand,
external and fiscal balances (Collier and Goderis, 2009; Berg et al., 2010). There is some
evidence that these effects are asymmetric suggesting that while negative shocks
necessarily reduce growth, the positive shocks do not necessarily positively impact on
long-run growth, especially in resource-rich countries where institutions are weak
(Collier and Goderis, 2007).
The impact of external shocks measured in particularly in terms of trade shocks on
output volatility has been examined in the literature using various quantitative and
multi-sector equilibrium approaches. Kose (2002) finds that world price shocks play an
important role in driving business cycles in small open developing economies. His
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results confirm the results of earlier work by Mendoza (1995) and Kose and Riezman
(2001).
Some studies have examined the relationship between terms of trade shocks and
variations in in GDP growth using vector auto-regression (VAR) models. Ahmed (2003)
applies a VAR model to analyze the sources of short-term fluctuations in the output for
six Latin-American countries and finds that changes in the terms of trade and external
output only play a moderate role in driving output fluctuations. Raddatz (2008), using a
panel VAR model, also find that terms of trade changes have a small effect, explaining
an estimated 13% of output volatility for a typical African country. Moreover, oil prices
are found to play a relatively more important role as drivers of output fluctuations for
oil exporting African countries. Broda (2004) also uses a panel VAR approach to
examine the role of exchange rate policies in shielding economies from real shocks. He
reports that terms of trade shocks can explain 30% of the real output volatility in the
long run in countries characterized by fixed exchange rate regimes against 10% in
countries with flexible exchange rate regimes.
Cross-country and panel data analysis has also been used to examine the relationship
between terms of trade shocks and GDP volatility. Easterly and Kraay (2000) find a
positive relationship between income volatility and terms of trade volatility in a crosscountry analysis. Rodrik (1998) interacts terms of trade volatility with openness in a
cross country study and finds that this variable has a positive effect on GDP volatility
positively. However, Rodrik (2001) finds that the relationship between terms of trade
volatility and GNP volatility is positive but insignificant for Latin American economies.
Hausman and Gavin (1996) also focus on Latin American countries and their findings
reveal that terms of trade shocks have a weaker effect on GDP volatility in countries
with a more flexible exchange rate than in countries with a pegged the exchange rate
regime, coming findings by Broda (2004). Di Giovanni and Levchenko (2008) use
industry-level data and find that the risk content of exports is strongly positively
correlated with the variance of terms of trade and that export specialization affects
macroeconomic volatility.
Another set of studies have examined output volatility in partner countries as a
potential determinant of domestic volatility. Ahmed (2003) uses a VAR model that
includes the volatility of the aggregate real GDP of the eight largest trading partners as
a measure for external shocks. Calderon et al. (2005) includes the standard deviation of
the trade-weighted annual growth of the main trading partners. Bacchetta et al., (2009)
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we use the trade-weighted annual growth of all trading partners as a measure for
external shocks. These studies draw the conclusion that output volatility in partner
countries has a positive effect on exporters’ GDP volatility. Jansen et. al (2009) examine
the role of foreign demand shocks in partner countries in explaining economic volatility
in exporting countries. They find that the correlation between trading partners’ cycles is
more important in determining exporter’s GDP volatility compared to the volatility of
demand in individual export markets.
Collier and Goderis (2009) study the role of structural policies in mitigating the impact
on growth from adverse commodity export price shocks and large natural disasters for
130 countries during the period 1963-2003. Their findings reveal that adverse
commodity export price shocks and natural disasters substantially affect short-term
growth but no find evidence of a long run impact of commodity export price shocks
(volatility) on the level of GDP. This external commodity price shocks-growth nexus is
found to display asymmetric behavior. While positive price shocks have a positive
effect on growth, these effects are not significant. Collier and Goderis (2008) argue that
this asymmetry is explained by a country’s productive capacity. In particular, if an
economy is operating close to its short-term production potential, sudden upward shifts
in export earnings will usually not raise aggregate output. However, sharp and sudden
large decreases will normally reduce both export output and demand elsewhere in the
economy and these effects will reduce aggregate output. The only exception here is if
prices are highly flexible and resource allocation responds swiftly to the resulting price
changes. Natural disasters have a negative and significant effect on output, but this
effect is modest. Structural policies are found to play a key part in shaping the negative
short-term growth effects from shocks. In particular, regulations that delay the speed of
firm closure increase the short-term growth loss from adverse price shocks in
commodity-exporting countries. Regarding natural disasters, labour market regulations
that prevent an efficient re-allocation of workers tend to increase the negative effect on
short-term growth from these shocks.
Dabla-Norris and Gunduz (2012) develop a vulnerability index to quantify low-income
country risks to growth crises arising from external shocks. Two complementary
approaches, multivariate regression analysis and a univariate ‘signaling” approach, are
applied to aggregate information from a set of macroeconomic and institutional
indicators into an ‘early warning’ vulnerability index. The index is then applied to
assess vulnerabilities to growth declines in low-income countries during the period
1993-2011. Findings show that country fundamentals, exchange rate regimes,
institutional quality, and the size of shocks are important in explaining growth crises in
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low income countries. Moreover, the sensitivity of crisis risks to varying policy and
institutional fundamentals varies across countries. A strengthening of policy and
institutional frameworks contributes to a larger reduction in the crisis probability in
countries where buffers are initially weak. The study also finds that the vulnerability
index has declined significantly following its peak in the early 1990s. This is explained
by the improved policy and economic management as well as a more favorable external
environment in particular the improvements in terms of trade and debt relief. These
factors, Dabla-Norris and Gunduz (2008) argue have contributed to stronger policy
buffers and thus reduced vulnerabilities to external shocks. However, these buffers
have been increasingly expended to counter the impact of the global crisis leading to
elevated growth crises risks in low-income economies to levels well above the pre-crisis
years.
3. METHODOLOGY AND DATA
We use a recursive dynamic CGE model (Appendix 1 and 2) for Uganda based on the
2009/10 Social Accounting Matrix (SAM). We draw on a number of strengths from the
CGE modeling framework in our analysis. Firstly, the model simulates the functioning
of the economy as a whole and tracks changes in economic conditions and how these
changes are transmitted through price and quantity adjustments on a range of markets.
Secondly, since the basis of the CGE model is a SAM, we are able to discern the effects
of the changes in economic conditions on individual sectors of the economy. Thirdly,
the link of the model to household survey data enables an assessment of the impacts on
the welfare of households. Finally, the recursive dynamic nature of our model implies
that the behavior of its agents is based on adaptive expectations, rather than on the
forward looking expectations that underlie inter-temporal optimization models. Since a
recursive model is solved one period at a time, it is possible to separate the within-period
component from the between-period component, where the latter governs the dynamics
of the model. The CGE model used in this study is based on a standard CGE model
developed by Lofgren, Harris, and Robinson (2002) and adopted to Uganda by
Economic Policy Research Center. This is a real model without the financial or banking
system (See Table A1). GAMS software is used to calibrate the model and perform the
simulations.
3.1 Social Accounting Matrix
Consistent with other conventional SAMs, the 2007 Uganda SAM is based on a block of
production activities, involving factors of production, households, government, stocks
and the rest of the worldi. The various commodities (domestic production) supplied are
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purchased and used by households for final consumption (42 per cent of the total), but
also a considerable proportion (34 per cent) is demanded and used by producers as
intermediate inputs. Only 7 percent of domestic production is exported, while 11 per
cent is used for investment and stocks and the remaining 7 percent is used by
government for final consumption.
Households derive 64 per cent of their income from factor income payments, while the
rest accrues from government, inter-household transfers, corporations and the rest of
the world. The government earns 32 percent of its income from import tariffs – a
relatively high proportion, but a characteristic typical of developing countries. It derives
42 percent of its income from the ROW, which includes international aid and interest.
The remainder of government’s income is derived from taxes on products (14 percent),
income taxes paid by households (6 percent) and corporate taxes (5 percent).
3.2 Productions and commodities
For all activities, producers maximize profits given their technology and the prices of
inputs and outputs. The production technology is a two-step nested structure. At the
bottom level, primary inputs are combined to produce value-added output using a CES
(constant elasticity of substitution) function. At the top level, aggregated value added is
then combined with intermediate input within a fixed coefficient (Leontief) function to
give the output. The profit maximization gives the demand for intermediate goods,
labour and capital demand. The detailed disaggregation of production activities
captures the changing structure of growth due to the pandemic.
The allocation of domestic output between exports and domestic sales is determined
using the assumption that domestic producers maximize profits subject to imperfect
transformability between these two alternatives. The production possibility frontier of
the economy is defined by a constant elasticity of transformation (CET) function
between domestic supply and export.
On the demand side, a composite commodity is made up of domestic demand and final
imports and it is consumed by households, enterprises, and government. The
Armington assumption is used here to distinguish between domestically produced
goods and imports. For each good, the model assumes imperfect substitutability (CES
function) between imports and the corresponding composite domestic goods. The
parameter for CET and CES elasticity used to calibrate the functions used in the CGE
model are exogenously determined.
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3.3 Factors of production
There are 6 primary inputs: 3 labour types, capital, cattle and land. Wages and returns
to capital are assumed to adjust so as to clear all the factor markets. Unskilled and selfemployed labor is mobile across sectors while capital is assumed to be sector-specific.
Within the model, producers instantly adjust to changes in rates of returns for factors of
production for each sector. The model does not take into account adjustment costs of
switching resources between sectors.
3.4 Institutions
There are three institutions in the model: households, enterprises and government.
Households receive their income from primary factor payments. They also receive
transfers from government and the rest of the world. Households pay income taxes and
these are proportional to their incomes. Savings and total consumption are assumed to
be a fixed proportion of household’s disposable income (income after income taxes).
Consumption demand is determined by a Linear Expenditure System (LES) function.
Firms receive their income from remuneration of capital; transfers from government
and the rest of the world; and net capital transfers from households. Firms pay
corporate tax to government and these are proportional to their incomes.
Government revenue is composed of direct taxes collected from households and firms,
indirect taxes on domestic activities, domestic value added tax, tariff revenue on
imports, factor income to the government, and transfers from the rest of the world. The
government also saves and consumes.
3.5 Macro closure
Equilibrium in a CGE model is captured by a set of macro closures in a model. Aside
from the supply-demand balances in product and factor markets, three macroeconomic
balances are specified in the model: (i) fiscal balance, (ii) the external trade balance, and
(iii) savings-investment balance. For fiscal balance, government savings is assumed to
adjust to equate the different between government revenue and spending. For external
balance, foreign savings are fixed with exchange rate adjustment to clear foreign
exchange markets. For savings-investment balance, the model assumes that savings are
investment driven and adjust through flexible saving rate for firms.
3.6 Recursive dynamics
To appropriately capture the dynamic aspects of aid on the economy, this model is
extended by building some recursive dynamics by adopting the methodology used in
previous studies on Botswana and South Africa (Thurlow, 2007). The dynamics is
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captured by assuming that investments in the current period are used to build on the
new capital stock for the next period. The new capital is allocated across sectors
according to the profitability of the various sectors. The labour supply path under
different policy scenarios is exogenously provided from a demographic model. The
model is initially solved to replicate the SAM of 2007.
3.7 Limitations of the model
CGE models have some weaknesses (Thurlow, 2008). The main criticism of the static
model is that its core formulation is closely tied to the Walrasian ideal of equilibrium
(Dervis et al, 1982). In a pure neoclassical setting, producers and consumers react
passively to prices in order to determine their demand and supply schedules. Markets
are therefore assumed to clear through the interaction of relative prices, such that
equilibrium is achieved in both goods and factor markets. The model accommodates
prices in relative terms and therefore cannot adequately address issues related to
inflation.
3.8 Simulations
Our analysis is based on a series of scenarios each representing an exogenous change in
economic conditions and are compared to a baseline scenario of business as usual.
Running scenarios allows us to conduct a sort of controlled experiment of various types
of impacts. These impacts are then ascertained in terms of changes in: average sectoral
growth patterns, various components of the external account, household welfare, and
employment. These changes are then compared to the baseline. This baseline scenario
assumes no specific changes to policy and the absence of an external shock. Consistent
with the growth patterns in 2010, we calibrate the model to generate about 6.14% for
real GDP growth under the baseline for the simulation period. The government finances
its activities from domestic and foreign sources in a manner that is designed to be
compatible with macroeconomic stability.
We compare the baseline to three simulations: (i) changes in terms of trade (tot); (ii)
movements in international oil prices (poil); and (iii) changes in development assistance
inflows (faid).
Under the ‘Terms of Trade’ (tot)simulation, the changes in terms of trade that are
simulated here are informed by trends in price movements for major export and import
commodities between 2010 and 2012. In particular, we simulate a 10% reduction in the
9
average price of Uganda’s exports and a 10% increase in the average cost of imports.1
The key exports considered here include coffee, fish, tea, cotton, and flowers which
jointly accounted for an estimated 65% of the total export receipts in 2012. The imports
included in the simulation include the following products: petroleum, chemical,
manufactured rubber, textiles and vehicles. These products accounted for an estimated
70% of total imports in 2012.
The second simulation (poil) examines the impact of changes in international oil prices.
The 2012 World Economic Outlook (WEO) reports that international oil prices increased
by 28% per barrel in 2010, 32% in 2011, and 10% in 2012. The 2012 WEO further projects
a reduction in international oil prices by 4% in 2013 and we assume a similar reduction
for 2014 and 2015.
Our third simulation – ‘Foreign Aid’ (faid) evaluates the impact of the 2012 aid-cut
backs. We simulate a 50% reduction in aid in 2012, compared to the 2010 aid levels,
following the suspension of development aid equivalent. In line with the FY 2013/14
budget which projects that 80% of public spending will be financed by domestic
resources, further reductions in aid of 75% (compared to the 2010 levels) are simulated
for the period 2013-2015.
4. FINDINGS
4.1 Impact on GDP Growth
Figure 1 shows the average real GDP growth rates for the period 2010-2015 under the
baseline and the three simulations considered here. The tot and faid simulations report
average real GDP growth rates that are lower than the baseline. The baseline assumes
no change the policy environment and framework of 2010 is maintained throughout the
simulation period and also assumes no external shocks.
1
Sensitivity analyses in which the changes in terms of trade are varied to 15% and 25% are conducted and the
study’s conclusions are not affected.
10
Figure 1. Average real GDP Growth: 2010 - 2015 (%)
5.72
5.70
5.68
5.66
5.64
5.62
5.60
5.58
5.56
5.54
5.52
5.71
5.70
5.65
5.59
base
tot
poil
faid
Source: Model simulations and author computations
The ‘poil’ simulations yields average real GDP growth rates similar to the baseline
scenario during the simulation period. The similarity in real GDP growth rates for the
‘base’ and ‘poil’ could result from the contrasting impact of the increase in international
oil prices (period 2010-2012) versus the project reduction in the same prices during the
latter half of the simulations period (period 2013-2015). Thus, where these two opposing
factors offset each other, changes in oil prices in this case will not affect trend real GDP
growth, as is the case with simulations ‘poil’ and ‘base’ in this case. Two channels in
particular could explain the inverse relationship between real GDP growth and
international oil prices. First, petroleum products, particularly fuel, comprise core
inputs in the production process, and thus changes in oil prices will directly affect
productivity. Second, rising oil prices are likely to put pressure on the non-farm wage
rate and thus affect growth in the non-agriculture sector. It is plausible that the
11
Figure 2. Real GDP Growth (year-over-year, %)
8.00
7.00
6.00
5.00
4.00
3.00
2.00
1.00
0.00
2010
2011
2012
2013
2014
2015
base
6.14
4.12
4.50
5.50
7.00
7.00
tot
5.78
4.13
4.41
5.42
6.92
6.92
poil
5.85
4.08
4.71
5.63
7.00
7.00
faid
6.14
4.14
4.44
5.45
6.90
6.89
Source: Model simulations and author computations
Figure 2 illustrates the trends in real GDP growth across the three simulations during
the simulation period. Real GDP growth mirrors similar trends across the simulations
and also when compared to the baseline. A more thorough assessment of the channels
through which the three shocks considered here affects real GDP growth requires
examining the trends in sectoral contributions to GDP growth. This impact is illustrated
in Figures 3-5.
Figure 3 indicates that trends in the contribution to real GDP growth from the
agriculture sector across the three simulations does not differ much and is also similar
to the baseline simulation.
12
Figure 3. Agriculture - Contribution to real GDP growth (%)
1.40
1.20
1.00
0.80
0.60
0.40
0.20
0.00
2010
2011
2012
2013
2014
2015
base agr
1.10
0.78
0.74
0.88
1.18
1.20
tot agr
1.04
1.05
0.77
0.90
1.22
1.24
poil agr
1.09
0.78
0.75
0.88
1.18
1.19
faid agr
1.11
0.85
0.75
0.93
1.20
1.21
Source: Model simulations and author computations
However, agriculture’s contribution to real GDP growth depicts an increase between
2010 and 2011 before reverting to the path traced by the other simulations for the
reminder of the simulation period. This finding could be due to the impact of
deterioration of terms of trade on agriculture exports and import substitutable
agriculture products. In particular, the deterioration in terms of trade reduces foreign
exchange receipts and all else the same, increases the import bill, leading to a
depreciation of the local currency. The local currency depreciation makes exports
cheaper and imports expensive leading to growth in exports including in the
agriculture sector and growth in import substitutable sectors. These events combined
could explain the increased contribution of agriculture to real GDP growth between
2010 and 2011. However, the drought driven food shortages and the resulting surge in
food inflation experienced during the second half of 2011 appears to have more than
offset the impact of terms of trade changes on agriculture sector growth thus leading to
a reduction in this sector’s contribution to real GDP growth in 2012.
In the industry sector, two key findings emerge. First, the contribution of this sector to
real GDP growth under the ‘faid’ simulation exceeds the baseline scenario and the two
other simulations during the period 2011-2014 but seems to revert to ‘trend’ in 2015.
Second, the sector’s contribution to real GDP growth decreases markedly between 2010
and 2011 under the ‘tot’ simulation. The first observation (strong contribution of the
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industry sector to real GDP growth under the ‘faid’ simulation) can be explained by the
depreciation of the real exchange rate associated with a reduction in development
assistance, and indeed a reduction in external inflows. In particular, real exchange rate
depreciation supports export-oriented and/or import substitutable manufacturing as
imports become expensive while exports become more costly.
Figure 4. Industry - Contribution to real GDP growth (%)
1.60
1.40
1.20
1.00
0.80
0.60
0.40
0.20
0.00
2010
2011
2012
2013
2014
2015
base ind
0.93
1.11
0.85
0.90
1.23
1.32
tot ind
1.31
0.49
0.88
0.93
1.20
1.27
poil ind
1.11
1.14
0.72
0.81
1.23
1.32
faid ind
0.96
1.25
1.13
1.09
1.27
1.35
Source: Model simulations and author computations
The second observation (industry sector’s contribution to real GDP growth decreases
markedly between 2010 and 2011 under the ‘tot’ simulation) can also be explained the
impact of terms of trade changes on export-oriented and import-substitutable industry
sub-sectors. However, in this case, the deterioration in terms of trade leads to a
depreciation of the real exchange rate via a reduction in foreign exchange receipts.
Figure 5 reveals that the services sector’s contribution to real GDP growth mirrors
similar trends across the three simulations.
14
Figure 5. Services - Contribution to real GDP growth (%)
5.00
4.50
4.00
3.50
3.00
2.50
2.00
1.50
1.00
0.50
0.00
2010
2011
2012
2013
2014
2015
base ser
4.08
2.33
3.07
3.97
4.69
4.54
tot ser
3.40
2.74
2.97
3.83
4.56
4.43
poil ser
3.62
2.24
3.41
4.19
4.69
4.54
faid ser
4.04
1.89
2.98
3.68
4.54
4.38
Source: Model simulations and author computations
However, two key results standout. The first key finding here is that the reduction in
the sector’s contribution to real GDP growth between 2010 and 2011 is greatest under
the ‘faid’ simulation and smallest under the ‘tot’ simulation. Reductions in external
inflows, including development assistance contribute to a depreciation of the real
exchange rate, all else the same. This tends to benefit, all else the same, growth in
export-oriented and import-substitutable sectors. Thus, tradable sectors become more
attractive as private investors seek to cash in on the reduced relative costs in the export
sectors and/or in the import-competitive sectors as a result of the low real exchange
rate and expensive import competition. The upshot of these factors is that private
capital will tend to flow towards the tradable sectors including manufacturing and
export oriented agriculture and away from the non-tradable sectors such as domestic
oriented services. Thus given that the service sector is the least tradable, it tends to
suffer most from the reduction in foreign inflows including external development
assistance.
The second key finding relates to the contribution of the services sector to real GDP
growth which appears to be higher under the ‘poil’ simulation during the period 2012 –
2015. Changes in oil prices have a direct relationship with inflation. Thus, reductions in
15
the international price of oil (as assumed under the ‘poil’ simulation for the period 20132015) will likely contribute to growth in the off-farm due to the consequent reduction in
costs of production, all else the same.
In conclusion, the impact of the three external shocks considered in this paper impacts
the three economic sectors (agriculture, industry and services) in diverse ways and in
most cases, these impacts offset each other via the effects on the exchange rate, cost of
production, and economic competiveness. This could explain the minimal deviations in
real GDP trends between the baseline and the three simulations during the simulation
period.
4.2 Impact on Exports and Imports
The discussion in the preceding section is primarily anchored on the effect of the three
simulations on the tradable sectors, in particular the export-oriented and import
dependent sectors. In this section, we examine the trends in imports and exports across
the three simulations and the possible drivers of these trends. As discussed earlier,
Figure 6 also indicates that growth rate of imports decreases across all simulations
between 2010 and 2011 followed by a gradual increase during the simulation period
post-2012. Changes in the real effective exchange rate are among the key drivers of the
trends depicted in Figure 6. Published data confirms that the Uganda shilling
depreciated markedly against the US Dollar during the first three quarters of 2011 –
leading to a slowdown in the rate of growth in imports – before appreciating 13%
during the fourth quarter of 2011 – which explains the reversal in the decline of the
growth rate for imports.
Figure 6. Growth in Real Imports (year-over-year, %)
7.00
Axis Title
6.00
5.00
4.00
3.00
2.00
1.00
0.00
2010
2011
2012
2013
2014
2015
base
5.55
3.12
4.54
5.98
6.47
6.33
tot
3.30
3.12
4.51
5.92
6.41
6.26
poil
4.79
3.05
5.10
6.34
6.45
6.33
faid
5.46
2.43
4.46
5.60
6.37
6.23
16
Source: Model simulations and author computations
Figure 7 shows the trends in the export growth rate and these are also consistent with
changes in the value of the local currency against the US Dollar and other major
currencies. As would be expected, depreciation in the real effective exchange rate
during the first three quarters of 2011 resulted in an increase in the export growth rate
with the 2011 Q4 appreciation and subsequent stability of the local currency during
2012 contributing to a slowdown in the growth rate of exports.
Axis Title
Figure 7. Real Exports Y.o.Y Growth (%)
16.00
14.00
12.00
10.00
8.00
6.00
4.00
2.00
0.00
2010
2011
2012
2013
2014
2015
base
9.57
9.64
5.05
4.42
9.87
11.10
tot
13.88
9.89
4.88
4.29
9.57
10.77
poil
12.07
9.83
3.35
3.33
9.87
11.11
faid
10.12
14.39
5.13
6.47
9.71
10.84
Source: Model simulations and author computations
In summary, our findings confirm that the impact of the three external shocks studied
here is transmitted to GDP growth through various channels including changes in the
real effective exchange rate and its effect on imports and exports.
4.3 Effects on Household Welfare
Figures 8 and 9 illustrate the impact of the three external shocks on household welfare
as measure by the equivalent variation and changes in real per capita consumption
respectively. Both these measures indicate household welfare declines in 2011 following
the on-set of the shocks and but gradually increases during the simulation period post2012. However, household welfare, as measured by the equivalent variation, remains
permanently below ‘trend’ under the ‘tot’ and ‘faid’ simulations. As discussed earlier,
the appreciation of the exchange rate post-2011 increases imports and hurts exports as
17
well as the import-substitutable sectors as imports become cheaper. Consequently, the
tradable sectors become less attractive owing to the increased costs in the exportoriented and import competitive sectors due to the appreciated real effective exchange
rate and less expensive imports. Thus, private capital is driven towards the nontradable sectors such as construction and domestic-oriented services as opposed to
manufacturing including food processing.
Axis Title
Figure 8. Equivalent Variation per capita (% of base year consumption spending
per capita) - Household
9.00
8.00
7.00
6.00
5.00
4.00
3.00
2.00
1.00
0.00
-1.00
-2.00
2009
2010
2011
2012
2013
2014
2015
base
0.83
1.89
1.39
1.49
2.71
5.34
8.02
tot
0.83
0.09
-0.87
-0.84
0.29
2.80
5.36
poil
0.83
0.63
-0.11
0.93
2.80
5.48
8.19
faid
0.83
1.74
-0.15
-0.17
0.29
2.70
5.15
Source: Model simulations and author computations
Moreover, the average growth rates for wages for labor all three labor categories
examined here (less-than-secondary, secondary, and tertiary education) are below the
baseline growth rates for the ‘tot’ and ‘faid’ simulations. This the reduced investments
and the resulting lower productivity in the tradable sectors and lower growth labor
remuneration under these two simulations explains the lower household welfare.
18
Figure 9. Growth in real per-capita Consumption (year-over-year, %) Household
3.00
2.50
2.00
1.50
1.00
0.50
0.00
-0.50
-1.00
-1.50
-2.00
-2.50
2009
2010
2011
2012
2013
2014
2015
base
0.83
1.89
-0.50
0.10
1.20
2.56
2.55
tot
0.83
0.09
-0.95
0.04
1.14
2.50
2.49
poil
0.83
0.64
-0.74
1.03
1.85
2.61
2.57
faid
0.83
1.74
-1.86
-0.02
0.46
2.40
2.39
Source: Model simulations and author computations
In summary, the external shocks examined via the three simulations in this paper affect
household welfare at the on-set of the shocks in 2011 and also result in lower welfare
under two simulations for the entire simulation period.
5. CONCLUSIONS
The role of external shocks in explaining output fluctuations is contingent upon several factors
such as policy stability and effectiveness. African countries were relatively resilient to the 2008
global financial and economic crisis due to the strong policy buffers that were established
following sustained periods of prudent policy design and implementation prior to the onset of
the crisis. However, these cushions that relatively minimized the impact of the 2008 global
recession have weakened. Fiscal and external balances are weaker, debt levels are trending up,
foreign exchange reserve levels are lower, and the fiscal space has diminished. Using a
computable general equilibrium model for Uganda, this study seeks to examine the growth and
welfare effects of three macroeconomic shocks: changes in terms of trade, changes in the price of
oil, and changes in development assistance inflows.
Our analysis reveals three key findings. First, the opposite and often offsetting effects of the
three external shocks examined in our paper on the agriculture, industry and the services
sectors yields only short-term as opposed to permanent deviations from trend growth in real
GDP. Second, the primary channel through which these three external shocks are transmitted to
19
the domestic economy and GDP growth is via changes in the real effective exchange rate and
the associated effect on the non-tradable (domestic) and tradable (both import-dependent
and/or export-oriented) sectors. Third, household welfare decreases permanently during the
entire simulation period for two of the three simulations considered by this study.
These findings should be of interest to a diverse audience of policy makers both in
Uganda and the region for at least two reasons. First, by identifying and quantifying the
impact of key external shocks in explaining output volatility is critical for informing
policy responses to smooth out the effects of these shocks as well as structural reforms
aimed at reducing Uganda’s exposure and vulnerability to these shocks. Examining the
potential gains in terms of reduced volatility from implementing programs aimed at
smoothing the impact of these shocks has the potential of informing the prioritization of
reforms.
In particular, our findings – and based on the assumptions that we make in this paper –
reveal that policy makers should focus on designing and implementing policies that
reduce the severity of external shocks on the vulnerable and poor. Such policies as
social safety nets and micro insurance including in the agriculture and non-tradable
services sub-sectors and construction have the potential of offsetting the permanent
effect of external shocks on household welfare. Moreover, due to Uganda’s long
standing structural imbalance between imports and exports, policies to diversify
economic activity and exports in particular remain critical to ensuring stability of the
exchange rate. Exchange rate stability will contribute to the closure of one of the
primary channels through which external shocks are transmitted to the domestic
economy.
20
APPENDICES
Appendix 1: CGE Model Sets, Parameters, and Variables
Symbol
Sets
Explanation
Symbol
Explanation
Activities
Activities with a Leontief
function at the top of the
technology nest
c  CMN ( C )
Commodities not in CM
c  CT ( C )
Transaction service
commodities
c C
Commodities
c  CX ( C )
Commodities with
domestic production
c  CD ( C )
Commodities with domestic
sales of domestic output
f F
Factors
c  CDN ( C )
Commodities not in CD
i  INS
c  CE ( C )
Exported commodities
i  INSD( INS )
Institutions (domestic and
rest of world)
c  CEN ( C )
Commodities not in CE
i  INSDNG ( INSD)
c  CM ( C )
Aggregate imported
commodities
h  H ( INSDNG )
Households
qdstc
Quantity of stock change
a A
a  ALEO( A)
Domestic institutions
Domestic nongovernment institutions
Parameters
dwtsc
Weight of commodity c in the
CPI
Weight of commodity c in the
producer price index
icaca
Quantity of c as intermediate
input per unit of activity a
cwtsc
icdcc '
icecc '
icmcc '
intaa
ivaa
mps i
mps01i
Quantity of commodity c as
trade input per unit of c’
produced and sold domestically
Quantity of commodity c as
trade input per exported unit of
c’
Quantity of commodity c as
trade input per imported unit of
c’
Quantity of aggregate
intermediate input per activity
unit
Quantity of aggregate
intermediate input per activity
unit
Base savings rate for domestic
institution i
0-1 parameter with 1 for
institutions with potentially
flexed direct tax rates
pwec
Export price (foreign currency)
pwmc
Import price (foreign currency)
 aa
Efficiency parameter in the CES activity
function
qg c
qinvc
shif if
shiiii '
Base-year quantity of
government demand
Base-year quantity of
private investment
demand
Share for domestic
institution i in income of
factor f
Share of net income of i’ to
i (i’  INSDNG’; i 
INSDNG)
taa
Tax rate for activity a
tinsi
Exogenous direct tax rate
for domestic institution i
tins01i
0-1 parameter with 1 for
institutions with
potentially flexed direct
tax rates
tmc
Import tariff rate
tqc
Rate of VAT tax
trnsfri f
Transfer from factor f to
institution i
 crt
21
CET function share parameter
 cac
Efficiency parameter in the CES valueadded function
Shift parameter for domestic
commodity aggregation function
 cq
Armington function shift parameter

CET function shift parameter
 ava
t
c
 vafa
 chm
 ac
 aa
 ava
CES value-added function share
parameter for factor f in activity a
Subsistence consumption of marketed
commodity c for household h
Yield of output c per unit of activity a
CES production function exponent
a
Capital sectoral mobility factor
 chm
Marginal share of consumption
spending on marketed commodity c for
household h
 cac
Domestic commodity aggregation
function exponent
 aa
CES activity function share parameter
 cq
Armington function exponent
 acac
Share parameter for domestic
commodity aggregation function
 ct
CET function exponent
 crq
Armington function share parameter
f
Capital depreciation rate
 afat
CES value-added function exponent
Sector share of new capital
Exogenous Variables
CPI
Consumer price index
DTINS
Change in domestic institution tax
share (= 0 for base; exogenous variable)
MPSADJ
QFS f
FSAV
Foreign savings (FCU)
TINSADJ
GADJ
Government consumption adjustment
factor
WFDIST fa
IADJ
Investment adjustment factor
Endogenous Variables
Average capital rental rate in time
AWFfta
period t
Change in domestic institution savings
DMPS
rates (= 0 for base; exogenous variable)
Savings rate scaling factor (= 0 for base)
Quantity supplied of factor
Direct tax scaling factor (= 0 for base;
exogenous variable)
Wage distortion factor for factor f in
activity a
DPI
Producer price index for domestically
marketed output
QHAach
EG
Government expenditures
QINTAa
EH h
Consumption spending for household
QINTca
EXR
Exchange rate (LCU per unit of FCU)
QINVc
Government consumption demand for
commodity
Quantity consumed of commodity c by
household h
Quantity of household home
consumption of commodity c from
activity a for household h
Quantity of aggregate intermediate
input
Quantity of commodity c as
intermediate input to activity a
Quantity of investment demand for
commodity
GSAV
Government savings
QM cr
Quantity of imports of commodity c
QFfa
MPSi
PAa
PDDc
PDSc
PEcr
Quantity demanded of factor f from
activity a
Marginal propensity to save for
domestic non-government
institution (exogenous variable)
Activity price (unit gross
revenue)
Demand price for commodity
produced and sold domestically
Supply price for commodity
produced and sold domestically
Export price (domestic currency)
QGc
QH ch
QX c
Quantity of goods supplied to
domestic market (composite
supply)
Quantity of commodity
demanded as trade input
Quantity of (aggregate) valueadded
Aggregated quantity of
domestic output of commodity
QXACac
Quantity of output of
QQc
QTc
QVAa
22
commodity c from activity a
PK ft
Aggregate intermediate input
price for activity a
Unit price of capital in time
period t
PM cr
Import price (domestic currency)
TINSi
PQc
Composite commodity price
TRIIii '
Direct tax rate for institution i
(i  INSDNG)
Transfers from institution i’ to i
(both in the set INSDNG)
WF f
Average price of factor
YF f
Income of factor f
YG
Government revenue
PINTAa
RWF f
Real average factor price
TABS
Total nominal absorption
PXACac
Value-added price (factor income
per unit of activity)
Aggregate producer price for
commodity
Producer price of commodity c
for activity a
QAa
Quantity (level) of activity
YIi
QDc
Quantity sold domestically of
domestic output
YIFif
QEcr
Quantity of exports
K afat
PVAa
PX c
23
Income of domestic nongovernment institution
Income to domestic institution
i from factor f
Quantity of new capital by
activity a for time period t
Appendix 2: CGE Model Equations
Production and Price Equations
QINTc a  icac a  QINTAa
(1)
PINTAa   PQc  icaca
(2)
cC

vaf
QVAa   ava     va
f a   f a  QF f a
 f F

W f WFDIST fa
QFf a

 ava



-
1
ava
(3)

vaf
 PVAa  QVAa     va
f a   f a  QF f a
 f F '



  van
van
f a
  van

f a     f f ' a  QF f ' a
 f 'F

-

 ava
1

va
vaf
   f a   f a  QFf a



 ava 1
(4)
1
 van
f a
(5)
1
W f ' WFDIST f ' a

  van 
  van 1
 W f WFDIST f a  QFf a     fvanf '' a  QFf '' a f a    fvanf ' a  QFf ' a f a
 f ''F

(6)
QVAa  ivaa  QAa
(7)
QINTAa  intaa  QAa
(8)
PAa  (1  taa )  QAa  PVAa  QVAa  PINTAa  QINTAa
(9)
QXACa c   a c  QAa
(10)
PAa

 PXAC
cC
ac
ac
(11)

ac 
QX c   cac     aacc  QXACa c  c 
 a A


1
cac 1
(12)

ac 
ac
PXACa c = PX c  QX c   aacc  QXACacc    aacc  QXACacc 1
 a A '

PEcr  pwecr  EXR   PQc  icec 'c
1
c 'CT
(13)
(14)
1
t

ct
ct  c
t
t
QX c =      cr  QE cr + (1-   cr )  QD c 
r
 r

t
c

1 -   crt
QE cr  PE cr
r
=

 ct
QD c  PDS c

(15)
1
 ct 1




(16)
24
QX c = QDc   QEcr
(17)
r
PX c  QX c  PDSc  QDc   PEcr  QEcr
(18)
r
PDDc  PDSc

 PQ
c'
c 'CT
PM cr  pwmcr   1  tmcr

 icdc 'c
(19)
 EXR 
 PQ

-q
-q 
QQ c =  cq     crq  QM cr c + (1-   crq )  QD c c 
r
 r


QM cr  PDD c
 cq
=

QD c  PM c 1 -   crq
r

QQ c = QD c   QM cr
PQc   1  tqc
QT c =
r
  QQc
-
c c'
 icmc ' c
(20)
1
cq
(21)
1
 1+cq




(22)
(23)
 PDDc  QDc   PM cr  QM cr
r
  icm
c 'C '
c'
c 'CT
 QM c '  icec c '  QEc '  icdc c '  QD c '

(24)
(25)
CPI   PQc  cwtsc
(26)
DPI   PDSc  dwtsc
(27)
cC
cC
Institutional Incomes and Domestic Demand Equations
YFf =  WFf WFDIST f a  QFf
a A
(28)
a
YIFi f = shifi f  YFf  trnsfrrow f  EXR 
YI i =
 YIF
f F
i f


i '  INSDNG '
(29)
TRIIi i '  trnsfri gov  CPI  trnsfri row  EXR
(30)
TRIIi i ' = shiii i '  (1- MPSi ' )  (1- tins i ' )  YIi '
(31)


EH h = 1   shiii h   1  MPSh   (1- tins h )  YI h
 i  INSDNG

(32)


PQc  QH c h = PQc   chm   chm   EH h   PQc '   cm' h 
c 'C


(33)
QINVc = IADJ  qinvc
(34)
QGc = GADJ  qg c
(35)
25
EG   PQc  QGc 
c C

i  INSDNG
trnsfri gov  CPI
(36)
System Constraints and Macroeconomic Closures

YG 
i  INSDNG
tins i  YI i 

cCMNR
tmc  pwm c  QM c  EXR   tqc  PQc  QQc
c C
(37)
  YFgov f  trnsfrgov row  EXR
f F
QQc   QINTc a   QH c h  QGc  QINVc  qdstc  QTc
(38)
 QF
(39)
a A
a A
f a
h H
 QFS f
YG  EG  GSAV
 pwmcr  QM cr   trnsfrrow f 
r cCMNR

i INSDNG
f F



r cCENR
pwecr  QEcr 
 trnsfr
iINSD
(40)
i row
 FSAV
MPSi  1  tins i  YI i  GSAV  EXR  FSAV   PQc  QINVc   PQc  qdstc
cC
MPSi  mpsi  1  MPSADJ 
c C
(41)
(42)
(43)
Capital Accumulation and Allocation Equations



 QFf a t 

AWF   
WFf t WFDIST f a t 

QFf a' t 
a
 

a'



 
QFf a t   a  WFf ,t WFDIST f a t

a
f at  




1


  1
a
 


QF
AWF

f
a'
t
f
t

 

 
 a'

  PQc t  QINVc t 

a
a
K f a t   f a t   c


PK f t




QINVc t
PK f t   PQc t 
c
 QINVc' t
a
ft
(44)
(45)
(46)
(47)
c'
 K af a t

QFf a t+1  QFf a t  1 
 f 
 QFf a t



  K f a t
QFS f t 1  QFS f t  1  a
 f

QFS f t


(48)





(49)
26
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ENDNOTES
i
The first SAM for Uganda was developed in 2002 by the Uganda Bureau of Statistics and later updated
by the International Food Policy Reserach Institute in 2007. The 2007 SAM reflects the current production
structure of Uganda which has not changed significantly over the past four years.
29