The optimal lag length selected by Akaike. Information Criterion (AIC) in this case is 2 lags. Ignoring the vector of co
Bank of Uganda Working Paper Series Working Paper No. 04/2017 The Macroeconomic responses to Petro Shocks for Uganda Nyanzi Sulaiman and Thomas Bwire
May 2017
Working papers describe on-going research by the author(s) and are published to elicit comments and to further debate. The views expressed in the working paper series are those of the author(s) and do not in any way represent the official position of the Bank of Uganda. This paper should not therefore be reported as representing the views of the Bank of Uganda or its management
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Bank of Uganda
WP No. 04/2017
The Macroeconomic responses to Petro Shocks for Uganda Nyanzi Sulaiman and Thomas Bwire Bank of Uganda, Research Department
Abstract This paper analyzes the dynamic effects of oil shocks to Uganda’s macroeconomy, employing a Structural Vector Autoregression (SVAR) analysis based on an identification strategy developed by Blanchard and Quah. The key findings of the study are twofold; first, an oil shock operates through the supply channel and thus has stagflationary effects on the economy. Secondly, the oil shock is relatively more important in explaining fluctuations in output compared to movements in the Consumer Price level. These results have two policy implications. First, Uganda should develop efficient and reliable non-oil energy sources as a structural policy response to mitigate the long-run impacts. Secondly, given the relatively less important role of oil shocks in explaining consumer price level, at least in Uganda, then in event of these shocks, monetary policy ought to behave counter cyclically for short-run output stabilization, but without compromising credibility. Keywords: Petro Shocks, Structural Vector Autoregression, supply and demand channels, Uganda. JEL classification: C23, E40, E50, Q41, Q43
Correspondence Address: Research Department, Bank of Uganda, P.O. Box 7120, Kampala, Tel. 256 414 230791, Fax. 256 414 230791. About the Authors: Sulaiman Nyanzi (
[email protected]) is a principal Economist while Thomas Bwire (
[email protected]) is a senior principal Economist. The authors are grateful to Professor Peter Pedroni of Williams College, Massachusetts, and several seminar participants at the bank of Uganda who heard and advised on the evolving versions of the paper. The usual disclaimer applies. The data used in the analysis are available on request. 2|Page
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Introduction The recent plunge in the international price of oil from over US$100/ barrel in 2014 to
below US$30/ barrel in January 2016 has renewed debate on the impact of oil price shock to the macro-economy. Questions regarding the transmission channel, as well as the size and importance of their effects have been at the core of this debate since the recession of the 1970s in the United States. Recently, there has been considerable contention as to whether the effects of oil price shock are symmetric or still as important as they were in the past in magnitude. Answers to these questions are not straightforward. They may be highly sensitive to the identification strategy used to disentangle oil shocks from other shocks, the estimation method employed, the sample period used, and the structure of the economy in question. Nonetheless, oil shocks, overall, have a significant bearing on macroeconomic policy-making across the globe. These shocks, no matter their source (global demand or supply), constitute a major source of external risks facing macro and microeconomic policy-making, particularly in small open net-oilimporting economies, where the magnitudes of the effects are unclear. These small economies face a double tragedy that comes with oil shocks. The first tragedy comes from the oil price shock itself, while the second emanate from shocks associated with policy responses to this shock by big economies. Prior to the oil price shock in 1970s, economic fluctuations in industrialized countries were perceived to be entirely from aggregate demand. This view broke down due to its inconsistency with the stagflation that afflicted the industrialized countries at the time, following the oil price shock of 1970s. Since then, a shock that increases the international oil price is understood by some economists to be a global shock that initially shifts the aggregate supply curve of a net oil-importing economy, leading to a fall in real output and an increase in the price level. This is a consequence of tightening the aggregate supply conditions due to higher real marginal costs facing producers. The eventual reduction in oil or energy imports due to a price increase induces the alteration of the optimal production possibilities, rendering capital obsolete and reducing the full employment labor; which have been shown theoretically to reduce potential output (see for example Tatom (987); and Estrada and Hernández (2009)). Indeed, a number of empirical studies have supported this view, arguing that sufficiently large oil price shocks may cause a durable fall in output and cause the domestic price level to increase in net oil importing countries (see for example; Peersman and Robays (2012); Tang, Wu 3|Page
and Zhanga (2010); DePratto, Deresende and Maier (2009)). However, understanding the propagation mechanisms of oil price shocks is one step in a strategy to mitigate its effects. The second step is to understand the magnitude of its effects to the economy in question to be able to deploy appropriate and proportionate policy responses. Most analyses geared to answering some of the questions posed above have to a great extent focused on industrial and/or emerging countries, with little or no research on low income economies. For instance, Blanchard and Galí (2010) note that, despite the occurrence of oil price shocks of similar sign and magnitude in the recent past as in the 1970-80s, the magnitude of the negative effects of oil shock on output, employment, wages and prices has been diminishing over time in industrial countries, at least in part due to increased credibility of monetary policy, increased flexibility in the labor markets and declining share of oil in final and intermediate consumption (Blanchard and Galí 2010). The implication of Blanchard and Galí’s findings is that one way to lessen the adverse effects of oil price shocks in small open net-oil importing economies is to modernize their monetary policy regimes to enhance credibility. Uganda is one such country and will be the focus of this study. In 2011, it abandoned the money-targeting monetary policy regime in favor of inflation-targeting lite in transition to a fully-fledged inflation-targeting. This policy regime requires credible macroeconomic forecasts. The forecasts themselves demand better understanding of the importance and transmission of different shocks to the economy to guide the conduct of monetary policy – whether to be procyclical or countercyclical in face of such shocks. One such shock that is a source of major concern and risks to monetary policy-making in Uganda is the oil shock. To our knowledge, the effects of oil shocks in Uganda, to date, have not yet been analyzed. The objective of this paper therefore, is to analyze the nature and importance of oil shocks to Uganda’s economy in a dynamic framework. We employ a Structural Vector Autoregression analysis (SVAR) following an identification strategy developed by Blanchard and Quah (1989). Due to data limitations, this paper does not attempt to analyze the evolution of the size and symmetry of the effects. The standard aggregate demand-supply framework is a useful theoretical configuration to understand the nature and transmission channels of an oil shock (Tatom 1987). The framework indicates that a rise in international oil prices cannot be an aggregate demand shock to a net oil 4|Page
importing economy. If it were so, the adjustment mechanism would be counterintuitive as both output and price would fall, which is highly suspicious. As Figure 1 indicates, a more plausible intuition is that an unanticipated increase in the international oil price would shift the aggregate supply curve of a net-oil importer upwards to the left from AS0 to AS1 along an unchanged aggregate demand (AD) curve. This would lead to a fall in output from y0 to y1 and an increase in the price level from P0 to P1. Equilibrium would then be restored through wage adjustments. The vertical section of the AS curve indicates the long-run AS curve, which also shifts if the oil shock is large enough and permanent, while the upward sloping section indicates the short-run AS curve. Figure 1 about here The figure supports the view that a permanent oil shock is supply in nature to a net oil importer, as it affects price and output in the opposite direction. Economists suggest that oil shocks that increase international oil price can affect potential output of oil-importing countries through negative impacts on productivity, capital stock utilization, and structural employment (see for example; Estrada and Hernández (2009), Blanchard and Galí, (2010)). In the literature, an investigation of the relationship between international oil prices and the macroeconomy has taken several approaches, ranging from those with an economic theoretical backing in identifying oil shocks to those which estimate reduced-form relationships. Hamilton (1983, 2005) uses Granger causality tests and dynamic OLS to subject the relationship between output and unemployment to the data. He intimates that nine out of ten US recessions were preceded by an oil shock and uncovers a negative relationship between international oil price and US real output. The resultant relationship, however, is not causal – raising questions regarding symmetry and linearity of the effects of oil shock to output due to the instability observed in the relationship over time and to the failure of oil price decline to bring about a proportionate increase in output. In an attempt to address possible non-linearity between real output and oil price, Hamilton (2000) estimates a flexible regression and accounts for the GARCH components of the oil price. He uses exogenous disruptions in oil supply due to war as an instrument variable. His conclusion is that the relationship is non-linear and that the oil shock rather operates through the aggregate
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demand channel, based on the view that oil shocks disrupted certain spending by consumers and firms. DePratto, Deresende, and Maier (2009) estimate a general-equilibrium model for Canada, US and the U.K and find that oil prices affect the real output both in the short-run and in the long-run and that these effects are transmitted through the supply side channel. Though the direction of the effect on output found by the tripartite is in agreement with many findings such as Tatom (1987), Estrada and Hernández (2009), Khan and Ahmad (2014) and Roubini and Setser (2004), the transmission channel contradicts with the demand channel stressed by Hamilton (2000). We think, as in Blanchard and Galí (2010) that an oil shock may hit the economy with other shocks at the same time so that Hamilton’s (2000) conclusion is rather unconvincing. In the first place, he demonstrates that output falls due to an increase in international oil price, but fails to show that the price level also falls in response. Moreover, the evidence of effects on certain expenditure by firms and consumers as presented is not an outcome of the model analyzed, especially because these expenditures are not variables in any of the estimated regressions in the paper. The fall in the expenditures by firms could be due to substitution of oil-intensive with non-oil intensive capital, while the fall in expenditures by consumers could be a secondary effect resulting from a fall in employment. If this is true, then the fall in certain expenditures is not sufficient to justify the claim that oil shocks are demand in nature. Another question pertinent to analyzing the macroeconomic effects of oil shocks is whether these effects are as important today as they were in the past. Blanchard and Galí (2010) use a bivariate rolling VAR with a break in the period of analysis to answer this question for United States, Germany, France, United Kingdom, Italy and Japan. They identify an oil shock as one that has no contemporaneous effects on output or employment for any of the countries in question. Their general conclusion is that the oil shock that increase the price of oil contracts real output and employment but increases prices. They further document that the magnitudes of the impact of the oil shock on the macroeconomy has generally diminished over time on the back of improved monetary policy credibility, reduced oil intensity, and increased labor market flexibility – as in Roubini and Setser (2004). Regarding developing and emerging markets, Tang, Wu and Zhanga (2010) analyze the short-run and long-run effects of the oil shock in China. They analyze a five-variable VAR using 6|Page
zero restrictions on the impact matrix to understand the short-run effects of the oil shock. The zero restrictions to identify the oil shock are imposed in such a way that no other shock can contemporaneously affect the net change in international oil price. While the identification of an oil shock as such is reasonable in the context of China’s large economy, the mix of stationary (CPI, Net change in International oil price) and non-stationary variables (Industrial value added, real Interest rate and Investment) in the VAR all in levels raises questions about the consistency of the impulse responses. Moreover, little attempt is made to economically define the other four shocks in the estimated SVAR, leaving them to be understood as unanticipated changes in the respective variables under consideration. Since cointegration does not imply causality, the longrun impact of oil shocks is reduced-form, rather than causal, since they infer this from an estimated cointegration relationship between investment, oil price and industrial value added (a prior for output). Khan and Ahmad (2014) estimate a seven-variable SVAR based on zero restrictions on the impact matrix to study the effects of oil price shocks in Pakistan. They conclude that oil price shocks increase inflation and negatively affect output proxied by Index of industrial production. However, like in Tang, Wu, and Zhanga (2010), their identification suffers from uncertainty regarding the timing of the impacts of different shocks. Secondly, there is no explicit explanation given as to how shocks idiosyncratic to a small economy like Pakistan do affect international oil price at any time horizons. Our exploration of the literature reveals, first, two divergent views on the nature of oil shocks: one arguing that oil shocks operate through the aggregate demand channel while the other contends that oil shocks operate through the supply channel. The advantage of the methodology we pursue here is that it does not require a prior decision about whether the oil shock is demand or supply, but rather the conclusion is inferred from the results; second that the effect of the oil shock may be non-linear, which, we however do not explore; third, that the identification strategies employed by some studies suffer from lack of transparency in the sense of economic definition of some shocks and uncertainty regarding the timing of impacts. To circumvent this, our approach is focused on the long-run restrictions - which offer the advantages of transparency and clarity. The application of Granger non-causality restriction, to ensure that a small open economy does not influence the international oil price, is an added advantage to reinforce this clarity. Finally, there seems to be an agreement that the impact of oil shocks on the 7|Page
macroeconomy has diminished over time in magnitude, a dimension this study does not pursue due to data limitations. The rest of the paper is organized as follows. Section 2 gives an exploration of the data and the empirical strategy used to disentangle the oil price shock from other shocks, and Section 3 presents results from a structural VAR. The study conclusions are drawn in Section 4. 2. Trends of Uganda’s Oil Sub-sector According to the Uganda’s Ministry of Energy and Mineral Development (2012), oil provides about 10 percent of Uganda’s energy requirements – the rest is sourced from the small and underdeveloped and unreliable electricity sub-sector and the cheap biomass energy. The oil sector was also deregulated in 1994, under the broad structural reforms implemented by the Government of Uganda, which effectively eliminated oil prices subsidies. Uganda is endowed with commercially-viable oil reserves, but domestic oil production is in embryonic stages. Consequently, all of the oil-energy needs of the country are satisfied by imports.
Figure 2 depicts the evolution of the international oil price (UK Brent) measured in US dollars and Uganda’s oil imports by volume and value as a percentage of GDP, on annual basis. The value of oil imports as a percentage of GDP has more than doubled over the last 15-years, from about 1.7 percent of GDP in 2001 to about 4 percent of GDP in 2014, constituting about 20 percent of the cost of the goods imports. The recent plunge in international oil price reduced this ratio to under 3.5 percent of GDP in 2015. The noticeable rise in the oil balance as a percentage of GDP when the oil price sharply rises is an indication that oil price increases may be an important source of pressures to Uganda’s Balance of Payments, and therefore to the exchange rate. This may further suggest that oil shocks constitute sources of volatility to output and prices in Uganda.
Figure 2 about here
The volume of oil imports exhibits an increasing trend, with the rate of increase sharpening whenever the price of oil falls. This may be an indicator of increasing oil intensity in 8|Page
the production process. The sharp increase in the volume of oil imports whenever the international oil price falls could be signifying that oil shocks affect the aggregate supply and/or demand conditions of Uganda’s real economy. The production process and final consumption expenditure in Uganda are not greatly oilintensive, given the large and highly subsistence-based agricultural sector (23 percent of GDP). The oil-intensive sectors in Uganda include manufacturing, construction, transport and mining, which consume over 75 percent of the total oil supply in their production processes. These three sectors respectively consume 28.4, 21.7, 16.2 and 9.4 percent of the total oil supply. However, these sectors combined account for 18 percent of GDP. Moreover, oil and its products constitute 8 percent of total intermediate consumption. In Final consumption expenditure, oil constitutes 1.9 percent of total final consumption expenditure as reported in the 2009/10 Supply Use Table (SUT) but constitutes about 2.9 percent in the 2009/10 rebased CPI basket.
2.1 Data and Empirical Strategy The framework employed to analyze the dynamic effects of international oil prices on Uganda’s economy consists of estimating a Structural Vector Auto Regression (SVAR) model for international oil price, Uganda’s real output and the domestic price level. The methodology proceeds in three steps: first, specification and estimation of the reduced form VAR; second, identification of structural shocks; and finally, studying the SVAR model properties accomplished by impulse responses and variance decomposition analyses. The theoretical framework reviewed in the previous section, together with data properties and availability, dictates the VAR specification to be estimated in the first step.
2.2 Data and Properties
A three-variable VAR system in elements of Vector z t pto
yt
pt , is estimated, but
after establishing the order of integration of the elements of 𝒛 to ensure that the estimated system is stable. The variables in 𝒛 are observed at a quarterly frequency for the period spanning 1999Q3 to 2015Q2, the lower case indicating natural logarithmic transform. In 𝒛, variable p o is
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the real international oil price, obtained by deflating the international oil price (UK Brent) in current US dollars by US Producer Price Index (PPI). Other studies such as Tang, Wu and Zhanga (2010) and Khan and Ahmad (2014) use the real price of oil in local currency, arguing that it is the local currency real price of oil which matters in production process. In contrast, Blanchard and Galí (2010) argue that endogenous variables of exchange rate and domestic level price level used to make the necessary adjustment to obtain real price of oil in local currency can potentially contaminate the effect of the oil shock. On the other hand, Hamilton (2005) argues that using nominal or real price of oil may not constitute a significant difference, what matters is the magnitude of the change in nominal international oil price. Therefore, to strike a balance between these arguments, we used the real price in US dollars rather than in Uganda shillings. The current US dollar oil price is sourced from the International Monetary Fund (IMF) International Financial Statistics (IFS) database. The variable y t represents the real Gross Domestic Product (GDP) while p t represents the domestic price level, proxied by the consumer price index (CPI). These are obtained from the Uganda Bureau of Statistics (UBOS). The choice of the three variables is based on the object of interest and the length of the available and reliable time series in light of the need to optimize the degrees of freedom. High frequency indicators of economic activity are not available for Uganda before 1999. Besides, quarterly GDP which is only available after 1999Q2, Index of Industrial production (IOP) at higher frequency is also unavailable before this period. Though available at a monthly frequency, trade indicators become inappropriate given the larger proportion of non-traded sector, and thus low level of real openness. An investigation of the presence of seasonality in the series reveals that the three series in vector z exhibit seasonal patterns. All series are therefore adjusted for seasonality using the X-12 method by the Census Bureau of Statistics, as a preliminary step. The idea is that the analysis focuses on business cycles. This implies that performing an analysis on variables with seasonality would be incorrectly characterizing cyclical behavior and the ensuing results would be spurious (see Dejong and Dave (2007)). Another preliminary step in analyzing cyclical behavior of the variables is to characterize their long-run properties for eventual removal of trends if available. Trend removal is a difficult exercise, since the different methods of trend removal depend on whether the trend is fixed or not. When breaks are present in trends, trend removal by especially detrending introduces strong 10 | P a g e
persistence in the cyclical part of the variable, while the use of filters like Hodrick-Prescott (HP) and Band-Pass (BP) on mean reverting processes can also introduce spuriousness (Dejong and Dave 2007). For this reason, we make a graphical exposition and conduct unit root tests on the variables to uncover their patterns and order of integration. Figure 3, top panel depicts the variables in levels, while the bottom panel displays variables in first differences. Output and domestic price level do not seem to exhibit breaks in their upward trends during the period under consideration. The international price of oil on the other hand, exhibits substantial fluctuations around an increasing trend. In first differences, all variables seem to be mean reverting. This roughly provides some support for the use of differencing to remove the trends. Periods of oil shocks are visible during the periods 1999, 2002 to 2008 and 2014.
Figure 3 about here
A more formal way of characterizing the long-run properties of the variables is to conduct unit root tests. In the paper, this is accomplished using the Augmented Dickey Fuller (ADF) and Philip-Peron (PP) t-Test of the Autoregressive Spectral OLS method tests. As shown in Table 1, small sample biases notwithstanding, the tests confirm that 𝑝𝑜 , 𝑝 and 𝑦 are all
𝐼(1) in levels
but stationary in first differences.
Table 1 about here
2.3
Empirical Strategy
Following the conclusion from the unit root tests, the covariance stationary vector z is specified as
z t pto
yt
pt
with ∆ denoting the difference operator. The variables
enter z as quarter on quarter growth rates, annualized for ease of interpretation. This three variable VAR can generally be represented as
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z t c j1 R j z t j μ t ...........................................................................................................(1) p
where 𝑝 is an optimal lag selected by some information criteria to make μ t (3X1) a vector of white noise innovations in the three elements of z t . The optimal lag length selected by Akaike Information Criterion (AIC) in this case is 2 lags. Ignoring the vector of constants, c , the reduced form VAR can be written as;
R L
R 1 L1 R p Lp z t RLz t μ t .............................................................................(2) where R is a 3 3 p matrix-valued polynomial in positive powers of lag operator L . Since 0
0
z is a covariance stationary process, the Wold representation theorem ensures the existence of
at least one Vector Moving Average (VMA) representation. The reduced form VMA is one of these and can be written as; z t R( L) 1 μ t FLμ t ................................................................................................................(3) where F is a 3 3 matrix in lag polynomial L and E μ t μt I 3 but 𝑭(0) = 𝑰𝟑 .
2.3.1 Identifying Structural Shocks
In the second step, we specify a structural VMA to identify shocks in such a way that disentangles the international oil shock from all other shocks hitting the economy. Our approach follows the identification strategy developed by Blanchard and Quah (1989), later applied by Gali (1992), Clarida and Gali (1994) and extended to a panel structure by Pedroni (2013). The strategy focuses on long-run restrictions appealing to economic theory. The criticism to this strategy notwithstanding, as pointed out by Mishra, et al. (2014), this identification is appealing over Cheloski decomposition because of the transparency of the theoretical underpinnings for the restrictions made therein. Cheloski decomposition recursive assumptions are based on initial impacts of the shocks and are marred with uncertainty regarding the timing of the impacts. The structural VMA is specified as,
z t A( L)ε t ..................................................................................................................................(4) where ε t toil tAS tAD is a vector of structural disturbances, namely oil price shock,
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Uganda supply and demand shocks, respectively. 𝑨(𝑳) is a 3 × 3 matrix of polynomial lags 𝐿, which when estimated facilitates recovery of expressions of ∆𝒛 in terms of past and current structural disturbances. The first identifying restriction for the structural shocks is the assumption that the structural disturbances are orthonormal such that,
E ε t ε t' I 3 ...........................................................................................................................5
Equations (3) and (4) imply that,
z t F( L)μ t A( L)ε t .................................................................................................................(6) Equations (5) and (6) evaluated at L 0 yield the following equation A0A0 ..............................................................................................................................7
Eqn. 7 delineates the relationship between the initial impact matrix of the structural disturbances and the variance covariance matrix Ω𝜇 , which can be estimated from the reduced
ˆ T 1 μμ . form VAR by In effect, (7) yields six non-redundant equations with nine unknowns, since Ω𝜇 is symmetric. Therefore, three additional restrictions will be required to recover the structural disturbances in(4). These are imposed on the long-run (stead-state) impact matrix following a strategy developed by Blanchard and Quah (1989) and are obtained by evaluating A(L) at L 1 and relating it to the long-run impact matrix corresponding to the reduced-form VMA. Evaluating F(L) at 𝐿 = 1 while appealing to assumptions in equation (5) yields,
1 F1 F1 A1A1 1......................................................................................8 where 𝐀(1) and 𝐅(1) are the 3x3 long-run effects matrices corresponding to the structural and the reduced form shocks, respectively. Ω𝜀 (1) and Ω𝜇 (1) are the long-run variance covariance matrices, respectively. The three additional necessary identifying restrictions are compactly summarized in equation (9):
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p o * A111 0 0 oil 0 AS ......................................................................................(9) y * A211 A22 1 p * A311 A32 1 A33 1 AD
Where the asterisk refers to levels of variables in the steady state
The rationale for making these restrictions is as follows: i.
An oil price shock ( toil ), regardless of the source (demand or supply) is a global shock to a net oil importing country like Uganda. This should affect Uganda’s output in the short-run and, if permanent, should affect it in the long-run.
ii.
The AS shock ( tAS ), on the other hand, is any other aggregate supply shock hitting Uganda’s economy capable of affecting Uganda’s aggregate output in the long-run, but cannot influence the international oil price at any horizon. The AD shock ( tAD ) is defined as a shock that has no permanent effect on output (𝑦) and does not influence the international on oil price at any horizon. These two assumptions on the influence of
tAS and tAD on the international oil price follow from the small country assumption, i.e. 𝐴12 (1) = 𝐴13 (1) = 0 . iii.
AD shocks do not affect Uganda’s output in the long run, but may affect Uganda’s price level in the long run. That is to say 𝐴23 (1) = 0 .
The purpose of decomposing Uganda’s real shock into demand or supply is to be able to characterize the nature of the oil shock, whether it behaves like a demand or supply shock in the Ugandan context. This is crucial for appropriate policy prescription. The above three additional zero restrictions are enough to recover the structural shocks, but we make two more necessary over-identifying restrictions to ensure that Uganda’s aggregate supply and demand cannot influence the international oil price at any horizon. That is to say,
tAS
and tAD do not Granger cause oil price due to the small country assumption, as
summarized compactly in equation (10).
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pto . 0 0 t oil AS yt . . . t ......................................................................................................(10) pt . . . t AD
3.0 Empirical Results We will now in this section, provide structural evidence on the macroeconomic effects of an
oil shock to output and consumer prices in Uganda, the main object of this study. In addition, dynamic effects of Uganda supply and demand shocks are provided for purposes of giving a complete picture of structural identification of the shocks and uncovering the nature of oil price shock. This is accomplished by impulse response analysis to understand the size of the effect and propagation of the shocks. Variance decomposition is also conducted to harness the importance of the oil shock.
3.1 Impulse Responses Functions Figure 4 provides the estimated impulse response functions (IRFs). The vertical axis shows accumulated responses (i.e. responses for variables in levels) on annualized basis while the horizontal axis indicates the time horizon in quarters after the shock impacts. Our estimates of the IRFs seem to fit pretty well the conventional understanding of the effects of an increase in international oil price to a net-oil importing country. One of the important results revealed is that oil shocks are transmitted through the supply channel, confirming results by DePratto, Deresende, and Maier (2009), but contrary to Hamilton’s (2000) findings.
Figure 4 about here
A permanent increase in the international price of oil is associated with a fall in real output, with a maximum decline experienced two quarters after the peak of the international oil price, which occurs in the second quarter from the time the shock hits. The maximum effect of an oil shock on real output is about 5 fold the initial effect. Thereafter, the decline in output subsides until real output reaches its new lower steady state level. On the other hand, consumer 15 | P a g e
prices rise initially, persists and peaks after five quarters from when the shock occurs, with the peak effect of over 3 times the initial shock. Thereafter, the impact wanes until the price level reaches its new higher steady state level. Specifically, an oil shock that permanently increases the international oil price by 50 percent leads to a 0.7 percent fall in real output in the first quarter. The fall in output persists until it finds its bottom at 2.8 percent after four quarters. In the long run, real output is 2.2 percent lower relative to the level before the shock impacted. In growth terms, an oil shock of this size causes growth in output to fall by about 0.8 percentage points in one-year horizon from the time the shock impacts. This estimate is broadly consistent with the 0.3 to 1.0 percentage points range estimated for US and G7 GDP growth (see Roubini and Setser (2004)) Consistent with conventional wisdom, an oil shock of the same size (i.e. which leads to a 50% annualized increase in international price of oil) leads to a one percent initial increase in the level of consumer prices. The impact peaks at 3.1 percent before finding a new long-run equilibrium price level that is 2.9 percent higher relative to the price level before the impact of the shock. The magnitudes of the impacts of the oil shock estimated for Uganda are broadly in line with those estimated by Blanchard and Galí (2010) for the United States, especially before 1984. Consistent with the standard view of the dynamic effects of aggregate supply and demand on real output and price level, the estimated impulse responses fits rather very well. Aggregate supply shocks have more powerful effects on real output than does the demand shocks, while aggregate demand shocks have more powerful effects on prices. The estimated impulses responses conform to the identifying restrictions for which Uganda supply and demand do not Granger cause the international oil price. The estimates indicate that a supply shock which increases the steady state level of real output by 10 percent leads to a 3.3 percent initial reduction in the price level, with a maximum impact of 5.4 percent reduction on the price level realized after two quarters. When real output is at its new higher steady state level, the price level is 4.4 percent lower than the level before the shock impacted. Conversely, an aggregate demand shock that increases the consumer price level by 10 percent in a one-year horizon and in the long-run leads to a 1.4 percent initial short-run increase in real output. This positive impact wanes thereafter, before completely vanishing for output to return to its original steady state after ten quarters. The temporary nature of the effect 16 | P a g e
of aggregate demand shock on real output is not a surprise but a reflection of the identifying restriction constraining the demand shock not to have a permanent impact on real output.
3.2 Variance Decomposition
A statistical way of analyzing the importance of shocks to fluctuations in a given variable is by decomposing its forecast error variance to different shocks. Table 2 presents the results of the 𝑘 quarter ahead forecast error variance in output and price due to the three disturbances. By construction, all of the forecast error variance in the international oil price not shown here for brevity is entirely due to oil disturbance. This is due to the Granger non-causality restriction which imposes the small country assumption to ensure that Uganda supply and demand disturbances do not influence the international oil price at any horizon. Also the long-run identifying restrictions ensure that the contribution of Uganda demand disturbances to the variance of output decays to zero as the horizon approaches the long-run. The rest of other features of the model are unrestrained.
Table 2 about here
The estimates indicate that oil price disturbances are not an important determinant of output fluctuations in the first two quarters. Their importance, however, is marked thereafter and in the medium to long-run. Specifically, their relative contribution to the movement of output is about 13 percent four quarters ahead. This contribution increases over time, but at slower pace, and settles at about 20 percent forty quarters ahead. On the other hand, the estimates suggest a weak role of oil disturbances in explaining fluctuations in the Uganda Consumer price level in the short to long-run. Their relative contribution to domestic price movements ranges between 4 and 5 percent in the short-run and just over 6 percent in the medium to long-run. Supply disturbances idiosyncratic to Uganda, on the other hand, are revealed to be the most powerful in explaining economic fluctuations in the country. They account for between 84 and 95 percent of the variability in output in the one-year horizon, and about 80 percent in the medium- to long-run horizon. Demand disturbances are similarly important in explaining
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variability in the consumer price level, accounting for about 87 to 90 percent of the variability in the short to long-run. The results of the variance decomposition in regard to oil shock are not entirely unexpected, given the structure of Uganda’s economy. Oil and its products constitute 8 percent of total intermediate consumption and 10 percent of energy requirements. In addition, oil is crucial to electricity supply in Uganda because hydro-electricity is unreliable and insufficient. This implies little or no substitutability of oil with hydro-electric energy in production in case of adverse oil shock, which could justify the long-run 20 percent variance in output due to oil shocks. Regarding consumer prices, the small percentage of variance in consumer prices due to oil shocks is justified by the small weight of oil in the CPI basket. Oil constitutes about 1 percent in the 2009/10 rebased CPI basket, of which 0.8 percent is oil for personal transportation and 0.2 percent a source of liquefied energy at home. These numbers are not surprising given that over 75 percent of the population live in rural areas and depend mainly on wood and charcoal as a source of energy, and that rates of car ownership are generally low. Moreover, the main source of short-run volatility in the Uganda CPI is weather-related factors affecting food prices. This leaves the bulk of fluctuations in the core consumer prices (Comprising over 80 percent) explained by demand. The prominence of aggregate supply shocks is not unexpected, since the country depends entirely on nature in the largely dominant agriculture sector featuring rudimentary technology. This makes the country’s economic activity susceptible to productivity shocks in agriculture emanating from soil fertility exhaustion due to increasing population and climate change. In addition, structural reforms implemented over the last two decades, technology improvements associated with increasing foreign direct investments over the period under review and the accompanying spillovers should favor the dominant role of supply shocks in explaining fluctuations in output.
4.0 Conclusion and Extension This study analyzed the nature, magnitude of effects, and importance of oil shocks to Uganda’s macroeconomy using Structural Vector Autoregression Regression analysis (SVAR) by making long-run identifying restrictions to disentangle the oil shock from other shocks. The 18 | P a g e
results of the analysis match closely the conventional wisdom on the effects of oil shocks on output and prices to a net-oil importing economy. An increase in international oil price exerts a negative short-run and long-run effects on output and inflationary effects on consumer prices. In addition, the estimated IRFs fit pretty well the standard aggregate demand-supply analytical framework. The main results of the study are that;
Oil shocks are transmitted through the supply channel, as a shock that increases the international price of oil leads to opposite movements in real output and consumer prices in Uganda,
Oil price shocks are relatively more important in explaining fluctuations in economic activity than are movements in consumer prices. The effects can be even greater with enlargement of the oil-intensive sectors and continued low development of alternative non-oil energy sources for industries. Due to data limitations, we could not establish whether the shocks are transmitted to output through unemployment or capital obsolescence. The weak role in explaining fluctuations in consumer price is not surprising given the small weight of oil and petroleum products in the Consumer Price Index basket, a reflection of the largely poor and rural population. However, the importance of oil shocks on prices may increase with increased urbanization in the country and improvements in income levels.
These results carry some policy implications: policy makers ought to prioritize development of non-oil energy sources as a long-run policy intervention to mitigate the impact of oil shocks on economic activity. On the other hand, given the weak role of oil shocks in explaining domestic consumer price movements, monetary policy should place less weight on international oil price in the inflation equation, in the magnitude of 6 to 7 percent, as informed by the variance decomposition analysis and largely play a short-run output stabilization role in the event of oil price shocks, without compromising credibility. While the estimated model for this study has appealing properties and its results are worthwhile, the short time span considered may compromise the reliability of the inference and the ability to decompose the demand shocks further. One remedy is to extend this study to a panel SVAR, as proposed by Pedroni (2013), to improve these estimates. This could also enable extending the system to include more variables and shocks to enhance further decomposition of the shocks. Some shocks that might be important to understand in this regard are the monetary 19 | P a g e
and international food price shocks. Further studies can also employ non-linear specifications of VAR system to address asymmetry of responses.
References
Blanchard , Olivier J. , and Jordi Galí. 2010. "The Macroeconomic Effects of Oil Price Shocks: Why are the 2000s so Different from the 1970s?" Edited by Jordi Gali and Mark J. Gertler. National Bureau of Economic Research (Universityof Chicago Pres) 373 - 421. Blanchard, Olivier Jean, and Danny Quah. 1989. "The Dynmic Effects of Demand and Supply Disturbances." The American Economic Review (American Economic Association) 79 (4): 655673. Clarida, Richard, and Jordi Gali. 1994. "sources of Real exchange rate Flactuations: How important are nominal shocks." Carnegie-Rochester Conference on Public Policy (Elservier science BV) 1-56. Dejong, David N, and Chetan Dave. 2007. Structural Macroeconometrics. Princton, New Jersey: Princeton University Press. DePratto, Brian, Carlos Deresende, and Philipp Maier. 2009. "How Changes in oil prices affect the Macroeconomy." Bank of Canada Working paper 200-33 (Bank of Canada) 1-34. www.bankbanque-canada.ca. Estrada, Ángel, and Pablo de Cos Hernández . 2009. "Oil Prices and their effects on potential output." Documentos Ocasionales. N.º 0902 (Banco de España ) 1-24. Accessed 01 13, 2015. http://www.bde.es. Gali, Jordi. 1992. "How Well does IS-LM Model Fit Postwar U.S. Data?" The Quarterly Journal of Economics (MIT Press) 107 (2): 709-738. Hamilton, James D. 2005. "Oil and the Macroeconomy." dss.ucsd.edu. San Diego University of California. August 24. Accessed January 07, 2015. http://econweb.ucsd.edu/~jhamilton/JDH_palgrave_oil.pdf. Hamilton, James D. 1983. "Oil and the Macroeconomy since World War II." The Journal of Political Economy ( The University of Chicago Press) 91 (2): 228-248. http://www.jstor.org/stable/1832055. Hamilton, James D. 2000. "What is an oil shock?" NBER Working Paper Series (National Bureau of Economic Reserach) 7755: 1-59. Khan, Arshad Muhammas, and Ayaz Ahmad. 2014. "Revisiting the macroeconomic effects of oil and food price shocks to Pakistan economy: a structural vector autoregressive (SVAR) analysis." OPEC Energy Review 38 (2): 184-215. Ministry of Energy and Mineral Development. 2012. "The Energy Policy for Uganda." Policy Document, Kampala. file:///C:/Users/Snyanzi/Downloads/The%20Energy%20Policy%20for%20Uganda.pdf.
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Mishra, parachi, Peter Montiel, Peter Pedroni, and Antonio Spilimbergo. 2014. "Monetary Policy and Bank Lending rates in Low-Income Countries: Heterogeneous panel estimates." Journal of development Economics (Elservier B.V) 117-131. Pedroni, Peter. 2013. "Structural Panel VARs." Econometrics 2: 180-206. Peersman, Gert, and Ine Van Robays. 2012. "Cross-country differences in the effects of oil shocks." Energy Economics 34 (5): 1532–1547. Roubini, Nouriel, and Brad Setser. 2004. The effects of recent oil price shock on U.S. and global economy. Unpublished Draft. Accessed January 01/06/2016, 2016. http://www.stern.nyu.edu/globalmacro/. Tang, Weiqi, Libo Wu, and Zhong Xiang Zhanga. 2010. "Oilprice Shocks and their short- and long-term effects on chinese economy." Energy Economics (Elservier) xxx (xxx): xxx-xxx. Accessed 01 16, 2016. www.eleservier.com/locate/eneco. Tatom, John A. 1987. "The macroeconomic effects of the recent fall in oil prices." Federal reserve bank of St. Luis (Federal reserve bank of St. Luis) xxx (xxx): 34-45.
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Table 1: Unit root tests with trend in levels and intercept in first difference specifications ADF PP Order of Variable
Level
1st Diff
Level
1st Diff
integration
𝑝𝑜
-0.9030
-5.3758***
-1.6088
-5.5851***
𝑰(1)
𝑦
-2.3307
-2.9120**
-2.3670
-10.2677***
𝑰(1)
𝑝
-2.3299
-2.8465*
-2.3161
-8.7340***
𝑰(1)
*Significant at 10%, ** significant at 5%, *** significant at 1% Table 2: Variance decomposition of Output and Consumer price
Percentage of Variance in Output due to
Percentage of Variance in CPI due to
Horizon Oil
Uganda
Uganda
Oil
Uganda
Uganda
shock
Supply
Demand
shock
Supply
Demand
1
1.0
94.9
4.1
4.1
8.4
87.6
2
1.8
95.0
3.3
3.9
7.6
88.5
3
9.6
87.9
2.5
4.7
5.4
89.9
4
13.6
84.5
1.9
5.6
4.4
90.0
8
16.3
82.6
1.1
6.2
3.4
90.4
12
17.6
81.6
0.8
6.3
3.1
90.6
40
19.7
80.1
0.2
6.4
2.8
90.8
(Quarters)
Figure 1: The Aggregate Supply Response of Output and Price to a Shock that Permanently Increases the International Oil Price Price level
22 | P a g e
Type equation here.
Real Output
Figure 2: International Oil Price and Uganda’s Oil Imports (Volume and Value), 2002-15
4.5 340
4.0
Index 2005=100
290
3.5
240
3.0
190
2.5
140
90
2.0
40
1.5
2002
2004
2006
2008
Oil imports (% GDP, Right )
2010
2012
Int. Oil Price (Left)
2014 Volume (Left)
Source: BoU and author’s calculations
Figure 3: Trends in real GDP(Y), CPI (P) and real Oil price (RPO_USA), 1999Q3-2015Q2 Y
RPO_USA 4.8
P
9.6
5.6
9.4
5.4
9.2
5.2
4.4
5.0
9.0
4.0
4.8 8.8 4.6
3.6 8.6
4.4
8.4
3.2 2000
2002
2004
2006
2008
2010
2012
2000
2014
2002
Dif f erenced RPO_USA
2004
2006
2008
2010
2012
2014
4.2 2000
2002
Dif f erenced Y
.4
2004
2006
2008
2010
2012
2014
Dif f erenced P
.08
.08
.2
.06 .04
.0
.04
-.2
.00
.02
-.4
.00 -.04
-.6
-.02
-.8
-.08 2000
2002
2004
2006
2008
2010
2012
2014
Source: UBOS, IMF and author calculations
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-.04 2000
2002
2004
2006
2008
2010
2012
2014
2000
2002
2004
2006
2008
2010
2012
2014
Figure 4: Accumulated impulse responses (quarterly Annualized and unscaled)
Response of int. Oil price to Oil shock
Response of int. oil price to UG - Supply shock
1.00
Response of int. oil price to UG - Demand shock
1.00
67.5 0.50
0.50
0.00
0.00
-0.50
-0.50
62.5 57.5 52.5 47.5
-1.00 1
2
3
4
5
6
7
8
-1.00
9 10 11 12 13 14 15 16 17 18 19 20
1
2
3
4
Response of GDP to Oil shock
-0.5
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20
Response of GDP to UG - Supply shock
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20
Response of GDP to UG - Demand shock
1.75
8.0
-1.0
1.25 -1.5
7.0
-2.0
0.75
6.0
-2.5 0.25
5.0
-3.0 -3.5
4.0 1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20
Response of CPI to oil shock
3.5
-0.25 1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20
Response of CPI to UG - Supply shock
-1.6
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20
Response of CPI to UG - Demand shock
13
-1.8
3.0
11 -2.0
2.5
-2.2 2.0
9
-2.4 7
1.5
-2.6
1.0
-2.8 1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20
Source: Author calculations
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5 1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20
1
2
3
4
5
6
7
8
9
10 11 12 13 14 15 16 17 18 19 20
