Back Matter

Back Matter

Author(s):
Tetsuya Konuki, and Mauricio Villafuerte
Published Date:
August 2016
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    Appendix I: Data Sources and Definition of Variables

    The data source for the analysis is the IMF’s 2015 Spring World Economic Outlook database, except for institutional quality and trading partners’ output volatility. The country sample comprises 43 sub-Saharan African countries: all sub-Saharan African countries excluding South Sudan and Zimbabwe. The sample period extends from 2000 to 2014 for all countries, except for Cabo Verde (2002–14), Chad (2004–14), Malawi (2002–14), Niger (2006–14), and São Tomé and Príncipe (2001–14).

    Gross domestic product (GDP): Series Nominal Gross Domestic Products (NGDP) is used for countries other than resource-rich countries. See footnote 4 for the definition of resource-rich countries and which countries are classified in that category.

    Nonresource GDP: GDP excluding value added of the commodity sector is used for resource-rich countries.

    Output gap: Difference between actual real GDP and potential real GDP in percent of potential GDP. NGDP is deflated by the GDP deflator to calculate real GDP. Potential real GDP is the trend of real GDP calculated by the Hodrick-Prescott filter. See footnote 6.

    Nonresource output gap: Difference between actual real nonresource GDP and potential nonresource real GDP in percent of nonresource potential GDP. Nominal nonresource GDP is deflated by the nonresource GDP deflator to calculate real nonresource GDP. Potential real nonresource GDP is the trend of real GDP calculated by the Hodrick-Prescott filter.

    Primary fiscal balance: Measured as government revenue excluding grants minus primary expenditure. Primary expenditure is total expenditure and net lending minus interest payments by the government.

    Nonresource primary fiscal balance: Measured as government revenue excluding grants, and commodity revenue minus primary expenditure.

    Institutional quality (IQ): The World Bank’s Worldwide Governance Indicators for the period of 1996–2012 are the data source. Chapter 4 explains how the index of IQ for each country is calculated.

    Financial depth: Measured as credit to the private sector in percent of GDP. See Chapter 4.

    Financial openness: Measured with the Chinn-Ito financial openness index (Chinn and Ito 2006), which measures a country’s degree of capital account openness. The data cover the period 2001–11.21

    Debt-to-GDP ratio: Measured as total general government debt in percent of GDP at the end of year.

    Foreign reserves coverage: Measured as the holdings of foreign exchange under the control of monetary policy (gross international reserves) at the end of the year in months of imports of goods and services in the current year.

    Terms-of-trade volatility: Measured as the variance of series TT (terms of trade, goods, and services) from 2015 Spring World Economic Outlook database during the period of 2001–13.

    Trading partners’ output volatility: Measured as the variance of an index of real GDP growth of each of the country’s five biggest trading partners, following Ilzetzki and Vegh (2008). Trade partners’ growth was weighted by the share of the country’s total exports to each of its trading partners, taken from the IMF’s Direction of Trade Statistics. Finally, each country’s weighted-trade-partner growth was deflated by the country’s average ratio of exports to GDP over the sample period to calculate the index of the real GDP growth of trading partners’ growth.

    Appendix II: Endogeneity Concern: Do Output Shocks Drive Fiscal Policy, or Do Fiscal Policy Shocks Drive Output Shocks?

    Following Ilzetzki and Vegh (2008), this paper relies on a few econometric tests to show supporting evidence for the notion that output shocks drive fiscal policy among sub-Saharan African countries.

    First, a panel GMM estimation is run to see whether the behavior of fiscal policy in sub-Saharan African countries is reacting to output shocks or causality is running from the opposite side in a following specification:

    where yi,t is the cyclical component of real output of country i in year t, gi,t is the cyclical component of real primary government spending, and β is the parameter of interest, which reflects the cyclicality of primary spending, the fiscal policy instrument of the government. Cyclical components of output and primary spending are measured as the percentage deviation from the trend calculated by Hodrick-Prescott filter. If the coefficient β turns out to be significantly positive even after properly instrumented, it will indicate that fiscal policies in sub-Saharan African countries are procyclical. The instrument for this GMM estimation is the weighted real output growth of each country’s trading partners, change in each country’s terms of trade (TOT), and change in real interest rate on six-month U.S. Treasury bills (proxy for the global liquidity condition).22

    Table A1 reports the result of this panel GMM estimation. An over-identification test does not reject the null that the instruments are valid at a conventional significance level. The coefficient on the cyclical component of real output is positive and significant at the 5 percent level. This implies that output shocks are causing fiscal policy shocks among sub-Saharan African countries even after properly instrumented.

    Table A1.Panel GMM Estimates
    VariableCoefficientt-statistics
    Constant−0.194−0.879
    Real GDP cycle1.8572.227 **
    Statistics
    Hansen’s J-statistics (p-value in brackets)3.512 [0.173]
    Number of observations (unbalanced panel)826
    Number of countries43
    Source: Authors' estimation.Notes: Estimations are performed using panel GMM with country-fixed effects.*, **, and *** indicate statistically significant at 10 percent, 5 percent, and 1 percent levels, respectively. Dependent Variable: Cyclical components of real government primary speding. Instrumented Variable: Cyclical components of real GDP.Instruments: Weighted average of real GDP growth of trading partners; Change in TOT; Change in real interest rate on 6-month U.S. Treasuries.

    Second, a Granger causality test of the cyclical components of real output and real primary fiscal spending is conducted. Table A2 reports the results. At the 5 percent significance level, the null that output shock does not Granger-cause fiscal primary spending shock can be rejected. Meanwhile, the null that fiscal primary spending shock does not Granger-cause output shock cannot be rejected at a conventional significance level.

    Table A2.Pairwise Panel Causality Tests
    Null hypothesisZbar statisticsp-value
    Real government primary spending cycle does not Granger-cause real GDP cycle−0.7620.446
    Real GDP cycle does not Granger-cause real government primary spending cycle2.0540.040 **
    Number of observations860
    Source: Authors' estimation.Notes: Tests are performed using Dumitrescu-Hurlin (2012) approach.*, **, and *** indicate statistically significant at 10%, 5%, and 1% levels, respectively.

    Those econometric tests provide evidence supporting the implicit assumption used in this paper: output shocks drive fiscal policy among sub-Saharan African countries.

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    Institutional quality would comprise the presence and effectiveness of fiscal institutions (that is, public financial management systems, fiscal rules, Sovereign Wealth Funds [SWFs]). On the latter, it is worth noting that only 20 SSA countries have a fiscal rule or SWF in place, with the majority of those corresponding to general supranational convergence criteria (IMF Fiscal Affairs Department’s Fiscal Rules and Fiscal Councils Database).

    Some authors (for example, Rigobon 2004) claim, by contrast, that fiscal policy shocks drive output and not the other way around. This reverse causality consideration might be particularly relevant in countries where (nonresource) economic activity is dominated by government spending. By contrast, other authors (for example, Ilzetzki and Vegh 2008) claim that causality goes in both directions and that the evidence on the cyclicality of fiscal policy is robust to endogeneity considerations. Appendix II provides empirical evidence to support the validity of the assumption that output shocks drive fiscal policy.

    For instance, a lower nonresource deficit in nominal terms might come hand in hand with a higher nonresource deficit-to-GDP ratio if, as a result of a decline in international resource prices, nominal GDP falls more proportionally than the nonresource deficit.

    For the purpose of this paper, resource-rich countries are the ones for which resource revenue accounted for at least 10 percent of total fiscal revenue (excluding grants). Oil-exporting countries include Angola, Cameroon, Chad, Republic of Congo, Equatorial Guinea, Gabon, and Nigeria. Mineral-exporting countries include Botswana, Guinea, Mali, Namibia, Niger, Sierra Leone, and Zambia.

    The behavior of the (capital-intensive) resource sector’s real output should be driven by fully exogenous factors, including the resource project’s life cycle, making it awkward to mix it with nonresource output in determining a “business cycle.” Furthermore, the literature (for example, Husain, Tazhibayeva, and Ter-Martirosyan 2008 and IMF 2015b) suggests the importance of resource prices (not volumes) on economic activity through government spending.

    The Hodrick-Prescott (H-P) filter was chosen because it is simple, is transparent, and continues to be the most commonly used filter in empirical studies and policy analysis. To address the endpoint problem of the H-P filter, GDP annual time series projections up to 2019 were based on the IMF’s WEO.

    This paper follows Fedelino, Ivanova, and Horton (2009) in linking the change in the cyclically adjusted (nonresource) primary balance (that is, the fiscal impulse) to changes in the (nonresource) output gap to assess the cyclicality of the fiscal response. In contrast, Alberola and Montero (2006) study the link between fiscal impulses and the level of the output gap. The authors of this paper find the former approach more appealing, in part because the estimation of the direction of changes in output gaps is arguably more reliable than the estimation of the specific level of the output gap.

    As in the previous section, the nonresource primary balance and output measures are used for mineral-resource-rich countries while overall primary balance and output measures are used for other countries.

    This paper follows the literature on the cyclical behavior of fiscal policy, which implicitly assumes that output shocks drive fiscal policy, but fiscal policy shocks could potentially drive output. Appendix II provides empirical evidence to support the validity of that implicit assumption.

    The seemingly countercyclical fiscal policy for Chad since 2005 is explained by an expansionary fiscal policy stance between 2005 and 2009 while economic growth was lagging due to civil conflict first and the global financial crisis later. A subsequent gradual tightening of fiscal policy coincided with an acceleration in economic growth. In Angola, the countercyclical stance between 2010 and 2014 was the result of a continued and gradual fiscal tightening following the global financial crisis together with a recovery in economic activity.

    See Appendix I for a detailed explanation of the data sources and how dependent, independent, and instrumental variables are calculated.

    As was done in the previous chapter, nonresource primary balance and output measures are used for mineral-resource-rich countries while overall primary balance and output measures are used for other countries.

    Total GDP is used for all countries for the denominator of the ratio of private credit to GDP.

    Nonresource real GDP is used for resource-rich countries while overall real GDP is used for other countries.

    Total GDP is used for all countries for the denominator of the ratio of public debt to GDP.

    As argued in Frankel, Vegh, and Vuletin (2013), procyclical fiscal policies could increase the chances of debt crises during busts. Turmoil typically associated with debt crises could exacerbate corruption, thus weakening the foundations of an efficient public administration.

    The statistical insignificance of the IQ index does not seem to be caused by multicollinearity among the explanatory variables. The Variance Inflation Factor (VIF) of each explanatory variable, which is a commonly used measure of the degree of multicollinearity (a higher VIF means a more serious problem), does not reveal any serious multicollinearity problem in the regressions in Tables 7 and 8; the maximum value of VIF among all explanatory variables is 2.52, well below the widely used threshold of 5 (or 10). Pair-wise correlations among the explanatory variables show that no explanatory variables are strongly correlated with each other; only IQ and financial depth are moderately correlated (r = 0.67). However, when regressions similar to Tables 7 and 8 are run with the financial depth dropped from the explanatory variables, the IQ index remains statistically insignificant; its t-ratios are far from the 10 percent significance level.

    See the IMF’s recent Botswana country report (IMF 2016).

    See IMF country reports on Chile (IMF 2013a and 2015a).

    Ossowski and others (2008) and Bova, Medas, and Poghosyan (2016) find that adoption of SFIs does not seem to reduce procyclicality in a significant way, but the quality of political institutions does matter.

    Database of Chin-Ito index was updated in 2013 is used for this paper.

    Choice of this set of instrumental variables follows Ilzetzki and Vegh (2008).

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