Journal Issue
Share
Chapter

4. What Explains the Degree of Fiscal Policy Procyclicality among Sub-Saharan African Countries?

Author(s):
Tetsuya Konuki, and Mauricio Villafuerte
Published Date:
August 2016
Share
  • ShareShare
Show Summary Details

This chapter examines factors that may influence the way fiscal policy has been conducted over the business cycle—that is, the degree of fiscal policy procyclicality—in each of the sample sub-Saharan African countries.11 As for the measure of the degree of fiscal procyclicality (dependent variable) of each sample country during 2001–14, two sets of country-by-country OLS regressions are run, similar to the panel regressions in the previous chapter:

  • With cyclically adjusted (nonresource) primary balance in percent of (nonresource) GDP as the dependent variable and (nonresource) output gap and constant as explanatory variables
  • With (nonresource) primary balance in percent of (nonresource) GDP as a dependent variable and (nonresource) real GDP growth and constant as explanatory ones12

As discussed in the previous chapter, the estimated value of the coefficient on output gap (growth) represents the degree of fiscal procyclicality: the lower (higher) this coefficient is, the more (less) procyclical the fiscal policy has been over the business cycle.

Following Frankel, Vegh, and Vuletin (2013), this analysis looks into four sets of explanatory variables aimed at capturing alternative theories regarding the cyclicality of fiscal policy.

First, it includes institutional quality (IQ) of each country in the set of explanatory variables. Many researchers have pointed to the importance of IQ in determining various aspects of fiscal policy. Frankel, Vegh, and Vuletin (2013) find that IQ plays a key role in explaining the cyclical behavior of fiscal policy among advanced and emerging market countries. An index of IQ is constructed by calculating the average of six normalized variables from the World Bank’s Worldwide Governance Indicators: voice and accountability, political stability and absence of violence/terrorism, government effectiveness, regulatory quality, rule of law, and control of corruption. The IQ index ranges between −2.5 (lowest institutional quality) and +2.5 (highest institutional quality). Period average of IQ index during 2002–12 for each sample country is used as one of the explanatory variables.

Second, the analysis controls for the degree of financial depth and openness. Lack of access to credit markets in bad times would inevitably leave governments with no choice but to cut spending and/or raise taxes. Caballero and Krishnamurthy (2004) have argued that lack of financial depth could limit a country’s ability to borrow domestically and run countercyclical fiscal policies. They provide empirical support for this viewpoint using data of advanced and emerging market economies. In the same spirit, many researchers, including Gavin and Perotti (1997), have pointed out that limited access to international capital markets, particularly during bad times, may limit the ability of government to conduct countercyclical macroeconomic policies. Financial depth is measured here using the ratio of credit to private sector to GDP (average during 2001–13)13 following Caballero and Krishnamurthy (2004), and financial openness using the Chin-Ito (2006) financial openness index (average during 2001–11) following Frankel, Vegh, and Vuletin (2013).

Third, the analysis control for the volatility of tax revenue, proxied by output volatility. Talvi and Vegh (2005) argue that the larger the revenue volatility, the more procyclical fiscal policy will be, as policymakers try to reduce fiscal surpluses in good times in the presence of political distortions. Output volatility is measured here by using the variance of the cyclical component of (nonresource) real GDP during 2001–13.14

Fourth, two economic vulnerability indicators are also included: the public-debt-to-GDP ratio15 and gross international reserves (GIR) in months of imports of goods and services (average during 2001–13). As Frankel, Vegh, and Vuletin (2013) point out, low debt-to-GDP ratios and ample GIR may contribute to reduce a country’s default risk and provide room to run countercyclical fiscal policy.

This analysis addresses potential endogeneity problems in all of the explanatory variables. Countercyclical (procyclical) fiscal policies that tend to stabilize (destabilize) the economy might improve (worsen) institutional quality; the causality may run from cyclical behavior of fiscal policy (dependent variable) to IQ (independent variable).16 Similar arguments could be made regarding the endogeneity of other explanatory variables; for example, procyclical policies could raise the debt-to-GDP ratio and lower the GIR import coverage. These endogeneity concerns are addressed by instrumenting all of the explanatory variables. Except for output volatility, instrumenting is done by lagged values IQ by its average during 1996–2000, financial depth by its value at 2000, financial openness by its average during 1991–2000, debt-to-GDP ratio by its value at 2000, and GIR import coverage by its value at 2000. As for the output volatility, it is instrumented by each country’s terms-of-trade volatility during 2001–13 and its trading partners’ output volatility during 2001–13 and during 1991–2000, following Ilzetzki and Vegh (2008).

Cross-country Generalized Method of Moments (GMM) regressions are run with two alternative measures of the degree of fiscal policy procyclicality as the dependent, and a set of explanatory and instrumental variables explained above. Tables 7 and 8 report the results, with the sensitivity of cyclically adjusted (nonresource) primary balance in percent of (nonresource) GDP to (nonresource) output gap used as the dependent variable in Table 7, and the sensitivity of (nonresource) primary balance in percent of (nonresource) GDP to (nonresource) real GDP growth as the dependent variable in Table 8. In both tables, the Hansen’s over-identification test cannot reject the null hypothesis that instruments are valid at a conventional significance level. After properly correcting for endogeneity of all explanatory variables, the coefficient on financial depth is positive (expected sign) and significant at the 5 percent level in Table 7, while the coefficient on GIR import coverage is positive (expected sign) and significant at the 1 percent level in Table 8. All other variables are not significant at a conventional significance level in these tables.

Table 7.Cross-Country GMM to Identify Determinants of Degree of Procyclicality (Dependent Variable: Degree of Fiscal Cyclicality Measured As The Sensitivity of Cyclically Adjusted Primary Balance to Output Gap)
VariableCoefficientt-statistics
Constant−1.258−1.394
IQ average during 2001-2013−1.079−1.489
Financial depth during 2001-20130.0312.043 **
Financial openness during 2001-20130.0420.214
Output volatility during 2001-2013 1/−0.010−0.596
Debt-GDP ratio during 2001-2013−0.003−0.797
Foreign reserves coverage during 2001-2013−0.014−0.228
Statistics
Hansen's J-statistics (p-value in brackets)4.522 [0.104]
Number of countries43
Source: Authors' estimation.Notes: *, **, and *** indicate statistically significant at 10 percent, 5 percent, and 1 percent levels, respectively. Instruments: IQ average during 1996-2000; Financial depth at 2000; Financial openness at 2000; Variance of Terms-of-Trade during 2001-2013; Trading partners’ real GDP variance during 1991-2000; Trading partners’ real GDP varianceduring 2001-2013; Debt-GDP ratio at 2000; and Foreign reserves coverage at 2000.

Volatility of nonresource GDP for resource-rich countries, volatility of total GDP for others.

Source: Authors' estimation.Notes: *, **, and *** indicate statistically significant at 10 percent, 5 percent, and 1 percent levels, respectively. Instruments: IQ average during 1996-2000; Financial depth at 2000; Financial openness at 2000; Variance of Terms-of-Trade during 2001-2013; Trading partners’ real GDP variance during 1991-2000; Trading partners’ real GDP varianceduring 2001-2013; Debt-GDP ratio at 2000; and Foreign reserves coverage at 2000.

Volatility of nonresource GDP for resource-rich countries, volatility of total GDP for others.

Table 8.Cross-Country GMM to Identify Determinants of Degree of Procyclicality (Dependent Variable: Degree of Fiscal Cyclicality Measured As The Sensitivity of Primary Balance to Real GDP Growth)
VariableCoefficientt-statisticst-statistics
Constant−0.458−1.178
IQ average during 2001-20130.0190.079
Financial depth during 2001-20130.0070.835
Financial openness during 2001-20130.0600.666
Output volatility during 2001-2013 1/0.0060.495
Debt-GDP ratio during 2001-2013−0.001−0.454
Foreign reserves coverage during 2001-20130.0632.738 ***
Statistics
Hansen’s J-statistics (p-value in brackets)1.085 [0.581]
Number of countries43
Source: Authors' estimation.Notes: *, **, and *** indicate statistically significant at 10 percent, 5 percent, and 1 percent levels, respectively. Instruments: Same as those in Table 7.

Volatility of nonresource GDP for resource-rich countries, volatility of total GDP for others.

Source: Authors' estimation.Notes: *, **, and *** indicate statistically significant at 10 percent, 5 percent, and 1 percent levels, respectively. Instruments: Same as those in Table 7.

Volatility of nonresource GDP for resource-rich countries, volatility of total GDP for others.

Three points are worth noting. First, more financial depth significantly helps reduce the degree of fiscal policy procyclicality among sub-Saharan African countries: there is a causal link running from deeper financial markets to less procyclical fiscal policy. In other words, lack of financial depth constraints a country to follow the standard Keynesian prescription to pursue expansionary fiscal policies during downturns. This is consistent with the empirical findings by Caballero and Krishnamurthy (2004) with advanced and emerging market economies. Second, ample GIR holdings significantly help reduce the degree of fiscal policy procyclicality. Third, contrary to the findings by Frankel, Vegh, and Vuletin (2013) with advanced and emerging markets, the difference in institutional quality would not play a significant role in explaining the difference in the cyclical behavior of fiscal policy among sub-Saharan African countries. This is likely due to the fact that there is little difference in the value of the IQ index among most of the sub-Saharan African countries.17

Some policy implications for sub-Saharan African countries could be derived from the first two points discussed above:

  • Progress in deepening financial markets could help countries enhance macroeconomic stability—allowing for less procyclical fiscal policies to mitigate boom–bust cycles—as well as enhance medium-term growth prospects.
  • Ample international reserves coverage may reduce default risks of sub-Saharan African countries and allow them to run less procyclical fiscal policies (that is, with no need for abrupt and massive fiscal consolidation in downturns). This is consistent with the textbook policy recommendation to build up reserves buffers to reduce vulnerabilities.
  • At the same time, to promote financial depth and build up reserves buffers, countries should accumulate financial savings for rainy days (through overall fiscal surpluses) on the basis of part of revenue windfalls in good times.
  • Although the regressions above fail to find a statistically significant role of institutional quality in reducing the cyclicality of fiscal policy, existing literature and the country case studies in the following chapter would suggest that strong institutions and political commitment help successfully implement fiscal policy aimed at smoothing spending and saving for rainy days.

The next chapter looks into three country case studies regarding the authorities’ efforts to establish and implement fiscal frameworks to save for rainy days, in light of the policy implications derived from the empirical analysis above.

    Other Resources Citing This Publication