Preliminary results

The preliminary estimation is carried out using fixed effects estimation to control for time-invariant country effects. As mentioned above, the corruption indices are ordinal and not cardinal. We therefore created three dummy variables by grouping the countries into high, medium and low levels of corruption. Countries on the TI index with corruption levels of 3 and below are coded as ‘highly corrupt’, those with corruption levels of above 3 and below 7 are coded as having ‘medium’ levels of corruption, and those with corruption levels of 7 and above are treated as having ‘low’ levels of corruption. Similarly, on the ICRG index, countries with corruption levels of 2 and below are coded as ‘highly corrupt’, those with corruption levels of above 2 and below 5 are coded as having ‘medium’ levels of corruption, and those with corruption levels of 5 and above are treated as having ‘low’ levels of corruption. Countries with ‘low’ levels of corruption are the benchmark groups. The results are reported in Table 2.

Table 2 Fixed effects estimation with dummy variables Full size table

Control variables are included for per capita income and human capital to account for the level of development of the country, government expenditure to GDP is entered because high public spending can strain the financial system of a country and countries for which the rule of law is weak are likely to be more corrupt. With the exception of three variables (corruption, rule of law indices and secondary school enrollment shares), the variables have been transformed logarithmically.

The coefficients on the dummy variables for countries with ‘high’ and ‘medium’ levels of corruption are negative and statistically significant. The results in Panel A indicate that relative to the group with ‘low’ corruption, countries with ‘high’ and ‘medium’ corruption experience less financial sector development. In column (1), for example, private credit in the ‘high’ corruption group is 5% lower than in the ‘low’ corruption group, and private credit is 2% lower in the group with ‘medium’ levels of corruption than in the ‘low’ corruption group. The larger negative coefficient on the ‘high’ corruption group relative to the group with ‘medium’ levels of corruption, suggests that private-sector credit availability increases as a country becomes less corrupt. Similarly, the marginal effect of corruption on M2 and liquid liabilities is larger for the ‘high’ corruption group than in the group with ‘medium’ levels of corruption, indicating that as a country becomes less corrupt, the financial sector size variables increase. Taking a look at the coefficients on the interest rate spread and non-performing loans, the larger positive coefficients on the ‘high’ corruption group relative to the group with ‘medium’ levels of corruption suggest that interest rate spreads and non-performing loans decline as a country becomes less corrupt. Those results provide evidence in favor of the ‘sand the wheels’ hypothesis. Similar results are obtained in Panel B for the estimations using the ICRG index, providing additional support for the ‘sand the wheels’ hypothesis.

Taking a look at the control variables, the results indicate that a country’s level of development as measured by per capita income and secondary school enrollment rates and a stronger rule of law have positive and significant effects on financial sector development. Higher per capita incomes, educational attainment and closer adherence to the rule of law lead to increases in private credit availability, M2 and liquid liabilities, as well as to narrower interest rate spreads and fewer non-performing loans, suggesting that higher levels of economic development, greater financial literacy and a stronger rule of law all are associated with larger financial sector size and greater efficiency. More government spending leads to an increase in the supply of money, liquid liabilities and private credit, implying that larger governments (relative to GDP) leads to an increase in financial sector size, but not efficiency. This is not an unreasonable result, as the continued reliance of the government on state owned banks can lead to a lack of competitiveness, and rising overhead costs and interest margins (Cooray 2011). Given that the results are qualitatively similar using both corruption indices, we report results only using the TI index in subsequent estimation.

Next, we test our hypothesis that the effects on the financial sector depend on governance quality by interacting the ‘rule of law’ variable from Kaufmann et al. (2012) with the TI corruption index, as in Méon and Sekkat (2005), Méon and Weill (2010) and Aidt (2009).Footnote 8 If the sand the wheels hypothesis holds, corruption will have a negative effect on financial sector development when the quality of governance is low. If, on the other hand, the grease the wheels hypothesis holds, corruption will have a positive effect on financial sector development when the quality of governance is low. We also include state ownership of banks and interact that variable with the corruption index, as direct government intervention into the financial sector can lead to banking system inefficiency. The results are reported in Table 3.

Table 3 Fixed effects estimation with dummy variables and interaction terms Full size table

The coefficients on the corruption dummy variables are statistically significant. Consistent with the results reported in Table 2, compared to the group of countries with ‘low’ corruption, countries with ‘high’ and ‘medium’ corruption experience lower levels of financial sector development. In column (2), for instance, M2, which measures the magnitude and depth of the financial sector, in the ‘high’ corruption group is 4% less than in the ‘low’ corruption group; and M2 is 2% lower the group with ‘medium’ levels of corruption than in the ‘low’ corruption group. Once again, the larger negative coefficient on the ‘high’ corruption group compared to the ‘medium’ corruption group suggests that, as a country becomes less corrupt, it experiences a higher level of financial sector development. The same result is found for private credit and liquid liabilities. Similarly, the coefficients on the interest rate spread and non-performing loans are larger in the ‘high’ corruption group than in the ‘low’ corruption group. While larger interest rate spreads and more non-performing loans are observed in the ‘medium’ corruption group than in the ‘low corruption group, the coefficients are smaller than those for the ‘high’ corruption group, suggesting that as the level of corruption falls, non-performing loans and interest rate spreads also fall.

The interaction between corruption and rule of law is negative for the financial sector size variables and positive for the financial sector efficiency variables for both groups of countries. The negative marginal effects on the financial sector size variables and positive marginal effects on the financial sector efficiency variables for the ‘high’ corruption group, however, are larger than in the ‘medium’ corruption group, suggesting that as corruption declines, institutions have a stronger positive effect on financial sector development. That conclusion is supported by the marginal effects on the ‘high’ corruption group, which range from 0.006 for private credit to 0.001 for M2, while the marginal effects on the ‘medium’ corruption group are 0.008 for private credit and 0.002 for M2. The interaction between corruption and government ownership of banks suggests that in the ‘high’ corruption group, direct government regulation of banks has a negative effect on financial sector development, while in the group with ‘medium’ levels of corruption, the coefficients are not statistically significant. Per capita income, the rule of law and human capital have positive and significant effects on financial sector development; government expenditure has a positive and significant effect on the financial sector size variables. Government ownership of banks has a negative effect on the financial sector size variables.

Robustness checks

Table 4 reports system GMM estimates. We repeat the estimation in Table 3 using system GMM.

Table 4 System GMM estimation with dummy variables and interaction terms Full size table

The results are similar to the results reported above under the fixed effects estimation, suggesting support for the sand the wheels hypothesis. Once again, the coefficients on the corruption dummy variables are statistically significant. The countries with ‘high’ and ‘medium’ levels of corruption experience lower levels of financial sector development than those with ‘low’ levels of corruption. The larger negative marginal effect in the ‘high’ corruption group compared to the group with ‘medium’ levels of corruption suggests that as a country becomes less corrupt, it experiences higher levels of financial sector development.

The interactions between corruption and rule of law are negative for financial sector size in both groups of countries and negative for financial sector efficiency. The larger negative marginal effect in the ‘high’ corruption group suggests that as the rule of law strengthens, the negative effect of corruption on the financial sector starts decreasing. The interaction between corruption and government ownership of banks indicates that direct government regulation of banks has a negative effect on financial sector development in the ‘high’ corruption group, while it has no effect on the ‘medium’ corruption group. Per capita income, the rule of law and human capital have positive and significant effects on financial sector development, and government expenditure has a positive significant effect on financial sector size variables. Government ownership of banks has a negative effect on financial sector size.

Bun and Windmeijer (2010) showed that equations estimated in levels can suffer from a weak instrument problem when the variance ratio of the individual fixed effects to the errors is large. In the presence of weak instruments, the estimators are biased and the results are inaccurate. We perform the Cragg–Donald Wald F test and the Kleibergen (2005) Lagrange multiplier (LM)Footnote 9 test in order to see if our instruments are weak. From the underidentification test, we can conclude that the excluded instruments are relevant. Newey and Windmeijer (2009) showed that these test statistics are robust to weak instrument asymptotics. The Hansen test and the serial correlation test confirm that the moment conditions cannot be rejected.

To account for regional factors that may affect the relationship between corruption and financial sector development, we group the countries on the basis of their geographical locations. The regions are classified according to the World Bank: Latin America and the Caribbean, Eastern Europe and Central Asia, Middle East and North Africa, South Asia, East Asia and the Pacific. The results are reported in Table 5.

Table 5 System GMM estimation with regional dummies Full size table

Consistent with the previous results, the interaction between corruption and rule of law is negative for all countries. The coefficient on the ‘high’ corruption group is larger than that for the ‘medium’ corruption group, suggesting that the negative effect of corruption on the financial sector starts weakening as the rule of law strengthens. The interaction between corruption and government ownership of banks suggests that in the ‘high’ corruption group, direct government regulation of banks has a negative effect on financial sector development. The results on the other control variables are similar to those obtained above.

The estimated regional coefficients indicate that in Africa the financial sector size variables are significant and smaller than in the benchmark group, Eastern Europe and Central Asia, and that non-performing loans and the interest rate spread are significantly larger than in the base group. Evidence shows that the banking systems in Africa are concentrated and in general inefficient at financial intermediation, which explains the lack of depth and larger spreads and margins of these banks (European Investment Bank 2013). In the Middle East and North Africa, the estimated coefficients on the financial sector size variables are significantly larger than in Eastern Europe and Central Asia, and the coefficients on non-performing loans and the interest rate spread are smaller than in the base group. In Latin American and the Caribbean, the coefficients on money supply and non-performing loans are positive and significant, suggesting that both are larger than in the benchmark group. In South Asia, the coefficient on money supply is positive and significant, while in East Asia and the Pacific the coefficients on non-performing loans and the interest rate spread are negative and significant. Higher non-performing loans in Central Asia and Eastern Europe compared to East Asia and the Pacific and the Middle East and North Africa, could be due to unemployment and inflation associated with economic transition.

Additional robustness checks

As discussed by Nunn and Puga (2012), it is possible that the results reported herein are driven by outliers. We test for that possibility by excluding outlying observations from the sample. We first estimate the models using OLS and obtain Cook’s D for each observation. We then drop the observations with Cook’s distance exceeding 1. The iteration process begins by calculating weights based on absolute residuals. The iteration stops when the maximum change in weights from one iteration to the next is below tolerance.Footnote 10 Two types of weights, the Huber weight and biweights are employed. In Huber weighting, observations with small residuals receive a weight of 1 and larger residuals are assigned lesser weights. With biweighting, all cases with a non-zero residual are down-weighted. The models are reestimated by dropping the most influential points and down-weighting the large absolute residuals (Bruin 2006). The basic conclusions do not change.

Next, based on the general to specific work of Hoover and Perez (1999) and Hendry and Krolzig (2005), we use the ‘genspec’ command put forward by Clarke (2013) in STATA, which permits running a series of regressions and settling at our final model. We first made sure that the general unrestricted model (GUM) did not suffer from any diagnostic problems on the basis of heteroscedasticity, non-normality and incorrect functional form. No mis-specification was observed in the GUM and this reduced to the final model, which was checked by a Doornik–Hansen test for normality of errors, the Breusch–Pagan test for homoscedasticity of errors, the RESET test for the linearity of coefficients and an in-sample and out-of-sample stability F-test for normality of errors. The models pass these diagnostic tests.

Owing to problems associated with the TI and ICRG corruption indices associated with changing methodologies and inter-year variation, along with reliance on different underlying sources for constructing the indices (Triesman 2007), we also estimated the models using the Kaufmann et al. (2012) Corruption Index (results not reported). The basic conclusions do not change.

We also carried out the estimation for countries with weak institutions, i.e., countries ranking 6 or below on the polity index. The coefficients on the interaction terms are consistent with the results reported in previous tables. The positively signed coefficients on per capital income, and secondary school enrollment ratio suggest that all have positive effects on financial sector development, while government expenditure negatively impact such development.