*Non-Parametric Regression*

The non-parametric regression estimated in the study explained efficiency scores with the help of various financial ratios and macroeconomic variables (see Table 6). A set of dummy variables representing the ownership status, financial crisis of 2007–2008, and regional banks is also included.


**Table 6.** Non-parametric regression results. GDP—gross domestic product.

Model I: country fixed effect; Model II: country fixed effect and country crises dummies; Model III: regional fixed effects (dummies). Note: In Model II, the details of the country crisis dummies are as follows: Indonesian crisis 1997–1999, Malaysian crisis 1997–1999, Philippines crisis 1997–2001, Thailand crisis 1997–2000, Brazil crisis 1999, Argentina crisis 1999–2002, Colombia crisis 1999, Turkey crisis 2001, Egypt crisis 2011–2013, Morocco crisis 2009, Czech Republic crisis 2009–2011, and Greece crisis 2009–2013.

Note that the effects of individual independent variables were tested against the null hypothesis of no effect over the entire domain of the regression function (Racine 1997). The *p*-values corresponding to the derived test statistics also referred to responses across all domains. Also, note that the non-parametric regression allows the effects of individual independent variables to vary locally. Both these considerations have to be borne in the mind when interpreting the non-parametric regression results presented in Table 6 (and in Figure 4). Table 6 reports the band width and the *p*-values for individual independent variables.

The efficiency of banks may also depend on the regulatory regime, which could vary from country to country (Barth et al. 2008).<sup>3</sup> There are some very detailed World Bank surveys that provide a rich set of indicators of the bank regulatory environments for a large number of countries. These surveys capture the various features of the regulatory regimes as they existed at specific points in time, i.e., 1999, 2002, and 2006 (ibid. p. 5). This study used a research design based on a panel of annual bank-level data from 24 emerging economies from 1999 to 2013. The integration of both datasets for use in model estimation, where the other bank characteristics were observed annually, is not a trivial matter.<sup>4</sup> However, failing to control for the regulatory regime in some way may bias the results. We tried to control for the country-specific regulatory environment by introducing country fixed effects in Model I and Model II (see Table 6).

The Model I results sugges<sup>t</sup> that bank size significantly affected efficiency with a *p*-value of 0.075, which was somewhat above the conventional threshold of 5%. The ownership status (public bank) was also highly significant. No other bank characteristic had a statistically significant effect. Among the two indicators of macroeconomic environment, inflation had a significant effect on bank efficiency and the GDP growth rate variable was insignificant. The impact of the 2007–2008 financial crisis on efficiency was highly significant.

While the financial crisis of 2007–2008 had global effects, individual countries also had financial crises whose effects were less contagious. Model II included country financial crisis dummies. For the country in question, these dummy variables took the value of 1 during the crisis period, and 0 otherwise. Model II was also estimated with country fixed effects. The results presented in Table 6 sugges<sup>t</sup> that, in addition to size and ownership status, capital adequacy was a highly significant determinant of bank efficiency. None of the macroeconomic variables had statistical significance, and the dummy variable for the 2007–2008 financial crisis had a *p*-value of 0.08, making it significant at the 10% level. Among the country financial dummies, only the dummy variables for Egypt and Thailand were statistically significant, with the latter only marginally so. The model *R*<sup>2</sup> value was only 0.48 compared to the *R*<sup>2</sup> of 0.80 for Model I.

In contrast to Models I and II, Model III did not specify country fixed effects, and instead included dummy variables for regions to capture the regional heterogeneity of efficiency scores. All regional dummies were statistically highly significant. The model *R*<sup>2</sup> was 0.78, which was comparable to that of Model I. The bank characteristics that had statistically significant effect on efficiency were found to be capital adequacy and ownership status, with *p*-values of 0.06 and <0.0001, respectively. Both variables reflecting macroeconomic conditions were statistically highly significant. The dummy variable representing the impact on bank efficiency of the global financial crisis was also highly significant.

We present the partial regression plots for Model I in Figure 4. The plots for Models II and III can be found in the Appendix A. The graphs from all plots show a high degree of similarity.

Increases in the capital adequacy ratio raised technical efficiency across the entire domain (top right panel of Figure 4). However, the imprecision of this effect increased, especially for capital adequacy ratios higher than 0.3. Several other studies found a positive relationship between capital adequacy and bank efficiency, for example, see (Gropp and Heider 2010; Kleff and Weber 2008).

<sup>3</sup> We would like to thank an anonymous referee for this point.

<sup>4</sup> The surveys contain a wealth of information that, when suitably combined with other sources of bank-level data, could offer rich possibilities for further research. We hope to explore some of these possibilities in our own future research. Models I and II that incorporated country fixed effects in an attempt to capture the individual country regulatory environment, while not fully capturing the dynamics of the regulatory regimes, were motivated by Barth et al. (2008).

Figure 4 also provides a graphical representation of the relationship between bank technical efficiency and the macroeconomic environment in which the banks operated. These graphs reveal interesting empirical regularities in different segments of the domain of the relationship. The banks operating in stagnant or contracting economies had poor efficiency scores (top panel), which were also highly volatile in that part of the domain.<sup>5</sup> As GDP growth entered positive territory, the bank efficiency score also increased. This pattern was observed to be positive at growth rates of up to about 5%. Vu and Nahm (2013) pointed out that high growth led to more savings and, hence, more deposits with the banks at a relatively low cost. For growth rates exceeding 5%, however, the technical efficiency scores became considerably more volatile and showed a somewhat declining trend.

**Figure 4.** The relationship between technical efficiency, bank characteristics (size, capital adequacy, liquidity and public bank dummy), macroeconomic indicators (GDP growth rate and inflation rate), and the financial crisis of 2007–2008.

Higher bank efficiency scores were found at low to moderate rates of inflation. Vu and Nahm (2013) also found that low inflation was associated with high levels of bank efficiency. As inflation exceeded 6%, the relationship became somewhat more volatile and declining efficiency scores were observed.<sup>6</sup> With inflation rates in excess of 10%, efficiency scores showed grea<sup>t</sup> variation and declined

<sup>5</sup> In our sample, the economies of countries such as Greece, Hungary, Czech Republic, Turkey, and UAE experienced negative growth rates and poor bank efficiency during various sub-periods.

<sup>6</sup> In our sample, the economies of countries such as Turkey, Argentina, Indonesia, Malaysia, and Pakistan experienced high inflation at various sub-periods and highly volatile technical efficiency scores for banks.

sharply. This finding is consistent with that of Batir et al. (2017) who pointed out that high inflation caused the efficiency of banks to decline.

The ownership status of banks indicates that public banks performed significantly poor at the 1% level of significance as compared to private banks. Several other studies pointed out the poor performance of public banks; for example, private banks were more efficient than public banks in Czech Republic and Poland (Weill 2003); public banks were less efficient compared to private banks in 15 East European transition countries (Fries and Taci 2005); joint-stock banks which were not owned by the governmen<sup>t</sup> were found to be more efficient than state-owned banks in China (Xiaoqing Maggie and Heffernan 2007).

The results of Model III suggested that the financial crisis of 2007–2008 caused efficiency scores to decline in emerging economies. The regional banks in East Asia, Southeast Asia, and Latin America performed significantly better with *p*-values < 0.01. The banks in these regions had a lower proportion of non-performing loans in their portfolios. In contrast, banks in the regions of South Asia and Emerging Europe performed significantly poorly with *p*-values < 0.01.<sup>7</sup> The banks in these regions had a high proportion of non-performing loans (see Table 4). This suggests that the negative impact of the crisis on bank efficiency was associated with the accumulation of non-performing loans.
