2.3 Methodology and Data
2.3.3 Definition and Measurement of Variables
The dependent variable, loan growth is the log difference in year-end gross loan portfolio (gross loan portfolio represents total amount of all loans outstanding). All MFI-specific variables are drawn from the MIX dataset. Four MFI-level variables are used: lagged loan growth, credit risk, capital asset ratio and return on equity. In addition, the level of market concentration in the MFI sector is proxied by the Herfindahl-Hirschman index (Basega-Pascual et al., 2015; Wagner and Winkler, 2013). It is computed using the following formula; 𝐻𝐻𝐼 = ∑𝑛𝑖=1𝑆𝑖2 where 𝑆𝑖 is the market share of firm i in total n firms in the country being considered.
The effect of competition on loan growth is mixed. High competition in a saturated market adversely affects loan growth. However, higher competition can also mean higher efficiency in the delivery of loans.
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Lagged loan growth is used to capture persistence in loan growth over time or conditional convergence. Ideally, loan growth at time t contains some information from its previous values
(𝑔𝑡−1, … . 𝑔𝑡−𝑝). Due to persistence in loan growth, the value of loan growth in previous periods
is expected to predict the current level of loan growth (Lane and McQuade, 2014). The coefficient on lagged loan growth indicates the speed at which the loan growth reverts to the long-run equilibrium (Chikalipah, 2018). Literature on conditional convergence (see Fung, 2009; Asongu, 2013; Asongu and Nwachukwu, 2016) suggests that convergence is established when two criteria are met. Firstly, 𝛼1 should be statistically significant. Secondly, the absolute
value of the estimated coefficient on the lagged dependent variable should be within the interval of zero and one (0<|𝛼1|<1). However, the speed of convergence can be derived by subtracting 1 from the estimated coefficient on the lagged dependent variable (𝛼1− 1).
Credit risk is measuredas the sum of portfolio at risk and the write-off ratio (Gonzalez, 2011; Wagner and Winkler, 2013; Sinkey and Greenwalt, 1991). Portfolio at risk is the proportion of loans in the gross loan portfolio of an MFI that has been overdue for more than 30 days while the write-off ratio is the share of loans in the portfolio that are written off. Credit risk
measures the quality of an MFI’s loan portfolio and gives the probability that the MFI loan assets will suffer from default. The relationship between credit risk and loan growth is embedded in the real business cycle theory which postulates that MFIs will suffer a high default risk due to reduced household and firm earnings during a recession. In response to increased risk exposure, MFIs tend to reduce lending by raising the credit standards and lending rates of interest in order to minimize further likelihood of default. Thus, it is expected that there will be a negative relationship between credit risk and loan growth.
Capital asset ratio is the proportion of total equity in total assets.It isused to account for an MFI’sstability (Amidu, 2014). A higher capital asset ratio boosts an MFI’s solvency, meaning
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that it holds a sufficient capital buffer to support its assets. According to Mishkin (2013), a highly capitalised firm faces less risk exposure because the owners have an incentive to pursue less risky ventures. It does this by becoming more stringent in underwriting loans and monitoring them, which reduces lending growth but minimizes loan default. Therefore, it is expected that there will be an inverse relationship between the capital asset ratio and loan growth.
Return on equity is a proxy for management efficiency (Love and Ariss, 2014). It is expected that correlation between loan growth and return on equity will be either positive or negative. Efficiency in lending may lead to a decrease in loan growth in view of the lemons problem.
Return on equity can also be associated with an increase in lending if profitability is associated with an economic upswing combined with an increase in demand for credit.
This study considers three macroeconomic variables, which are drawn from the World Development Indicators database of the World Bank. These variables are GDP growth,
inflation and money supply. Following Ahlin et al. (2011) and Wagner and Winkler (2013), this study controls for GDP growth, which is measured as the annual percentage change in real GDP per capita. GDP growth captures business cycle effects. Both business cycle theory and evidence support procyclicality between economic expansion and lending growth (Hofmann, 2001; Calza et al., 2003; Njoroge and Kamau, 2010; Ahlin et al., 2011).
Inflation is measured by the annual percentage change in the consumer price index (CPI). The effect of inflation on loan growth is ambiguous. The positive effect works via two channels. The first channel is based on the Phillip curve hypothesis, which postulates an inverse relationship between inflation and unemployment. Higher inflation is associated with lower unemployment and higher capacity to service loans. The second channel works through the effect of inflation on the real value of the loan. The real value of the loan tends to fall when
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inflation is high (Wagner and Winkler, 2013). High inflation can also adversely affect loan growth. This occurs because high inflation reduces real incomes and therefore adversely affects loan servicing ability (Chaibi and Ftiti, 2014).
Money supply is broad money (M3) as a percentage of GDP. It is used to capture financial sector depth or the size of the financial sector (Wagner and Winkler, 2013). The relationship between loan growth and financial depth is mixed (Ahlin et al. 2011). Demand for microcredit may fall where financial development opens up opportunities for microentrepreneurs in formal financial institutions. Conversely, MFIs might be pushed by the developed banking sector to lend to the micro-entrepreneurs.
Two institutional variables are used in this study: regulatory quality (drawn from the World Governance Indicators of the World Bank) and ease of getting credit (drawn from Doing Business Indicators of the World Bank). It is expected that these indicators will have a positive correlation with loan growth because good institutions have been hypothesized to smoothen the functioning of factor and product markets as well as the operations of the state (McMullen et al., 2008; North, 1990; Ahlin et al. 2011). Regulatory quality is a perception index ranging from -2.5 (weak governance performance) to +2.5 (strong governance performance). It measures “the ability of the government to formulate and implement sound policies and regulations that accelerate the development of the private sector” (Indicators, 2015). Ease of getting credit is measured in terms of distance to the frontier on a 0 to 100 scale. It measures “the legal rights of borrowers and lenders with respect to secured transactions and the reporting of credit information” (Business, 2017).
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