Data source and sample

The MFI data used in this study were extracted from the Microfinance-Information eXchange Database (MIX), which is accessible through the World Bank’s data catalogue.Footnote 1 The MIX market is a global, web-based information platform that provides accounting information from MFIs [16]. It is the main source of microfinance data used in many microfinance studies [7, 10, 15, 16, 49]. The researcher also uses data collected by hand from each MFI’s website, which is not included in the Mix Market database. Macroeconomic indicators such as gross national income and inflation data come from the World Development Indicators database.Footnote 2

At the time of data collection, the MIX Market contained the accounting data of nearly 3,237 MFIs worldwide from 1999 to 2019, but no more data will be collected from the platform in the last year of 2019 (December). On the other hand, not all MFIs reported the complete data to MIX and some important variables were missing [14]. Therefore, some adjustments are needed to obtain the complete information. These include excluding MFIs that lack information on balance sheet items and performance, and not including the incomplete last year of 2019. After these adjustments, the final balanced dataset consists of 106 MFIs from 25 SSA countries over the period from 2014 to 2018 (see Table 4 in the Appendix).

Variables and measurement

Financial performance

Empirical researchers are agreed in viewing profit or value creation from two perspectives: Accounting perspectives and Market perspectives, each of these presents its own unique challenges. However, there is no generally accepted best/unique method of measuring the financial performance of MFIs [29].

The financial performance of MFIs has been widely studied for its relation with various determinants. In their book, Armendáriz and Morduch [5] identify five financial ratios that are commonly used to measure the financial performance of MFIs. These are operational self-sufficiency (OSS) ratio, financial self-sufficiency (FSS) ratio, return on asset (ROA) ratio, the portfolio at risk (PAR 30 days) ratio and portfolio yield ratio. The return on asset is a conventional measure of financial performance, and it measures how well the MFI uses all its resource (assets) to generate income [5]. The OSS measures the ability of MFIs in generating operating revenue to cover its operating costs, while the FSS consider additional adjustments to operating revenue and costs. The FSS measures how well the institution can cover its cost without ongoing subsidy [5, 16].

The study uses the accounting-based measure of financial performance, return on asset (ROA) for two reasons. (1) The study examines the quality of asset management and the funding capacity for generating income. (2) it is commonly used in the microfinance literature [13, 15, 16, 19, 20, 24, 49]. In banking literature, the return on asset ratio is also widely used to measure the financial performance of banks, along with asset and liability management indicators [3, 8, 42, 45]. In terms of ROA formula, the study uses the MIX market formula as it uses data from the MIX market database. Accordingly,

$${text{ROA}} left(text{%}right)text{=}frac{text{Net operating income} , – , text{income taxes}}{text{Average Assets}}$$

Asset and liability management

This study uses the statistical cost accounting (SCA) models based on the description of [30]. The rates of return on assets are positive and vary across assets, while the rates of cost on liabilities are negative and also vary across liabilities. The statistical cost accounting (SCA) model is widely used in the banking literature to measure asset-liability management of financial institutions [8, 32, 41,42,43,44,45]. Moreover, the asset-liability management principles applied in commercial banks are similar to those applied in non-profit microfinance institutions [12]. Therefore, in this study, asset management is represented by cash and cash equivalents, net loan portfolios, net fixed assets and other assets,liability management is also represented by deposits, borrowings, other liabilities and other current financial liabilities. The study uses the MIX market definition for each asset and liability account as shown in Table 1 (below).

Control variables

As for the firm-specific variable, this study uses the size of the MFI (represented by the natural logarithm of total assets) as the control variable. This is because MFI size controls for the effects of differences in technology, investment opportunities and economies of scale across microfinance institutions [20]. The evidence for this result is mixed. Larger MFIs achieve better financial performance (ROA) [16, 20, 36]. However, Hartarska [25] found an insignificant influence.

As the study focuses on Sub-Saharan Africa, the macroeconomic environment there may also influence MFI performance [2]. Therefore, in this study, macroeconomic conditions are represented by gross national income per capital (GNIPC) and inflation. This is because these indicators are commonly used in microfinance research [2], Vanroose and D’Espallier [18, 51]. Theoretically, the overall effect of macroeconomic conditions on MFI performance is unclear. On the one hand, a growing GNI rate creates investment opportunities and technological progress that make small entrepreneurs more profitable. As a result, the loan repayment performance of MFI borrowers will improve, which has a positive impact on the financial performance of MFIs. On the other hand, higher GNI growth may enable micro entrepreneurs to finance themselves and push them to look for new financial institutions such as banks, which has a negative impact on MFI financial performance.

Ahlin et al. [2] found both a positive and a negative effect of the macroeconomic environment on the financial performance of MFIs. Others, such as Vanroose and D’Espallier (2009), observed a negative and significant relationship between gross growth rate and MFI return on assets. In contrast, Xu et al. [53] find that the macroeconomic environment has a positive impact on MFIs’ financial performance. Empirical evidence on the relationship between inflation and financial performance is also mixed. Vanroose and D’Espallier [51] and Cull et al. [18] find a negative relationship between inflation and MFI returns. In contrast, a study by Hartarska and Nadolnyak [26] contradicts this result. They found a significant and positive effect of inflation on the operational self-sufficiency of MFIs. However, Cull et al. [17] also find no evidence of the impact of inflation on MFI performance.

Model ALM is used as an independent variable in the study. Therefore, SCA is applied to measure ALM as described in Eq. (1).

$${text{Y}}_{{{text{lt}}}} {text{ = }}alpha_{1} {text{ + }}Sigma alpha_{{{text{2i}}}} {text{A}}_{{{text{ilt}}}} {text{ + }}Sigma alpha_{{{text{3j}}}} {text{L}}_{{{text{jlt}}}} {text{ + e}}_{{{text{lt}}}}$$



({varvec{Y}}) denotes the net income of the MFI; ({{varvec{A}}}_{{varvec{i}}}) represents the ith asset, i = 1, 2, 3,… m, whereas; ({{varvec{L}}}_{{varvec{j}}})denotes the jth liability, j = 1, 2, 3, … n; ({varvec{l}}) is the number of microfinance institutions, l = 1, 2, 3… k; ({varvec{t}}) is the period of time, t = 1, 2, 3,… T; ({{varvec{delta}}}_{2{varvec{i}}}) represents the rates of return and shows the variations in the MFI’s performance by replacing one unit of cash with one unit of the ith asset and is expected to be positive; ({{varvec{delta}}}_{3{varvec{j}}}) indicates the rate of cost of liabilities and indicates the changes in the MFI’s profit by adding one unit of cash and one unit of jth liability and is expected to be negative; ({{varvec{delta}}}_{1}) represents a constant term, and ({{varvec{e}}}_{{varvec{l}}{varvec{t}}}) denotes a stochastic (error) term.

In Eq. (2), all ALM variables are divided by average total assets to avoid inefficiency in the estimation of coefficients associated with heteroscedasticity [30, 32]. Thus, the appropriate fixed effect model written as follows,Footnote 3

$$begin{gathered} ROA_{it} = beta_{0i } + beta_{ } A1_{it} + beta_{2 } A2_{it} + beta_{3 } A3_{it} + beta_{4 } A4_{it} + beta_{5 } L1_{it} + beta_{6 } L2_{it} + beta_{7 } L3_{it} + beta_{8 } L4_{it} + beta_{9 } LogTA_{it} + beta_{10 } GNICP_{it} + beta_{11 } INF_{it} + e_{it} hfill \ end{gathered}$$



represents the return on asset of MFI
at year
({{varvec{beta}}}_{1boldsymbol{ },2,3,4})
represents the rates of return on earning asset and shows the variations in the performance of MFI;
indicates the rate of cost of liabilities and indicates the changes in the ROA;
({{varvec{beta}}}_{9},boldsymbol{ }{{varvec{beta}}}_{10boldsymbol{ }},boldsymbol{ }{{varvec{beta}}}_{11})
are coefficients of natural logarithms of total assets (LogTA), GNI per capita growth (annual %) (GNICP) and Inflation, consumer prices (annual %) (INF) respectively;
denotes individual MFI;
refers time; and
denotes the error term.

The numerical data collected in this study analysed quantitatively using both descriptive and inferential analysis of statistical tools. The study run Hausman specification tests to make choice between random effect model and fixed effect model [27]. The Hausman test result depicts that the P-value (Prob > Chi2 = 0.0000) is statistically significant at the 0.01 level. Therefore, the result rejected the null hypothesis and confirms that the fixed effect model is appropriate than random effect model to get efficient and consistent parameter estimates in the regression. Further, the fixed effect model widely used in the asset and liability management studies [32, 41, 43].

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