However, Indonesia has the largest number of small credit banks (1,619 rural banks as of 2017) in the world after China, India and the United States. Some of the previous studies such as Alessandrini et al. 2009) argue that the distance between banks' lending branches and local borrowers as well as internal distance between a local branch and bank headquarters in the local credit market is a significant factor for lending transactions. As a result, banks earn a larger market share, leading to higher concentration in the market.
In the case of Euclidean distance, consumers are assumed to be spread across the market area and the distance between banks represents an appropriate measure of the average distance to a given consumer (Richards et al., 2008). The identification of determinants of bank profitability is important because of the role of banks in the economy. A study by Maudos and de Guevara (2004) based on the Lerner Index showed that the power of the banking market has a positive effect on interest margins in the banking sector of the European Union.
Rural banks in Indonesia
Rural banks operate in rural communities mostly to serve the interests of local people and SMEs. The Indonesian Deposit Insurance Corporation (IDIC) guarantees deposits at a maximum of 8.25% for rural banks and 5.75% for commercial banks. The third party funds in rural banks are mostly in the form of time deposits, which constitute 68.9% of total deposits in rural banks.
In 2017, rural banks charged an average interest rate of more than 20% per annum, while the average interest rate of commercial banks was around 11% per annum.
Competition measures
- Concentration measures
- Lerner Index
- Panzar and Rosse H-statistic
- Boone Indicator
There are four zones based on the economic potential and level of banking competition in a region. Furthermore, the last indicator is the Boone Indicator (Boone, 2008), which assumes that more efficient firms will have higher performance and attract larger market shares. CRk measures the market share of the top k banks in the industry (aggregating only the market shares of the largest k banks in the market).
It is the sum of the elasticities of the reduced-form income equation with respect to all input prices.
Hypothesis Development
Problems associated with this model have Bikker et al. 2012), who argue that the Panzar-Rosse approach is not very informative about the degree of competition in long-run equilibrium and is likely to give ambiguous conclusions about the level of market competition. The Boone indicator assumes that more efficient banks (banks with lower marginal costs) have higher profits or performance and attract larger market shares (Berger & De Young, 1997). The estimate of is expected to be negative, but may be positive in empirical implementation (as more efficient, less marginal cost).
Marginal costs cannot be observed directly, so the translog cost function is used to derive marginal cost. The Boone indicator is calculated on bank-level data as the percentage change in profits or market share due to a one percent change in marginal costs (Boone and van Leuvensteijn, 2010; van Leuvensteijn et al., 2011). This indicator can reflect market dynamics and is easily applied to a limited number of observations.
Capital positively affects bank performance as higher levels of capital may indicate banks with riskier assets. Lending positively affects bank performance, however it is procyclical and banks with higher profits will lend more generously. Diversification into non-interest income is associated with poorer bank profitability and negatively affects performance.
Liquidity, which is usually associated with lower rates of return, thus negatively affects the bank's performance. Gross domestic product (GDP) is expected to have a positive impact on bank operations as it encourages investment.
Data and Methodology 1 Data and Sample
Variables
ROA is chosen as a profitability indicator due to the fact that it has proven to be a key indicator for assessing bank profitability (Athanasoglou et al., 2008). The results are loans, other profitable assets and non-interest income. translog formulation originally proposed in Christensen et al. 1973) is the most widely used functional form in bank competition studies. Many previous studies have used the ratio of personnel costs to total assets, where the number of employees is often not available.
Thus, in this study, the cost of labor is calculated as the ratio of personal and administrative costs to total assets because labor is likely to be a much more important component for banks engaged in non-traditional industries (Titotto, D., & Ongena, S., 2017). The ratio of total depreciation expense to fixed assets as the cost of physical capital. With regard to the price of funds, the cost of borrowed funds is assumed to correspond to the ratio of interest expenses to total deposits including deposits from other financial institutions.
Spatial competition is measured by physical distance from bank to nearest rival bank and the market boundary area of each bank in a province. The distance is calculated as the physical distance using the Haversine formula in meters between the location of the bank and its nearest rival bank in a province by assigning longitude and latitude coordinates. Lerner index = The Lerner index is defined as the difference between a bank's price and the marginal cost divided by the price.
However, liquid assets are usually associated with lower rates of return, and thus a negative relationship between this variable and profitability is generally expected. Inflation is expected to negatively affect bank efficiency due to the fact that under inflationary conditions banks may feel less pressure to control their inputs and therefore become less efficient.
Model and Methodology
The Lerner index is the difference between the price of total assets (the ratio of total revenue to total assets) and the marginal cost of total assets divided by the price of total assets (Maudos & Fernandez de Guevara, 2007; Delis & Tsionas, 2009). ; Koetter et al., 2012). Banks with a larger spread between price and marginal cost could be considered to have a greater degree of monopoly power. Pit is the ratio of total income (interest and non-interest income) to total assets for bank i at time t following the example in Carbó et al.
After the estimation results were obtained, MC was calculated by differentiating the total costs. This indicator is based on the efficiency hypothesis developed by Demsetz (1973) and shows the strength of the relationship between efficient banks (measured in terms of their marginal costs) and performance. The Boone (2008) Indicator aims to capture the impact of competition on bank performance.
Intuitively, the profitability of banks with lower marginal costs (higher efficiency) is expected to increase, which means that β should be negative. Lower market power (more competition) means that the value of β is higher (more negative) in absolute terms, so β serves as a constant indicator of market power. Marginal costs are unobservable, so the translog cost function was obtained to estimate marginal costs.
Second, I investigate how banking competition affects bank efficiency
This paper chose the translog cost function, which reflects the interaction between explanatory variables and explained variables. Where TC is defined as the total cost; Pi is the vector of input prices; Qi is a vector of variable outputs. After the cost efficiency of each bank is obtained, I estimated the effect of spatial competition and other factors (bank-specific characteristics, industry-specific characteristics and macroeconomic variables) on bank efficiency using panel data regression model.
Following Batesse and Coelli (1995), the error is distributed as zero truncations of the distribution, with the mean assigned as , where is a vector of variances affecting efficiency and is the vector containing the parameters to be estimated. This study includes quasi-fixed net assets of each bank (N1), which represents a proxy of physical capital. Therefore, the inefficiency, , is obtained by truncation (at zero) of the normal distribution with mean,.
Results
Variables and selected descriptive statistics a. Descriptive Statistics
This table contains summary statistics for the variables used in the analysis of the effect of spatial competition on bank profitability and efficiency. This number is slightly higher than the average ROA for all rural banks in Indonesia in 2017 which is 2.45%. ROE and NIM of rural banks in Indonesia are high due to their high interest rate characteristics.
The Effect of Spatial Competition on Bank Profitability
The effect of bank-specific characteristics on bank profitability in these three models largely supports the hypothesis. This result is consistent with a study by Smirlock (1985), which found a positive and significant relationship between bank size and profitability. Lending, expressed as the loan to asset ratio, shows positive ROE and NIM.
The negative signals mean that this has a negative impact on banks' profitability, as the more diversified banks push banks to set lower margins. The macroeconomic variables, GDP and inflation, have a positive and significant influence on bank profitability in all models. Claessens et al., 2001; Schwaiger & Liebig, 2008) have also shown a positive and significant relationship between inflation and/or GDP and profitability.
This table presents the regression estimates of the effect of spatial competition on bank profitability (ROA, ROE and NIM).
The Effect of Spatial Competition on Bank Efficiency
The negative coefficient of the exogenous variable in the regression indicates that banks with larger values of the variables tend to have a lower level of inefficiency (they are more efficient).
Conclusion
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