Determinants of Bank Lending Interest Rates in Tanzania

The study seeks to examine the determinants of bank lending interest rates in Tanzania, largely focusing on identifying the key determinants and their relative importance. Techniques employed comprise interest rates decomposition and econometric estimation using banks’ annual balance sheet data. Results on interest rates decomposition suggest that, the main drivers of lending rates are operating costs, non-performing loans; and costs of funds (deposits interest rates). The three factors accounted for 70.4 percent of small banks’ average lending rates in 2014-17; while for medium and large banks, they constituted about 69.5 percent and 67.4 percent of the lending rates, respectively. Statutory minimum requirement ratio (SMR) appears to play an important role in all banks’ lending rates, but its share has been declining overtime consistent with the expansionary monetary policy measures pursued since 2014. With respect to econometric estimations, the findings confirm the role of operating costs, non-performing loans, and cost of funds in explaining bank lending rates dynamics. Operating costs, cost of funds, and inflation have a statistically significant positive effect on bank lending rates, while bank size and level of liquidity have a negative influence. SMR ratio is statistically significant but bears a negative sign except for locally owned banks. In relative importance, the main determinants of bank lending rates could be ranked as follows: inflation with an average positive impact of 0.432 on lending rates for a unit change in inflation, trailed by operating costs (0.261), and cost of funds (0.255). Bank size has the largest negative effect of 0.288 for every unit increase in the variable. The implication of the findings is that effort should be directed at improving operational efficiency aiming at reducing banks operating costs. The key areas of attention are with respect to employees’ salaries and benefits, as well as rental and depreciation expenses related to premises and equipment. Banks may consider to take advantage of ICT advancement in the country to cut on costs of “mortal and brick” and employees. Priority could be put on utilizing the growing agent banking framework, and digital How to cite this paper: Mbowe, W. E., Mrema, A., & Shayo, S. (2020). Determinants of Bank Lending Interest Rates in Tanzani. American Journal of Industrial and Business Management, 10, 1206-1236. https://doi.org/10.4236/ajibm.2020.107081 Received: May 27, 2020 Accepted: July 20, 2020 Published: July 23, 2020 Copyright © 2020 by author(s) and Scientific Research Publishing Inc. This work is licensed under the Creative Commons Attribution International License (CC BY 4.0). http://creativecommons.org/licenses/by/4.0/ Open Access


Introduction
Tanzania embarked on a series of financial reforms in the 1990s with a view to supporting the development of a market-based financial sector (Bank of Tanzania [BoT], 2011) 1 . With the reforms, the ratio of banks credit to the private sector to gross domestic product (GDP) increased from 4.1 percent in 2001 to 16.0 percent in 2016 (Mbowe, 2018). Despite the achievement, the credit level is still far below that of comparable countries in the region. In 2017 for example, the share of credit to GDP for Kenya was 29.3 percent while those of Mozambique, Namibia, and South Africa were 25.64 percent, 63.76 percent and 147.7 percent, respectively. Compared with selected regional averages, the same situation reveals as Sub-Saharan Africa had 48.3 percent. Meanwhile, the lower middle-income group to which Tanzania has graduated and the aspired middle-income group registered 43.7 percent and 99.3 percent of GDP, respectively 2 . Cihak and Podpiera (2005) attribute the limited extent of lending in Tanzania to high intermediation costs including interest rate spreads, which according to Manamba (2014), are significantly higher after the adoption of financial liberalization. As discussed in Section 2, the spreads have been much elevated since 1998 contributed by lending interest rates rigidity especially from 2003.
High interest rate spreads signal banking sector inefficiency and, when that occurs, it hampers not only financial development but also economic growth as credit to productive use is constrained due to high lending rates which are a cost to investors (Nanjunga et al., 2016). Lending interest rate to charge also matters to a commercial bank since profit banks earn-the interest income-makes a significant component of their revenues (Bhattarai, 2015;Nanjunga et al., 2016). 1 King and Levine (1993aLevine ( , 1993b, Demirguc-Kunt and Maksimovic (1998), and Levine and Zervos (1998) urge that well-functioning markets not only support economic development, but also enhance the effectiveness of monetary policy since they provide a mechanism for mobilization and allocation of financial resources. Progress has also been registered in money supply, banks assets, credit to private sector, and deposits mobilization (Figures 1-3). In absolute terms or ratios, an upward trend is evident for extended broad money supply, banks assets, credit to private sector, and deposits. This is an indication of increasing financial intermediation in the country. Credit is fairly distributed across many sectors of the economy, although dominance of personal, trade and manufacturing activities cannot be denied.
The developments have implications on interest rates primarily through the interplay of supply and demand factors. Half of banks' credit was absorbed by 4 Banks were followed by development financial institutions which held 3.0 percent of the banking sector assets; financial institutions (1.9 percent); microfinance institutions (0.6 percent); and community banks (0.3 percent). American Journal of Industrial and Business Management   the private sector largely in support of personal, trade and manufacturing activities. However, five large banks contributed nearly 52 of the total banks credit, which together with the banking sector reign, signal considerable concentration in the financial sector that may adversely affect credit supply and delivery of competitive interest rates.

Evolution of Commercial Banks Interest Rates
During the period of State control of the financial sector , credit was  directly rationed and allocated to specific sectors of the economy at preferential interest rates. Evidently, the adoption of the comprehensive economic reforms in 1986 saw interest rates rising suggesting a carry-on of the negative effects of delays in financial reforms partly related to a sustained pursuit of multiple monetary policy objectives and lack of requisite independence to discharge traditional central banking functions. With the start of comprehensive financial reforms in 1991, interest rates initially increased until when money markets were introduced in 1993/94, during which interest rates were completely liberalized. In 1995, BoT was mandated to carry out the traditional central bank role and functions, refocusing the monetary policy objectives towards the single primary objective of price stability (BoT, 1996). Here, the monetary policy is the main macroeconomic stabilization tool, largely via the money markets.
Specifically, banks' lending rates rose initially to an average rate of 36 percent in 1995 before taking a downward trend to about 17.8 percent in 2017, whereas average deposits rates edged upward to 27 percent and declined to about 10 percent in the similar period ( Figure 4). The developments notwithstanding, interest rate spreads remained much higher during reform period particularly from 1998 and were associated with high and rigid lending interest rates. Compared with other East African Community (EAC) member countries (Burundi, Kenya, Rwanda and Uganda), bank lending rate in Tanzania over ten years to May 2019 was an average of 16.03 percent, being the second lowest after Kenya's 15.61 percent 6 . However, as portrayed in Figure 5, lending rates in Tanzania exhibited an upward shift starting December 2016, while trending above those of other EAC member countries except Uganda. 6 In the same period, Uganda registered an average lending rate of 22.14 percent, Rwanda (17.05 percent) and Burundi (16.13 percent). American Journal of Industrial and Business Management   What could be explaining the observed tendency in lending interest rates in Tanzania? This is what this study endeavors to answer using banks' annual balance sheet data. To aid in answering the research questions and objectives, the literature review together with the study approach are taken up first.

Literature Review
The main theoretical underpinnings which underscore how interest rate is de-W. E. Mbowe et al. American Journal of Industrial and Business Management termined can be grouped under: the classical, loanable, and rational expectations theories. The classical approach stems from the fact that interest is the reward for the productive use of capital. Since physical capital is purchased with monetary funds, the rate of interest is taken to be the annual rate of return over money capital invested in physical capital assets. At this point, the savings investment theory is key, in which the rate of interest is determined by two forces of demand for and supply of capital. Whereas the demand for investable capital draws from investment decisions of the business sector, the supply of capital results from supplies of savings derived mainly from households (Friedman & Kuttner, 1991;Rose, 2003).
Relatedly, loanable funds theory presupposes that interest rates are determined by supply of loanable funds and demand for credit; this is an improvement on the classical theory of interest 7 . This recognizes that money can play a disturbing role in the savings and investment processes and thereby causes variations in the level of income (Peng et al., 2002). The loanable funds theory considers the rate of interest as the function of four variables: savings, investment, the desire to hoard money and supply of money.
As for the rational expectations theory, it is based on the premise that people formulate expectations based on all the information that is available in the market. Thus, the best estimation for future interest rate is the current spot rate and that changes in interest rates are primarily due to unexpected information or changes in economic factors (Irungu, 2003). Rational expectations theory has limiting factors, though: the difficulty in gathering information and understanding how the public uses its information to form expectations (Caplan, 2000).
Two theoretical approaches in modeling determinants of interest rates are worth underscoring: the monopoly model by Klein (1971) and Monti (1972), and Ho and Saunders (1981)'s dealership model. The former approach assumes a profit maximizing firm whose primary business is to offer deposits and loan services. The monopolistic power of the bank in providing credit and deposits services in the market can somehow affect the operation of the businesses. In contrast, the dealership model views a bank not as a firm but as an intermediary between firms and households. The intermediation operations lead to uncertainty in the bank resulting from lack of coordination between the deposits and credit (loans) that leads to interest rate risk. Uncertainty may also arise from default by borrowers. Since, the bank does not have full information about its customers, this increases the likelihood of default that exposes the bank to credit risk. The more the bank faces credit risk, the more it widens its interest rate spread to avoid credit risk partly by increasing the lending rate.
Some other variables have also featured in similar studies that have modeled factors influencing lending interest rates in which deposits interest rate is treated 7 According to Turnovsky (1985), loanable funds are the sums of money supplied and demanded at any time in the money market, where: funds available for lending are influenced by the savings of the people and the additions to the money supply (normally through credit creation by banks), while demand for loanable funds is determined by the need for investment plus desire for hoarding. American Journal of Industrial and Business Management as an independent variable or when the interest rates spread (the difference between lending and deposits interest rates) is instead treated as endogenous to the model. The explanatory factors can be categorized in three categories: 1) individual bank-specific factors, including operating or administrative costs, non-performing loans, return on assets, structure of the balance sheet, non-interest income or non-core revenues, bank size, and bank liquidity; 2) aspects specific to the banking industry comprising the degree of competition or market concentration, regulatory requirements such as statutory reserve requirements or regulated minimum deposit rates and; 3) macroeconomic indicators like growth rate of gross domestic product (GDP), inflation rate and taxes.
While some studies have focused on one category of the factors, others considered two or all the three categories of factors. Differences also exist in type of data and modeling techniques-i.e. time series against panel data approaches.
Using cross section and panel data, for example, studies such as Cihak (2004); Gambacorta (2008); Georgievska et al. (2011);Mbao et al. (2014) underscore the importance of bank size, liquidity, capital adequacy, foreign ownership, market share, non-performing loans, banks' costs, deposit rates, interest rate volatility, bank efficiency, credit and interest risks, and permanent changes in income in explaining lending interest rate variation. As for time series-based studies, Matemilola et al. (2015), used the momentum threshold autoregressive and asymmetric error correction models and found that bank lending rate adjusts to a decrease in the money market rate in South Africa. However, commercial banks adjust their lending rate downward but the lending rate appears rigid upward supporting the customer reaction proposition.
In Tanzania, Manamba (2014) focused on co-integration analysis using macro-level quarterly data covering 1986-2013 period and found that, interest rate

Methodology
Duo approaches are followed in this study to track the determinants of bank lending interest rates in Tanzania. First, lending interest rates are decomposed to identify contribution of specific accounting factors at the level of peer groups of banks as in Cihak and Podpiera (2005). The second technique involves econometric estimation with the lending interest rates treated as endogenous at bank-by-bank level (see for example, Cihak & Podpiera, 2005;Samahiya & Kaakunga, 2011;Ongeri, 2012;Were & Wambua, 2013;Ahokpossi, 2013;and Nanjunga et al., 2016).

Interest Rates Decomposition
Interest rates decomposition is undertaken along two main banking institutions' characteristics or groups: size (small, medium and large) and ownership structure (local and foreign banks). The asset draining components are then analyzed over 2005 to 17 due to data unavailability. The main components considered in this study are operating costs, deposits interest rate (cost of funds), non-performing loans, provision for bad debts and SMR. The variables are derived as explained in Annex 1.
A contribution of a cost component in each category of banks is computed by multiplying the weight of the average value of a component by average lending rate in a specific period as shown in Equation (1). The weight is obtained by dividing the value of the component by the sum of values of all components in a group.
where, ijt cc is contribution of component i in group j , period t ; w , weight of component i in group j , period t ; LR, average lending rate in group j , period t ; while , , , and 1 4 t = ⋅⋅⋅ , , .

Econometric Model
The starting point for panel data estimators is pooled ordinary least squares (OLS), which assumes away fixed effects or parameters (cross-section specific and time-invariant component) and non-fixed parameters, i.e. indiscriminate drawings from a certain probability distribution (random effects). If the assumption holds that the unobservable individual bank-specific effects are not very different, pooled OLS estimations is the most simple and efficient method for panel data analysis (Onuonga, 2014). This approach has been found to be inadequate, so that further estimations and tests are usually recommended with the view to accounting for fixed and random effects of the data (Greene, 2007;Cottrell & Jack, 2016). The rule of thumb is that, if the panel compares observa- tions on a fixed and relatively small set of units of interest (say, banks), there is a presumption in favor of fixed effects. If it compares observations on a large number of randomly selected individual units (in this case, banks), there is a presumption in favor of random effects. The advice is followed in this study.
In equation form, the pooled OLS may be specified as: with it Y being the observations on the dependent variable for cross-sectional unit i in period t ; it X , a vector of independent variables; and it u is an error term specific for each unit over the period. α are treated as fixed parameters, random effect model treats them as random drawings from a given probability distribution ( i v ). Therefore, fixed and random models can be written as in Equations (3) and (4): In modeling, the endogenous variable is bank lending interest rates, while explanatory variables comprise bank characteristics, industry-wide and macroeconomic factors as summarized in Annex 1. In answering the research objectives, a factor is considered to be useful in explaining movement in bank lending interest rates if it is statistically significant at the conversional test statistics (i.e. 1% or 5% or 10%) and bears the expected sign. The relative importance is evaluated basing on the magnitude of the factor coefficients or share of the factor for the case of lending rates decomposition.
Bank level annual data are employed spanning the period 2001 to 2017, mainly drawn from annual financial statements of commercial banks, community banks, microfinance institutions and development finance institutions, which were in operation during the study period. This is a population of sixty institutions some of which have information over 17 years. Separate estimations are made to account for differences across ownership structure (local banks vis-à-vis foreign banks); and size (small banks vis-à-vis medium and large banks). Share of assets to the industry's total is used to separate banks across size categories. A large bank is the one with assets market share greater than or equal to 4 percent; a medium size bank, assets share of less than 4 percent but greater than one percent; and a small bank has assets share of less than 1 percent of the industry assets. With these mixed descriptive results, further enquiry is made using lending rates decomposition. Panel data econometric estimation approach is also important to determine the causal effect of the explanatory variables on the dependent variable.

Descriptive and Correlation Statistics
W. E. Mbowe et al.

Unit Root Tests
Hadri LM test was employed to test for stationarity of all panels, with the null hypothesis (Ho): "All panels are stationary". Since the test requires strongly balanced data only tests for SMR ratio, real GDP, inflation, treasury bill rate, and market concentration indicators are reported. The results are as summarized in Table 3 and, they indicate that the variables are stationary at 1 percent level.
This information together with the fact that the remaining variables are in ratios or rates, suggest that the variables may be considered at their levels or growth rates in econometric estimation.

Decomposition Results
Here, we identify contribution of specific accounting components (     average lending rate rose to 10.9 percent from 3.5 percent in 2005-08 due to operating costs, non-performing loans, and costs of funds measured by deposits rate, which together accounted for 70.4 percent of the lending rates in this category in 2014-17. The three factors also play a great role in other categories contributing on average 69.5 percent and 67.4 percent of the lending rates in medium and large banks sub-groups, respectively. SMR ratio appears to play an important role in lending rates across bank categories, but its share has been declining overtime consistent with the expansionary monetary policy measures pursued since 2014 to spur credit growth in which SMR ratio was reduced for the first time to 7.0 percent from the long prevailing rate of 10.0 percent. The main reasons behind cost of funds could partly be due to increased banks' competition for deposits partly following tight liquidity conditions experienced by banks especially from 2016, largely due to cumulative impact of substantial decline in net foreign budgetary inflows, transfer of public institutions' deposits from commercial banks to the Bank of Tanzania and heightened expenditure management. This trend prompted for pursuance of accommodative monetary policy with a view to increase banks liquidity and support growth of credit to the private sector. Meanwhile, non-performing loans increased to 10.5 percent in June 2017 from 6.4 percent in June 2008 contributed by a combination of in-cluding global financial crises; credit screening weaknesses; a decrease in supply of loans partly contributed by factors such as liquidity tightness, and decline of effective demand for loans attributed to domestic fiscal consolidation and disciple enhancement measures; drought that adversely affected agricultural production (especially in 2015 to 2016); capital enhancement measures including adoption of capital charge for operational risk, introduction of capital buffer of 2.5 percent and anticipation of increased provision following due to adoption of IFRS 9.
The high operating costs is largely driven by costs related to employees' salaries and benefits which accounted for an average of 43.7 percent of the banking industry's operating costs in the five years to 2017 and have been increasing overtime (Table 7). Other notable costs components are rental expenses on premises and equipment, depreciation of premises and equipment, and utilities expenses, which together contributed another 16.2 percent in the banking industry operating costs. Employees' salaries and benefits costs are much higher for small banks at 44.4 percent of operating costs compared to 42.5 percent and 43.9 percent for medium size and large banks, respectively (see, Annex 3).

Econometric Results
In this sub-section, further enquiry is done covering components used in the interest rates decomposition exercise and other industry-level and macroeconomic variables. Since the decomposition of lending rates and interest rates spread yield qualitatively similar results, econometric estimations are only made with lending rates as an endogenous variable.  standard errors setting to take care of possible heteroskedasticity and autocorrelation in the data. The average results across all banks or bank categories are provided in Figure 6 and   rates on banks loans (il). The explanatory variables are: operating cost/total assets (opcr); deposits interest rates (id), a proxy of cost of funds; return on assets (ras); non-performing loans/total loans (nplr); bank size (siz); liquid assets/total assets (lqr); treasury bill rate, a measure of monetary policy rate (itbl); statutory minimum requirement (smr); assets market concentration index (HHI_AS); inflation (infl); and growth rates of real gross domestic product (rgdp) 8 .
The findings indicate that operating costs, deposit rates (cost of funds), and inflation have a statistically significant positive effect on banks' lending rates, while bank size and level of liquidity have a negative influence. Although SMR ratio is statistically significant it puzzlingly bears a negative sign, implying that an increase in SMR ratio could lead to a decline in lending rates. Thought differently, the negative sign on SMR ratio coefficient could be a reflective of lag effect of active use of the instrument particularly in the second half of 1990s to control excess liquidity in the economy partly to reduce credit risk. Looking at the econometric results, this thinking could be more relevant to foreign owned banks than local banks. This is because one percent increases in SMR ratio would be accompanied by a rise in lending rates by an average of 1.248 percent for local banks compared to a decline of 1.85 percent for foreign banks. Noteworthy, the negative effect seems to outweigh the positive effect when banks are grouped along size (Table 9).
Basing on the general model results (Table 8), and sticking to only variables which are statistically significant and bear the expected signs, the main determinants of lending rates could be ranked as follows: inflation with an average positive impact of 0.432 on lending rates for a unit increase in inflation, followed by operating costs (0.261), and deposits rate (0.255). Other factors with a positive effect are NPLs, policy rate, bank size, and market concentration. Bank size has the largest negative effect of 0.288 on lending rates ( Figure 6).
The results along banks characteristics suggest that the most important factors for local banks are increase in SMR ratio, policy rate and market concentration, which tend to influence lending rate positively, and inflation that acts in the negative direction. In contrast, foreign banks' lending rates increase with a rise in operating costs, deposits rate (cost of funds), and market concentration, while a rise in banks liquid would lead to a decline in lending rates. A growth in cost of funds, operating costs, non-performing loans and market concentration also tend to lead to increase in lending rates by small banks while improvement in liquid strength and RGDP, as well as inflation would lower the cost of loans. Operating costs, deposits rate, and market concentration likewise matter for medium size banks in lending rate increase, whereas improvement in bank's size and liquid strength tend to influence lending rates negatively. For large banks, lending rates increase by 0.943 percent due to a percent increase in operating costs, while for non-performing loans it rises by 0.232 percent; 0.009 percent (market concentration), while lending rates decrease by 2.171 percent and 0.364 percent due to increases in inflation and RGDP by one percent, respectively.
The results on Tanzania corroborate the situation revealed in some other East African Community member states. According to the study by National Bank of Rwanda of 2018, the drivers of lending rates in Rwanda are operating costs, market concentration, provisions for bad debts, and deposits rate. In Kenya, the factors are operating costs, NPLs, inflation, interest caps, and liquidity level (Central Bank of Kenya, 2018).

Conclusion and Policy Implications
This study attempts to investigate the determinants of bank lending interest Lending rates decomposition results suggest that the main drivers of bank lending rates are operating costs, non-performing loans; and costs of funds. The three factors accounted for 70.4 percent of small banks' average lending rates in 2014-17, while for medium and large banks; they constituted about 69.5 percent and 67.4 percent of the lending rates, respectively. SMR ratio appears to play an important role in all banks' lending rates, but its share has been declining overtime consistent with the expansionary monetary policy measures pursued since 2014. Econometric results point to a combination of factors that influence banks' lending rates. In particular, operating costs, cost of funds, and inflation have a statistically significant positive effect on bank lending rates, while bank size and level of liquidity have a negative influence. SMR ratio is statistically significant but bears unexpected negative sign except for locally owned banks. The negative sign on SMR ratio coefficient could reflect a lag effect of active use of the instrument particularly, in the second half of 1990s, to control excess liquidity in the economy. In relative importance, the main determinants of lending rates could be ranked as follows: inflation with an average positive impact of 0.432 on lending rates for a unit change in the variable, tailed by operating costs (0.261), and deposits rate (0.255). Other factors with a positive effect on banks lending rates are increase in NPLs, policy rate, and market concentration. Bank size has the largest negative effect of 0.288 for every unit increase in the variable. These factors are also significant but with some variation across bank categories.
The main reasons behind high deposits rates include increased banks' competition for deposits partly following tight liquidity conditions experienced by banks especially from 2016, largely due to cumulative impact of substantial decline in net foreign budgetary inflows, transfer of public institutions' deposits from commercial banks to the Bank of Tanzania and heightened expenditure management. Factors affecting non-performing loans comprise global financial crises; credit screening weaknesses; a decrease in supply of loans partly contributed by factors such as liquidity tightness, and decline of effective demand for loans ascribed to domestic fiscal consolidation and disciple enhancement measures; capital enhancement measures including adoption of capital charge for operational risk, introduction of capital buffer and anticipation of increased provision following due to adoption of IFRS 9. Meanwhile operating costs are largely driven by costs related to employees' salaries and benefits which account for an average of 43.7 percent of the banking industry's operating costs and have been increasing overtime. Other notable costs components are rental expenses on premises and equipment, depreciation of premises and equipment, and utilities expenses. Employees' salaries and benefits costs are much higher for small banks at 44.4 percent of operating costs compared to 42.5 percent and 43.9 percent for medium size and large banks, respectively. American Journal of Industrial and Business Management The implications of these findings are that banks should intensify efforts towards improving operational efficiency targeted at reducing banks operating costs particularly employees' salaries and benefits as well as rental and depreciation expenses related on premises and equipment. In this, banks may consider to take advantage of ICT advancement in the country in services provision so as to cut on costs of "mortal and brick" as well as wages. Priority could be put on utilizing the growing agent banking framework, and digital banking technology.
Prudent consolidation of small banks could as well help cut on operating costs, improving efficiency, and enhancing liquidity levels.
Meanwhile, measures need to be taken to reduce non-performing loans including through enhancing borrowers screening mechanisms aided by credit management frameworks at bank-level to reduce credit risk. Relatedly, strengthening of the regulatory and supervisory role is important, largely targeting on ensuring adequate liquidity in the banking system to square daily needs. Since SMR is not remunerated and so it is a tax on banks deposits, it is recommended to cautiously (consistent with the economy's absorption capacity) reduce SMR so as to enhance banks' lending ability thus reducing an upward pressure on lending rates. The EAC statutory reserve requirement convergence target is 5 percent by 2021, the target already attained by Burundi with a rate of 3 percent, Rwanda (5.0 percent), and Kenya (5.25 percent). Sustaining the macroeconomic stability through higher and sustainable economic growth and low and stable inflation could as well boost demand for credit and improve loan repayment capabilities, thus reducing credit risk.
This study has contributed to the literature on loanable funds and interest rate determination, largely focusing on determinants of bank lending rates and their relative importance. The study does not however claim to be exhaustive. Further empirical studies can be undertaken to evaluate in detail factors which influence operating costs at bank level, cost of funds (deposits rates), and non-performing loans.

Annex 1. Analysis Variables and Expected Signs
Variable Explanation Expected sign

Dependent
Lending rate, (il) Weighted average interest rate on banks loans. This is a price to a borrower.

Independent variables
Operating cost to total assets ratio, opcr Measures the cost of providing a loan unit by a bank and depends on the productivity of staff and other operating costs. This is the key indicator of efficiency of commercial bank so that the lower the ratio, the higher the efficiency of the commercial bank.

Positive
Cost of funds Deposit interest rate, id is use to capture the cost of funds for a bank computed as weighted average interest rate on retail deposits by each bank.

Positive
Return on assets, ras Increasing return on assets is likely to enhance bank's ability to cushion its assets against unexpected risks thus reducing lending rates.

Negative
Default risk It measures the effect on lending of a possibility of default due to a change in the financial health or condition of the borrower following normal or unexpected swings in the overall level of economic activity. Default rate on total loan and advances is proxied by non-performing loans to total loans ratio (nplr).

Positive
Bank size Computed as a ratio of bank's assets to industry total assets (siz), it captures the effect of bank's size on lending rate. As the size of a bank increases the likely that it will be able to cushion it's assets from falling following unexpected occurrences and can meet its loan obligations with less difficulties. Another candidate variable in this area is liquid assets to total assets (lqr). Liquid assets comprise vault cash, treasury bills and bonds, bills receivable, clearing account balances and claims on banks.

Negative
Bank rate (monetary policy effect) Proxied by weighted average treasury bills rate (itbl) to capture the influence of monetary policy stance on lending rate. An increase in the central bank rate will signal policy tightening to commercial banks, thus lending rate or interest rate spreads are expected to increase.

Positive
Regulatory constraints Proxied by statutory minimum requirement (smr) to capture effects of regulatory requirements on lending rate. Another variable that could explain severity of regulation is provision for bad loans as a ratio of total loans (provr).

Positive
Market concentration Market concentration (comp) approximates the level of competition in an industry, with lower market concentration resulting in higher competition thus pushing down spreads. HHI is used to measure degree of concentration, computed as the sum of squared market shares of all the firms in the market scaled from 0 to 10,000.
Negative Inflation Inflation (infl) is used as the cost of doing the business in the economy. High levels of inflation are expected to lead to high lending rates or interest rate spreads as it causes banks to charge a risk premium. Also, when the general prices of goods and services increase these lead to significant reduction in disposable income and the purchasing power of income earners. This ultimately leads to low level of savings and high rate of loan defaults, negatively affecting the financial performance of lenders.

Positive
Real GDP Growth of economic activity (rgdp) can affect lending rates by: increasing the demand for loans leading to high lending rates; and by making projects more profitable which reduces defaults and increase the deposits that further reduce interest rate spreads.