EVIDENCE OF NONLINEAR RELATIONSHIP BETWEEN NONINTEREST INCOME AND PROFITABILITY OF COMMERCIAL BANKS IN PAKISTAN
^{1}Research Scholar Karachi University Business School, University of Karachi, Pakistan
^{2}Associate Professor Karachi University Business School, University of Karachi, Pakistan
ABSTRACT
Pakistan's commercial banks are lately facing hindrance in earning substantial profits due to lowinterest rates and lowinterest margins on Government Securities which is evidently reflected in the low Earning per Share and low share prices of commercial banks. To confronting this, the banks are forced to diversify their income. The past studies show the mixed inferences about the reliance on noninterest income can be profitable for commercial banks in Pakistan's case. This research fills the gap for the existence of a nonlinear relationship between the noninterest income and profitability of banks in Pakistan. Threshold Regression Model is applied on a panel data of 13 commercial for the period 20072017. The results have shown that optimal diversification benefit can be attained by reaching to a certain level of noninterest income proportion. The findings of the study are: (1) there exist a single threshold, confirming the nonlinear relationship between the NonInterest Income ratio (NIR) and profitability (ROE). (2) The NIR impacts positively on profitability (ROE) when NIR (≤61.1%) and beyond this value i.e. NIR (>61.12%) the relationship is negative. The study can help the Pakistani banks in exploiting their maximum level of diversification and in earning large profits in unfavorable times.
Keywords:Noninterest income, Banks’ profitability, Threshold regression model, Nonlinearity, Income diversification, Panel data, Threshold effect.
JEL Classification: G21, C24, C10.
ARTICLE HISTORY: Received: 22 November 2018, Revised: 24 December 2018, Accepted: 28 January 2019, Published: 18 March 2019
Contribution/ Originality: This study investigates the nonlinearity in the relationship between NonInterest Income for banks in Pakistan and their profitability to exploit the optimal level of noninterest income ratio in order to diversify income profitably.
The banks in broader sense generate their revenues in the form of interest and noninterest income. Both of the sources are mainly altered by internal and external factors as suggested by the study of Gul et al. (2011) that both internal and external factors have significantly strong relations with the profitability of the banks in Pakistan.
Recently the lowinterest rates along with lowinterest margin on Government Securities imposed by the existing government made a dip in the interest income of the banks in Pakistan which impacted the profitability of the banks. This also reflected in the lowering earning per share and low share price of the banks in the few years. To coup up with it, banks are aiming for noninterest income more.
The financial globalization has developed so much that it is apparent for banks to benefit from the diversification of tradition business because of the competition they are confronting and to have some ground providing cushion for the credit risk. It also seems that noninterest income is more volatile and low riskadjusted than interest income. But this doesn't mean that one cannot take benefit from it if not going too far and seeing it as complementing the core income source not supplementing it. The banks seem to depend on noninterest income more when their net interest margins are low and use noninterest income as an implement of marketing by reducing fees and service charges to attract customers and remain competitive in the market. So this makes sense that maybe if there is a negative correlation or weak correlation between interest and noninterest income and the banks can benefit from relying on noninterest income as generating higher profits. The noninterest income contains (1) proceeds generated as fees and commission against the financial services and consultation, by introducing new products and services. (2) Trading revenues and (3) improve income from gains or losses of exchange and profits or losses from changes of fair value and other business. It also seems that trading revenues are very volatile and it makes noninterest income more erratic than interest income. Besides all of the doubts, there are empirical evidence (Vithyea, 2014; Senyo et al., 2015) that are suggesting that banks profitability can be enhanced by reliance on noninterest income in a period of lowinterest rates.
Since banks tend to rely more on noninterest income, their profitability would also depend upon the fluctuations in noninterests income and the factors that affect those fluctuations. This is more pertinent in view that noninterest income is more volatile. Hence increases the riskiness of the banking sector. Banks during the times of low interest margin seeks more income from shifting their activities towards generating noninterest income. But the banks can't just earn the revenue without bearing the cost of it and it is said that the return generated from noninterest activities is more than offset by the cost of it. For this, banks should be careful when it comes to investing their resources in traditional activities, it should be efficient and effective. Banks should focus more on those areas of noninterest activities that contribute to less volatility. Furthermore, banks should consider their expertise when expanding their business plus center their endeavors to be more proficient in the areas in which they are not well skilled. In case of being more diversified, banks have to be vigilant about the increased risks as expanding businesses expands the array of risks, try not to be missed out on the investment opportunities. Because losing the noninterest income would ultimately make the banks to drop on total income especially in regimes of lowinterest rates. Recently this has been observed in dropping earning per shares and share prices of commercial banks in Pakistan and reason reported is lower noninterest income along with lowinterest income.
Smith et al. (2003); De Young and Rice (2004); Stiroh and Rumble (2006); Mndeme (2015); Trivedi (2015) explicitly stated that noninterest income can't be counted on as a reliable income source and can't replace the corebusiness income for commercial banks. But lately, there are many types of research claiming that noninterest income has a productive influence on the performance of banks and helps them in reducing their risk and the banks should consider generating more income from noninterest activities (Sanya and Wolfe, 2011; Govori, 2013; Saunders et al., 2016). There are studies (Govori, 2013; Vithyea, 2014; Senyo et al., 2015) claiming that especially in the times of depression banks can find buffer against credit risk and manage to increase their liquidity by increasing their noninterest income. In Pakistani context, Ismail et al. (2015); Aslam et al. (2015) empirically proved that noninterest income can expand the growth of business, profitability, and diversification for commercial banks while the study of Raza et al. (2013); Afzal and Mirza (2012) contradicts these claim. All of the mentioned literature has inferences using linear modeling that did not take into account the limited benefit of noninterest income. They seek whether the noninterest income is profitable or not. In order to take into account the feature of limited benefit, the nonlinear approach of modeling seems to be more appropriate than linear. For the sake of it, this paper moves forward a step further and is inferring the relation between the proposed variables using the nonlinear approach of modeling. Hence, this paper explores the nonlinearity in the relationship between noninterest income and the profitability of commercial banks in Pakistan which is hardly explored in this context so far. The Nonlinear model is earlier adapted by Sun et al. (2017) on this subject. They took Hansen's Model of Panel Threshold Regression (briefly discussed in section.4) and split their data into three intervals; taking two thresholds. Then they observed the changing effect of NIR on ROE in those threshold intervals. They found the association between the two variables is found to be negative but the negative coefficients reduce after excelling each threshold. This means that the negative association weakens as the noninterest income ratio grows further. Their results still lack the evidence of limited benefit and fail to find the optimal point of benefit attained by diversifying into noninterest income. Moreover, no such endeavor was done on Pakistan. Being a developing country, Pakistan may offer new insights on this nonlinearity phenomenon. This paper allowed for the turning points in the relationship between the two variables and determining the optimum level of income diversification since most of the researchers used linear model and concluded on the basis of their results which are contrary to each other and inferred mixed views about going for the income diversification.
The objective of this research is to explore the presence of a nonlinear relationship between NonInterest Income and profitability of banks as to determine the optimal level, a commercial bank can reach in order to fully take advantage from diversifying their income. It is concluded by Ismail et al. (2015) that greater number of Pakistani commercial banks has not attained the position of utmost diversified income. Either the banks mostly focused on interest income or the noninterest income, they don't seek the balance that can get them the greater benefit in terms of profit from diversification in times of adversity. The examination of nonlinearity between the two variables caters the assumption of changing the effect of NonInterest Income proportion on the profitability of commercial banks. This paper helps the commercial banks in Pakistan to get an idea of maximum limit that they can reach to profitably diversify their income.
Since the perception of greater the noninterest income, greater the profitability of banks is not right as discussed in the above sections. This leads to the query if there is a limited benefit that can be attained from diversifying into noninterest activities then what is the optimal limit for it? What is the maximum level for the noninterest income proportion that can be exploited as an income diversification benefit? The hypotheses of the paper are, (1) there exists nonlinearity in the relationship between noninterest income ratio (NIR) and profitability of banks? If there exists then (2) what are the effects of structural change in NIR on the profitability of commercial banks in Pakistan?
In this paper Panel Threshold model is used to test nonlinearity and to estimate threshold points (points of structural change) on the data of 13 commercial banks as panels over the period of 20072017. The data is collected from the financial reports of the banks provided on their websites. All of the empirical work is derived from Stata. Return on Equity (ROE) as profitability indicator is the response variable in the model and NonInterest Income Ratio (NIR) as explanatory variable, other control variables are Capital Adequacy Ratio (CAR) and Nonperforming Loan Ratio (NPLR). Generalized Least Square Regression represented as NoThreshold model is also calculated to compare with the Threshold Regression model and observe the behavior of all explanatory variables on the dependent variable (ROE) with no threshold effect. Section 2 discusses the literature of the studies that have been established in the past in the same context. Section 3 constructs the theoretical framework of how noninterest income makes a difference in the profitability of banks. Section 4 discusses the methodologies in detail. Then the following section 5 discusses the result derived from the proposed models in the paper. Detailed discussion on results validating/contradicting past studies is presented in section 6 and then in the last conclusion and recommendations are given in section 7.
Past researches on this subject banks (Smith et al., 2003; De Young and Rice, 2004; Stiroh, 2004; Stiroh and Rumble, 2006; Mndeme, 2015; Trivedi, 2015) explicitly stated that noninterest income can't be counted on as a reliable income source and can't replace the corebusiness income for commercial banks, either because of high correlation between noninterest income and interest income or presence of greater volatility than interest income. Noninterest income can be complementary used with interest income which happens to be the core business for the commercial. Mndeme (2015) reported his study that counting on noninterest income can severely affect the banks' profitability. Some agree with the idea that although noninterest income influences the profitability in a positive manner its impact is not essentially significant for riskadjusted measures and hence considered fluctuating income source (Trivedi, De Young, and Rice). It is suggested that banks with good management tend to move slowly into noninterest income activities (De Young and Rice). But lately, there are many types of research claiming that noninterest income has a productive influence on the performance of banks and helps them in reducing their risk and the banks should consider generating more income from noninterest activities (Sanya and Wolfe, 2011; Govori, 2013; Saunders et al., 2016). There are studies (Govori, 2013; Vithyea, 2014; Senyo et al., 2015) claiming that especially in the times of depression banks can find buffer against credit risk and manage to increase their liquidity by increasing their noninterest income, found in case of Cambodia, Kosovo & Ghana respectively.
In Pakistani context, Ismail et al. (2015) and Aslam et al. (2015) empirically proved that noninterest income can expand the growth of business for commercial banks while the study of Raza et al. (2013) contradicts these claim and said that noninterest income has a significantly negative impact on profitability. Afzal and Mirza (2012) claimed that noninterest income can’t be associated with risk reduction. There has to be a balance between interest and noninterest income in order to have profitably diversified income (Ismail et al., 2015) signifying the limited benefit of income diversification. All of the above discussion establishing the sense of limited advantage from noninterest income, as sticking to core business (lending money) guarantees more stable and reliable income and assures profitability for banks and in order to seek more income sources one should be very vigilant about moving into nontraditional activities, taking it as complimentary & not counterbalancing.
This section discusses the conclusion of past studies establishing the association between NonInterest Income Ratio (NIR) and profitability (ROE) of banks for Pakistan’s case and for globally. There are mixed views about the effect of noninterest income on profitability and considering it a reliable and stable source to go for income diversification. Some literature is reported below.
Stiroh (2004) studied the linkage between on nontraditional operating income of U.S. banks and their performances at both aggregate and individual bank level and suggested prudence from increasingly relying on the noninterest income because it is more volatile and correlated with the interest income, signifying little diversification benefits from shifting towards nontraditional business activities. Davis and Tuori (2000) stated that the relation of profitability and noninterest income inclines to be positive for less restricted financial systems. Smith et al. (2003) supported the idea that income produced by noninterest activities does stabilize profits for most European countries’ banks in the years 1994 to 1998 but not for all types of banks. It is negatively correlated with interest income however more volatile and does not fully counterbalance the decrease in interest income. Furthermore, De Young and Rice (2004) took data from 1989 to 2001 of U.S. commercial banks and concluded that banks with good management move slowly into noninterest income activities because although there is an association of a marginal increase in noninterest income and higher performance there is a poor tradeoff of risk and return. Mndeme (2015) wrote in his paper that greater reliance on noninterest income unfavorably affected the performance of all the categories of banks in Tanzania. Trivedi (2015) in his article inferred that enhanced noninterest income share has an essential positive effect on profitability but not on a riskadjusted one hence does not assure the stability of returns.
However, on the other side, the investigation of Saunders et al. (2016) deduced with larger sample of U.S. banks, data from 20022013, that higher noninterest income ratio is linked with high profitability and lower risk. Sanya and Wolfe (2011) provided evidence that shifting towards noninterest income improved not only profitability but also reduced liquidity risk. Alpera and Anbarb (2011) empirically supported that noninterest income has a positive and notable impact on the profitability of banks in Turkey. Govori (2013) wrote in his paper that rates of return of Kosovo's commercial banks are directly and significantly impacted by noninterest revenues and banks of Kosovo should go for a stream of diversified income to provide some cushion for the credit risk. Vithyea (2014) investigated the contribution of noninterest income in profitability of Cambodian banks and stated that in the period of depression, for higher profit banks should focus on nontraditional activities more because taking more weight on traditional activities might make banks to suffer from default loan by more exposure of credit risk. But it is also suggested that offbalance sheet activities and fees charged against services should also be carefully observed because some banks might encounter low transaction concentration in a period of less growth. Misra (2015) paper also go with the same statement that noninterest income has a significant impact on the return on assets (ROA) and return on equity (ROE) depicting the profitability of the banks in India. Sun et al. (2017) took panel data of 16 commercial banks of China for the period 20072013 and found that the relation between noninterest income and profitability of banks has structural change. Their paper stated that crossing the two threshold values, the negative relation between noninterest income and profitability weakens and the coefficients tend to be zero and might even be positive as rising the noninterest income ratio. This supports the idea of noninterest income benefiting profitability. Gichure (2015) stated that there is an insignificant and negative relationship between noninterest income and profitability for banks of Kenya rejecting the idea of noninterest income as profitable income diversification. Ozek (2017) confirmed the positive relation of noninterest income with high profitability for commercial banks in Turkey. Chinese researchers (Sheng and Wang, 2008) believe that the increase of the noninterest income proportion can productively enhance the performance of the business for banks. Senyo et al. (2015) stated in their article that noninterest income has an additive role in the periods of lowinterest income, in the context of Ghana.
There are some studies conducted prior, establishing the relation of the two variables for Pakistan’s case. Like (Ismail et al., 2015) reported that noninterest income can profitably diversify the income for banks of Pakistan. Aslam et.al also provided evidence supporting the positive relation of noninterest income and growth of the business. On the contrary, Raza et al. (2013) stated that nontraditional business activities have a significantly negative association with the profitability of banks in Pakistan. Afzal and Mirza (2012) deduced that banks cannot benefit from the income diversification as it has no essential relationship with the risk reduction.
All of the above literature inferred using linear modeling. This paper moves forward a step further and is inferring the relation between the proposed variables using the nonlinear approach of modeling. The idea is to find the existence of nonlinear association by introducing threshold points and determining their significance then observes the impact in those changing structural points. For this, a panel of 13 banks is constructed for the period of 20072017. Panel Threshold Regression Model is applied and the results are compared with the Generalized Least Square Regression's result as the comparison of the threshold model with nothreshold model.
Following are the ways NonInterest Income Effects on the Profitability of Commercial banks.
The obvious two streams of earning revenues for banks are interest income generating activities and noninterest income generating activities. When the banks confront limitation from perceived competition and the regulatory restrictions, the banks pursue noninterest income as an augmented source. Noninterest income earns income by (1) proceeds generated as fees and commission against the financial services and investment consultation, by introducing new products and services. (2) Trading revenues and (3) from gains/losses of exchange, changes in fair value of assets and other business. It also comes with the expenses like salary for labor, marketing and administration cost. The banks need to employ teams to market new services and attract clients. All of this cost is more than what comes with the core business of the banks which is lending loans. Plus changes in market factors affect the returns from investment in bonds and in stocks. This ultimately affects the overall income for the bank. Banks then need the expertise to manage the possible risk and be prudent about market changes.
There is evidence of higher volatility induced in the noninterest income. It is said that noninterest income business has a poor tradeoff between risk and return and provides low riskadjusted returns than the interest income. All of these conclusions made ground for doubt about stabilize income. The reasons why noninterest income is volatile are illustrated by some researchers: 1) Noninterest income business does not pledge fixed assets as security and hold regulatory capital in case of loss. So this increases the risk associated with the return on noninterest activities. 2) Switching cost is much lower than that of a cost bearing with interest income as these activities do not base on the relationship. 3) The cost for generating noninterest products is mostly either fixed or quasifixed; that increases with no of labors increased rather than variable so this makes the noninterest income to bear more operating leverage than the interest income. All of these aspects are accounted for the increased volatility of the noninterest income.
Noninterest income business expansion requires investing in labor and facility which constitutes its expense, most fixed. This makes high operating leverage induced than the interest income business. When the profit for industry drops, this severely influences the profitability in an adverse direction, increasing the operating risk for noninterest income business.
Developing innovative and new products and services also contribute to the cost of noninterest income business. The new product and services should also be able to be changed along with customer needs and more types of businesses make it complex for the banks to manage properly also increased the management cost for it. Noninterest Income can move in the direction of increasing total income in the beginning but with the expansion of it, it's per unit cost also increases rather than it's per unit income so the increased operating cost leads to lower net income. It is found that banks with a relatively higher or lower ratio of interest income can perform more efficiently. Another view also holds that increased commission income to overall income can adversely affect the profitability of banks.
To take into account the possible structural change in the relationship between noninterest income and the performance of the commercial banks, the threshold points are introduced to form a specific model. This will help in capturing the effect of threshold which is the state in which the proposed variables are associated with each other in a way and after crossing that value (threshold point) the association changes into another. In this case, the association of profitability (ROE) of banks and noninterest income ratio (NIR) is to be found when NIR (≤ γ) and NIR (> γ). The data used in this paper has multiple individuals (banks) observed over multiple years so the model applied is Panel Threshold Model. The model is proposed by Hansen (1999). Hansen developed a technique that is more appropriate for threshold regression applicable on panel data. He applied his technique to investigate the changing effect of cash flows on investment for firms differing in extent of financial constraint. His theory implies that firms which are debtconstrained have cash flows positively related to their investment as they finance their investment out of their cash flow otherwise the cash flows are irrelevant to the investments because the firms then can easily borrow from the market of external financing. Also, banks are not willing to finance firms having large debts. This theory is contrary to the classical models of the firms supposing the presence of perfect financial markets which provide the needed sources for the investment projects. This problem was earlier addressed by Fazzari et al. (1988) he divided the firms into groups according to the hypothetical level of dividend to income ratio and observed whether the effect of cash flows on investments of each group of firms differs or not. They proposed that firms that pay a low dividend are retaining their earnings more because they are facing limitations from the external financial market, hence are financially constrained. Hansen (1999) pointed out two problems with Fazzari et al. (1988) model 1) assuming dividend to income ratio as an exogenous variable while it was actually an endogenous variable treated as threshold variable and might have biased the results. He used debt level instead as a threshold variable which is exogenous in nature showing the debtconstrained firms. 2) Fazzari et al. used a hypothetical value of dividend to income ratio for each group of firms rather than estimating it from the sample. Hansen estimated the values of the threshold variable through boot strapping technique. The model of Hansen (1999) has two benefits in comparison with the earlier Threshold Effect Regression Model. First, the endogenous variables and exogenous variables are not needed to be separated in this threshold regression model, so the dependency of threshold and estimated parameters only lies on endogeneity. Second, asymptotic distribution theory is proposed by this model to find the confidence interval of parameters along with the statistical significance of the threshold points, estimated by the model.
The model of this research is based on Hansen’s Panel threshold model to determine the association of noninterest income and profitability of banks. The specific single threshold model is written as follows:
yit = uit + x’it β1 + eit , qit ≤ γ(4.1.1)
yit = uit + x’it β2 + eit , qit > γ(4.1.2)
In the above equations, the letter i indicates banks, t indicates years, yit indicates dependent variable, x’it is an independent variable vector of order1. qit is the threshold variable which is, in this case, noninterest income ratio (NIR). Besides NIR the other explanatory variables are CAR (Capital Adequacy Ratio) and NPLR (NonPerforming Loan Ratio). The response variable used in this paper is ROE (Return on Equity) as the measure of profitability.
Since we are taking NIR as a threshold variable so it plays its role as a regime dependent variable and segregates the data into groups where the regime changing effect can be observed. Particularly for single threshold model, the different regimes are indicated by two states where the threshold variable is lower than the specific value and where the threshold variable is greater than the specific value.
Di (γ) is defined as a dummy variable which equals to (qi ≤ γ) or and ( ∙ ) is indicator function. This indicator function divides the data into 0 and 1. It indicates 0 when (qi ≤ γ) and indicates 1 when (qi > γ).
This makes the above equation expressed as follows:
yit = uit + x’it β+ x’it dit (γ) θ + eit , eit ~iid ( 0,σ2) (4.1.3)
In this, β = β2; θ = β1 – β2. With any estimated threshold value, we can get the model fit and find the residual sum of squares (RSS). Following Hansen, the value that minimizes the RSS we take that value as the optimal threshold.
The model for this research is proposed as follows,
ROEit = uit + β1 CARit + β2 NPLRit + β3 NIR (qit ≤ γ) +β4 NIR (qit > γ) + eit , eit ~iid ( 0,σ2) (4.1.4)
In this test, it is considered that there is no threshold, one threshold, two thresholds or triple thresholds. The F1 statistics is used to check first for no threshold against one threshold. Since the threshold value is not known so bootstrapping is used to approximate the asymptotic distribution and then the pvalue is found. If F1 is rejected in favor of one threshold then one threshold is further tested against two thresholds and so on.
Here, S0 is RSS derived under the null hypothesis. Under the second null hypothesis the maximum likelihood ratio statistics are.
Hansen provided a simple formula to calculate a rejection region. If LR1 (γ) ≤ c (α) the null hypothesis cannot be rejected where c (α) = 2lnwhere α is the significance level.
Hausman Test of endogeneity (Hausman, 1978) also illustrated as the Hausman test for specification, is used to choose between the model of fixed effect and the random effect model. This selection is grounded on the information about the presence of endogeneity in the independent variables i.e. correlation between the error term and the explanatory variables in the panel data model. The estimators of these two models (i.e. fixed effect model and random effect model) depict specific properties which are; random effect estimator is efficient, consistent and unbiased if there is no endogeneity in the explanatory variables in the panel data model (means Cov(ei, xi = 0)) and fixed effect estimator is efficient, consistent and unbiased if there is endogeneity in the explanatory variables (means Cov(ei, xi ≠ 0)). The formula for Hausman statistics is (BfeBre)'[(V_BfeV_Bre)^(1)](BfeBre). This statistics is Chisquare distributed and is critically valued against χ2 (k), where k is the degree of freedom equal to the number of factors. The null hypothesis under this test is H0 = There is no systematic difference between the estimators of the Fixed Effect Model and Random Effect Model i.e. random effect model is preferred. While the alternate hypothesis is H1 = There is a systematic difference between the estimators of the Fixed Effect Model and Random Effect Model i.e. fixed effect model is preferred. The test statistics is chisquare χ2. The decision is made on the basis of corresponding pvalue; if the pvalue is greater than 0.05 we accept the null hypothesis concluding there is no endogeneity.
Stationarity of every variable is required when panel threshold regression model is opted to use. This is because panel data has time series element besides having crosssectional. Since there is a time series component (of longer period) it is necessary to test for stationarity (means no trend, no seasonal fluctuations and constant variance over time) in order to get consistent results. For this LevinLinChu test of unit, root is used. The null hypothesis is there is unit root in the panel and the alternate hypothesis is there is no unit root in the panel. All the variables are stationary at a level as pvalue of all variables is less than α= 0.05 level of significance. This rejects the null hypothesis in favor of alternate, which is that the panels are stationary.
Table5.1. Unit Root Test for Stationarity of Variables (ROE, NIR, CAR, NPLR).
LevinLinChu unitroot test for all variables  
Ho: Panels contain unit roots  Number of panels = 13 
Panel means: Included 
Ha: Panels are stationary  Number of periods = 11 
Time trend: Not included 
AR parameter: Common  Asymptotics: N/T > 0 
ADF regressions: 1 lag 
LR variance: Bartlett kernel, 7.00 lags average (chosen by LLC)  
ROE  Statistic 
pvalue 
Unadjusted t  7.5543 

Adjusted t*  3.6839 
0.0001 
α= 0.05 

NIR  Statistic 
pvalue 
Unadjusted t  6.9305 

Adjusted t*  2.7249 
0.0032 
α= 0.05 

CAR  Statistic 
pvalue 
Unadjusted t  7.9226 

Adjusted t*  5.5652 
0 
α= 0.05 

NPLR  Statistic 
pvalue 
Unadjusted t  9.3706 

Adjusted t*  4.2239 
0 
α= 0.05 
Table5.2. Hausman Test for Endogeneity of Explanatory Variables (NIR, CAR, NPLR).
 Coefficients  

(b) 
(B) 
(bB) 
sqrt(diag(V_bV_B)) 

Fixed effect 
Random effect 
Difference 
S.E. 

NIR 
0.1842984 
0.1433904 
0.040908 
0.0213556 
CAR 
1.701292 
1.256267 
0.4450248 
0.4948553 
NPLR 
1.506597 
1.777582 
0.2709854 
0.3102926 
b = consistent under Ho and Ha; obtained from xtreg.
B = inconsistent under Ha, efficient under Ho; obtained from xtreg.
Test: Ho: difference in coefficients not systematic.
chi2(3) = (bB)'[(V_bV_B)^(1)](bB) = 5.39
Prob.>chi2 = 0.1457.
In order to avoid the bias estimates of the model, it is needed to check for endogenous variables in the model which means if there is any predictor variable whose value is determined by the other regressor in the model i.e. if the explanatory variables are correlated with the error term or not. For this, Hausman test for endogeneity is used; the null hypothesis is there is no systematic difference in the coefficients of fixed effect and the random effect and the alternate hypothesis is there is systematic difference in coefficients of fixed effect and random effect. The above results show that the pvalue 0.1457 is greater than 0.05 accepting the null hypothesis that there exists no endogeneity.
Table5.3. Test for existence of Threshold Points.
Threshold effect test (bootstrap = 1000 1000 1000): 

Threshold 
RSS 
MSE 
Fstat 
Prob 
Crit10 
Crit5 
Crit1 
Single 
5.7395 
0.0435 
60.99 
0.0000*** 
9.4299 
13.1695 
21.4899 
Double 
5.6380 
0.0427 
2.38 
0.9100 
21.4010 
25.2091 
31.9711 
Triple 
5.5812 
0.0423 
1.34 
0.9710 
22.5134 
36.5679 
208.3800 
Single threshold exist 
This test determines the existence of threshold points for NIR in the model if there does any. The estimated threshold points will be the change in structure points in the model. And the relationship of the performance of the bank and noninterest income is required to be found at those structural points to determine the diversification benefit. For this we have to consider that there might be 3 turning points, defining the number of thresholds in order to specify our model. The bootstrapping of 1000 replicates is conducted and the model estimated the threshold effects for single double and triple along with their significance presented by pvalue and Fstatistics. It can be seen that the existence of a single threshold point is significant up to 1% indicated by (***). While double and triple thresholds are not significant even at 10% level of significance (pvalue is greater than α= 0.01*** or 0.05** or 0.1*).
The estimated single threshold is 0.6112 that is the structural change is observed in the relationship between the noninterest income and performance of bank when the NIR is lower than 61.12% or greater than 61.12%.
Table5.4. Threshold Regression estimates.
Explanatory variables 
Coef. 
Std. Err. 
t 
P>t 
CAR 
1.55535 
0.5778898 
2.69 
0.008*** 
NPLR 
0.1727 
0.4136686 
0.42 
0.0677* 
NIR (≤61.12%) 
0.68598 
0.1013131 
6.77 
0.000*** 
NIR (>61.12%) 
0.561 
0.1250735 
4.49 
0.000*** 
The above result shows the different relationships of noninterest income ratio with the profitability of banks, lower and greater the threshold point. The impact of NIR is positive on the profitability of the bank until the ratio goes up to 61.12%. Above this value the relationship is negative; it means Pakistani commercial banks can strive to increase their profitability by increasing their NIR up to 61% and maintain to that bar in order to get diversification benefit from noninterest income. Increasing the proportion of noninterest income to the total income more than 61.12% can negatively impact the profitability. All the variables, except for NPLR, are highly significant at α= 1%, while NPLR is significant at 10% level of significance. CAR has a positive impact while NPLR has a negative impact on the profitability of banks indicated by ROE.
Table5.5. No Threshold Regression estimates.
ROE 
Coef. 
Std. Err. 
z 
P>z 
NIR 
0.14339 
0.089 
1.610 
0.100* 
CAR 
1.256 
0.484 
2.590 
0.009*** 
NPLR 
1.778 
0.279 
6.370 
0.000*** 
For comparison purpose the no threshold model is also estimated and the result is shown in the Table 5.5NIR and CAR are positively related with the ROE, While NPLR is negatively associated with the ROE. All the variables are significant at α= 1% and 10%, separated by *** and *.
This research is first ever effort in Pakistan to probe the differing effect of Noninterest income on profitability. Panel data of 13 listed commercial banks for the period of 20072017 is used. First, the Hausman test of endogeneity is applied to find a correlation between the explanatory variables and the error terms. Results signify no presence of endogeneity which is going with the results of Sun et al. (2017) using the same variables. Next Table 5.3 shows the significant number of thresholds. An only single threshold is appeared to be significant in the test. In Table 5.4, the results of Panel Threshold Regression are shown, considering the significance of only a single threshold model; two intervals are introduced in the model which are NIR≤61.12 and NIR>61.12. The differing coefficients in each interval indicate the changing effect of NIR on ROE. In the first interval (NIR≤61.12%), the coefficient is 0.6859 indicating the positive significant influence on the ROE. While in the second interval (NIR>61.12%) the coefficient is 0.561 showing the negative significant association of NIR and ROE. The presence of threshold is validated with the results of Sun et al. (2017). However the effect of threshold is quite different, the value of threshold indicates that banks can profitably diversify their income if they seek to increase their nonincome proportion up to 61%. Surpassing that value would not help the banks in escalating their income. The relation of noninterest income in the No Threshold model on profitability is negative as shown in the results of Raza et al. (2013); Mndeme (2015).
Other than NIR, the effect of other variables like Nonperforming Loan Ratio (NPLR) and Capital Adequacy Ratio (CAR) is observed on profitability. The effect of NPLR is appeared to be negative on the profitability in both the models (i.e. linear and nonlinear for comparison purposes) which is also agreed by the study of Sun et al. (2017); Alpera and Anbarb (2011); Saunders et al. (2018). This is expected because NPLR is asset quality indicator and it shows the proportion of defaulted loans in the total loan portfolio. Greater of that ratio increases the credit risk for the banks and hence declines the performance of the banks. The positive effect of CAR on profitability (ROE) is validated by Alpera and Anbarb (2011); Saunders et al. (2018). CAR shows the capitals strength of the banks; the capability of absorbing the adverse conditions in terms of losses and managing the risk exposure. Hence the higher ratio shows the greater performance of the banks.
The aim of this study is to find the existence of a nonlinear relationship between noninterest income ratio and profitability of commercial banks of Pakistan in order to achieve optimal diversification benefit from noninterest income; so called the nontraditional source of income. For this, the data of 13 listed commercial banks are taken for the period of 2007 to 2017. The Panel Threshold Regression model is applied to find the significant no. of threshold points (change in structural points) and then the change in the relationship between NIR (NonInterest Income Ratio) and profitability indicated by ROE (Return on Equity) is observed at those points.
It appears that single threshold exists, confirming the hypothesis of the presence of a nonlinear relationship between noninterest income and profitability in case of Pakistan's commercial banks. The model estimated the threshold value of 0.6112 and the threshold effect is analyzed at two intervals i.e. lower than 61.12% and higher than 61.12%. It is found that the impact of NIR (<61.12%) on ROE is significantly positive and it changes to negative as the noninterest ratio goes up the 61.12% i.e. NIR (>61.21%). This means that optimal diversification benefit from noninterest income can be attained if the ratio of noninterest income to total income is escalated up to and maintained at the estimated percentage of around 61%. Above this value, the banks can impair the profitability as it would be too much deviation from the traditional operations of banks, resulting in volatility of returns and increased chances of default.
For comparison sake, the nothreshold model is also estimated using Generalized Least Square Regression (GLS). ROE has a significant and positive association with NIR and CAR which also competes with the past studies; establishing the relation between noninterest income ratio and profitability for Pakistan's case. The negative association is observed between ROE and NPLR as it is also expected that defaulted loans result in ceased interest payments which decrease interest income and ultimately affect the overall profitability of banks. The research suggests that banks can get maximum benefit from a nontraditional source of income if they strive to take the proportion of their noninterest income up to a certain level especially in regimes of lowinterest income when the lowinterest margins can substantially affect the profitability. The banks should come up with more innovative ways in terms of financial products and services to generate noninterest income and place their resources efficiently taking in consideration their proficiency and vigilantly supervise to reduce the risk induced with it.
Funding: This study received no specific financial support. 
Competing Interests: The authors declare that they have no competing interests. 
Contributors/Acknowledgement: Both authors contributed equally to the conception and design of the study. 
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