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Dividend policy as a multi-purpose mechanism; the case of conventional and Islamic banks before and after the 2008 crisis.

1. Introduction

Conventional and Islamic banks differ in the components of their capital structures. Conventional banks (CBs) receive deposits and treat them as liabilities. In this case, the depositor is a lender, and the bank is the borrower bearing all the risks. Therefore, to avoid interest payment default, CBs strike a balance in their asset-liability management. Islamic banks (IBs), on the other hand, receive deposits under profit and loss sharing agreements. According to these agreements, depositors are considered partners, implying a possible loss of part, or even all, of the amount deposited without legal obligation on the bank. Because clients bear the risks, IBs are tempted to take more risks with these deposits for the reward of making higher returns. Therefore, IBs presumably enjoy more profitability during times of economic prosperity. Conversely, at times of economic downturns they struggle more for recovery.

With this background, there is reason to believe that CBs and IBs differ in the way they set their financial policies including, among others, the dividend policy. There is also reason to believe that they responded differently to the recent global financial crisis that started in 2008. To provide evidence of these beliefs, the determinants of dividend policy of the two bank types were investigated before and during the time of the crisis. The paper addresses several important questions. What are the determinants of dividend policy for CBs and IBs before and after the crisis? Did their purposes of paying cash dividends change in response to the crisis? If they did, what are they? And why did they change?

The results of this research should add more to the understanding of how dividend policy is set by CBs compared to IBs and provide theoretical and practical contributions.

The next section of the paper provides a discussion of the relevant literature on dividend policy focusing on the banking industry, and on the mixed banking systems in particular. The objective of the review is to develop our research conceptual framework and hypotheses which set out in a separate section. Following that, the scope of the data and the research methodology is addressed before presenting the results of the various tests and model estimations. The last section concludes with conclusions drawn from the research and some observations.

2. Literature review

In this paper, two strands of research relevant to the dividend policy of banks are recognized. The first is concerned with the relationship between firm value and its dividend policy that was initiated by Miller & Modigliani (1961). Their original proposition is that dividend policy is irrelevant to value. They claim that, if shareholders prefer an appreciation in stock market value, then profit should be retained by the firm to fund further growth. Therefore, the firm should pay no dividends. Alternatively, if they prefer cash, then they can always sell some of their shares. Again, no cash dividend distribution is necessary. They argue that shareholders are indifferent to the dividend policy of the firm. This argument implies that when firms pay, cut, increase or decrease cash payouts, stock traders do not respond and the stock market price remains unchanged. In the literature of corporate finance, this is called the "dividend irrelevance" theory. The first to criticize this theory were Lintner (1962) and Gordon (1963). They both believe that the Miller & Modigliani (1961) proposition is based on unrealistic assumptions. They believe that investors value a dollar of dividend today more than a dollar of price appreciation in the future. Miller & Modigliani (1961) disagreed and called this "bird-in-the-hand" theory. A third theory is called the "Tax preference" theory. It maintains that, where dividends are taxed more than capital gains, some investors prefer firms that retain profit to finance further growth. Because when they get cash dividend, a significant part of it will be deducted as taxes. This theory implies that an increase of dividend payout leads to lower value.

For those who believe that the dividend policy does matter to firm value (e.g., Barely et al., 1995; Fama & French, 1998; Dickens et al., 2003; and Bhattacharyya, 2007 for a review), it is crucial to investigate the determinants of this policy. This forms the second strand of the literature. To explain changes in dividend policy, the literature recognizes three main theories: the "clientele" theory, the "signaling" theory, and the "agency" theory. A review of the literature on these theories is summarized by Bhattacharyya (2007).

When Miller & Modigliani (1961) introduced their famous dividend-irrelevance proposition, it was criticized for being unrealistic as they ignored the effect of tax treatments. Opposing scholars (e.g., Peterson et al., 1985; Amihud & Murgia, 1997; Allen et al., 2000; Baker & Wurgler, 2004; DeAngelo & DeAngelo, 2006; and Komrattanapanya & Suntraruk, 2014) believe that dividend policy is relevant and that different clienteles have different preferences. Other researchers provided proofs of the signaling effects of cash payouts. Bhattacharyya (1979) developed a signaling proposition based on the notion of information asymmetry. He argued that managers have deeper and more detailed information about the future cash flows of the firm and they convey this information by means of additional dividends as a signaling mechanism. A similar argument was made by Miller & Rock (1985), William (1988), and Bernheim & Wantz (1995). Advocates of the agency hypothesis believe that profitable firms with no further growth opportunities should pay a cash dividend to prevent executives from misusing in non-profitable investments. The hypothesis implies that accumulation of cash is associated with an increased agency cost. This notion is supported by Rozeff (1982), Easterbrook (1984), Moh'd et al. (1995), La Porta (2000), DeAngelo et al. (2004).

As the main driver of economic strength, the banking industry was extensively investigated in relation to dividend policy. For those who believe that dividend distribution is relevant to the value of the firm, it was important to understand how dividend policy is determined. It is even more important when there is a mixed banking market. This is particularly true for the Gulf Cooperation Council (GCC) region and other Islamic countries where most of the Islamic banks in the world coexist with their conventional counterpart. Many research attempts, within these mixed-banking markets, have been made to study the differences in dividend policies of IBs versus CBs. Hassan et al. (2003), for example, provided evidence of dividend signaling and agency cost effects in an interest-free banking system. By studying the dividend policy of the Malaysian mixed-banking market, Ameer (2008) provided evidence that the decision to cut or increase dividend is determined differently. He did not differentiate between IBs and CBs. A comparison between the dividend policies of IBs and CBs was later investigated by Eng, Yahya, & Hadi (2013). The dividend policy of IBs was found to be affected only by lagged dividends, implying the signaling effect, while CBs' dividend was found to be affected only by profitability.

Zameer et al. (2013) explored the determinants of dividend policy in the mixed-banking industry of Pakistan. They found that profitability, pervious dividends, and ownership structure have a positive impact on dividend while liquidity had a negative impact. Size, risk, growth, and agency cost were found irrelevant to dividend policy. In his short paper on modeling dividend policy in the banking industry of the GCC region, Al-Ajmi (2010) did not differentiate between Islamic and conventional banks but provided mixed results about signaling and agency effects. He did, however, provide evidence of the effect of current earnings and previous dividend on cash payout policy. Athari et al. (2016) conducted a study of dividend policy in a dual-banking system covering some GCC countries in addition to Egypt and Jordan. They provided two agency explanation for the dividend payout. They found that IBs used dividends to mitigate risk and agency problem associated with accumulation of cash. Alternatively, CBs were found to follow the outcome model of dividend policy when dividend policy was more stable.

Al-Kayed (2017) compared the determinants of dividend policies of IBs to CBs. She found that IBs' dividend policy is affected by profitability, previous dividends, and leverage. She also found that for CBs, the dividend policy was affected by the same determinants in addition to liquidity and growth. The Agency theory was not tested.

Although some of the studies reviewed have covered the period before and after the 2008 global financial crisis, none of them has catered for its effect on bank dividend policy, especially when comparing between IBs and CBs. In the next section, we make use of the reviewed literature to develop our conceptual framework, dependent and explanatory variables, and research hypotheses.

3. Conceptual framework, variables, and hypotheses

The research framework in this paper recognizes three main propositions to explain dividend decisions exercised by Islamic and conventional banks in a mixed banking system. With the assumption of the fundamental differences between the capital structures of IBs versus CBs, one would assume different dividend policies and determinants for the two types of banks based on the status of the economy (before and after the 2008 global financial crisis). To pursue the investigation, the model will be based on the three most established theories of dividend policy: the signaling, pecking order, and the agency effect theories. If we can provide evidence of the adoption of different forms of effects in different economic occasions, then banks are using dividend payouts for different purposes according to different needs.

As we are focusing on the GCC region where taxes are of a lesser concern, we excluded the clientele theory. We elected nine explanatory variables that were discussed in the literation, three variables to represent each theory. These variables are:

For signaling effect, we chose

- Previous dividend ([div.sub.t-1]). This variable is calculated as the dividend per share in the previous year. When firms maintain a certain pattern in payouts despite variability in profitability performance, they signal their commitment to their shareholders (see for example Lintner, 1956; Miller & Modigliani, 1961; DeAngelo et al., 1992; Ben Naceur et al. (2006); and Al-Ajmi, 2010). Briston & Tomkins (1970) found that there was evidence of dividend policy stability in periods of a major change in the UK tax system. Eng, Yahya, & Hadi (2013) provided evidence of a positive association between payouts and previous dividend for IBs but not for CBs. Al-Kayed (2017) found a positive relation for CBs and IBs in Saudi Arabia. We, therefore, hypothesize that previous dividend positively affects dividend policy.

- Future earnings (geps). This is calculated as the change in earnings per share for the current year. There is ample evidence that the payment of dividends is seen by stock traders as a signal for future prospects of the firm. Miller & Modigliani (1961) argue that investors interpret change in dividend payout as a change in management view of future earnings. A statistically significant interaction effect between earnings and dividend announcement was reported by Kane et al. (1984). Nissim & Ziv (2001) reported similar results. Our hypothesis concerning this predictor is that future earning positively affects dividend policy.

- Deposits (lndep). It is calculated as the natural log of this year's deposits. Dividends signal on the future availability of cash (more deposits for banks). This variable could also be used to test the agency effect. The Miller & Modigliani (1961) argument on future cash flows could be applied to deposits for banks. Bhattacharyya (2007) provides a review discussion of this variable. Eng, Yahya, & Hadi (2013) did not support the hypothesis for either CBs or IBs. The hypothesis here is that deposits positively affect dividend policy.

For the pecking order effect, we chose:

- Profitability (eps). This variable is defined as the year's earnings per share. Companies prefer internal funding over external funding to finance dividend distribution. So, higher profitability is associated with more dividend payouts. DeAngelo et al. (1992) argue that earnings do explain variation in dividend payouts. Various profitability proxies were employed by Barclay et al. (1995), Adedeji (1998), and Al-Malkawi (2007). Ameer (2008) provides evidence to support this hypothesis for Malaysian banks. He did not compare between CBs and IBs. Eng, Yahya, & Hadi (2013) did not support the hypothesis for either CBs or IBs. Al-Kayed (2017) found a positive relation for CBs and IBs in Saudi Arabia. For non-banking firms, Ben Naceur et al. (2006) provided evidence of the positive effects of earnings. The hypothesis here is that profitability positively affects dividend policy.

- Investment (invtota). This is a proxy for a greater investment opportunity calculated as the percentage of investments on total assets. The relationship with dividend payouts is negative; when opportunities exist, they distribute less dividends. The hypothesis was supported by Al-Ajmi (2010) for most of the banks in the GCC region. However, he did not attempt to compare between CBs and IBs. Our hypothesis relevant to this predictor is that the investment ratio negatively affects dividend policy.

For agency effect, we chose:

- Agency cost (ac). The capital ratio is used as a proxy for agency cost. Agency theory stipulates that higher agency cost indicates management misconduct and may be relevant to the accumulated cash when there are less profitable opportunities. The association to payout ratio could go either way. It could be positive supporting the argument that more cash encourages paying more dividends. Or, it could be negative supporting the argument that with management misconduct lesser dividends, than it should be, is paid out to shareholders. As in Berger & Patti (2006) and Pratomo & Ismail (2007). The hypothesis here is that agency cost negatively affects dividend policy.

- Size (lnta). Large firms, typically measured by the natural log of their assets, generate more cash and the accumulated cash encourages management misconduct when profitable opportunities do not exist. The most of the existing literature on the size (a representative variable of agency) supports that larger firms should pay more dividends to overcome the agency problem. Eriotis (2005), Al-Malkawi (2007), and Denis & Osobov (2008) provided evidence that size is positively associated with higher cash payouts supporting the agency concept. Zameer et al. (2013) did not provide support for this hypothesis. Zameer et al. (2013) provided no support for this hypothesis. Our research hypothesis relevant to this predictor is that size positively affects dividend policy.

- Fixed Assets Growth (fatota). This variable is calculated as the percentage of net fixed assets over total assets. If firms have no growth investment opportunities (investing in fixed assets) and still pay dividends, then there is a reason to believe they want to avoid agency problems. Although Zameer et al. (2013) did not support it as a hypothesis in his research, Eng, Yahya, & Hadi (2013) found a positive association for the case of CBs but not for IBs. Al-Kayed (2017) found a positive relation for CBs, but not for IBs, in Saudi Arabia. Our hypothesis regarding this predictor is that fixed assets positively affect dividend policy.

- Risk index (ri). The ri as proposed by Sinkey (1988) and Sinkey & Nash (1993) is defined as([[sigma].sub.ROA] + E/A)/[[sigma].sub.ROA]. We add this predictor as we believe that an increase of risk index, which implies a decrease in the level of general risk the bank is facing, should be positively associated with cash payouts. The argument here is that banks may use cash payouts to lower the risk leading to lower agency problems. Our hypothesis with this predictor is that higher risk index (lower level of risk) positively affects dividend policy.

For the dependent variable, we chose:

- Dividend yield (dy). This variable is calculated as the current of divided per share over current price. Most of the research use the dividend payout ratio (current dividend divided by net income) as the dependent variable (see, e.g., Amidu & Abor (2006) and Komrattanapanya & Suntraruk, 2014).

A description of the variables is presented in Table 1.

4. Data and methodology

The data was collected using Bayanati financial & banking data from the Institute of Banking Studies in Kuwait. This is an online (Institute of Banking Studies, 2018) database for all companies listed in the Kuwait Stock Exchange. It also provides fundamental data for all banks in the GCC region. Initially, the attempt was to include all listed banks in the GCC region for the period from 2003 to 2016. Unfortunately, some were omitted because of either their short lives or lack of significant data. We also targeted strongly balanced panels, but this would have cut the sample even further. We ended up with a total of 76 banks, 50 of which are CBs and 26 are IBs.

Ultimately, the objective was to understand how the dividend policy of each bank type is set and to explore to what extent the 2008 global financial crisis affected how dividend policy is determined by each bank type.

The investigation starts with avoiding some of the common problems associated with causal model estimations. Therefore, we start with removing any multicollinearity among the proposed nine predictors for each bank type. This is done by estimating an OLS model regressing the dependent variable, dividend per share, on all nine predictors. The objective is to perform a variance inflation factor (VIF). Cutoff values of less than 10, 5, or even 2.5 are commonly used to eliminate variables with possible multicollinearity among predictors. We use a value of less than 2.5. We also use Fisher-type unit root method based on augmented Dickey-Fuller tests to test for data stationery. Variable remains when the null hypothesis of unit root existence is rejected. The Shapiro-Wilk W test for normal data is selected as it handles unbalanced panel data. This is important for deciding on which method to use for testing differences in means. The student t-test is preferred when data is normal. Alternatively, the nonparametric Kruskal-Wallis equality-of-populations rank test must be employed.

To explore causal effects, the nature of the data compiled for this study calls for panel data analysis. This type of analysis takes two forms, the random effect form and the fixed effect for. We adopt the Hausman test to select the appropriate model. This is a test that analyses the coefficients resulting from estimating tow OLS regressions, one with random effects and another with fixed effect. When the hypothesis of the test is rejected, a fixed effect model is more appropriate. Otherwise, the random effect model is more efficient, but the fixed effect model can still be consistent.

A general panel data fixed effect model may be written as following:

[y.sub.it] = [[alpha].sub.i]+[X.sub.it][beta] + [u.sub.it] for t = 1,...,T and i =1,...,N (1)

Where [y.sub.it] is the dependent variable for panel i at time t, [[alpha].sub.i] is the unobserved panel effect, [X.sub.it] is 1 x k explanatory variables matrix, [beta] is the calculated 1 x k coefficients matrix, and [u.sub.it] is the error term.

The general panel data random effect model can be written as

[y.sub.it] = [[alpha].sub.i] + [X.sub.it][beta] + [u.sub.it] + [[epsilon].sub.it] (2)

Where [u.sub.it] is the between-entity error and [[epsilon].sub.it] is the within entity error.

5. Diagnostics and preliminary testing

We start our diagnostics by checking the multicollinearity among the predictors for the CBs dataset. The results of the OLS regression and VIF test are presented in Table 2.

The results indicate high VIF value for four predictors with possible collinearity. By excluding these variables, we end up with six possible predictors. Table 3 presents the results of re-estimating the model with the six predictors. The new VIF values indicate the problem is solved.

After the VIF examination, we end up with six predictors for CBs causal investigation. To decide on fixed or random effect modeling, we perform the Hausman test. This test requires running equations (1) and (2) and comparing their estimates. The results of this test are presented in Table 4.

The conclusion of the Hausman test is that it not possible to use the random effect option to the model panel data regression. The fixed-effect model is more appropriate. The results of this test show a [chi square] score of 93.45 and a p-value of 0.000 rejecting the null hypothesis to accept random effect.

Regressing dps on geps, lndep, eps, debtota, invtota, ac, Inta, and ri with the VIF test in the 1st run produced the VIF values for IBs dataset with an obvious multicollinearity problem in four predictors as shown in Table 5.

The 2nd run of the OLS regression after removing three of the four problematic variables, produced VIF values for the remaining predictors higher than the 2.5 cutoff point. We have ended up with seven potential explanatory variables for IBs causal investigation.

To decide on which effect model to adopt, again, we use the Hausman test for the IBs dataset in the manner we did with the CBs dataset. The results of this test are shown in Table 6.

As indicated at the bottom of the Table 6, a [chi square] of 22.00 with a p-value of 0.0025 rejects the null hypothesis that the random effect appropriate. Therefore, for IBs dataset we would be using the panel data regression with fixed effect for causal investigation.

Table 7 presents the correlation matrix of the dependent variable and the potential explanatory variables for the CBs dataset.

A significant high positive correlation value between the dependent, dps, variable and the predictors; [div.sub.t-1] and eps indicate potential positive causal effects. The relatively low correlation values among the potential independent variables confirm the independency of the variables and low collinearity. A summary statistics of the variables for CBs dataset are presented in Table 8.

The zero minimum values for the dps and [div.sub.t-1] designate nonpayment of cash dividend in some years. The high standard deviation of the investment to total assets ratio designates the volatility of the profitability indicator of CBs which is possibly caused by the global financial crisis during the period of the study. A further and more detailed description of the variables (Table 9) shows a comparison of the variables' means before and after the crisis.

Most of the variables exhibit a logical decline which can be explained by damaging consequence of the global crisis in 2008. However, we can observe that CBs exhibited a remarkable increase in the growth of earning per share ratio indicating a rapid recovery from the crisis.

The correlation matrix for the selected variable of the IBs dataset is presented in Table 10. The significant correlation values between the dependent variable [div.sub.t-1] and two predictors eps and lnta indicate possible causal effects. The low correlation values among predictors confirm variables' independence.

Summary statistics of the selected variables for the IBs dataset is presented in Table 11. A high standard deviation value for the risk index indicates on the high variability in the level of risks, which is possibly a result of the financial crisis.

A comparison of the means before and after the crisis for the IBs dataset is illustrated in Table 12. Unlike CBs, IBs exhibit a noticeable deterioration in the growth of their earning per share ratio after the crisis signifying a struggle to recover from the crisis. A simple comparison of the same ratio between the two types of banks, demonstrates that CBs exhibit better resilience. The decline in most of the values following the crisis is logical.

Before moving on to the causal models, we present two preliminary tests of normality and data stationary for the CBs dataset and IBs dataset. The results of the Shapiro-Wilk normality test for CBs dataset is depicted in Table 13.

The normality null hypothesis is rejected for all variables. The result implies that, for a comparison between means, a nonparametric rank test is more appropriate that the student t-test for the CBs dataset. Table 14 presents the results of the unit root test for data stationery, again, for the CBs dataset.

The test rejects the null hypothesis that data contains unit root implying that data is stationary for all selected variable of the CBs data set. The results of the nonparametric Kruskal-Wallis-of-populations rank test is shown in Table 15. The table is useful for detecting the variables that changed significantly in response to the 2008 crisis.

The Table 15 shows that except for the fixed assets ratio and risk index, the ranks of all remaining variables are significantly different indicating significant differences in means for CBs.

The results of the Shapiro-Wilk W test of normality for IBs (Table 16) shows that data of the selected variables are not normally distributed which, again, means that we cannot use parametric tests to compare means.

The results of the Fisher unit root test (Table 17) indicate that only [div.sub.t-1] variable contains a unit root for IBs dataset. According to this result, this variable should be removed as a predictor.

Table 18 exhibits the results of the Kruskal-Wallis rank test which indicates that [div.sub.t-1] variable is significantly different after the crisis. Similarly, dps, geps, eps, and lnta exhibit significant ranks differences.

6. Panel regression estimation

The causal fixed effect model represented in equation (1) will be estimated for three datasets. The first dataset contains conventional banks only, without considering the effect of the crisis. This is important for a comparison of these results with those after the inclusion of the crisis' effects. The second dataset includes only the panels representing CBs for the years before 2008. The third dataset includes the CBs for the period from 2008 to 2016. This is important to show the significant effect of the crisis when the periods of economic stability is separate from the period of economic instability. It may also provide evidence of how important it is to reflect such effects. Many financial studies were conducted using the time period covering the years 2008 and neglected the effect of the crisis.

The results of estimating the model for CBs using the 1st dataset is shown in Table 19. Two variables only ([div.sub.t-1] and eps) are positively significant at the 5% significance level.

The results imply that the dividend policy of CBs is determined only by previous dividend and the current earnings per share. The question here is whether these results will hold when separating the data into two datasets: before and after the crisis. To answer this question, we re-estimated the model for the "before-crisis" dataset. The results of this estimation are presented in Table 20. The previous dividend effect did not hold for this dataset. Only the current earnings per share have a positive and significant effect of the dividend policy of conventional banks.

Now, the question is "What factor can predict dividend policy for the third dataset?". The results of the third run of the panel regression model are depicted in Table 21. Four variables have significant positive effects. These are previous dividends, earning per share, investment ratio, and the risk index.

We follow the same procedures to investigate the dividend policy of IBs. The results of estimating the fixed effect panel regression for the complete dataset of IBs are presented in Table 22. A significant F value of 7.27 is indicative of good model specification.

Three predictors have a positive significant effect on dividend policy. These are earnings per share, investment ratio, and size. The risk index is also positively significant at the 10% level.

Running the model for "before-crisis" dataset reveals weak model specification as indicated by the F value of 0.61 with prob>F = 0.7177 (Table 23).

The weak specification may be a result of a relatively small number of observations compared to 16 groups. For Islamic banks, the weak data sample is undermining any possible inference relevant to the period before the crisis. The reason for a smaller sample for the period before 2008 is the fact that there were fewer banks at that time. Gradually, the increase of the number of listed banks led to the larger sample size after 2008. This is reflected in the next run of the model.

The results for the "after crisis" dataset show that the earning per share predictor is the only predictor that is significant at the 5% level (Table 24). Though, two other predictors (growth in earnings per share and size) are significant at the 10% level.

7. Discussion

We start our discussion with a summary of the causal model results as illustrated in Table 25.

We can understand from the outcome of this study that the dividend policy of CBs robustly confirms the pecking order hypothesis. Conventional banks seem to continue adopting the policy of preferring internal funding over external funding. The association of higher dividend payouts with the higher earnings is evident from the correlation relationship discussed earlier, and the significant causal effect of earning per share on cash dividends paid out to shareholders. Earnings per share variable is the only robust predictor that appeared to be statistically significant in all three data sets. The result confirms the pecking order effect evidence provided by Ameer (2008) in the study conducted on Malaysian banks. Although to a lesser extent, the previous dividend per share predictor can also be considered robust as it appeared to be statistically significant in two of the three datasets, the "entire-dataset" and the "after crisis" dataset. This result implies that CBs, despite the dramatic decrease in profitability following the crisis, were striving to signal their ability to maintain a pattern of cash payment for shareholders. This is an evidence of the adoption of a cash payout as a signaling instrument. It confirms previous earlier propositions made by Lintner (1956) and the evidence provided by DeAngelo et al. (1992) from their study on the NYSE. The final interesting result for CBs is the positive and significant effect of the risk index on dividend payouts. We note here that a higher risk index implies a lower level of risks associated with lower agency problem. What we may understand from this result is that, and in response to the global financial crisis, CBs adopted the agency effect with cash payouts to mitigate management misconduct at times of economic instability. The result is consistent with the arguments made by Rozeff (1982) and DeAngelo & DeAngelo (2004).

For IBs, earning per share is the only robust predictor with a significant positive effect on dividend policy. It appeared statistically significant for the "entire" dataset and the "after crisis" dataset. The argument here is that IBs in the GCC region are following the pecking order to distribute cash dividends to shareholders. They prefer internal funding over external funding. Looking at the "entire" dataset, we can observe that IBs use dividend payouts to signal the strength of their financial positions. This is confirmed by the significant and positive effect of the investment ratio on dividend policy. Our explanation for the positive effect is that IBs use dividend payouts to signal their ability to pay a cash dividend in the presence of investment opportunity which also requires funding. This latter result contradicts with the evidence of negative association provided by AL-Ajmi (2010) for the same region. The positive and significant effect of bank size on dividend policy for the "entire" dataset implies that IBs are gradually adopting payment of cash dividends as a mechanism to mitigate management misconduct and avoid potential agency problems. This result is consistent with the arguments made by Zameer et al. (2013) but contradicts the evidence provided by Al-Kayed (2017) who found a positive association for CBs only.

8. Research limitations

This study suffers from two obvious limitations. The first one is the, relatively, small sample of IBs compared to that of CBs. This limitation is tied to the shorter time series of some of the IBs included in the sample which may undermine the confidence in the results generalization relevant to IBs in the GCC region. The second limitation is the fact that it focuses on a regional market instead of considering the wider global market. Investigating this subject at the global market level would, certainly, increase the confidence to generalize on the results beyond the regional market.

9. Concluding remarks

Considerable literature has been accumulating following Miller & Modigliani (1961) controversial argument on dividend policy. Since then, the majority of this literature provided evidence on its relevance to firm value. Many forms of dividend effects were progressively developed, including, but not limited to, signaling, pecking-order, and agency. Guided by these forms of effects, we developed a conceptual framework to investigate the applicability of these forms in a mixed banking industry before and during a major economic downturn. The main proposition was that, if CBs and IBs differ in their capital structures they must be different in the way they set their dividend policies. We adopted a panel data regression model with fixed effect to test this proposition.

The results reveal that, at times of economic prosperity (before the 2008 crisis), CBs used dividend payout as a signaling instrument and a pecking-order instrument. At times of economic instability (after the 2008 crisis), they used it as an agency-protection instrument. A logical interpretation of this result is that CBs realized that agency problems during the time of economic instability is more damaging to the value of the firm calling for more controlling measures. For IBs, the results indicate that dividend policy is used in the form of pecking-order mechanism irrelevant to the status of the economy. It is interesting to note that, only at the time of economic stability, IBs used dividend payout as an instrument against agency problems. A possible interpretation of this result is that IBs have realized the agency problem during this period and acted sooner to mitigate its effects. The absence of this instrument after the crisis may be due to the significant decrease in IBs' growth of earnings compared to CBs as discussed earlier.

The main conclusion of the results is that dividend policies of CBs and IBs are determined differently. Dividend policy for each bank type differed according to economic status and the individual needs of bank types. The change in the form-of-effects implies that dividend policies are used as a multi-purpose mechanism. That is the form of effect changes as the need changes. One important theoretical implication of this argument is that the multi-purpose notion developed in this paper may be a practical interpretation of the so-called "dividend puzzle" of Black (1976).

One final note on the results is that, for both bank types and irrelevant to economics status, earnings per share is the only robust predictor of dividend policy serving the purpose of pecking-order effect. This result indicates that, when it comes to considering new investment opportunities, banks in the GCC region, prefer internal funding over external funding.

The results of this paper show that dividend payouts represent an important instrument that can be informative to investors about the future perspectives of the firm. They also serve as a growth funding instrument and a protection instrument against agency risks. To maintain the objective of value maximization, and depending on their particular needs, bankers should know which form of dividend policy to adopt. For instance, in certain occasions, dividend policy can serve only as a signaling instrument, while in other occasions it should be adopted as an investment funding instrument or as a protection mechanism against management misconduct.

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Talla M Aldeehani

Department of Finance & Financial Institutions, College of Business Administration, Kuwait University

corresponding e-mail: talla[at]cba(dot)edu{dot}kw

address: Department of Finance & Financial Institutions, College of Business Administration, Kuwait University, P.O.Box 13055, Safat 13055

DOI: http://dx.doi.org/10.15208/beh.2019.3
TABLE 1. VARIABLES DESCRIPTION

VARIABLE           DESCRIPTION                     PROXY

[div.sub.-1]t    Previously paid    Percentage of previous dividend over
                    dividend        price

geps            Growth in future    Change in earnings per share
                    earnings

lndep           Size of deposits    Calculated as the natural log of
                                    deposits

eps               Profitability     Calculated as net income over number
                                    of shares

invtota        Size of investments  Calculated as the percentage of
                                    investments over total assets

ac                 Agency cost      Calculated as the capital ratio

Inta              Size of bank      Natural log of total assets

fatota            Fixed assets      Calculated as net fixed assets to
                     growth         total assets

ri                 Risk Index       Calculated as the standard deviation
                                    of return on assets added to the
                                    equity ratio all divided by the
                                    standard deviation of return on
                                    assets

dy               Dividend yield     Percentage of current dividend per
                   (dependent)      share over price

VARIABLE                           USED BY

[div.sub.-1]t  Lintner (1956); Miller & Modigliani (1961); DeAngelo et
               al. (1992); Ben Naceur et al. (2006); Al-Ajmi (2010);
               Briston & Tomkins (1970); Eng, Yahya, & Hadi. (2013)

geps           Miller & Modigliani (1961); Kane et al. (1984); Nissim &
               Ziv (2001)

lndep          Bhattacharyya (2007); Eng, Yahya, & Hadi. (2013)

eps            DeAngelo et al. (1992); Barclay et al. (1995); Adedeji
               (1998); Ben Naceur et al. (2006); Al-Malkawi (2007);
               Ameer (2008); Eng, Yahya, & Hadi. (2013); Al-Kayed (2017)

invtota        AL-Ajmi (2010)

ac             Berger & Patti (2006); Pratomo & Ismail (2007)

Inta           Eriotis (2005); Al-Malkawi (2007); Denis & Osobov (2008);
               Zameer et al. (2013)

fatota         Zameer et al. (2013); Eng, Yahya, & Hadi. (2013);
               Al-Kayed (2017)

ri             Sinkey (1988); Sinkey & Nash (1993)

dy             Amidu & Abor (2006); Ben Naceur et al. (2006);
               Komrattanapanya & Suntraruk, (2014)

TABLE 2. RESULTS OF THE PRELIMINARY OLS REGRESSION AND VIF FOR
CONVENTIONAL BANKS (CBS)

VARIABLE         COEF.    STD. ERR.    T    P>| T |  [95% CONF.
                                                     INTERVAL]

[div.sub.t-1]   .494052   .0324784   15.21   0.000    .4302682
geps            .0004283  .0004727    0.91   0.365   -.0004999
lndep          -.0012079  .0271931   -0.04   0.965   -.054612
eps             .1639327  .0151275   10.84   0.000    .1342239
debtota         .8141158  .9596008    0.85   0.397   -1.070431
invtota         .0584972  .044357     1.32   0.188   -.028615
ac              .9347817  .9575347    0.98   0.329   -.9457076
lnta           -.0022739  .027766    -0.08   0.935   -.0568031
fatota          .0295739  .8918612    0.03   0.974   -1.72194
ri              .0008221  .0003175    2.59   0.010    .0001984
_cons          -.8186654  .9618839   -0.85   0.395   -2.707696

VARIABLE       [95% CONF. INTERVAL]   VIF

[div.sub.t-1]        .5578358          1.49
geps                 .0013565          1.01
lndep                .0521962         43.38
eps                  .1936414          1.52
debtota             2.698663         102.44
invtota              .1456093          1.44
ac                  2.815271          99.50
lnta                 .0522554         37.58
fatota              1.781088           1.15
ri                   .0014457          1.17
_cons               1.070365

TABLE 3. RESULTS OF THE PRELIMINARY OLS REGRESSION AND VIF FOR
CONVENTIONAL BANKS (CBS) AFTER REMOVAL OF PROBLEMATIC VARIABLES

VARIABLE         COEF.    STD. ERR.    T    P>|T|  [95% CONF. INTERVAL]

[div.sub.t-1]   .4915182  .031974    15.37  0.000    .4287257   .5543107
geps            .0004483  .0004719    0.95  0.343   -.0004785   .001375
eps             .1615718  .0146366   11.04  0.000    .1328275   .190316
invtota         .0534518  .0375203    1.42  0.155   -.0202328   .1271364
fatota          .3403701  .8422678    0.40  0.686  -1.313726   1.994467
ri              .0009154  .0003068    2.98  0.003    .0003129   .0015179
_cons          -.0227208  .0168801   -1.35  0.179   -.055871    .0104293

VARIABLE       VIF

[div.sub.t-1]  1.45
geps           1.01
eps            1.42
invtota        1.03
fatota         1.03
ri             1.09
_cons

TABLE 4. RESULTS OF THE HAUSMAN TEST FOR CONVENTIONAL BANKS (CBS)

VARIABLE       COEFFICIENTS        (b-B) DIFFERENCE  sqrt(diag(V_b-V_B))
                (b) fe    (B) re                            S.E.

[div.sub.t-1]  .3320877  .4915182     -.1594304           .0199423
eps            .0000308  .0004483     -.0004174           .0000959
ri             .1774903  .1615718      .0159186           .0124859
invtota        .1328597  .0534518      .0794079           .0736235
fatota         .7859691  .3403701      .445599            .7769467
geps           .0018788  .0009154      .0009635           .0011805

Notes: b = consistent under Ho and Ha; obtained from panel regression
model estimator. B = inconsistent under Ha, efficient under Ho;
obtained from panel regression model estimator. Test: Ho: difference in
coefficients not systematic. [chi square] (6) = (b-B)'[(V_b-V_B)^(-
1)](b-B)=93.45, Prob>chi2 = 0.0000.

TABLE 5. THE VIF TESTS BEFORE AND AFTER THE REMOVAL OF PROBLEMATIC
PREDICTORS

1ST RUN: INCLUDING ALL VARIABLES

VARIABLE        VIF     1/VIF

ac             127.50  0.007843
debtota        122.56  0.008159
lnta             7.04  0.142083
lndep            6.24  0.160272
[div.sub.t-1]    2.19  0.456677
eps              1.94  0.515272
invtota          1.49  0.673159
fatota           1.26  0.796422
ri               1.20  0.833043
geps             1.08  0.924165
Mean VIF        27.25

2ND RUN: AFTER REMOVING HIGH VIF VARIABLES

VARIABLE       VIF    1/VIF

[div.sub.t-1]  2.10  0.476941
eps            1.94  0.516417
lnta           1.43  0.698236
invtota        1.18  0.845868
fatota         1.10  0.909413
ri             1.09  0.915711
geps           1.06  0.942521
Mean VIF       1.41

TABLE 6. RESULTS OF THE HAUSMAN TEST FOR ISLAMIC BANKS (IBS)

VARIABLE       COEFFICIENTS          (b-B) DIFFERENCE     sqrt(diag
                (b) fe     (B) re                      (V_b-V_B)) S.E.

[div.sub.t-1]   .4521232   .5722702     -.120147           .0262
geps           -.0001974  -.0004162      .0002188         .0005405
eps             .1004533   .1290801     -.0286268         .0161353
invtota         .1439666   .0978009      .0461658         .0956167
lnta            .0113903   .0084274      .0029628         .0105557
fatota         -.0645429  -.1350706      .0705277         .6684216
ri              .0015018   .0000782      .0014236         .0010676

Notes: b = consistent under Ho and Ha; obtained from panel regression
model estimator. B = inconsistent under Ha, efficient under Ho;
obtained from panel regression model estimator. Test: Ho: difference in
coefficients not systematic. [chi square] (6) =
(b-B)'[(V_b-V_B)^(-1)](b-B) = 22.00, Prob>chi2 = 0.0025.

TABLE 7. VARIABLES' CORRELATION MATRIX FOR CONVENTIONAL BANKS (CBS)
DATASET

VARIABLE         dps    [div.sub.t-1]   geps      eps    invtota

dps             1.0000
[div.sub.t-1]   0.6920      1.0000
geps           -0.0078     -0.0584      1.0000
eps             0.6242      0.5337     -0.0027   1.0000
invtota         0.0612      0.0495     -0.0090  -0.0122   1.0000
fatota         -0.0300     -0.0397     -0.0209  -0.0279  -0.1517
ri              0.2883      0.2563     -0.0365   0.2336   0.0627

VARIABLE       fatota     ri

dps
[div.sub.t-1]
geps
eps
invtota
fatota          1.0000
ri             -0.0665  1.0000

TABLE 8. SUMMARY STATISTICS FOR CONVENTIONAL BANKS (CBS) DATASET

VARIABLE       OBS.    MEAN      STD. DEV.      MIN      MAX

dps            668     .1865023    .1979475   0            2.266229
[div.sub.t-1]  617     .1934515    .199378    0            2.266229
geps           618     .6762588  11.2603     -8.423469   265.6776
eps            668     .4439088    .4232255  -2.867297     2.735895
invtota        668     .2707241    .1468702    .0287392     .9203521
fatota         668     .0092773    .006408     .0004146     .0827879
ri             668   25.2879     18.33361    -2.510582   123.5363

TABLE 9. COMPARISON OF MEANS FOR CONVENTIONAL BANKS (CBS) DATASET

VARIABLE       CRISIS STATUS     MEAN     STD. ERR.  [95% CONF.
                                                     INTERVAL]

dps               Before        .2424889   .0161877    .2106991
                   After        .1539455   .0084091    .1374314
[div.sub.t-1]     Before        .2412436   .0128616    .2159856
                   After        .1664017   .0100051    .1467535
geps              Before        .253935    .1504851   -.041591
                   After        .9167617   .705402    -.4685226
eps               Before        .5906036   .0338167    .5241936
                   After        .3723012   .0182767    .336409
invtota           Before        .2993945   .0105474    .2786813
                   After        .2466978   .0066143    .2337084
fatota            Before        .009396    .0005038    .0084067
                   After        .0091001   .0002844    .0085416
ri                Before      25.05314    1.266225   22.5665
                   After      25.23576     .8864034  23.49503

VARIABLE       [95% CONF. INTERVAL]

dps                   .2742787
                      .1704595
[div.sub.t-1]         .2665015
                      .18605
geps                  .549461
                     2.302046
eps                   .6570136
                      .4081934
invtota               .3201076
                      .2596871
fatota                .0103853
                      .0096585
ri                  27.53978
                    26.9765

TABLE 10. CORRELATION MATRIX FOR ISLAMIC BANKS (IBS) DATASET

VARIABLE         dps    [div.sub.t-1]   geps      eps    invtota   lnta

dps             1.0000
[div.sub.t-1]   0.8079      1.0000
geps            0.0491      0.0157      1.0000
eps             0.6892      0.6676      0.1678   1.0000
invtota        -0.0220     -0.0342     -0.0095  -0.1365   1.0000
lnta            0.4035      0.4323      0.0346   0.3475  -0.3236  1.0000
fatota          0.1145      0.1456     -0.0480   0.1075  -0.1373  0.2240
ri             -0.0586     -0.0901      0.0862  -0.0242  -0.1778  0.0908

VARIABLE       fatota    ri

dps
[div.sub.t-1]
geps
eps
invtota
lnta
fatota         1.0000
ri             0.1831  1.0000

TABLE 11. SUMMARY STATISTICS FOR ISLAMIC BANKS' (IBS') VARIABLES

VARIABLE       OBS.     MEAN     STD. DEV.       MIN      MAX

dps            267     .104675     .152217     0             .7
[div.sub.t-1]  241     .1091911    .157669     0             .7
geps           241    -.0788719   3.650523   -37.72849     19.74044
eps            267     .2389055    .3234104    -.5473449    2.292303
invtota        267     .1425336    .0977161    0             .6437339
lnta           267    8.656587    1.321699     5.242333    11.41535
fatota         267     .0135623    .013323     0             .075341
ri             267   20.98328    20.07043      1.582506   130.0466

TABLE 12. COMPARISON OF MEANS FOR ISLAMIC BANKS (IBS) DATASET

VARIABLE       CRISIS STATUS     MEAN     STD. ERR.  [95% CONF.
                                                     INTERVAL]

dps               Before        .1610499   .0248299    .1121374
                   After        .0869177   .0092883    .0686207
[div.sub.t-1]     Before        .1505052   .0248887    .1014769
                   After        .093933    .0102524    .0737368
geps              Before        .3667386   .1758421    .0203477
                   After       -.243444    .3147877   -.8635437
eps               Before        .4782576   .0576427    .3647074
                   After        .1631106   .0148684    .1338215
invtota           Before        .1431949   .0108466    .1218282
                   After        .139813    .0069763    .1260705
lnta              Before       8.230411    .1550768   7.924926
                   After       8.989282    .0908015   8.810412
fatota            Before        .0142881   .0020761    .0101983
                   After        .013389    .0009128    .0115909
ri                Before      17.97171    1.329654   15.35243
                   After      20.10519    1.457966   17.23314

VARIABLE       [95% CONF. INTERVAL]

dps                   .2099623
                      .1052146
[div.sub.t-1]         .1995334
                      .1141293
geps                  .7131295
                      .3766556
eps                   .5918077
                      .1923998
invtota               .1645616
                      .1535555
lnta                 8.535897
                     9.168152
fatota                .0183779
                      .015187
ri                  20.59099
                    22.97723

TABLE 13. RESULTS OF THE NORMALITY TEST FOR CONVENTIONAL BANKS (CBS)
DATASET

VARIABLE       OBS.     W        V       z     PROB.>Z

dps            668   0.83098   73.874  10.475  0.00000
[div.sub.t-1]  617   0.83388   67.567  10.223  0.00000
geps           618   0.05801  383.699  14.438  0.00000
eps            668   0.83929   70.240  10.352  0.00000
Invtota        668   0.92983   30.670   8.335  0.00000
fatota         668   0.75375  107.626  11.391  0.00000
ri             668   0.80632   84.653  10.807  0.00000

TABLE 14. RESULTS OF DATA STATIONARY TEST FOR CONVENTIONAL BANKS (CBS)
DATASET

VARIABLE       Z-STATISTIC  P-VALUE

dps              -5.1015    0.0000
[div.sub.t-1]    -4.2699    0.0000
geps            -17.3496    0.0000
eps              -6.0794    0.0000
invtota          -5.8474    0.0000
fatota           -5.9968    0.0000
ri               -4.3335    0.0000

TABLE 15. KRUSKAL-WALLIS EQUALITY-OF-POPULATIONS RANK TEST FOR
CONVENTIONAL BANKS (CBS) DATASET

VARIABLE       STATUS  OBS.  RANK SUM   [chi square]  PROB.

dps            Before  272   104122.50     28.746     0.0001
               After   396   119323.50
[div.sub.t-1]  Before  223    80378.00     29.080     0.0001
               After   394   110275.00
geps           Before  223    77282.00     15.028     0.0001
               After   395   113989.00
eps            Before  272   109136.00     54.871     0.0001
               After   396   114310.00
invtota        Before  272   103355.00     25.486     0.0001
               After   396   120091.00
fatota         Before  272    92008.00      0.175     0.6760
               After   396   131438.00
ri             Before  272    89054.00      0.620     0.4310
               After   396   134392.00

TABLE 16. RESULTS OF THE NORMALITY TEST FOR ISLAMIC BANKS (IBS) DATASET

VARIABLE       OBS.     W        V       z     PROB.>Z

dps            267   0.83194   32.299   8.111  0.00000
[div.sub.t-1]  241   0.83463   29.040   7.823  0.00000
geps           241   0.35057  114.046  11.000  0.00000
eps            267   0.82710   33.230   8.177  0.00000
Invtota        267   0.94230   11.089   5.616  0.00000
lnta           267   0.98836    2.238   1.880  0.00000
fatota         267   0.78765   40.811   8.657  0.00000
ri             267   0.72752   52.367   9.239  0.00000

TABLE 17. RESULTS OF DATA STATIONARY TEST FOR ISLAMIC BANKS (IBS)
DATASET

VARIABLE       Z-STATISTIC  P-VALUE

dps              -1.9207    0.0274
[div.sub.t-1]    -0.9812    0.1632
geps            -12.3417    0.0000
eps              -5.1928    0.0000
Invtota          -1.4542    0.0729
lnta             -8.9637    0.0000
fatota           -2.9242    0.0017
ri              -10.7401    0.0000

TABLE 18. KRUSKAL-WALLIS RANK TEST FOR ISLAMIC BANKS (IBS) DATASET

VARIABLE       STATUS  OBS.  RANK SUM  [chi square]  PROB.

dps            Before   82   12557.00      7.266     0.0070
               After   185   23221.00
[div.sub.t-1]  Before   65    8691.00      2.957     0.0722
               After   176   20470.00
geps           Before   65    8952.00      5.122     0.0236
               After   176   20209.00
eps            Before   82   14279.00     31.968     0.0001
               After   185   21499.00
invtota        Before   82   10960.00      0.002     0.9614
               After   185   24818.00
lnta           Before   82    8226.00     22.517     0.0001
               After   185   27552.00
fatota         Before   82   10121.50      2.216     0.1366
               After   185   25656.50
ri             Before   82   11843.00      2.158     0.1419
               After   185   23935.00

TABLE 19. RESULT OF ESTIMATING MODEL (1) FOR THE 1ST DATASET OF
CONVENTIONAL BANKS (CBS) CBS

VARIABLE              COEF.     STD. ERR.    t      P>|t|

[div.sub.t-1]        0.3320877  0.0376833   8.81  0.000 (***)
geps                 0.0000308  0.0004816   0.06  0.949
eps                  0.1774903  0.0192387   9.23  0.000 (***)
invtota              0.1328597  0.0826328   1.61  0.108
fatota               0.7859691  1.1458890   0.69  0.493
ri                   0.0018788  0.0012197   1.54  0.124
_cons               -0.0482333  0.0368321  -1.31  0.191
[R.sup.2](between)   0.8426        Number of observations
F(6,561)            10.88          Number of panels
Prob.>F              0.0000

VARIABLE             [95% CONF. INTERVAL]

[div.sub.t-1]        0.2580701    0.4061054
geps                -0.0009150    0.0009767
eps                  0.1397016    0.2152791
invtota             -0.0294478    0.2951673
fatota              -1.4647880    3.0367260
ri                  -0.0005170    0.0042746
_cons               -0.1205790    0.0241124
[R.sup.2](between)              617
F(6,561)                         50
Prob.>F

Note: (***) Significant at 1% level, (**) significant at 5%, and (*)
significant at 10%.

TABLE 20. RESULT OF ESTIMATING MODEL (1) FOR THE 2ND DATASET OF
CONVENTIONAL BANKS (CBS)

VARIABLE              COEF.     STD. ERR.    t      P>|t|

[div.sub.t-1]        0.0270311  0.1186488   0.23  0.820
geps                -0.0011633  0.0055047  -0.21  0.833
eps                  0.1618791  0.0336563   4.81  0.000 (***)
invtota              0.0475939  0.1931634   0.25  0.806
fatota              -2.1843960  2.4803760  -0.88  0.380
ri                  -0.0010567  0.0025095  -0.42  0.674
cons                 0.1734069  0.0848220   2.04  0.042 (**)
[R.sup.2](between)   0.2695        Number of observations
F(6,169)             8.98          Number of panels
Prob>F               0.0004

VARIABLE             [95% CONF. INTERVAL]

[div.sub.t-1]       -0.2071936    0.2612557
geps                -0.0120302    0.0097036
eps                  0.0954382    0.2283200
invtota             -0.3337301    0.4289178
fatota              -7.0809060    2.7121150
ri                  -0.0060107    0.0038972
cons                 0.0059597    0.3408541
[R.sup.2](between)              223
F(6,169)                         48
Prob>F

Note: (***) Significant at 1% level, (**) significant at 5%, and (*)
significant at 10%.

TABLE 21. RESULT OF ESTIMATING MODEL (1) FOR THE 3RD DATASET OF
CONVENTIONAL BANKS (CBS)

VARIABLE              COEF.     STD. ERR.    t      P>|t|

[div.sub.t-1]        0.1331740  0.0346830   3.84  0.000 (***)
geps                 0.0002902  0.0003220   0.90  0.368
eps                  0.2104372  0.0249786   8.42  0.000 (***)
invtota              0.1710216  0.0870423   1.96  0.050 (**)
fatota               1.7996050  1.5483340   1.16  0.246
ri                   0.0025853  0.0012116   2.13  0.034 (**)
_cons               -0.0706368  0.0400253  -1.76  0.078 (*)
[R.sup.2](between)   0.7673       Number of observations
F(6,338)            19.78         Number of panels
Prob>F               0.0000

VARIABLE             [95% CONF. INTERVAL]

[div.sub.t-1]        0.0649522    0.201396
geps                -0.0003432    0.000924
eps                  0.1613041    0.25957
invtota             -0.0001912    0.342234
fatota              -1.2459790    4.84519
ri                   0.0002021    0.004969
_cons               -0.1493668    0.008093
[R.sup.2](between)              394
F(6,338)                         50
Prob>F

Note: (***) Significant at 1% level, (**) significant at 5%, and (*)
significant at 10%.

TABLE 22. RESULT OF ESTIMATING MODEL (1) FOR THE 1ST DATASET OF ISLAMIC
BANKS (IBS)

VARIABLE              COEF.     STD. ERR.    t      P>|t|

geps                -0.0015464  0.0018472  -0.84  0.403
eps                  0.1842663  0.0300565   6.13  0.000 (***)
invtota              0.2628021  0.1308466   2.01  0.046 (**)
lnta                 0.0285231  0.0131881   2.16  0.032 (**)
fatota              -0.0544825  0.9028336  -0.06  0.952
ri                   0.0021673  0.0012704   1.71  0.089 (*)
_cons               -0.2680603  0.1312568  -2.04  0.042 (**)
[R.sup.2](between)   0.4235        Number of observations
F(6,209)             7.27          Number of panels
Prob>F               0.0000

VARIABLE             [95% CONF. INTERVAL]

geps                -0.0051879    0.002095
eps                  0.1250135    0.243519
invtota              0.0048539    0.52075
lnta                 0.0025243    0.054522
fatota              -1.8343100    1.725345
ri                  -0.0003371    0.004672
_cons               -0.5268172   -0.0093
[R.sup.2](between)              241
F(6,209)                         26
Prob>F

Note: (***) Significant at 1% level, (**) significant at 5%, and (*)
significant at 10%.

TABLE 23. RESULT OF ESTIMATING MODEL (1) FOR THE 2ND DATASET OF ISLAMIC
BANKS (IBS)

VARIABLE              COEF.     STD. ERR.    t    P>|t|  [95% CONF.
                                                         INTERVAL]

geps                -0.0095361  0.0164576  -0.58  0.565  -0.0427259
eps                  0.0695102  0.0735905   0.94  0.350  -0.0788992
invtota              0.1190729  0.5041191   0.24  0.814  -0.8975802
lnta                 0.0498091  0.0440894   1.13  0.265  -0.0391057
fatota              -0.7941536  2.0391940  -0.39  0.699  -4.9065800
ri                   0.0022663  0.0037625   0.60  0.550  -0.0053215
_cons               -0.3250799  0.3583253  -0.91  0.369  -1.0477120
[R.sup.2](between)   0.6516           Number of observations
F(6,43)              0.61             Number of panels
Prob>F               0.7177

VARIABLE            [95% CONF. INTERVAL]

geps                      0.023654
eps                       0.21792
invtota                   1.135726
lnta                      0.138724
fatota                    3.318273
ri                        0.009854
_cons                     0.397552
[R.sup.2](between)       65
F(6,43)                  16
Prob>F

TABLE 24. RESULT OF ESTIMATING MODEL (1) FOR THE 1ST DATASET OF ISLAMIC
BANKS (IBS)

VARIABLE              COEF.     STD. ERR.    t       P>|t|

geps                -0.0021066  0.0011165  -1.89  0.061 (*)
eps                  0.3294499  0.0470530   7.00  0.000 (***)
invtota              0.0882965  0.0868488   1.02  0.311
lnta                -0.0323122  0.0168495  -1.92  0.057 (*)
fatota              -0.1171132  0.8354682  -0.14  0.889
ri                  -0.0004156  0.0009566  -0.43  0.665
_cons                0.3207109  0.1619881   1.98  0.050 (***)
[R.sup.2](between)   0.4539        Number of observations
F(6,144)             8.40          Number of panels
Prob>F               0.0000

VARIABLE             [95% CONF. INTERVAL]

geps                -0.0043135    0.0001
eps                  0.2364460    0.422454
invtota             -0.0833668    0.25996
lnta                -0.0656165    0.000992
fatota              -1.7684790    1.534253
ri                  -0.0023064    0.001475
_cons                0.0005293    0.640893
[R.sup.2](between)              176
F(6,144)                         26
Prob>F

Note: (***) Significant at 1% level, (**) significant at 5%, and (*)
significant at 10%.

TABLE 25. SUMMARY RESULTS OF THE CAUSAL MODEL

                      HYPOTHESIS SUPPORTED FOR CBS
                                 DATASET
PREDICTOR         ENTIRE         BEFORE          AFTER

[div.sub.t-1]  ([check]+***)                 ([check]+***)
geps
eps            ([check]+***)  ([check]+***)  ([check]+***)
invtota                                      ([check]+**)
lnta
fatota
ri                                           ([check]+**)

                   HYPOTHESIS SUPPORTED FOR IBS
                             DATASET
PREDICTOR         ENTIRE      BEFORE      AFTER

[div.sub.t-1]
geps                                   ([check]+*)
eps            ([check]+***)          ([check]+***)
invtota        ([check]+**)
lnta           ([check]+**)            ([check]+*)
fatota
ri              ([check]+*)

Note: ([check]) Hypothesis is supported; (+) Positive effect; (***)
Significant at 1% level, (**) significant at 5%, and * significant at
10%.
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Author:Aldeehani, Talla M.
Publication:Business and Economic Horizons
Geographic Code:7SAUD
Date:Jan 1, 2019
Words:10073
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