This article is published by the Zamfara International Journal of Humanities.
By
Prof. Fodio I. Musa
Department of Accountancy & Finance
Nasarawa State University, Keffi.
NasarawaNigeria,
Prof. J.E.I Abbah
Department of Business Administration
Nasarawa State University, Keffi.
NasarawaNigeria
&
MUSA, Sadiq AbdulKhadir
Department of Business Administration
Nasarawa State University, Keffi.
NasarawaNigeria
Abstract
Over the years, the nature of dividend payment of quoted nonfinancial firms in Nigeria has been observed: while some firms pay a dividend, others are not regular and some do not pay at all. The general objective of this research, therefore, is to determine the factors (previous dividend, return on assets, financial leverage, firm growth, tax, firm age, and firm size) responsible for the variance in payments of divided among the quoted nonfinancial firms in Nigeria. This study adopts the ex post facto research design. The population for the study is the quoted nonfinancial firms on the Nigerian Stock Exchange (NSE) from 2010 through 2019. There were a hundred and thirteen (113) firms. The purposive sampling technique is employed based on firms with records of declaration of dividend payment for at least six years within the period under review (20102019). On this basis, a total of fiftythree (53) firms spread across the ten (10) sectors were selected to form the sample size of the study. Secondary data was collected from the firms’ published annual reports. The panel regression model was used to estimate the data. After the Hausman test was conducted, the fixedeffect statistical model was adopted for the overall model of quoted nonfinancial firms in Nigeria. After the test of hypotheses, the results reveal financial leverage, tax, firm size, previous dividend, and return on assets have a significant and a positive effect on dividend payout in descending order. While firm age and firm growth have an insignificant effect. It is recommended among others, that the board of directors and the management should be mindful of the obligation to bondholders and not concentrate only on paying a dividend to shareholders only.
Keywords: Dividend Policy, Dividend Payout, Previous dividend, Return on Assets, Financial Leverage.
Introduction
Dividend is the portion of profit firms declare as a return to shareholders. Dividend policy, therefore, is one of the most important financial decisions firms are expected to make. The decision of how much to retain or disburse from its earnings in the form of a dividend to stakeholders has remained a contending issue for ages. Three parties are pivotal in dividend decisions: the firm itself, shareholders, and creditors (Musa, 2005). The firm operates based on generated free cash flow which is used for financing its growth needs. These funds are funds that the shareholders expect the firms to disburse as dividend. Failure to disburse will signal a poor performance to the relevant public, as some may not be willing to invest since the purchasing power has been reduced by nonpayment of dividends.
To meet dividend obligations, firms are faced with several variables (previous dividend, return on asset, financial leverage, firm growth, tax, firm age, and firm size) which determine how it pays. Dividend payout is a measure of a firm’s dividend policy; it denotes the proportion of earnings a firm is willing to bequeath shareholders as dividends. Previous researches and dividend theories suggest that: previous dividend is used to establish the nature of dividend payment of a firm. Most firms are believed to want to maintain a stable policy to signal good performance to the public. A firm’s return on asset (ROA) is a measure of profitability ratio. It is obvious when a firm makes a profit, only then dividend can be paid therefore, the more profit firm makes the more it is expected to pay a dividend and vice versa.
Leverage is an important determinant of dividend payout because debt holders will require funds from the firms’ profit to pay interest, as result; this might cause a reduction in the amount of dividend payout because instead of the firms to pay a substantial amount as a dividend, it would need to set aside another fund for servicing its debts.
Another variable expected to affect dividend payout is sales growth. Firms with higher growth opportunities tend to pay a lower dividend. Conversely, some with fewer growth opportunities pay higher dividends to prevent managers from overinvesting funds available to the firm to reduce the agency cost.
Tax is an imposed levy by the government on business earnings. Once a business makes some earnings, then a proportion is to be paid in tax to the government. The extent to which businesses are charged tax affects the amount left to be paid as dividend. Firm age also is considered another determinant of dividend payout. Young firms with prospects are believed to be striving for expansion since they are new therefore, they may pay fewer dividends because they need to retain funds for expansion. A matured firm may be willing to pay more dividends to its shareholders since most of its investment opportunities may have already been explored.
Firm size is a determinant of dividend payout. Large firms are expected to pay more dividends because of their already large asset. Whereas, small firms in need of expansion pay less because they needed to retain earnings to finance the expansion. Firm size is denoted as the natural log of total assets. The financial manager must understand the various factors that determine dividend payout. (Akinsulire, 2014)
Over the years, the nature of dividend payment of overall quoted nonfinancial firms in Nigeria has been observed: while some firms pay a dividend, others are not regular and some do not pay at all. The hierarchical causes and nature of the overall firms’ dividend payment has remained a mystery that this research has attempted to unravel. Therefore, this study seeks to find out why nonfinancial firms do pay a dividend as well as why they don’t as the case may be. Nonfinancial firm was chosen because it is not as strictly regulated as the financial sector.
In the recent past, various studies have been carried out to explain dividend policy using different forms of variables. One thing common with these studies is the use of a similar statistical technique such as multiple regression and correlation analysis or ordinary least squares and panel regression technique method; secondly, the studies were mostly carried out in various countries, sectors and, timeframe. Only a few in Nigerian overall nonfinancial sector were recently carried out. Research results on the determinant of dividend policy by prior studies are confronted with mixed results: while some found a significant positive relationship, significant negative relationship, others reported an insignificant relationship; See, for example, Eliasu (2014), Zakir (2016), Adnan (2017), Kannadhasan et al. (2017), Abdulrauf (2021), etc. these mixed results apply to variables such as the previous dividend, return on assets, financial leverage, firm growth, tax, firm age and firm size.
The mixed result must have been as a result of the studies being carried out in different countries, different sectors which have different policies and legislations governing operations, and the application of inappropriate research methods in some cases. This calls for further investigation using a different domain, assorted variables, and more robust technique and by extending the time scope to ascertain the nature of the current state of affairs. This study hypothesised that previous dividend, return on assets, financial leverage, firm growth, tax, firm age, and firm size have no significant effect on the dividend policy of quoted nonfinancial firms in Nigeria.
That is to say, the general objective of the study is to determine the factors responsible for the variance in payments of divided among the quoted nonfinancial firms in Nigeria. The study is divided into five sections: this introduction is the first section, followed by literature review on the concepts of leverage, firm size, tax and return on assets then, the methodology of the study, results and discussion of findings and lastly, the conclusion and recommendation section.
Literature Review and Conceptual Clarifications
Concepts of Dividend Policy
Dividend refers to the distribution of the portion of profit to the shareholders as a form of reward in fulfilling the wealth maximisation objective of the shareholders. Dividend policy is the practice that management follows in making dividend payout decisions or, in other words, the size and pattern of cash distributions over time to shareholders (Lease, 2000). Furthermore, a dividend is defined as a portion of a firm’s net earnings, which is paid among the shareholders (Khan & Jain, 2007). Pandey (2005) defined dividends as the earnings distributed to shareholders. Furthermore, dividend policy remains one of the most important financial policies not only from the viewpoint of the company but also from that of the shareholders, the consumers, employees, regulatory bodies, and the government. It is a pivotal policy around which financial policies rotate (Alii, Kha & Ramirez, 1993).
A firm’s dividend policy has the effect of dividing its net earnings into two parts: retained earnings and dividends. The retained earnings provide funds to finance longterm growth. It is the most significant source of financing a firms’ investment in practice. Dividends are paid in cash. (Pandey, 2005).
The key measures of dividend policy can be divided into dividend yield and dividend payout. This work adopts dividend payout ratio as a measure of dividend policy because it is internal i.e., it is within the management control to decide the proportion of profit to declare as a dividend. The dividend yield is subject to external influence: price fluctuations, which is outside management control to a large extend. Dividend payout is defined as the ratio between total dividends and net profit (Bijana et al., 2021). The dividend payout ratio takes internal factors into considerations and it is therefore independent of external factors (Penman, 2009). Also, it tends to follow the life cycle of a firm and indicates the maturity of a firm. In addition, the retention ratio derived from it is used to estimate growth in future earnings (Labhane & Das, 2015).
Determinants of Dividend Policy
According to Ramli and Arfan (2011), the magnitude of the current dividend is based on the amount of dividends paid years ago as companies are trying to maintain or even increase the dividend payout ratio from the previous levels. The higher the dividend payout in the previous year, the steeper the amount of dividends received by shareholders of the current year.
The last year’s dividends payout was used as a proxy variable for previous dividends. The previous year’s dividends positively affect the current dividend payout ratio of a company. This shows that previous years as a measure should have a positive effect on dividends (Maladjian & Khoury, 2014). This study adopts the perspective of Ramli and Arfan (2011). This is because it echoes the study of consistency and pattern of a firm’s dividend payout relative to previous years. Rreturn on assets is one of the key measures of profitability ratios such as the gross profit margin, net profit margin, operating expense ratio, return on investment and return on equity. Return on assets, return on investment, and return on equity all relate profitability to investment. The term investment may refer to total assets or net assets (Pandey, 2005). The profitability ratios are calculated to measure the operating efficiency of the company. According to Rosikah et al. (2018), return on assets is used to measure the company's capability to create profits using total owned assets by a company in the future, higher ROA of a company performance will lead to more effective company
On the other hand, the debt ratio is a measure of financial leverage; it is used to analyze the longterm solvency of a firm. The firm may be interested in knowing the proportion of the interestbearing debt (also called funded debt) in the capital structure. It may therefore compute the debt ratio by dividing total debt by capital employed or total assets (Pandey, 2005). The ratio is used to assess the proportion of total funds (short and long term) provided by outsiders to finance total assets. Gugler and Yurtoglogly (2003) used the total debt to total assets ratio as a proxy for leverage. This study adopts the debt ratio measure as total debt divided by total assets. This is because they are an important determinant of a firm’s financial risk since they represent obligations and exert pressure on the firm and restrict its activities.
For firm growth, Denis and Stepanyan (2011) opined that dividend policy is strongly linked to fundamental firm characteristics such as growth opportunities. Growth in sales and the markettobook value is used as predictors of investment opportunities. A high growth rate of income on interest represents the signal to investors that a company is in the phase of open investment opportunities, whereby each new investment decreases the amount that remains for the dividend payout (Dewasiri et al., 2019). This means firms experiencing growth in sales revenue can signal more investment opportunities for firms to take advantage of; this will result in a reduction in firm earnings that would be shared as a dividend.
Consequently, this research adopts sales revenue as a measure of firm growth. This is because as stated earlier, it reflects both short and longterm changes in the firm and it is an objective measure if compared to others. It is operationalised as current revenue minus previous revenue divide by previous revenue. According to Nwadighoha (2011), tax can be defined as a compulsory levy imposed by a public authority on incomes, consumption, and production of goods and services, and such levies are made on personal income (consisting of salaries, business profits, interest income, dividends, royalties, etc.), company profits, petroleum profits, capital gains, and capital transfers. Masulis and Trueman (1998) argued that taxes affect the dividend payment of organisations. Changes in corporate dividend payment would be expected whenever the government changes its tax policy (Wu, 1996). Effective tax rate divided by profit before tax is used as proxy of tax in this study as used by (Mehar, 2007).
The life cycle variable age is used as a proxy for this study which is defined as the year from which the company has been into existence. Any firm has a welldefined life cycle and is fundamental to the firm life cycle theory of dividend. The mature firms have fewer investment opportunities, more accumulated profit, and retained earnings which cause them to pay more dividends. This shows that age has a positive effect on dividends (Labhane & Das, 2015). In contrast to this, younger firms are in the stage of new growth opportunities and need to build reserves of profit to finance their growth opportunities which result in less dividend payment. This means depending on the nature of a firm, it may have a negative effect as well.
In respect of firm size, this study adopts the natural log of the total asset as a measure of firm size because the total asset is not dependent on external influence such as market capitalisation, the total number of employees, and total sales that are affected by fluctuating market forces. Secondly, total asset measures the firm’s total resources procured by management to create value. Notably, large companies benefit from economies of scale when raising debt financing. With lower transaction costs and increased potential for agency problems, the size of the companies tends to be positively correlated with dividend payments.
To Ommeihodzic (2014) Firm size has the potential to influence a firm’s dividend policy. Larger firms have an advantage in capital markets in raising external funds, and therefore depend less on internal funds (Higgins, 1972). Furthermore, larger firms have a lower likelihood of bankruptcy and, therefore, should be more likely to pay dividends. This implies an inverse relationship between the size of the firm and its dependence on internal financing. This indicates that large firms can distribute higher dividends than smaller firms. This relationship is also supported by the transaction cost explanation of dividend policy.
Theoretical Framework: Signaling Theory
This theory was first developed by Solomon (1963:142). He contends that dividends may offer tangible evidence of the firm’s ability to generate cash. Managers use the change in cash dividends distribution rates as a way of delivering information to investors about the company. The foundation of the argument is the information asymmetry between managers (insiders) and outside investors. The managers tend to have private information about the current and future prospects of the company, which outsiders (shareholders) do not have. This theory asserts that managers are motivated to communicate this information to the market.
Methodology
The study used expost facto research design. The population of the study is the 113 quoted nonfinancial firms in Nigeria. This is because the aggregated data of firms were used. The sample size of this study includes 53 firms that were selected using a purposive random sampling technique. The nonfinancial sector was used in this study because it is not as strictly regulated as the financial sector. Secondary data between years 2010 through 2019 were collected from the published annual reports and the fact book of the Nigerian stock exchange. Data was collected based on firms that have paid dividend for at least six years within the period under review. The study used various procedures in analysing data such as descriptive statistics, correlation matrix, autocorrelation, multicollinearity and hausman test. The model of the study is as follows:
Yit= Î²_{0 }+ Î²_{1}PREVDit+Î²_{2}ROAit+Î²_{3}LEVit+ Î²_{4}GROWTHit+Î²_{5}TAXit+ Î²_{6}AGE+Î²_{7}SIZEit+ eit + Æ© …………………………………………………………………… (1)
Where: eit= Control for individual specific effect; Y = Dividend Payout ratio; Î²_{0}= Intercept; Î²_{1 }= Coefficient of previous dividend; Î²_{2 }= Coefficient of Return on assets; Î²_{3 }= Coefficient of Lev; Î²_{4 }= Coefficient of Growth; Î²_{5 }= Coefficient of Tax; Î²_{6}= Coefficient of Age; and, Î²_{7}= Coefficient of Size
Results and Discussions
Table 1
Measurement of Variables
Variable  Symbol  Measurement  Source  Expected Sign 
Dividend Payout  DIV  Paid Dividend/Net Income  Bijana et al. (2021) 

Previous Dividend  PREVD  Prior year (lagged) Dividend Payout  Raamli and Arfan (2016)  + 
Return on Asset  ROA  Profit after tax / Total Assets  Westerfield et al. (2015)  + 
Financial Leverage  LEV  Total Debt / Total Assets  Guglar and Yurtoglogly (2003)   
Firm Growth  GROWTH  Current Sales RevenuePrevious Sales Revenue/ Previous Sales Revenue  Delmar (1997)   
Tax  TAX  Total Tax Expenses/ Earning Before Tax  Mehar (2007)   
Firm Age  AGE  Date of Incorporation + 1 (lead time)  Ebenezer (2013)  + 
Firm Size  SIZE  Natural Log of Total Assets  Holder et al (1998)  + 
Source: Author’s Computation, 2022
Table 2
Normality Test
Variable  Observation  W  V  Z  Prob>z 
DIV  530  0.92634  26.116  7.863  0.00000 
PREVD  478  0.90262  31.458  8.273  0.00000 
ROA  530  0.54149  162.558  12.270  0.00000 
LEV  530  0.98453  5.485  4.102  0.00002 
GROWTH  530  0.39053  216.080  12.956  0.00000 
TAX  530  0.48596  182.247  12.546  0.00000 
AGE  530  0.98592  4.993  3.876  0.00005 
SIZE  530  0.80643  68.628  10.192  0.00000 
Source: Researcher’s computation using Stata 13.0, 2022.
Shapiro Wilk normality test was conducted for the individual variables; the result is presented above (table 2). The pvalue for each of the variables (DIV, PREVD, ROA, LEV,
GROWTH, TAX, AGE, and SIZE) is 0.000 which is less than 5% respectively. This means the data are not normally distributed. The data, therefore, are logged and used for analysis.
Table 3
Autocorrelation Test
Chi2 = 99.3390  Prob = 0.3610 
Source: Researcher’s computation using Stata 13.0, 2022.
The above table presents an autocorrelation or serial correlation test result summary. The Breusch Godfrey serial correlation test is used to check whether the model has an autocorrelation problem since the data used is panel data. Based on the results, the chisquare probability of more than 5% means there is an absence of serial correlation in the model. This means the regression coefficients are not spurious. The above 5% Pvalue for Model 1 implies the absence of serial correlation.
Table 4
Multicollinearity Test
VARIABLE  VIF  1/VIF 
AGE  1.08  0.923275 
LEV  1.06  0.944960 
SIZE  1.04  0.95905 
ROA  1.03  0.971379 
PREVD  1.03  0.974937 
TAX  1.01  0.987112 
GROWTH  1.01  0.987112 
MEAN VIF  1.04  0.988195 
Source: Researcher’s computation using Stata 13.0, 2022.
The table above shows the results obtained from the variance inflation factor test. The mean VIF value of 1.04 which is less than the benchmark value of 10 indicates the absence of multicollinearity. This means none of the independent variables are collinear.
Table 5
Heteroscedasticity Test
Prob = 0.0001 
Source: Researcher’s computation using Stata 13.0, 2022.
Table 5 shows the result obtained from the test of heteroscedasticity. The probability value of 0.0001 resulting from the test for heteroscedasticity implies that the model is free from the presence of unequal variance. It, therefore, implies that our probability values for drawing inference on the level of significance are reliable and valid.
Table 6
Descriptive statistics (overall)
 Mean  Median  Std. Dev.  min  max  N 














Div  .293  .214  0.306  0  0.99  530 
Prevd  .289  .214  0.304  0  0.99  478 
Roa  .207  .065  0.504  2.128  4.828  530 
Lev  .52  .545  0.232  .0329  .979  530 
Growth  .355  .059  1.785  .971  18.366  530 
Tax  .267  .297  0.405  1.9  6.992  530 
Age  40.066  41  18.784  1  96  530 
Size  22.637  23.294  2.403  13.38  25.319  530 
Source: Researcher’s computation using Stata 13.0, 2022.
Table 6 presents the overall summary statistics of all variables used in the study. The mean value of dividend payout for the sampled firms was 0.293 while the median value was 0.214. The standard deviation from the mean was 0.306. This means the dividend payout data is consistent. The minimum value of dividend payout was 0 while the maximum was 0.99. This means that some firms that do not pay dividends were included in the sampled firms. There were 530 observations.
The mean value of previous dividend (PREVD) for the sampled firms was 0.289 while the median value was 0.214. This means the data is free from an outlier. The standard deviation from the mean was 0.304. This means the previous dividend data was consistent. The minimum value of PREVD was 0 while the maximum was 0.99. This means that some firms that do not pay dividends were included in the sampled firms. There were 478 observations._{}
The mean value of return on assets (ROA) for the sampled firms was 0.207 while the median value was 0.65. This means the data is free from outliers. The standard deviation from the mean was 0.504. This means the data is consistent. The minimum value of ROA was 2.128 while the maximum was 4.828. This means that some firms that return loss and those that were efficient in asset utilization were included in the sampled firms. The standard deviation from the mean was 0.504. This means the data was consistent. There were 530 observations.
The mean value of financial leverage (LEV) for the sampled firms was 0.52 while the median value was 0.545. This means the data was free from outliers. The standard deviation from the mean was 0.232. This means the data was consistent. The minimum value of LEV was 0.032 while the maximum was 0.979. This means that some firms were not levered at a point in time while some that were highly levered were included in the sampled firms. There were 530 observations.
The mean value of firm growth (GROWTH) for the sampled firms was 0.355 while the median value was 0.059. This means the data is not free from outliers. The standard deviation from the mean was 1.785. This means the data is consistent. The minimum value of GROWTH was 0.971 while the maximum was 18.366. This means that some firms that were not growing and those experiencing growth at a point in time were included in the sampled firms. There were 530 observations.
The mean value of tax (TAX) for the sampled firms was 0.267 while the median value was 2.97. This means the data is not free from outliers. The standard deviation from the mean was 0. 405. This means data is consistent. The minimum value of tax was 1.9 while the maximum was 6.992. This means that some firms that were charged tax even at loss and those that pay at profit were included in the sampled firms. There were 530 observations.
The mean value of firm age (AGE) for the sampled firms was 40 while the median value was 41. This means the data is free from outliers. The standard deviation from the mean was 18. 784. This means the data is dispersed. The minimum value of age was 1 while the maximum was 96. This means that some firms that were new and older were included in the sampled firms. The standard deviation from the mean was 1.785. This means the data is consistent. There were 530 observations.
The mean value of firm size (SIZE) for the sampled firms was 22.637 while the median value was 23.294. This means the data is not free from outliers. The standard deviation from the mean was 2. 403. This means the data is consistent. The minimum value of size was 13.38 while the maximum was 25.319. This means that smaller and bigger firms were included in the sampled firms. There were 530 observations.
Correlation Matrix (overall)
To explore the determinants of dividend policy and the nature of the relationship, this study applied a correlation matrix to determine whether the variables have some degree of association or not. The decision rule is; that if the correlation coefficient is greater than or equal to 0.70, there is a strong correlation between the two variables. A correlation somewhere 00.19 means that the relationship is very weak; 0.200.39 is weak; 0.400.59 is moderate; 0.600.79 is strong; 0.80 – 100 is very strong. The below table is the overall correlation matrix of determinants of dividend policy of quoted nonfinancial industries in Nigeria.
Table 7
Correlation Matrix
Variables  Div  prevd  Roa  lev  Growth  tax  Age  size 
DIV  1.000 
 
PREVD  0.359  1.000 
 
ROA  0.194  0.124  1.000 
 
LEV  0.028  0.015  0.030  1.000 
 
GROWTH  0.025  0.045  0.012  0.009  1.000 
 
TAX  0.003  0.013  0.050  0.074  0.038  1.000 
 
AGE  0.061  0.090  0.085  0.203  0.047  0.030  1.000 

SIZE  0.050  0.043  0.071  0.097  0.072  0.033  0.162  1.000 

Source: Researcher’s computation using Stata 13.0, 2021.
Table 7 shows the correlation between DIV, PREVD, ROA, LEV, GROWTH, TAX, AGE and SIZE. The correlation between PREVD and DIV shows a weak positive correlation at 0.359 which shows the absence of multicollinearity. The correlation between ROA and DIV (0.195); ROA and PREVD (0.124) is positive and weak indicating the absence of multicollinearity. The correlation between LEV and DIV (0.028); LEV and PREVD (0.015); LEV and ROA (0.030) are very weak negative, very weak positive, and very weak negative respectively. The correlation between GROWTH and DIV (0.025) is weak positive; GROWTH and PREVD (0.045) is weak negative; GROWTH and ROA (0.012) is weak negative, while GROWTH and LEV (0.009) is very weak negative. The correlation between TAX and DIV (0.003) is weak negative; TAX and PREVD (0.013) is weak negative; TAX and ROA (0.05) is very weak positive; TAX and LEV (0.074) is very weak positive while that of TAX and GROWTH (0.038) is weak negative.
The correlation between AGE and DIV (0.061) is weak positive; AGE and PREVD (0.090) is weak positive; AGE and ROA (0.085) is weak positive; AGE and LEV (0.203) is positive; AGE and GROWTH (0.047) is weak positive; similarly, AGE and TAX (0.030) is weak positive. Lastly, the correlation between SIZE and DIV (0.050) is weak positive; SIZE and PREVD (0.043) is weak positive; SIZE and ROA (0.071) is weak positive; SIZE and LEV (0.097) is positive; SIZE and GROWTH (0.072) is positive; SIZE and TAX (0.033) is weak positive; while that of SIZE and AGE (0.162) is positive. Generally, correlation coefficients between the variables are mostly positive but few are negative. All the coefficients suggest the absence of multicollinearity.
Table 8
Hausman Test
Chi2 = 29.69  Prob = 0.0001 
Source: Researcher’s computation using Stata 13.0, 2021.
This section presents the results of the test of the hypothesis. Panel regression was used in which the Hausman test was used to select the best model between fixed and random effects. It was found as presented in the above table; the appropriate model for this study is the fixed effect model based on the Hausman test pvalue of less than 0.05%. therefore, the fixed effect model is used for analysis in this study.
Table 10
Panel regression results
VARIABLES  FIXED EFFECT 
PREVD  0.029** (2.28) 
ROA  0.017** (1.99) 
LEV  0.102*** (4.67) 
GROWTH  0.001 (0.60) 
TAX  0.064*** (3.55) 
AGE  0.000 (1.07) 
SIZE  0.040*** (5.66) 
CONSTANT  0.004* (0.002) 
FSTATISTIC  4159*** 
R^{2} WITHIN  0.8341 
Source: Researcher’s computation using Stata 13.0, 2021.
Note. *** p<0.01, ** p<0.05, *p<0.1. Where ***, **, and * indicates coefficient’s pvalue at 0.01%, 0.05% and 0.1% respectively. Values in parenthesis represent T or Z statistic.
This section discusses the previously established results (findings). This research studies the determinants of dividend policy of quoted nonfinancial firms in Nigeria. In the process, hypotheses were tested. Therefore, each independent variable size (magnitude), sign, and significance/insignificance of coefficient is discussed as follows:
Previous Dividend
Using the appropriate model, fixed effect for the overall model 1, the previous dividend has a significant effect on dividend payout. The effect is positive. This can be rationalized from the belief that companies pay a stream of dividends because investors perceive firms with stable dividends as stronger and more valuable. So, for the aggregated (overall) model 1, all the nonfinancial firms are believed to have a stable dividend policy. This finding is in line signaling theory. Also, it is in line with the finding of Bassam et al. (2018) and, Lestari (2018).
Return on Asset (ROA)
Using the fixed effect panel regression model for the overall model 1, ROA was found to have a statistically significant coefficient. The effect was positive. This implies the higher the utilization of assets by a firm in generating profit, the better their ability to pay a dividend. This finding conforms with the signaling theory as well as agency theory because the firms pay to send good news about the sector to the owners and public. This finding conforms with that of Adnan (2017) and, Rajesh and Sujit (2018)
Financial Leverage
The overall (Model 1) for the quoted nonfinancial firms shows a significant effect of financial leverage. The effect was positive. This simply means most of the industries in the sector are less levered and therefore restrictive covenants are not placed in the bond contract. The finding of this study is in line with Ahmed et al. (2018), but contrary to MarfoYiadom. The finding is supported by signaling theory.
Firm Growth
From the fixed effect overall (Model 1) growth portends a negative effect on dividend payout of quoted nonfinancial firms in Nigeria. This means the more the firms grow in terms of sales, the less they pay a dividend. However, the relationship is insignificant. This implies that for most of firms, growth is not an important determinant of the dividend payout of quoted nonfinancial firms in Nigeria. The result agrees with that of Lestari (2018) and Rulisma’rufatin (2018). Depending on the nature of investors, the finding is supported by the agency theory.
Tax
Using the fixedeffect model for the overall (Model 1), tax has a significant effect on dividend payout. The effect was positive. This means the overall firms manage their policy so as not to trigger investors’ decisions that can adversely affect stock prices, so they pay more dividends as a tax increase. The finding is in line with that of Zachan, Ch, Shaheezad and Wasimullah (2012) who found a positive effect; the finding is contrary to that of Lim (2013) who found a negative effect. The findings did not support the tax effect theory. But can be explained by signaling.
Firm Age
Using the fixed effect model for the overall (Model 1), firm age has an insignificant positive effect on dividend payouts. This means firm age is not an important determinant of dividend payout. The finding conforms to that of James and Onwuke (2019). These findings are supported by the signaling theory.
Firm Size
In the overall (Model 1), size has a significant effect on dividend payout. The effect is positive. This implies they are large firms because they can have access to finance cheaply due to their collateral base, so they can pay a dividend. The finding of this theses is in line with that of Abdulrauf (2021), Abraham (2017) and, Rajesh and Sujit (2018) who found a positive and significant effect. Firms must have been paying dividend to solve or reduce the agency problem. Therefore, this finding is explained by the agency as well as signaling theory.
Conclusion and Recommendations
This study examined the determinants of dividend policy of the quoted nonfinancial firms in Nigeria. It was found that financial leverage is the most significant determinant of dividend policy of quoted nonfinancial firms in Nigeria. This means the null hypothesis is rejected. The board of directors and management should be mindful of the obligation to bondholders and not concentrate only on paying a dividend to shareholders.
Tax is the second most significant determinant of dividend policy of quoted nonfinancial firms in Nigeria. This means the null hypothesis is rejected. It is therefore recommended to the board, to constitute an independent investigation panel to review the firms’ management strategies in the quoted nonfinancial sector to find out the rationale behind paying of more dividends even in the face of increasing tax.
Firm size is the third most significant determinant of dividend policy of quoted nonfinancial firms in Nigeria. This means the null hypothesis is rejected. Continued improvement of firm size should be ensured by management in all sectors through the expansion of their market reach which can result to acquisition of more assets.
Previous dividend is the fourth most significant determinant of dividend policy of quoted nonfinancial firms in Nigeria. This means the null hypothesis is rejected. The board of directors and management of firms in the sector should strive to have a stable/consistent dividend policy by establishing an earning reserve
Return on assets is the fifth most significant determinant of dividend policy of quoted nonfinancial firms in Nigeria. This means the null hypothesis is rejected. The board of directors and managements should continue with their mode of assets utilization in generating more profit to pay a dividend.
On the other hand, firm age is the most insignificant determinant of dividend policy of quoted nonfinancial firms in Nigeria. This is drawn from the test of the null hypothesis (H_{0}) which states that tax has no significant effect on the dividend payout of quoted nonfinancial firms in Nigeria. This is not rejected. However, management should ensure that their age lifecycle trend is echoed in their payouts by being continuously effective in performance at all times.
Firm growth is the second most insignificant determinant of dividend policy of quoted nonfinancial firms in Nigeria. This is drawn from the test of the null hypothesis (H_{0}) which states that tax has no significant effect on the dividend payout of quoted nonfinancial firms in Nigeria. This is not rejected. However, the management of firms should explore, strategize and take advantage of any growth opportunity in order to achieve shareholder wealth maximization objective.
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