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=
β_{0 }+ β_{1}PREVDit+β_{2}ROAit+β_{3}LEVit+ β_{4}GROWTHit+β_{5}TAXit+
β_{6}AGE+β_{7}SIZEit+ eit
+ Ʃ …………………………………………………………………… (1)
Where:
eit= Control for individual specific effect; Y = Dividend Payout ratio; β_{0}=
Intercept; β_{1 }= Coefficient of previous dividend; β_{2 }=
Coefficient of Return on assets; β_{3 }= Coefficient of Lev; β_{4 }=
Coefficient of Growth; β_{5 }= Coefficient of Tax; β_{6}=
Coefficient of Age; and, β_{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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