




































INDIAN JOURNAL OF FINANCE AND BANKING 10(1) (2022), 54-60 

54 

 

                       FINANCE AND BANKING 

                                                                  IJFB VOL 10 NO 1 (2022) P-ISSN 2574-6081  E-ISSN 2574-609X 
                                                  

        Available online at https://www.cribfb.com 

                                                                                                                                           Journal homepage: https://www.cribfb.com/journal/index.php/ijfb 
                                                                                                                                                                                                   Published by CRIBFB, USA 

DETERMINANTS OF DIVIDEND POLICY: AN EMPIRICAL 

INVESTIGATION OF INDIAN COMPANIES USING PANEL DATA 

ESTIMATION TECHNIQUE        

 
 Razique Anwar (a)    Sandeep Kumar (b)1   

 

(a) Research Scholar at Indian Institute of Management, Kashipur - 244713, India; E-mail: razique.phd1902@iimkashipur.ac.in 
(b) Assistant Professor at Jamshedpur Workers’ College, Jamshedpur – 831001, India; E-mail: sandijaiswal@gmail.com 

 

 
A R T I C L E I N F O 

 
 

Article History: 
 

Received: 19th May 2022  

Accepted: 29th June 2022 

Online Publication: 31st July 2022 

 
Keywords: 

 

Dividend Yield, Dividend Policy  

Indian Companies, Leverage  

Panel Data  

  
JEL Classification Codes: 

 

      G1, G3, M2 

 
  

 
A B S T R A C T 

 
The decision to pay dividends by companies is among the most researched and contentious topics in 

corporate finance as, according to some scholars, it has a bearing on companies' valuation. The purpose 

of this research is to find out important factors that act as determinants of the dividend policy of Indian 

companies. The study employs secondary time series data gathered from the latest data available from the 

CMIE (Centre for Monitoring India) Prowess database. The study has been divided into three time periods 

i.e. 1) 2000-01 to 2009-10, 2) 2010-11 to 2020-21, 3) 2000-01 to 2020-21, by merging the two time periods 
and the panel data regression technique has been applied. The results of the analysis suggest that Return 

on assets, Debt to equity ratio, Cash and cash equivalents & Debt to total capital ratio significantly affect 

dividend yield from 2000-01 to 2009-10 and from 2000-01 to 2020-21, whereas the results of the analysis 

from period 2010-11 to 2020-21 found Sales/Size, Return on the asset, and Debt to total equity ratio as 

determinants of dividend policy. These results corroborate the earlier findings that profitability, liquidity, 

and leverage are important factors in the decision-making of companies regarding the payment of 

dividends. 
 

 

© 2022 by the authors. Licensee CRIBFB, USA. This article is an open access article distributed under 
the terms and conditions of the Creative Commons Attribution (CC BY) license 
(http://creativecommons.org/licenses/by/4.0/).                           

 

INTRODUCTION 

The decision to pay dividends or not is one of the most contentious decisions for companies. “The harder we look at the 

dividend picture the more it seems like a puzzle, with pieces that just do not fit together” (Black, 1998). The puzzle is among 

the top ten unsolved problems of finance. So it is very hard to dissect the dividend policy of the companies. The decision of 

firms to declare dividends or to retain earnings for future growth has always been dynamic. A vast amount of literature 

available on this study suggests theoretical and empirical support for variables such as corporate size, age, ownership 

structure, market risk, cash holdings, leverage, growth opportunities, net working capital, and profitability of the firms as 

important factors as determinants of dividend policy for companies. 

In light of the brief above argument presented, the present study intends to find out the factors that act as 

determinants of dividend policy for 356 Indian companies under study using the panel data regression analysis technique. 

The analysis of this study has been divided into three time periods i.e 1) From the financial year 2000-01 to 2009-10,  2) 

From the financial year 2010-11 to 2020-21,  3) From the financial year 2000-01 to 2020-21 by merging the above two time 

periods to find out the significant factors that act as determinants of dividend payment by companies. In this study banking 

and financial services providing companies have been excluded as we assume that they differ in business practices and have 

different financial parameters than industries in other sectors. 

 

Trends in Dividend Yield  

Figure 1 below shows the average dividend yield of all 356 companies understudy from the financial year 2000-01 to 2009-

10. It shows that the average dividend yield showed an increasing trend though it started falling from 2005-06 till 2008-09 

                                                      
1Corresponding author: ORCID ID: 0000-0002-8292-0972 
© 2022 by the authors. Hosting by CRIBFB. Peer review under responsibility of CRIBFB, USA.  

https://doi.org/10.46281/ijfb.v10i1.1777 

 
To cite this article: Anwar, R., & Kumar, S. (2022). DETERMINANTS OF DIVIDEND POLICY: AN EMPIRICAL INVESTIGATION OF INDIAN 

COMPANIES USING PANEL DATA ESTIMATION TECHNIQUE. Indian Journal of Finance and Banking, 10(1), 54-60. 

https://doi.org/10.46281/ijfb.v10i1.1777 

https://orcid.org/0000-0002-8292-0972
http://creativecommons.org/licenses/by/4.0/)
http://creativecommons.org/licenses/by/4.0/)
https://orcid.org/0000-0002-8292-0972
https://doi.org/10.46281/ijfb.v10i1.1777
https://orcid.org/0000-0002-1573-0415


Anwar & Kumar., Indian Journal of Finance and Banking 10(1) (2022), 54-60 

 

55 

which could be attributed to the global financial crisis and India too was not fully immune to it and thus companies refrained 

from paying dividends. But again it picked up its increasing trajectory from there onwards. 

 

 
Figure 1. Above shows the average dividend yield of companies from 2000-01 to 2009-10. 

Figure 2 below shows the average dividend yield of all the companies understudy from the financial years 2010-

11 to 2020-21. It also shows an increasing trend though it was on the decline from the financial year 2015-16 till 2017-18 

which could be attributed to major structural changes within India’s economy like the implementation of Goods and Services 

Tax ( GST ) move toward the digital economy, tax amendments laws, etc. So the companies once again refrained from 

paying dividends as they had to adjust to these changes. 

 

 
Figure 2. Above shows the average dividend yield of companies from 2010-11 to 2020-21. 

Figure 3 below shows the average dividend yield of all 356 companies from the financial year 2000-01 to 2020-

2021 by merging the above two time periods. It shows a slight increasing trend in average dividend yield with a few ups 

and downs. 

 

 
Figure 3. Above the shows average dividend yield of companies from 2000-01 to 2020-21. 

0

2

4

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12

1999-00 2000-01 2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10

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%
)

Data Source : CMIE Prowess IQ

Average Dividend yield(%)

0

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2011-12 2012-13 2014-15 2015-16 2016-17 2017-18 2018-19 2019-20 2020-21 2021-22

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%
) 

Data Source : CMIE Prowess IQ

Average  Dividend yield(%)

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(%
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Data Source : CMIE Prowess IQ

Average  Dividend yield(%)



Anwar & Kumar., Indian Journal of Finance and Banking 10(1) (2022), 54-60 

 

56 

REVIEW OF LITERATURE 

According to some scholars, the decision to pay dividend has an important bearing on the valuation of the firms while others 

believe that dividend decisions are irrelevant and does not have any effect on variables affecting firms. A dividend decision 

is irrelevant in the absence of taxes, transaction costs, or other market imperfections, and the investment decisions of a firm 

are not affected by its dividend policy (Miller & Modigliani, 1961). Also, it has been found in many studies that dividend 

has no significant relation with size and industry classification, while it has an inverse relation with the growth of the 

companies. 

In the United States, Canada, United Kingdom, Germany, France, and Japan, the propensity to pay dividends is 

higher among larger and more profitable firms (Denis & Osobov, 2008). Also, profitable firms with more stable net earnings, 

ownership concentration, and market liquidity have a positive impact on dividend payout policy. The study done in Pakistan 

to find out determinants of dividend policy using the dynamic panel data analysis technique found stable net earnings, 

liquidity, and ownership concentration have positive effects whereas investment opportunity and leverage have negative 

effects on dividend payment decisions by companies (Yusof & Ismail, 2009). Also, the study done in Srilanka for 191 firms 

and 1337 firms year observation using binary logistic regression and fixed effect panel data technique found that past 

dividend decisions, earnings, investment opportunities, profitability, free cash flow (FCF), corporate governance, state 

ownership, firm size and industry influence as the key determinants of propensity to pay dividends. In addition to this past 

dividends, investment opportunities, profitability, and dividend premium are identified as the determinants of dividend 

payout. As per a study done for the U.A.E. (United Arab Emirates) firms, profitability and size are the most important 

considerations of dividend payout decisions. In another study done for 799 companies in 15 countries over 14 years, the key 

significant determinants of dividend payout decisions were found to be free cash flow, growth, liquidity, profitability, and 

size (Madra-Sawicka & Ulrichs, 2020). Also, a few other pieces of the literature suggest liquidity, size, and profitability 

have a positive relationship with dividend payment decisions by the companies. 

The study done in the Indian context using the panel data regression technique concludes that in the automobiles 

sector liquidity/cash flow, dividend distribution tax, investment opportunities, and retained earnings are the significant 

factors that influence the dividend payout ratio whereas in the pharmaceuticals sector profitability, debt to equity ratio, sales 

growth and retained earnings are the significant factor for deciding dividend payout ratio (Nathani & Gangil, 2019). 

Profitability, liquidity, leverage, risk, size of the firm, and inflation were also found to be the major determinants 

of dividend policy of selected NSE( National Stock Exchange) listed firms in India (Brahmaiah et al., 2018). Investment 

opportunity, financial leverage, size of the company, business risk, firm life cycle, profitability, tax, and liquidity were found 

to be the major determinants of the dividend policy for Indian companies (Labhane & Mahakud, 2016). The study done 

using pooled ordinary least square and fixed effect panel data technique found that size, profitability, and interest coverage 

ratios have a significant positive relation to dividend policy, and business risk and debt level have a significantly negative 

relationship with payment of dividends (Pinto & Rastogi, 2019). 

 

DATA & METHODOLOGY 

List of Variables  

The following is the list of variables with their definitions used in this study – 

 Dividend percent / Dividend Yield-  It is a financial ratio that shows the percentage of dividends a company pays 

out relative to its share price in the financial year ( Dividend / Share price). It is used as a dependent variable. 

 Sales- It is defined as the activity of selling products or services. It is also taken as a proxy for the size of the 

company in this study. 

 Return on assets- It is obtained as “net profit after tax divided by its total assets” of a company which shows the 

profitability of a company. 

 Current ratio- It is the liquidity ratio and obtained as all “current assets divided by its current liabilities of a 

company”.  

 Quick ratio- Also known as acid test ratio, it short-term or term liquidity position of a company. It is calculated 

as “current assets minus inventory divided by current liabilities”.  

 Debt to equity ratio- It is a ratio used to evaluate a company's financial leverage and is calculated as the 

“company’s total liabilities divided by its shareholder’s equity”. 

 Net cash flow from operating activities to total asset ratio- It is obtained as “net cash flow generated from 

operating activities of a business divided by its total assets”. 

 Cash & Cash equivalents- Cash and cash equivalents are a line item on the balance sheet stating the amount of 

all cash or other assets that are readily convertible into cash. 

 Retained earnings to total equity ratio- It measures how much retained earnings the company is keeping within 

when compared to its total equity. 

 Debt to total capital ratio- It is calculated as “interest-bearing total term liabilities ( short and long term) divided 

by total capital ( total debt + shareholder’s equity)”. It shows the financial leverage of the company. 

 

In this study, analysis is done for 356 companies across three time periods – 1) From the financial year 2000-01 to 

2009-2010, 2) From the financial year 2010-2011 to 2020-2021, 3) From the financial year 2000-01 to 2020-2021 by 

combining the above two time periods. Banking and financial services providing companies have been excluded from the 

study as we assume that they differ in business practices (lending and borrowing) and have different financial parameters 

than industries in other sectors. 



Anwar & Kumar., Indian Journal of Finance and Banking 10(1) (2022), 54-60 

 

57 

The panel data regression technique has been employed to find out the variables affecting dividend payment 

decisions by the firms. The results are reported based on the most robust model of panel data regression technique i.e model 

used is robust to standard errors disturbances being heteroscedastic or autocorrelated or both if the test indicates the 

presence of such disturbances. 

 

The equation used the for Panel data regression technique in the study is as follows- 

 

𝐷𝑖𝑣 𝑦𝑖𝑒𝑙𝑑𝑖,𝑡 =  𝛼 +  𝛽1𝑠𝑎𝑙𝑒𝑠𝑖,𝑡 + 𝛽2𝑅𝑒𝑡𝑢𝑟𝑛 𝑜𝑛 𝑎𝑠𝑠𝑒𝑡𝑖,𝑡 + 𝛽3𝐶𝑢𝑟𝑟𝑒𝑛𝑡 𝑟𝑎𝑡𝑖𝑜𝑖,𝑡 +  𝛽4𝑄𝑢𝑖𝑐𝑘 𝑟𝑎𝑡𝑖𝑜𝑖,𝑡

+  𝛽5𝐷𝑒𝑏𝑡 𝑡𝑜 𝑒𝑞𝑢𝑖𝑡𝑦 𝑟𝑎𝑡𝑖𝑜𝑖,𝑡 + 𝛽6𝑁𝑒𝑡 𝑜𝑝𝑒𝑟𝑎𝑡𝑖𝑛𝑔 𝑐𝑎𝑠ℎ 𝑓𝑙𝑜𝑤 𝑡𝑜 𝑡𝑜𝑡𝑎𝑙 𝑎𝑠𝑠𝑒𝑡 𝑟𝑎𝑡𝑖𝑜𝑖,𝑡

+  𝛽7𝐶𝑎𝑠ℎ & 𝑐𝑎𝑠ℎ 𝑒𝑞𝑢𝑖𝑣𝑎𝑙𝑒𝑛𝑡𝑠𝑖,𝑡 + 𝛽8 𝑅𝑒𝑡𝑎𝑖𝑛𝑒𝑑 𝑒𝑎𝑟𝑛𝑖𝑛𝑔𝑠 𝑡𝑜 𝑡𝑜𝑡𝑎𝑙 𝑒𝑞𝑢𝑖𝑡𝑦 𝑟𝑎𝑡𝑖𝑜𝑖,𝑡

+  𝛽9𝐷𝑒𝑏𝑡 𝑡𝑜 𝑡𝑜𝑡𝑎𝑙 𝑐𝑎𝑝𝑖𝑡𝑎𝑙 𝑟𝑎𝑡𝑖𝑜𝑖,𝑡 +  𝜂𝑖 + 𝜀𝑖,𝑡 

 
Where, 

 𝜂𝑖 shows unobservable firm effects, 𝜀𝑖,𝑡 shows the effect of unobservable cross-section variables changing over time. 𝛼 is 

a constant term, and 𝛽1 𝑡𝑜 9 are coefficients that show the magnitude of change in the dependent variable due to change in 

each of the individual independent variables respectively. 

 

The data source used in the study is CMIE (Centre for Monitoring India) Prowess. 

 

EMPIRICAL RESULTS & FINDINGS 

Return on assets, Debt to equity ratio, Cash and cash equivalents & Debt to total capital ratio were found to be significantly 

affecting dividend yield from the financial year 2000-01 to 2009-10 (result shown below in Table 1). 
 

Table 1. Below shows the results of Panel data regression results for the period 2000-01 to 2009-10. 

 
                              Robust 

   Divpercent        Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval] 

        Sales    -1.58e-06    1.24e-06      -1.27   0.205    -4.02e-06      8.68e-07 

          ROA     15.11666    4.000729     3.78    0.000     7.243503     22.98981 

 Currentratio     .2063839   .4004855      0.52    0.607    -.5817434    .9945112 

   Quickratio    -.4230762   .4866113     -0.87   0.385    -1.380693    .5345406 

Debttoequit~o    -.0014024   .0004352     -3.22   0.001    -.0022589    -.0005459 

     Nocftota     1.640437    2.588111     0.63    0.527    -3.452783     6.733656 

Cashandcash~s     .0000466   .0000134      3.48    0.001     .0000202     .0000729 

Retototaleq~y    -.0000535   .0000495     -1.08   0.280    -.0001508     .0000439 

Debttototal~o    -.0557873   .0155427     -3.59   0.000    -.0863743     -.0252003 

        _cons      9.28502    .5192132     17.88   0.000     8.263245     10.30679 

                                Source: Results obtained from Stata 16. 

 

The results above in Table 1 have been reported using the  “Fixed effect Cluster” variant of the panel data regression 

technique in Stata 16 as the Hausman test suggests the use of the fixed-effect model (Appendix A.1) and is the most 

appropriate variant to be used when the estimates of the standard errors are robust to disturbances being heteroscedastic and 

autocorrelated (Appendix A.2 and A.3 respectively).  (https://www.princeton.edu/~otorres/Panel101.pdf) 

Sales/Size, Return on the asset, and Debt to total equity ratio was found to be significantly affecting dividend yield 

from the financial year 2010-2011 to 2020-2021 (result shown below in Table 2 ). 
 

Table 2. Below shows the results of Panel data regression result for the period 2010-11 to 2020-21. 
 

                              Robust 

   Divpercent        Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval] 

        Sales     8.47e-06     2.75e-06      3.08   0.002     3.06e-06    .0000139 

          ROA     43.52311    8.825933     4.93   0.000     26.15453    60.89169 

 Currentratio     1.246671    1.246429     1.00   0.318    -1.206181    3.699522 

   Quickratio    -1.688633   1.592382     -1.06   0.290    -4.822287    1.445021 

Debttoequit~o     .0963707   .0469315      2.05   0.041     .0040142    .1887273 

     Nocftota     4.653729    3.83322      1.21    0.226    -2.889675    12.19713 

Cashandcash~s    -3.33e-08    .00001        -0.00   0.997    -.0000197    .0000197 

Retototaleq~o    -.0043646   .0022172    -1.97   0.050    -.0087279   -1.23e-06 

Debttototal~o    -.0115922   .0214151    -0.54   0.589    -.0537352    .0305507 

        _cons     7.251368   .8931895      8.12   0.000     5.493658    9.009078 

Source: Results obtained from Stata 16. 
 

The results above in Table 2 have been reported using the same variant of panel data regression technique as the 

Hausman test suggests the use fixed-effect model (Appendix B.1 ) and standard error estimates are robust to disturbances 

being heteroscedastic (Appendix B.2). 

 

https://www.princeton.edu/~otorres/Panel101.pdf


Anwar & Kumar., Indian Journal of Finance and Banking 10(1) (2022), 54-60 

 

58 

Return on assets, Debt to equity ratio, Cash and cash equivalents & Debt to total capital ratio were again found to 

be significantly affecting dividend yield of two time periods merged from the financial year 2000-01 to 2020-2021 (result 

shown below in Table 3) as consistent with the findings of the first study. 

 

Table 3. Below shows the results of Panel data regression results for the period 2000-01 to 2020-21. 
 

                              Robust 

   Divpercent        Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval] 

        Sales    -1.58e-06    1.24e-06     -1.27    0.205    -4.02e-06     8.68e-07 

          ROA     15.11666    4.000729     3.78    0.000     7.243503    22.98981 

 Currentratio     .2063839   .4004855     0.52     0.607    -.5817434    .9945112 

   Quickratio    -.4230762   .4866113    -0.87    0.385    -1.380693    .5345406 

Debttoequit~o    -.0014024   .0004352    -3.22    0.001    -.0022589   -.0005459 

     Nocftota     1.640437    2.588111     0.63    0.527    -3.452783    6.733656 

Cashandcash~s     .0000466   .0000134     3.48     0.001     .0000202    .0000729 

Retototaleq~y    -.0000535   .0000495    -1.08    0.280    -.0001508    .0000439 

Debttototal~o    -.0557873   .0155427    -3.59    0.000    -.0863743   -.0252003 

        _cons      9.28502    .5192132     17.88   0.000     8.263245    10.30679 

Source: Results obtained from Stata 16. 

 

The results above in Table 3 have been reported once again using the “Fixed effect Cluster” variant of the panel 

data regression technique same as in the first study as the Hausman test suggests the use of the fixed-effect model (Appendix 

C.1) and it is a robust method to be used where the standard errors disturbances are heteroscedastic and autocorrelated 

(Appendix C.2 and C.3 respectively). 

 

CONCLUDING REMARKS 

The following three conclusions can be made based on the above results of this study- 

 The dividend yield is directly related to the Profitability of the firms as per findings in earlier studies. As more 

profitable the firm, the more its ability to pay dividends to shareholders. 

 Leverage has a negative impact on the payment of dividends by firms once again consistent with the findings of 

the previous studies. 

 The liquidity of firms has a positive relationship with dividend yield as the more liquid the firms, they have more 

ability to pay cash dividends. 

 
Figure 4. Shows J.C. Van Horne's classification of factors affecting Dividend Policy. 

The above findings can be put into an above-shown figure of J.C. Van Horne's classification of factors affecting 

dividend policy (shown with blue arrows). 

 

 
Author Contributions: Conceptualization, R.A. and S.K.; Data Curation, R.A. and S.K.; Methodology, R.A.; Validation, R.A. and S.K.; Visualization, 

R.A. and S.K.; Formal Analysis, R.A.; Investigation, R.A.; Resources, R.A., Software, R.A.; Writing – Original Draft, R.A.; Writing – Review & Editing, 
S.K.; Supervision, S.K.; Project Administration, S.K.; Funding Acquisition, R.A. Authors have read and agreed to the published version of the manuscript. 

Institutional Review Board Statement: Ethical review and approval were waived for this study, due to that the research does not deal with vulnerable 
groups or sensitive issues. 

Funding: The authors received no direct funding for this research. 

Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. 

Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available 
due to restrictions. 

Conflicts of Interest: The authors declare no conflict of interest.  

                                                                                                                                        

REFERENCES 

Black, F. (1998). The Dividend Puzzle'. Streetwise: The Best of the J Portfolio Manag, 10. 

Brahmaiah, B., Srinivasan, P., & Sangeetha, R. (2018). Determinants of corporate dividend policy in India: a dynamic 

panel data analysis. Academy of Accounting and Financial Studies Journal, 22(2), 1-13. Retrieved from 



Anwar & Kumar., Indian Journal of Finance and Banking 10(1) (2022), 54-60 

 

59 

https://www.abacademies.org/articles/determinants-of-corporate-dividend-policy-in-india-a-dynamic-panel-

data-analysis-7138.html 

Denis, D. J., & Osobov, I. (2008). Why do firms pay dividends? International evidence on the determinants of dividend 

policy. Journal of Financial economics, 89(1), 62-82. https://doi.org/10.1016/j.jfineco.2007.06.006 

Labhane, N. B., & Mahakud, J. (2016). Determinants of dividend policy of Indian companies: A panel data 

analysis. Paradigm, 20(1), 36-55. https://doi.org/10.1177%2F0971890716637698 

Madra-Sawicka, M., & Ulrichs, M. (2020). Dividend payment policy across food industry companies–the case of European 

economies. Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu, 64(4), 146-159. 
https://doi.org/10.15611/pn.2020.4.11 

Miller, M. H., & Modigliani, F. (1961). Dividend policy, growth, and the valuation of shares. the Journal of Business, 

34(4), 411-433. Retrieved from https://www.jstor.org/stable/2351143 

Nathani, N., & Gangil, R. (2019). Determinants of Dividend Policy in Indian Companies: A Panel Data Analysis. 

In Proceedings of 10th International Conference on Digital Strategies for Organizational Success. 
http://dx.doi.org/10.2139/ssrn.3308721  

Pinto, G., & Rastogi, S. (2019). Sectoral analysis of factors influencing dividend policy: Case of an emerging financial 

market. Journal of risk and financial management, 12(3), 110. https://doi.org/10.3390/jrfm12030110 

Yusof, Y., & Ismail, S. (2016). Determinants of dividend policy of public listed companies in Malaysia. Review of 

International Business and Strategy, 26(1), 88-99. https://doi.org/10.1108/RIBS-02-2014-0030 

 
APPENDICES 

Appendix A 

1)  Hausman fixed random 

             Coefficients  
              

              

      (b)          (B)            (b-B)     sqrt(diag(V_b-V_B))      fixed        

random       Difference          S.E. 

       Sales     -1.58e-06    -5.68e-07       -1.01e-06        1.38e-06 

         ROA      15.11666     19.97453       -4.857872        .6234698 

Currentratio      .2063839     .2309103       -.0245264        .2066785 

  Quickratio     -.4230762    -.4337633        .0106871        .2268247 

Debttoequi~o     -.0014024    -.0019628        .0005604               . 

    Nocftota      1.640437     4.789302       -3.148865        .4447369 

Cashandcas~s      .0000466     .0000365        .0000101        7.02e-06 

Retototale~y     -.0000535    -.0000806        .0000271        .0000205 

Debttotota~o     -.0557873    -.0647497        .0089623        .0058542 

  b = consistent under Ho and Ha; obtained from xtreg         B = inconsistent under Ha, efficient under Ho; obtained from    

     xtreg     Test:  Ho:  difference in coefficients not systematic 

                  chi2(6) = (b-B)'[(V_b-V_B)^(-1)](b-B) 

                          =       81.36 

                Prob>chi2 =      0.0000 

                (V_b-V_B is not positive definite 

2)   xttest3 

Modified Wald test for groupwise heteroskedasticity in fixed effect regression model H0: sigma(i)^2 = sigma^2 for 

all i 

chi2 (300)  =   1.2e+06 Prob>chi2 =      0.0000 

3) xtserial  Divpercent Sales ROA Currentratio Quickratio Debttoequityratio Nocft 

> ota Cashandcashequivalents Retototalequity Debttototalcapitalratio 

Wooldridge test for autocorrelation in panel data 

H0: no first-order autocorrelation 

    F(  1,     296) =     11.166 

           Prob > F =      0.0009 

Appendix B 
1) Hausman random fixed 

 similar scale.                    Coefficients  

              

              

      (b)          (B)            (b-B)     sqrt(diag(V_b-V_B))      random       

fixed        Difference          S.E. 

       Sales      1.55e-06     8.47e-06       -6.93e-06               . 

         ROA      59.12369     43.52311        15.60058               . 

Currentratio     -1.104143     1.246671       -2.350813               . 

  Quickratio      1.071532    -1.688633        2.760166               . 

Debttoequi~o     -.0063944     .0963707       -.1027652               . 

    Nocftota       7.88746     4.653729        3.233731               . 

Cashandcas~s      4.48e-06    -3.33e-08        4.51e-06               . 

Retototale~o     -.0051832    -.0043646       -.0008186               . 

Debttotota~o     -.0351521    -.0115922       -.0235599               . 

https://doi.org/10.1108/RIBS-02-2014-0030


Anwar & Kumar., Indian Journal of Finance and Banking 10(1) (2022), 54-60 

 

60 

 b = consistent under Ho and Ha; obtained from xtreg   B = inconsistent under Ha, efficient under Ho; obtained from xtreg     Test:  Ho:  difference in 

coefficients not systematic 

                  chi2(7) = (b-B)'[(V_b-V_B)^(-1)](b-B) 

                          =      482.64 

                Prob>chi2 =      0.0000 

                (V_b-V_B is not positive definite) 

2)  xttest3 
Modified Wald test for groupwise heteroskedasticity in fixed effect regression model H0: sigma(i)^2 = sigma^2 for 

all i 

chi2 (301)  =   5.5e+05 Prob>chi2 =      0.0000 

3)  xtserial  Divpercent Sales ROA Currentratio Quickratio Debttoequityratio Nocft 

> ota Cashandcashequivalents Retototalequity Debttototalcapitalratio 

Wooldridge test for autocorrelation in panel data 

H0: no first-order autocorrelation 

    F(  1,     298) =      0.791 

           Prob > F =      0.3746 

Appendix C 
1) hausman fixed random 

Note: the rank of the differenced variance matrix (6) does not equal the number of coefficients being tested (9); be sure this is what you expect, or 

there may be problems computing the test.  Examine the output of your  estimators for anything unexpected and possibly consider scaling your 
variables so that the coefficients are on a similar scale.                   

  Coefficients  

 

              

              

      (b)          (B)            (b-B)     sqrt(diag(V_b-V_B))      fixed        

random       Difference          S.E. 

       Sales     -1.58e-06    -5.68e-07       -1.01e-06        1.38e-06 

         ROA      15.11666     19.97453       -4.857872        .6234698 

Currentratio      .2063839     .2309103       -.0245264        .2066785 

  Quickratio     -.4230762    -.4337633        .0106871        .2268247 

Debttoequi~o     -.0014024    -.0019628        .0005604               . 

    Nocftota      1.640437     4.789302       -3.148865        .4447369 

Cashandcas~s      .0000466     .0000365        .0000101        7.02e-06 

Retototale~y     -.0000535    -.0000806        .0000271        .0000205 

Debttotota~o     -.0557873    -.0647497        .0089623        .0058542 

   b = consistent under Ho and Ha; obtained from xtreg     B = inconsistent under Ha, efficient under Ho; obtained from     

    xtreg    Test:  Ho:  difference in coefficients not systematic 

                  chi2(6) = (b-B)'[(V_b-V_B)^(-1)](b-B) 

                          =       81.36 

                Prob>chi2 =      0.0000 

                (V_b-V_B is not positive definite) 

2)  xttest3 

Modified Wald test for groupwise heteroskedasticity in fixed effect regression model H0: sigma(i)^2 = sigma^2 for 

all i 

chi2 (300)  =   1.2e+06 Prob>chi2 =      0.0000 

xtserial  Divpercent Sales ROA Currentratio Quickratio Debttoequityratio Nocft Cashandcashequivalents Retototalequity Debttototalcapitalratio > ota 

Cashandcashequivalents Retototalequity Debttototalcapitalratio 

3) Wooldridge test for autocorrelation in panel data 

H0: no first-order autocorrelation 

    F(  1,     296) =     11.166 

           Prob > F =      0.000  

 

 

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