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The Impact Of Corruption On Debt Financing And Corporate Ownership.

Byline: Sarfaraz Ahmed Shaikh, Khurram Ali Mubasher, Meer Rujaib Naseem and Agha Amad Nabi

Introduction

In 1990's corporate governance does not exist, but still companies achieve their objectives and earn profits. Once the profits are earned investors, shareholders and customers do not ask about the corporate governance and same goes for the share prices, once they are increased or doubled stakeholders do not ask for management practices. It continued until the stock market collapses in the early 2000s and then investors and shareholders make their concerns about corporate governance and require good corporate governance for the improvement of share prices. It was observed that corporate governance has somehow impact on separation of ownership concentration with respect to the firm and other internal level (Basil and Najjar, 2018). It includes ownership structure, debt financing, dividend payout, etc. In such scenario firms' size shows a vital role in examining the association between internal and external stakeholders.

In a study in China it has been observed that corruption and debt is the most important problems in China. Proposed by (Booth 2001) and (Fan 2012) to control the effect of increasing debt a country's authorized organism must be protected and long term debt financing must be available in the corporations of that country. Corruption distort the rule of law decline the institutional foundations which means that modifying agency problem and also The Impact of Corruption on Debt financing and Corporate Ownership: A Study In Pakistan region 2 reduce the efficiency of financial markets, in this result the firm operating cost was increased by cost of licenses and permits and cost of debts. As said by (Freeman and Asiedu, 2009) corruption developed the operational cost, increase uncertainty and discourages the investors. To investigate the relationship among corruption and firm level financing, shows that corruption raise the cost of borrowing and companies developing cost.

Also study the company's liquidity and corruption, it shows that the company'sliquidity is less in countries with complex corruption control. The ownership structure is spread in small stockholders but the mechanism is determined in the hands of managers. The most commonly study explains that the firms pays dividends is the agency cost theory, the problems involved with the separation of administration and ownership and the differences in managerial and shareholders significances known as the principal-agent war. (Vishny and Shleifer 1997) claimed that while large stockholders, like family owners, hold most of the regulator, they have to make reserved remunerations of control (for example spending the company's cash flow, pays extreme wages to themselves, given to top managerial situations and mostly positions give to their family participants). The families won't to have more inspiration to take minority investors wealth than other large shares (Ramakrishnan et al 2016).

Literature Review

Corruption has been reflected an important factor in making state's authorized system, supply delivery and firm performance. Firm's capital structure is affected by corruption in two different ways. One is that, when shareholders aim to capitalize in a company, they think that on the way to gain their investment established on criteria computed in the agreement. Thus, if the agreement cannot be executed, although they completed, the investor receive back its investment and concentration through slightly correct options. Judicial corruption is one of the variable, which have an impact on contract performance and investors carry-out of right of option. In a corrupt countries, creditors suffers possibility that they will not get back the investment. However, currently, we emphasis a small however increasing thread of literature that happening to study the impact of corruption on firm side assessments.

The influence of corruption on firm's performance and determines that sales progress comprehensively decreased by corruption, small investment employment growth. While corruption defines that an offence of lawful means from a permissible perception, furthermore corruption defined as an offence of social means from a collective perception. It is characterized that we are commonly used the definition of corruption is that an influence of government controls or sale of government property or together with government administrator on behalf of private consumption or private benefits. (Yefengwang, yingzhang, 2017) take a sample of 8232 firms from 81 countries to explore the impact of government possession on financial restrictions and the impact of financial constraints on corporate performance. (Robert Kieschnick, RabihMoussawi, 2017) investigate the effect of corporate authority on corporate capital structure as using firm age.

In this study they use firm age as an independent variable and debt as a dependent variable. They check the direct effect of firm age and use of debt. The result shows the firm age is positively correlated with debt of firm uses. They also find that the insiders is more powerful have, the less debt that firm uses as it ages. (Dang, 2011) study the leverage, investment of the firm and debt maturity. The sample data collected from 678 firm of United Kingdom from the period of 1996 to 2003. The statistical technique use in this research is regression and the variables use in this study is size of the firm, volatility, quality and tangible assets. In this research they use firm size and leverage as an independent variable and dependent variable respectively.

The results revealed that the size of the firm and the leverage of the firm is significant impact. (Jared D. Smith, 2015) conducted a research on US firm financial policies investigate the association into corruption and leverage, they found that in an areas where the corruption level is high. Mostly firms who is involved in higher level of perceived corruption uses debt financing as compare to equity financing for financing. They have to borrowing fund in a short-term debt or in a long-term debt. (Feng Wei, Yu Kong, 2017) studied in china's two cities listed companies stock markets from 1998 to 2013, which is Shanghai and Shenzhen. They analyze the relation among corruption and financial development. The result shows that company's bank loans have significantly positive effect with corruption and financial development.

When examining the connections among financial development and corruption, they find that company's bank loans does not increase in an areas with a higher level of corruption with financial development. But, company's long-term bank loans is positively related with corruption and financial development. (Johannes Kepler, 2016) investigate the relation into corruption, debt and the shadow economy. In the research corruption use as an independent variable and the shadow economy and debt as a dependent variables. Furthermore, they investigate that shadow economy increase the effect of corruption on private debt. For investigation they used corruption perceived index (CPI). Corruption perceived index (CPI) proved that increased corruption leads to increases public debt. (Marcos Gonzalez-Fernandez, 2014) examine that corruption as determinant of debt in the Spanish self-governing communities. In this research they use corruption is an independent variable and debt is a dependent variable.

Furthermore, they construct a corruption perceived index (CPI). Data was collected for the years 2000-2012. The result find that corruption has a straight and substantial impact on debt financing. (TrangPhanQuynh, 2018) investigate the significant relationship between debt level and debt maturity was related with firm investment behavior's. Data was collected from Vietnam listed firm for the period of 2010 to 2016. The research use debt level and debt maturity as an independent variable and firm investment behavior as a dependent variable. They use regression for test the variables and the result shows that level of debt is expressively negative impact on firm investment but the maturity of debt is irrelevantly to investment rate. (Serraquerio and Nunes, 2012) study the impact of the firm age as the financing decisions elements of small and average size firms for using data 1999-2006 and sample size 495 young and 1350 SME's.

They use regression analysis as a statistical techniques and age, size, asset structure, growth, research and development, non-debt tax shield, profitability and default risk as a variables. The outcomes was that age is the main element for change of the short term financing and long term financing in the direction of the exact idea level; relationship among the distinctive element and short term financing and long term financing; and for the influence of financial inefficiency on the divergence of short term and long term leverage. The result also shows that older firm moves differentiated debt financing. According to Ramakrishnan. el al (2016) review the effect of family ownership and corporate misconduct of small firm in United States (US). In this research family ownership as an independent variable and corporate misconduct as a dependent variable. The research indicates that small family owned firm are smaller possibility to constrain misconduct than small non family owned firm.

Furthermore, the result revealed that an indirect relationship of family ownership misconduct. Further investigate that relatively established firms, only family businesses with older proprietors are fewer expected to obligate misdemeanors. (Feng Wei, Yu Kong, 2017) investigating the significant impact between financial improvement and corruption beside with the communication among these two determinants. For the research corruption use as a dependent variable and financial development as a dependent variable. In determining the element of company's investment structure in the country's permissible atmosphere. In the data they select listed companies of China form the year of 1998 to 2013. The empirical research is to inspect the influence among the corruption and the financial growth on firm's capital structure in Chinese legal background.

Hypotheses

H1a: Corruption has positive impact on debt financing

H1b: Corruption has positive impact on short term debt

H2: Corruption has positive impact on corporate ownership

Research Methodology

The objective of the research is to find out the impact of corruption on ownership structure decisions and debt financing of firm in the perspective of organization corporate in Pakistan Stock Exchange from 2010 to 2017.

Research Design And Data Collection

This study used quantitative approach to evaluate the significant impact of corruption on ownership structure and debt financing. This research have collected secondary data of the organizations listed in Pakistan Stock Exchange related to different sectors Automobile Sector, Cement Sector, FMCG, Oil and Gas Development Sector, Petroleum Sector, Pharma Sector and Textile Sector from the period of 2010 to 2017. The selected firms are currently listed in the Pakistan Stock Exchange. This study have selected the firms that have at least three consecutive years of available data over the study period have at least eight years in our sample data.

Statistical Technique

In order to test the hypothesis of this study uses a statistical software Eviews that have test the mathematical equation and will check the dependency of dependent variables over independent variables. Regression analysis will be used to ascertain the 18 association among the defined variables and simultaneously correlation analysis have identified the significance relationship between variables. (Tesfaye T. Lemma, 2015) identified the significant impact between corruption and debt financing consequently it is endorsed that correlation analysis have determined the significant impact.

Results And Discussions

This chapter will provide the consequences, associations and effects among the dependent and independent variables and will also discuss the influence of one variable on another. Forecasting of independent variables upon depending variable will be exposed in this chapter. The software used to run the test was Eviews and tests and methods used in this chapter are Descriptive Statistics, Hausman Test and Panel Generalized Method of Moments. The results and their conclusions are discussed below.

4.1.1 Descriptive Statistics

###TOT###TAX###FIR###GROW###FIRM###EARN'G###ASSET###ASSE###CPI

###AL###M###TH###S###S###TS

###LEV.###SHEI###SIZE###OPPR.###PROF###VOLATI###TANG###MATU

###LD###IT###LI.###AB.###R.

###Mean###0.523###0.029###16.34###0.054###0.061###10.719###0.271###8.827###-

###9###0.678

###Median###0.450###0.029###16.96###0.068###0.000###14.343###0.266###7.396###-

###4###0.639

###Maximum###4.055###0.159###24.89###5.781###0.420###22.045###0.939###47.427###-

###0###0.385

###Minimum###0.000###-0.03###0.000###-4.058###-0.212###0.000###0.000###-14.71###-

###1.154

###Std. Dev.###0.396###0.020###4.512###0.597###0.109###7.371###0.239###8.873###0.22

###6

###Sum###108.8###6.195###3400.###11.314###12.75###2229.7###56.52###1836.1###-

###6###141.1

###Observatio###208###208###208###208###208###208###208###208###208

###ns

The table 4.1.1 shows descriptive statistics of total leverage on different independent variables including corruption. 208 observation of 26 companies, from year 2010 to 2017. The mean of total leverage is 0.523480 and mean of Corruption perceived Index (CPI) is -0.67815. Moreover this table includes mean, median, maximum and minimum, standard deviation, sum and observation of control variables such as firm size, tax shield, growth opportunity, firm profitability, asset tangibility, assets maturity and earning volatility.

Table # 4.1.3 Descriptive Statistics

###SHOR###TAX###GRWT###FIRM###FIRM###EARNG###ASSETS###ASSE

###T###H###TS

###TERM###C

###SHEIL###OPPR.###SIZE###PROFI###VOLAT###TANGA###MAT###PI

###LEV.###D###T###I###B.###UR.

Mean###0.400###0.029###0.054###16.349###0.061###10.719###0.271###8.827###-0.678

Median###0.307###0.029###0.068###16.964###0.000###14.343###0.266###7.396###-0.639

Maximum###3.972###0.159###5.781###24.890###0.420###22.045###0.939###47.427###-0.385

Minimum###0.000###-0.028###-4.058###0.000###-0.212###0.000###0.000###-###-1.154

###14.71

###5

Std. Dev.###0.391###0.020###0.597###4.512###0.109###7.371###0.239###8.873###0.226

Sum###83.292###6.195###11.31###3400.6###12.75###2229.7###56.52###1836.1###-

###141.05

Observation###208###208###208###208###208###208###208###208###208

s

The table 4.1.2 shows descriptive statistics of short term Leverage on different independent variables including corruption. 208 observation of 26 companies, from year 2010 to 2017. The mean of short term Leverage is 0.400446 and mean of Corruption perceived Index (CPI) is -0.678125. Moreover this table includes mean, median, maximum and minimum, standard deviation, sum and observation of control variables such as firm size, tax shield, growth opportunity, firm profitability, asset tangibility, assets maturity and earning volatility.

Hausman Test

Hausman test will be applied to identify either the cross section will be fixed or random. If the probability will be greater than 0.05 random effects cross section will be used. But if the probability will be less than 0.05 fixed effects cross section will be used.

Hausman test for Total Leverage

Test Summary###Chi-Sq. Statistic###Chi-Sq.###Prob.

###d.f.

Cross-section###21.587193###8###0.005

random###7

Now to identify the impact of independent variables upon Total Leverage, Hausman test is implemented therefore, the results in Table 4.1.2.1 suggest that fixed effects cross section would be most appropriate for panel data regression.

Hausman test for Corporate Ownership

###Chi-Sq. Statistic###Chi-Sq.###Prob.

###d.f.

Cross-section###10.367655###9###0.321

random###5

Now to identify the impact of independent variables upon corporate ownership, Hausman test is implemented therefore, the results in Table 4.1.2.2 suggest that random effects cross section would be most appropriate for panel data regression.

Hausman test for Short Term Leverage

Test Summary###Chi-Sq. Statistic###Chi-Sq. d.f.###Prob.

Cross-section random###16.606492###8###0.034

Now to identify the impact of independent variables upon Short Term Leverage, Hausman test is implemented therefore, the results in Table 4.1.2.3 suggest that fixed effects cross section would be most appropriate for panel data regression.

Panel Data Regression

Panel data regression is applied in this study to identify the impact of dependent variables (Total Leverage and corporate ownership) and independent variables (firm size, firm age, tax shield, growth opportunity, firm profitability, asset tangibility, assets maturity, earning volatility, corruption perceived index, dividend payout firm level investment and long term leverage). The regression analysis is executed by using Generalized Method of Moments (GMM) which was recommended by Bond and Blundell (1998). (Tesfaye T. Lemma, 2015) in his study uses GMM Generalized Method Moment system to get robust results. The results from Hausman test suggested that fixed effects cross section was used for Total Leverage and random effect cross section was used for Share Capital.

Panel data estimation for Total Leverage

###Dependent Variable: TOTAL_LEVERAGE

###Variable###Coefficient###Std. Error###t-Statistic###Prob.

###TAX_SHEILD###-1.96941###1.199774###-1.641484###0.102

###5

###FIRM_SIZE###0.03969###0.006053###6.556999###0.000

###0

###GROWTH_OPPORTUNITY###-0.013553###0.029725###-0.455944###0.649

###0

###FIRM_PROFIT###0.008076###0.203061###0.039771###0.968

###3

###EARNING_VOLATILITY###-0.014043###0.004223###-3.325599###0.001

###1

###CPI###-0.156039###0.081462###-1.915491###0.057

###1

###ASSETS_TANGABILITY###0.845327###0.132373###6.385938###0.000

###0

###ASSETSMATURITY###-0.016584###0.003380###-4.907057###0.000

###0

###C###-0.105145###0.115063###-0.913804###0.362

###1

###R-squared###0.67069

###Adjusted R-squared###0.608234

###S.E. of regression###0.248196

###Sum squared resid###10.71865

The panel data regression analysis shows that the outcome represents a positive and significant relationship of firm size and asset tangibility. Corruption perceived Index has negative and significant relationship with total; leverage therefore, hypothesis H1 is accepted. Control variables such as tax shield and growth opportunity is negative and insignificant to total leverage. Moreover earning volatility has a significant but negative impact on total leverage. Asset Maturity was also found to have a negative and significant association with total leverage. At last firm profit has no significant impact on total leverage.

Panel data estimation for Corporate Ownership

Dependent Variable: CORPORATE_OWNERSHIP

###Variable###Coefficient###Std.###t-Statistic###Prob.

###Error

###TOTAL_LEVERAGE###-0.002701###0.019643###-0.137499###0.8908

LONG_TERM_LEVERAGE###0.279366###0.069296###4.031469###0.0001

###DIVIDENT_PAYOUT###-0.091547###0.146267###-0.625892###0.5321

FIRM_LEVEL_INVESTMENT###-0.028726###0.028914###-0.993512###0.3217

###FIRM_SIZE###-0.004424###0.001783###-2.481609###0.0139

###FIRM_AGE###-0.132182###0.045778###-2.887445###0.0043

###FIRM_PROFIT###0.156293###0.057879###2.700361###0.0075

EARNING_VOLATILITY###0.000883###0.001157###0.763637###0.4460

###CPI###-0.007612###0.025580###-0.297577###0.7663

###C###1.053909###0.172288###6.117145###0.0000

###Weighted Statistics

R-squared###0.150453

Adjusted R-squared###0.111838

S.E. of regression###0.067369

Sum squared resid###0.898626

###Unweighted Statistics

R-squared###-0.089666###Mean dependent###0.664414

###v

###a

###r

Sum squared resid###58.29005###Durbin-Watson###0.018937

###stat

The panel data regression analysis shows that the outcome represents a positive and significant relationship of firm profit and long term leverage. Corruption perceived Index has negative and insignificant relationship with corporate ownership therefore, hypothesis H2 is rejected. Control variables such as total leverage, dividend payout and firm level investment is negative and insignificant to corporate ownership. Moreover firm size has a significant but negative impact on corporate ownership.

Panel data estimation for Short Term Leverage

###Dependent Variable: SHORT_TERM_LEVERAGE

###Variable###Coefficient###Std.###t-Statistic###Prob.

###Error

###TAX_SHEILD###-1.386833###1.152109###-1.203734###0.230

###3

###GROWTH_OPPORTUNITY###-0.019384###0.028544###-0.679081###0.498

###0

###FIRM_SIZE###0.029612###0.005813###5.094345###0.000

###0

###FIRM_PROFIT###0.057435###0.194993###0.294547###0.768

###7

###EARNING_VOLATILITY###-0.011701###0.004055###-2.885597###0.004

###4

###ASSETS_TANGABILITY###0.793340###0.127114###6.241158###0.000

###0

###ASSETSMATURITY###-0.015956###0.003245###-4.916658###0.000

###0

###CPI###-0.169471###0.078225###-2.166444###0.031

###6

###C###-0.109088###0.110491###-0.987299###0.324

###9

###R-squared###0.688645

###Adjusted R-squared###0.629594

###S.E. of regression###0.238336

###Sum squared resid###9.883891

The panel data regression analysis shows that the outcome represents a positive and significant relationship of firm size and asset tangibility. Corruption perceived Index has negative and significant relationship with short term leverage therefore, hypothesis H1a is accepted. Control variables such as tax shield and growth opportunity is negative and insignificant to short term leverage. Moreover earning volatility has a significant but negative impact on short term leverage. Asset Maturity was also found to have a negative and significant association with short term leverage. At last firm profit has no significant impact on short term leverage.

Conclusion and Discussion

Corruption perceived Index has negative and significant relationship with total; leverage therefore, hypothesis H1 is accepted. Control variables such as tax shield and growth opportunity is negative and insignificant to total leverage. Moreover earning volatility has a significant but negative impact on total leverage. Asset Maturity was also found to have a negative and significant association with total leverage. At last firm profit has no significant impact on total leverage.

(Marcos Gonzalez-Fernandez, 2014) examine that corruption as determinant of debt in the Spanish self-governing communities. In this research they use corruption is an independent variable and debt is a dependent variable. Furthermore, they construct a corruption perceived index (CPI). The result also shows that older firm moves differentiated debt financing. (Vanacker and Manigart, 2010) corporate structure and size of leverage with high developing firms looking for financing. The data taken from 32,754 firms. In this study regression technique was used statistically. In this study company size, company age and fake industry as avariables. The outcome of this reveals that profitable industries mostly use and choose finance investment, however non profitable firm use and choose external equity by high debt ratio. The result also shows the significant relationship between size and age of firm with debt financing.

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Author:Shaikh, Sarfaraz Ahmed; Mubasher, Khurram Ali; Naseem, Meer Rujaib; Nabi, Agha Amad
Publication:The Dialogue
Article Type:Report
Geographic Code:9PAKI
Date:Jun 30, 2019
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