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\title{Human Capital, Capital Structure, Employee Pay: Empirical Evidence from Pakistan}
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             \author[1]{Talal  Tahir}

             \affil[1]{  Muhammad Ali Jinnah University}

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\date{\small \em Received: 10 February 2015 Accepted: 3 March 2015 Published: 15 March 2015}

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\begin{abstract}
        


This study examines effect of leverage on labor costs there by testing predictions of Titman (1984) and Berk, Stanton and Zechner (2010). The study covers period 2009 to 2013 for which firm level data of 84 non financial companies listed on Karachi Stock Exchange selected on the basis of data availability were examined using ordinary least square regression. Leverage is measured by debt to equity ratio of firm while labor costs considered as labor intensity are the total of salaries expense of the firm divided by total assets of firm. Influence of controlled variable like size of firm, Market to Book ratio, Physical capital intensity and Earning of firm per Asset is also investigated. Results reveal that in overall analysis leverage does not impact labor costs? thereby stating that prediction of Titman (1984) and Berk, Stanton and Zechner (2010) are not applicable in Pakistani context because of the unemployment conditions, ownership structure and level of corporate governance in the country.

\end{abstract}


\keywords{labor costs, capital structure, human capital.}

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\let\tabcellsep& 	 	 		 
\section[{Introduction}]{Introduction}\par
o raise capital at lowest cost is a major issue for corporate managers, with a view to maximize the value of firm. Corporate Finance literature mostly consists of developing an optimal capital structure for a company, defined as balance of debt and equity in a firm that reduces the weighted average cost of capital. As per trade off theory firms acquire debt to take advantage of tax shield benefits till the time level of debt increases bankruptcy costs of firm off-setting the benefits of tax shield. However empirical evidence shows that firms stop acquiring debt way before the point where bankruptcy costs off-sets the benefit of tax shield through debt. Thus authors have suggested indirect bankruptcy costs as a possible reason depriving firms from using debt to fully utilize tax shield benefit of debt or to acquire debt to the point where bankruptcy costs erode benefit of tax shield through debt.\par
Historically, managers and academicians have more focus on fundamental area of finance that are focusing on bankruptcy, firm size, leverage profitability etc. Human capital has got low attention to devise the policy about leverage. Employees are one of the biggest stakeholders and resource (factor of production) that a firm requires to move on and are always kept away from maximum studies of corporate finance. Although capital structure decision impacts almost all stakeholders especially employees as the large amounts of debt can cause bankruptcy for firm. And the bankruptcy costs borne by employees are much more still decision of capital structure is mostly done is keeping all stakeholders interest at par except shareholders and creditors.  {\ref Titman (1984)} argued that customers, workers and suppliers of firms are likely to suffer high costs in event of liquidation. Cost borne by employees due to bankruptcy can significantly affect firms capital structure in a setting where employees have firm specific human capital.\par
Formalizing  {\ref Titman (1984)} arguments Berk, Stanton, and Zechner (2010) developed a model that human capital costs associated with financial distress can be large enough to be a distinctive reason for firms to issue debt.\par
According to BSZ (2010) model as firms acquire debt the probability for bankruptcy increases and employees thus demand a premium against the increased risk of bankruptcy of the firm. This demand for premium is to cover the risk employees' face after bankruptcy of firm. \hyperref[b3]{Berk, Stanton, and Zechner (2010)} state that this premium paid to employees off sets the tax shield benefit created by debt. This eventually leads the firm to stop acquiring debt way before the point where bankruptcy costs off sets benefits of tax shield. 
\section[{II.}]{II.} 
\section[{Theoretical Background}]{Theoretical Background}\par
Firms finance their assets through equity, debt, other financial arrangement or a mixture of all. This financing combination of assets to maximize overall value of firm is referred to as Capital Structure of firm. Different capital structure theories attempt to explain variation in capital structure of firms over time and across regions. There is no specific methodology realized yet which mangers can use to determine optimal debt level and financing mix. Prominent Capital structure theories include MM Irrelevance theory, Trade Off theory and Pecking Order Theory. a) MM Irrelevance Theory \hyperref[b19]{Modigliani and Miller (1958)} showed that in perfect markets total value of firm remains same no matter how the capital structure of firm is divided among equity, debt and other claims. The support to this theory is based on the idea that both firms and investors can borrow at the same interest rate thus investors are able to substitute personal leverage for corporate financial leverage and have ability to replicate any capital structure firm might undertake. Furthermore, they argue that if value of firm depends on capital structure then in perfect capital markets arbitrage opportunities will be available. This theory is based on unrealistic assumptions which include no taxes, no transaction costs, no bankruptcy costs, same borrowing cost for investor and firm, symmetry of market information. 
\section[{b) Trade-Off Theory}]{b) Trade-Off Theory}\par
Since Irrelevance Theory is based on based on restrictive assumptions which do not hold in reality and when these assumptions are removed then choice of capital structure becomes important for determining value of firm.  {\ref Modigliani and Miller (1963)} suggested that due to tax deductible interest payments firms should use as much debt as possible. However excessive debt has its cost that is cost of bankruptcy thus based on hypothesis of  {\ref Kraus and Litzenberger (1973)} Trade-Off Theory evolved. Their hypotheses suggest that firms should consider a balance between tax saving benefits of debt and dead-weight costs of bankruptcy. According to Trade-Off Theory optimal leverage of firm is influenced by taxes, bankruptcy costs and agency costs and firms borrow debt up to the point where tax savings through debt equal cost associated with increase in debt and probability of financial distress.\par
i. Taxes Since interest is a tax-deductible expense a tax paying firm receives interest tax shield in form of lower tax paid. Interest expense thereby decreases tax liability and increases after tax cash flows. Firms in regions with higher tax rates will be highly levered to increase after tax cash flows and market value.\par
ii. Bankruptcy Costs With increase in amount of debt in capital structure of firm the possibility of the firm to default increases. If the firm is unable to pay the loan and value of assets of firm decline triggering default then to safeguard their interest bondholder's takeover the firm. This legal mechanism allowing creditors to takeover firms is referred to as Bankruptcy and Bankruptcy costs are cost associated with use of this mechanism. Bankruptcy costs are direct as well as indirect. Direct costs of bankruptcy include fees of lawyers, accountants, and other professionals administering bankruptcy. If firm is large in size then these costs are small however if firms is small in size then it has to consider direct bankruptcy cost while determining amount of leverage in its capital structure. Indirect costs include decline in sales, profits, unable to obtain credit line etc. These costs arise when firm foresees bankruptcy. To avoid bankruptcy it cut downs expense on research, advertisements, training of employees thus quality of product and service is hampered which decreases firm sales and profits and decrease in share price in market further pushing it towards bankruptcy.\par
iii. Agency Theory Agency costs are costs that arise due to conflict of interest between managers and shareholders because of manager's share of less than 100 percent in the firm. Capital Structure or firms leverage is dependent on role of mamagers depending on situations.\par
a. Free cash flow theory Managers, with less than 100 percent stake in business, after funding all projects with positive cash flow may utilize the left over cash flow (referred to as free cash flow) for their own use rather than using it to increase value of firm. This problem can be controlled by using debt in capital structure thus reducing the free cash flow available to the managers as suggested by Jensen  {\ref (1986)}. Thus the use of debt in this case is benefiting and decreasing agency costs. 
\section[{b. Overinvestment and Underinvestment problem}]{b. Overinvestment and Underinvestment problem}\par
According to Myer and Majluf (1984) management is responsible to shareholders and tries to increase the value of equity and is not concerned with overall value of firm. Thus management may invest in projects that are risky just to increase value of equity (overinvestment) and may avoid projects with safe net present value in which value of equity may decrease (underinvestment). This leads to bondholder expropriation hypothesis which states that shareholders gains advantage at cost of bondholder as management is only working for increase in value of equity. Thus bondholders refrain from investment in such firms. 
\section[{c) Pecking Order Theory}]{c) Pecking Order Theory}\par
Pecking Order Theory \hyperref[b22]{(Myers \& Majluf 1984}) states that firms follow a hierarchy to finance projects. Firms prefer to use internal financing depending on availability and prefer to issue debt instead of equity when external financing is required. This theory is based on the assumption that managers are better informed of firms' future prospect than outside investors and they act in best interest of existing shareholders. \hyperref[b22]{Myers and Majluf (1984)} state that there is an investor perception regarding managers that managers use private information to issue equity when it is overpriced. This perception leads to under pricing of new equity causing loss to existing shareholders. Thus firms avoid issuing equity for new projects and finance projects through internal funds and issue debt instead of equity if further financing is required. Issuing new equity for financing is the last resort for firms.\par
Further there is also a signaling effect which arises due to information on capital structure of firm. Since managers have better knowledge about income of firm issuing debt will generate a signal to outside investors that firm has suitably large income and pay off 
\section[{Year ( )}]{Year ( )}\par
C periodic installments and interest easily increasing confidence of outside investor and value of equity. Thus to increase investor's confidence and value of equity firms use higher level of debt in capital structure. 
\section[{d) Human Capital}]{d) Human Capital}\par
In 1960 economist Theodre Shultz invented the term Human Capital representing value of human capacities. According to him human capital is just like any other type of capital and investment in human capital would lead to improvement in production level and quality. Investment in human capital can be done through education, trainings and enhanced benefits. This concept also reflects the fact that all labor is not equal and quality of labor can be improved by investing in them. According to Romer (1989) rate of growth of output and investments of a firm are explained by level of human capital. According to Schultz (1971) and Sakamota and Powers (1995) human capital theory rests on assumption that formal education is necessary to improve production capacity of employees. Thus to improve output, firms train and educate their employees thereby making investment in human capital.\par
According to Berk, Staton and Zecher (2010) firms invest in employees and thus during bankruptcy this gives a loss of this investment also which is neglected by finance mangers. This loss is counted in indirect bankruptcy costs. The larger the investment in human capital the larger the bankruptcy cost abstaining such firms from decisions leading to bankruptcy. 
\section[{III.}]{III.} 
\section[{Employee Pay and Capital Structure}]{Employee Pay and Capital Structure}\par
Trade off theory suggests that bankruptcy costs are the main reason which abstain firms from using large amount of debt. However empirical evidence suggests that direct bankruptcy costs are too low to be an important disincentive for firms to use higher high amounts of debt. Thus researchers suggest indirect bankruptcy costs a reason to abstain firms from using large amount of debt.  {\ref Titman (1984)} developed a model showing that bankruptcy status of firm causes firms liquidation decision. He further argued that worker, supplier and customer are to suffer high costs in event of liquidation of firm and workers suffer a much higher cost if they are in a firm-specific worker environment. Formalizing this argument Berk, Staton and Zecher (2010) developed a model showing that to compensate the cost in event of liquidation workers demand an extra premium when they perceive bankruptcy of firm occurring due to incorporation of debt in capital structure. According to BSZ 2010 model this premium cost demanded by workers is large enough to offset the tax benefit of debt. Chemmanur, Cheng and Zhang (2012) tested this model empirically and found that incremental labor expense associated with increase in debt are large enough to offset the tax benefits of debt.\par
IV. 
\section[{Problem Statement}]{Problem Statement}\par
Indirect bankruptcy costs, such as salary premium, abstain non financial firm to incorporate large amounts of debt in capital structure. However we are unaware of the fact that whether such costs also exist in Pakistan making Pakistani non financial firms to resist large amounts of debt in their capital structure.\par
V. 
\section[{Research Question}]{Research Question}\par
This study addresses the question that how Leverage affects Human Capital Costs of firm in context of Pakistan? a) Research Objective ? To examine the impact of leverage on human capital. ? To examine the difference in labour intensity across the industries. ? To check the moderating role of leverage across the industries. 
\section[{b) Significance of Study}]{b) Significance of Study}\par
Debt is used by firms to maximize the value of firm. This level of debt in capital structure is influenced by theories mentioned above. In trade off theory finance researchers are largely concerned with direct costs of leverage neglecting indirect costs of leverage which prevent firms from taking on large amounts of debt. Still the question is un-answered that why firms don't take full advantage of tax benefit shield under trade off theory, what stops them way before the point where bankruptcy cost off sets the tax benefit shield of debt. Many scholars indentify such restriction as indirect bankruptcy cost that forces firm to stop use of debt before the point where bankruptcy costs rise and offset tax shield benefit but still these indirect bankruptcy cost are not identified individually.\par
This study will further support Trade Off Theory and will mention Human Capital Costs as a major restriction to leverage in firm thereby identifying part of indirect bankruptcy cost. Leverage will be treated as a determinant of Human Capital Costs of firm. Further according to Chemmanur, Cheng and Zhang (2012) their empirical study to test BSZ 2010 model was first study in literature thus this study will be second one. This study will be conducted for the first time in Pakistan using data from Pakistani firms.\par
This study is with the aim to empirically analyze that whether capital structure is important determinant of human capital costs in context of Pakistan. Thus informing whether indirect bankruptcy costs abstain firms from using debt in capital structure. And after evaluating if there would be significant relation among Human Capital variables and Capital Structure it would be justifiable that Human Capital should be incorporated I will further explore that at existing debt level, additional labor costs associated with increase in leverage are large enough to off-set incremental tax benefits of debt thus suggest Human Capital as one of the important factors or determinant of Capital Structure and major resistant to debt incorporation in firms and also that indirect bankruptcy cost causes firms to abstain from incorporating large amount of debt in capital structure. 
\section[{c) Plan of Study}]{c) Plan of Study}\par
Chapter 2 will provide literature review with hypothesis in end then Data and Methodology in Chapter 3 describing data, defining variables and methodology. Chapter 4 will provide Data Analysis and Results and Chapter 5 will conclude the study. 
\section[{VI.}]{VI.} 
\section[{Literature Review a) Capital Structure}]{Literature Review a) Capital Structure}\par
Capital structure defines the financing behavior of firms that is from where does a firm arrange finances for investing, decreasing the cost of capital to minimum and maximizing shareholder value. Research in capital structure is dominated by two theories: trade-off theory and pecking order theory. \hyperref[b19]{Modigliani and Miller (1958)} proved that capital structure is irrelevant that is the cost of capital and shareholder value is not impacted under the assumption that capital market is perfect and frictionless. As the market is imperfect in reality so tradeoff theory evolved based on hypothesis of  {\ref Kraus and Litzenberger (1973)} that considers a balance between tax saving benefits of debt and dead-weight costs of bankruptcy. Trade-off theory of capital structure refers to the idea of maintaining debt and equity by balancing the costs and benefits of debt that is creating a balance between the tax-shield benefit of debt and bankruptcy costs. Later Pecking theory emerged \hyperref[b22]{(Myers \& Majluf, 1984)} stating that firms follow a financing hierarchy.\par
Many researchers have found firms characteristics which determine the firms' capital structure. These include size of firm, liquidity and interest coverage ratio, median industry leverage, market-to-book assets ratio, profits, credit ratings, expected inflation and uniqueness of firm. \hyperref[b32]{(Titman \& Wessels, 1988;}\hyperref[b8]{Frank \& Goyal, 2009;}\hyperref[b15]{Kisgen, 2006;} {\ref Kila \& Mahmood, 2009)}. \hyperref[b8]{Frank and Goyal (2009)}, examined the significance of various factors in the capital structure decision of public traded American firms. This study based on the data from 1950 to 2003.\par
The most dependable factors i.e market leverage are; median industry leverage have positive effect of leverage, market to book assets ratio and profits have negative effect, tangibility, log of assets and expected inflation have positive effect on leverage. Furthermore they found that dividend paying firms tend to have lower leverage and when consider book leverage some time same effects are found. For book leverage; the impact of firm size, effect of inflation and market to book ratio are not reliable. An empirical fact appears logically reliable with some versions of the trade off theory of capital structure.  {\ref Kila and Mahmood (2009)}, in their study tested the determinants of capital structure for the listed firms in BMSB (Bursa Malaysia Securities Berhad) market from 2000 to 2005. Data was taken from financial statements of 17 listed companies, total observation was 102. Debt ratio is their dependent variable; while independent variables are growth, liquidity, interest rate and size. They applied pooled OLS estimations. Their result shows that their independent variables significantly negatively related to their dependent variable. Their study found insignificantly negative between capital structure and growth of the firm, by annual changes of earnings. The result of dummy variable show there are significant different in capital structure between those firms that adopt more debt and those who employ less leverage financing. \hyperref[b15]{Kisgen (2006)}, in his study of regarding impact of Credit rating on Capital structure empirically finds that credit rating of firms directly impact their capital structure decision. As per his result firms not near a credit rating change (upward/downward) issue debt relative to equity than firms near a change of credit rating.\par
However these determinants of capital structure vary from country to country because country specific factors also influence determinants of leverage \hyperref[b13]{( Jong, Kabir \& Nguyen 2008)}. In China, according to \hyperref[b5]{Chen (2004)} fundamental institutional assumptions underpinning Western Models are invalid. Financial constraint in banking sector and institutional differences influence leverage decisions thus Chinese firms follow "new pecking-order" -retained profit, equity and long term debt.\par
Sheikh \& Wang (2011) while investigating whether capital structure decisions of Pakistani firms are explained from models derived from Western Settings and the factors affecting Capital Structure decision state that Capital Structure models derived from western setting do provide explanation for financing behavior of Pakistani firm. The financing behavior is consistent with trade-off theory, pecking order theory and agency theory. Further according to them profitability, liquidity, earning volatility, tangibility and firm size impact debt ratios. Whereas non debt tax shield and growth opportunity do not impact debt ratios significantly. Results of \hyperref[b28]{Shah \& Khan (2007)} for determining factors affecting capital structure are in line also. Their results approve prediction of trade-off theory in case of tangibility, agency theory incase of growth and pecking order theory incase of profitability.\par
In my thesis I am exploring the relation between human capital costs and capital structure on basis of trade-off theory that indirect bankruptcy costs borne by employees associated with bankruptcy or financial distress can off-set firms decision to take over more debt. 
\section[{b) Human Capital}]{b) Human Capital}\par
Firms require financial capital as well as human capital to carry out business. In literal terms human capital can be simply stated as employees or workforce of a firm. Different researchers have described and measured human capital in different ways. It is taken in sense of labor intensity that is calculated by salary expense divided by sales, considered as investment made by firm on which firm makes investment in terms of salary. Human capital is also seen in terms of skills of employees and the type of contract through which they are hired that is temporary or permanent. Here we see human capital in terms of salary. c) Human Capital, Capital Structure And Employee Pay \hyperref[b19]{Modigliani and Miller (1958)} suggest that capital structure is irrelevant and it does not matter how a firm finances it operations under two main assumptions that there are no taxes and no bankruptcy costs. But over years researchers and academicians have found that capital structure becomes of much importance if these two assumptions are relaxed. Thus it becomes important for firms to make choices of how to finance its operations considering the benefits debt creates due to taxes and the bankruptcy related problems and costs caused by large amount of debt incorporated. As more and more debt is incorporated in capital structure the bankruptcy risks of firms increases and bankruptcy are costly sometimes even forcing liquidation of firm.\par
The bankruptcy costs mainly discussed in corporate finance are kept in circle of high legal and accounting expenses or liquidation of assets of value less than they worth. According to Branch (2002) while exploring magnitude of bankruptcy costs on firm states that bankruptcy process imposes costs on wide range of parties including shareholders creditor's suppliers, customers and employees. Further Less or and all other having contracts (including employees) with bankrupt firm are likely to absorb costs and losses as a result of bankruptcy. Researchers have also found that bankruptcy costs faced by employees of the firm is much more than the liquidation or direct bankruptcy costs of firms. When a firm becomes bankrupt its employees are left of strayed and such employees who are involuntarily separated from their jobs by mass layoff, plant closure or an employer going out of business are referred as displaced workers \hyperref[b16]{(Kletzer, 1998)}. These employees after job loss have to face large amount of unemployment costs that may include decrease in consumption, long delays before reemployment and significant wage losses after reemployment. Most displaced employees usually suffer great wage losses and the displaced workers who switch sectors suffer greater wage losses than those who find job in same sector after being displaced. \hyperref[b23]{Neal (1995)}, have conducted the displaced worker surveys, the results of that survey showed that wages cost of switching industries following displacement is strongly correlated with pre-displacement measures of both work tenure and experience. Workers actually receive reward for some skills that are neither completely general nor firm specific. Furthermore, displaced workers who find new jobs in their pre-displacement industry, postdisplacement returns to pre-displacement job tenure resemble cross-section estimates of the returns to current seniority. He suggested that firm-specific factors may contribute little to the experiential grade of wages tenure. And further the wage losses for switchers are strongly correlated with displaced workers experience and tenure in sector before displacement.\par
Thus as more and more debt is increased in capital structure of firm the bankruptcy risks of firm increases. As the bankruptcy risk increases employees risk of being displaced increases, or in others words it can be stated that as debt increases the probability of employees to become unemployed and bear the bankruptcy costs after unemployment increases. Therefore to mitigate the risk of being unemployed and bearing unemployment costs employees demand premium which is to be incorporated in their salary. So as debt is induced in capital structure employees demand compensation and thus we can infer that as debt in capital structure increases the salary of employee increases.\par
Berkovitch, Israel, and Spiegel (2000) investigated interaction between firms' capital structure and managerial compensation. In their model they show that risky debt affects manager's wage if he is retained by firm. As per their model's prediction managerial payperformance sensitivity is positively correlated with leverage, expected compensation, and expected cash flows.\par
Berk, Stanton, and Zechner (2010) while deriving optimal compensation contract in setting including equity and debt state that capital structure decisions trade off employees risk aversion against benefit of debt. In other words the debt can be incorporated in a firm till the time the benefit of tax shield due to debt equals the premium demanded by employees for a potential job loss after incorporation of debt.\par
Butt-Jaggia and Thakor (1994) developed optimal dynamic wage contracting and capital structure according to them wage contracts are to end at bankruptcy thus employees in firms requiring specific skills look for leverage of firm for deciding their compensation accordingly that is with respect to Chemmanu, Cheng, and Zhang (2012) while exploring whether human capital costs limit use of debt state that indirect bankruptcy costs arising from human capital can be one disincentive to the use of debt and empirically found that firms with higher debt pay higher wages to compensate for higher financial distress risk thus the incremental compensation associated with leverage is large enough to offset tax benefits of debt.\par
Agrawal and Matsa (2010) estimates, a total of about 57 basis points of firm value for a BBB rated firm as the average wage compensation for unemployment risks. They state that probability of a firm that it will encounter financial distress and subject workers to costly layoffs is decreased if leverage is reduced and managers are also able to lower the premium demanded by workers as compensation for bearing unemployment risk.\par
Although Hanka (1998) found that capital structure affects employment terms and lower wages are paid by those Compustat having large amounts of debt. Hovakimian and Li (2011) conclude that capital structure affects employee wage in China. Firms with more debt pay lower wages. The magnitude of this affect is defined by Ownership Structure and firms characteristics. The negative affect is forceful and strong in State-Owned firms and the negative affect in these firms' increases with large size, higher leverage ratios, lower profitability and less growth opportunities. Also debt serves as monitoring device mitigating managerial agency costs resulting in negative relation between leverage and low wage. Debt has negative affect on employees wage for financially constrained firms as such firms borrow from employees by paying low wages today in exchange of future higher wages. Debt protects wealth of shareholders from threat of unionization. Committing debt payments to creditors reduces free cash flow of the firms and limits the compensation managers can demand.  {\ref Matsa (2006)} state that high levels of corporate liquidity can encourage workers to raise their wage demands thus use of debt financing can improve a firm's bargaining power with workers. To reduce the impact of collective bargaining on profits, the firm has an incentive to undertake costly actions that reduce its owner's liquidity. It is also suggested by authors that firms entering distress zone lower employees wages to cover up interest payments to creditors.\par
As per scholars firms use debt to lower free cash flows available to managers thereby reducing agency costs and any excess demand of salary thus indicating inverse relation between leverage and employee pay. Khan, Kaleem \& Nazir (2012) collected panel data of 54 manufacturing firms from non financial sector of Pakistan for the period 2006 to 2010 and examined impact of financial leverage on agency cost free cash flow. Their results, consistent with free cash flow theory, reveal that in Pakistani firms leverage plays important role in reducing free cash flow that is under control of managers thus reducing agency cost of free cash flow.\par
These contrasting works are ex post effect of leverage on employee pay and do not contradict with ex ante relation, on which we focus, between same variables. According to Almazan, Suarez \& Titman (2004) terms of trades under which firms transacts with its customers and employees are affected by information and under normal conditions any good news improves these terms and however bad news worsens these terms of trade. Since information regarding leverage acquisitions to lower wages of employees is bad news for employees and if workers anticipate or get informed the move of equity holders to acquire debt to negotiate their wage downward then workers will demand higher expected wages to compensate them for bearing this risk as pointed out by Perotti and Spier (1993). Further they also pointed that firms are unable to use debt as bargaining tool to reduce employee pay if firms are earning large profits from existing assets. Since firm with large profits tend to be less inclined towards non bankruptcy while firms with less profit or negative profits are likely to be bankrupt we can divide are data in two parts bankrupt and non bankrupt firms. Firms falling in bankrupt zone will not pay higher wages and tend to use debt to lower down employee pay whereas firms in non bankrupt zone will not be able to use debt to lower down wages of employees.\par
Labor intensity is defined as the ratio between labor and pension expense over assets. Greater the salary expenses with respect to total assets more will be the firm labor intensive. Labor intensive firms in other words will be firms having much more labor or employees hired. Since more employees are hired so the unemployment costs of firm increases. Thus with increase in debt the premium to compensate unemployment risks will greater in firm that is more labor intensive than the firm which is less labor intensive. According to Agrawal and Matsa (2010) the impact of unemployment risk on financing decision is strong for firms that are more labor intensive. To reduce the premium of unemployment risks firms convert fix human cost to variable human cost that is they hire more temporary workers. \hyperref[b17]{Kuzmina (2011)}, in his study examined that how firms use of flexible contractual arrangements with a factor of production, labor affects its capital structure. They found that hiring more temporary workers lead firms to have more debt. Temporary workers, unlike permanent ones, it can be fired at a much lower cost , a firm can more easily meet its interest payments and avoid bankruptcy when faced with negative shock. They understand this result, flexible workforce decreasing operating leverage which in turn promotes financial leverage. \hyperref[b25]{Pratt (2011)} states that the salary given to employees by firms is like an investment done in human capital and loss of human capital creates a significant cost of financial distress. Labor intensive firms are therefore more exposed to these costs and they counter it by using less debt in capital structure. His results show that when moving from lowest to highest decile of labor intensity leverage drops by 21 percentage points significantly stating that high labor intensity leads to less use of debt. Further Anderson, Banker and Ravindran suggest that employees in non technological firms (labor intensive) earn more wages than in technological firms (capital intensive). Thus impact of debt on employee wages can be greater in labor intensive firms as compared to capital intensive firm which leads to further division of data between labor intensive firms and capital intensive firms. 
\section[{d) Hypothesis}]{d) Hypothesis}\par
After this we reach the following hypothesis i. Labor Intensity will increase with increase in leverage of firm.\par
ii. Labor Intensity will not increase in Bankrupt firms as firms will use debt as a bargaining tool. iii. Salary premium cost caused by increase in debt causes firms to abstain from incorporating large amount of debt in capital structure.\par
VII. 
\section[{Data Description \& Methodology a) Data Description}]{Data Description \& Methodology a) Data Description}\par
The research is descriptive type on the empirical analysis of secondary data. The sample is selected from listed firms in Karachi Stock Exchange of Pakistan. Data is taken for five years for eighty four companies from annual reports of firms. Labor Intensity defined as total wage paid divided by total assets. Pratt (2011) used labor intensity as the factor affecting leverage. According to Pratt (2011) as labor intensity increases leverage of firm decreases. Large value of labor intensity pose a large bankruptcy cost to firms thus firms decrease leverage in order to avoid bankruptcy. We use Labor Intensity as a proxy to measure salary of firms.\par
b. Independent Variable: Leverage Explanatory variable is leverage of firm defined as ratio of total debt to equity. Debt to equity ratio is the best ratio used by scholars around the world to measure leverage of a firm. According to Chemmanu, Cheng, and Zhang (2012) as debt to equity ratio increase salary of employees will rise increasing total labor cost of firm as employees demand premium against bankruptcy risk.\par
c. Control Variable: Size of firm, M / B Ratio, P. C Intensity, EBIT / Total Assets Ratio\par
? Size of firm Size of firm is natural log of total assets as firm. Chemmanu, Cheng, and Zhang (2012) state that big firms pay more salary to employees as compared to small firms. Thus to cover effect of size we use size of firm as control variable. 
\section[{? M / B Ratio}]{? M / B Ratio}\par
Market to Book Ratio (M/B Ratio) is calculated by dividing market value of equity with book value of equity. Book value of equity is given in annual reports of firms whereas market value of firm is calculated by multiplying total number of shares with share price as on close of business year. Market to book ratio is a proxy of growth opportunity of firm. According to Chemmanu, Cheng, and Zhang (2012) growing firms or firms with higher M/B Ratio will pay higher salaries. 
\section[{? P.C Intensity}]{? P.C Intensity}\par
Physical capital intensity is computed by dividing gross property, plant and equipment to total assets. There is a prediction by researchers that there is positive correlation between capital intensity and employee wage, as physical capital intensified firms have more output. 
\section[{? EBIT/Total Assets Ratio}]{? EBIT/Total Assets Ratio}\par
Earning of firm per asset that is ratio of earnings before interest and taxes to total assets. Increased EBIT to Total Asset ratio will represent higher profits and lesser firm bankruptcy risk (Rashid \& Abbas 2011) thus firms with higher earning per asset will have increased employee pay. 
\section[{b) Methodology}]{b) Methodology}\par
In order to understand clearly the role of the Human Capital on the corporate capital structure and relation between human capital and Leverage, we will carry out an empirical analysis by using panel data analysis with the following form: 
\section[{Salary of employees = F (Leverage of firm)}]{Salary of employees = F (Leverage of firm)}\par
The relation between average employee pay and leverage is tested through panel data analysis.LI it = Intercept + B1 (L it ) + B3 (FS it ) + B4 (M/B it ) + B5 (PCI it ) + B6 (EPA it )\par
With LI = Labor Intensity (Salaries/Total assets) L = Leverage of firm (Total Debt/Total Equity) M/B = Market to book ratio PCI = Physical Capital Intensity Earning per asset = Earning per asset (Earnings before Interest \& Taxes / Total Assets) t = time series i = cross section Further we will segregate the data in two parts bankrupt and non bankrupt firms through Z Score method and again apply panel data analysis separately on both data under same equation. According to scholars firms that are in bankrupt zone will use debt to lower down wages where as firms in non bankrupt zone will be earning profits and won't be able to use debt as a bargaining tool.\par
To check Z Score of our data we use Z Score model developed by \hyperref[b26]{Rashid and Abbas (2011)}. \hyperref[b26]{Rashid and Abbas (2011)}, have conducted a study to identify the Financial Ratios that are much significant in bankruptcy prediction for the non-financial sector of Pakistan. This study based on the sample of companies which became bankrupt from1996 to 2006. In these study 24 financial ratios covers four most important financial attributes i.e leverage ratios, profitability ratio, turnover ratios and liquidity ratios were examined for five years period prior bankruptcy. Their estimation provide evidence that the firms with below zero Z-value fall into bankrupt instead of these firms their Z-value is above Correlation table above shows the correlation matrix of the variables. The results state that there is positive correlation between Labor Intensity and all independent variables just as expected in literature except physical capital intensity and firm size. Labor Intensity has a higher value of positive correlation with the earnings per asset of the firm showing that with increase in earning per asset average pay will also increase. Same is the case with leverage and market to book ratio however the intensity of correlation is quite less predicting that increase in market value and leverage of firm will increase labor Intensity with a less intensity. Physical Capital Intensity and firm size however have a negative correlation with labor Intensity with a higher intensity than any other variable suggesting that as firms become more mechanized the labor intensity decreases and also increased firm size decreases labor Intensity. The correlation between Labor Intensity and Physical Intensity is opposite as expected in literature by BSZ (2010). According to BSZ (2010) prediction increase in Physical Capital Intensity average employee pay must increase thereby increasing Labor Intensity. As capital intensive firms tend to be more productive (Cronqvist, Heyman, Nillson, Svaleryd and Vlachos, 2009) the firms earning power increases thereby increasing employee benefits. However in case of Pakistan the relation is opposite. The main reason is unemployment caused by increase in Physical Capital Intensity as machines takeover the jobs of labor. This unemployment leads to increase in supply of labor in market. Unemployment rate increased from 5.2\% in 2008 to 6.2\% in 2012 with a growth rate of 4.5\% per anum .  
\section[{d) Panel Least Square Regression Overall}]{d) Panel Least Square Regression Overall}\par
Only Size of firm and Physical capital Intensity have significant negative impact on Labor Intensity but the value of coefficient is quite small. With increase in 1 unit of Physical Capital Intensity Labor Intensity decreases by 0.074 only and with increase in one unit of Firm Size Labor Intensity decreases by 0.039. These results are opposite to scholars prediction and research as according to them with increase in firm size and physical capital intensity labor wages shall rise thereby increasing Labor Intensity. These results can be due to the fact that large firms are more stable and are more likely to survive than small firms thus pay of wages at a minimum rate whereas increase in physical capital intensity further increases the unemployed work force in the country. This excess supply of work force ultimately decreases wage rates.\par
However leverage has no significant impact on Labor Intensity according to the results of our total sample thus our results are not consistent with theory and also the results of Chemmanu, Cheng, and Zhang (2012). Stating that our first hypothesis that with increase in leverage of firm Labor Intensity will increase Further the results conclude that model is fit as shown by value of F-Statistic. The value of R-Squared is 0.084 showing that the independent variables (leverage, Physical capital intensity, Earning per asset, Market to book ratio) explain 8.4\% of the variation in our dependent variable that is Labor Intensity. Now to check our second hypothesis that Labor Intensity will not increase with increase in Leverage in Bankrupt firms we divide our sample in two that is bankrupt observations and non bankrupt observations. Bankruptcy of firms is checked by value of Z score developed by \hyperref[b26]{Rashid and Abbas (2011)} for Pakistani firms as discussed earlier. Panel data for both bankrupt and safe firms is created by average Z score of five years as done by \hyperref[b26]{Rashid and Abbas (2011)}. Negative average Z score states distress firm whereas positive average Z Score indicates safe firm. 
\section[{g) Data analysis of Non-Bankrupt Sample: i. Descriptive Statistics}]{g) Data analysis of Non-Bankrupt Sample: i. Descriptive Statistics}\par
Mean value of Labor Intensity is 0.0822 which means that on average employees earn PKR 0.0822 against every PKR 1 of assets. Maximum value reaches to 1.88 that is against every PKR 1 assets of firm employees earn PKR 1.88. Minimum value rests at zero stating that a firm did not paid salary in a certain year. Firms in our sample vary from total assets of PKR 150 Million to PKR 209 Billion. Mean value of total assets of firms in our sample is PKR 7 Billion. Mean value of Earning per asset is about PKR 0.099, with firms earning up to maximum of PKR 0.55 per asset and generating maximum of loss of PKR 0.46 per asset. Market to Book ratio has a mean of 1.13. On average the gross amount of property, plant and equipment is 47.28\% of total assets with maximum of 93.60\% and minimum of 1.68\% of total assets. Mean leverage is at -3.47 that is for every PKR 1 of negative equity on average firms have a loan of PKR 3.33. Maximum leverage value is at 11.87 that is against every PKR 1 of equity firm has a debt of PKR 11.87. Descriptive Statistics keeping other variables constant is rejected. Thus the theory of BSZ (2010) that firms will not use large amounts of debt because of the increase in labour expenses with increase in debt offsetting benefits of 
\section[{g) Correlation of variables in Non bankrupt sample}]{g) Correlation of variables in Non bankrupt sample}\par
Correlation table of non bankrupt sample shows the correlation matrix of the variables in non bankrupt sample. The results state that there is positive correlation of Labor Intensity with Leverage, Market to Book Ratio Earning per asset. These correlations are justified by theory as increase in leverage will increase salary as employees demand premium against cost of bankruptcy due to leverage. Market to Book ratio represents growth of firm which also should have positive impact on salary when the M/B ratio rises. Earning per asset also increases salary as firms earning more will pay higher to employees. However all these correlations values are insignificant. Physical Capital Intensity and firm size however have a negative correlation with labor Intensity with a higher intensity than any other variable stating that there as firms become more mechanized the labor intensity decreases and also increased firm size decreases labor Intensity.\par
Further Physical Capital Intensity is negatively correlated with Earning per assets and firm size. Increase in Physical capital Intensity will decrease Earning per asset. Firm size is also intensely correlated with Market to Book Ratio. Increase in firm size will decrease market to book ratio. Leverage is highly positively correlated with Market to Book ratio of firm. Increase in leverage will increase Market to Book ratio showing that increase in leverage increases value of firm.\par
As per our results of correlation of bankrupt firms the regression equation to measure impact of independent variables on dependent variables is Only Size of has significant negative impact on Labor Intensity but the value of coefficient is quite small. With increase in one unit of Firm Size Labor Intensity decreases by 0.044. These results are again opposite to scholars prediction and research as according to them with increase in firm size labor wages shall rise thereby increasing Labor Intensity. These results can be due to the fact that large firms are more stable and are more likely to survive than small firms thus pay of wages at a minimum rate. All independent variables except size have no significant impact on Labor Intensity according to the results of our safe firm sample thus no variable is consistent with theory and also the results of Chemmanu, Cheng, and Zhang (2012) confirming that our first hypothesis that with increase in leverage of firm Labor Intensity will increase keeping other variables constant is rejected. Thus the theory of BSZ (2010) that firms will not use large amounts of debt because of the increase in labour expenses with increase in debt offsetting benefits of debt is not applicable in Pakistani listed firms as shown by our results. These result also confirm rejection of our third hypothesis that salary premium cost caused by increase in debt causes firms to abstain from incorporating large amount of debt in capital structure.LI it = Intercept + B1 (L it ) -B3 (FS it ) + B4 (M/B it ) -B5 (PCI it ) + B6 ( 
\section[{i) Panel Least Square Regression Non Bankrupt Sample}]{i) Panel Least Square Regression Non Bankrupt Sample} 
\section[{Variable}]{Variable}\par
Further the results conclude that model is fit as shown by value of F-Statistic. The value of R-Squared is 0.067 showing that the independent variables (leverage, Physical capital intensity, Earning per asset, Market to book ratio) explain 6.7\% of the variation in our dependent variable that is Labor Intensity.\par
We further see that Auto industry has the highest employee wage per asset among the firms in safe zone as shown in Regression Table Non Bankrupt across Industry (1). Auto industry is followed by Pharmaceutical industry. Beverages industry has lowest employee wage per asset among the firms in safe zone as shown by Regression Table Non Bankrupt across Industry \hyperref[b1]{(2)}.\par
We further check role of size across the industries in our non bankrupt sample. Regression Table  {\ref Non} Bankrupt across Industry with respect to size shows the impact of size on Labor Intensity Industry wise. Expect Auto, Household and Pharmaceutical Industry in all other industries size has negative impact on labor intensity. However there is no significant impact of size on labor intensity in any industry individually.  
\section[{j) Regression}]{j) Regression} 
\section[{Data Analysis of Bankrupt Observations a) Descriptive Statistics}]{Data Analysis of Bankrupt Observations a) Descriptive Statistics}\par
Mean value of Labor Intensity is 0.058 which means that on average employees earn PKR 0.058 against every PKR 1 of assets. Maximum value reaches to 0.28 that is against every PKR 1 assets of firm employees earn PKR 0.28. Minimum value rests at zero stating that a firm did not paid salary in a certain year. Firms in our sample vary from total assets of PKR 9 Million to PKR 209 Billion. Mean value of total assets of firms in our sample is PKR 5.5 Billion. Mean value of Earning per asset is about PKR 0.092, with firms earning up to maximum of PKR 0.65 per asset and generating maximum of loss of PKR 0.27 per asset. Market to Book ratio has a mean of 3.5. On average the gross amount of property, plant and equipment is 55\% of total assets with maximum of 99.86\% and minimum of 10.99\% of total assets. Mean leverage is at 2.13 that is for every PKR 1 of equity on average firms have a loan of PKR 2. \hyperref[b12]{13} As per our results of correlation of bankrupt firms the regression equation to measure impact of independent variables on dependent variables is bargaining tool to reduce salary is confirmed as with increase in leverage of firm salary decreases however the intensity of decrease in wages to total assets ratio is quite less with increase in leverage. With increase in one unit of leverage labor intensity decreases by 0.0008 units only at 5\% level of significance. This means there is 95\% probability that with increase in leverage in distress firms labor intensity will decrease. All control variables (firm size, Market to book ratio, Physical Capital Intensity and Earning per asset) have highly significant relation with labor intensity that is they impact labor intensity at 1\% level of significance.LI it = Intercept -B1 (L it ) -B3 (FS it ) + B4 (M/B it )\par
Market to book ratio used as proxy of growth has significant relation with labor intensity however the coefficient is very small. At 1\% level of significance one unit increase in market to book ratio increases labor intensity by 0.003 unit. The result is in line with theory stating that as firm maximizes its equity value showing signs of growth salary of employees also increase. Profitability has significant positive relation with labor intensity in line with theory and literature. At 1\% level of significance one unit increase in profitability labor intensity increases by 0.086 units.\par
Physical Capital intensity however opposite of theory shows highly significant effect of firm mechanization on salary of employees. As per theory with increase in physical capital intensity output of firm increases thereby increasing sales and profitability but in case of Pakistan the results are opposite which is due to the fact of high and increasing level of unemployment. Increase in physical capital intensity by one unit at 1\% level of significance labor intensity decreases by 0.093 units. Size of firm also significantly negatively impacts labor intensity. Increase in one unit of size of firm, labor intensity decreases by 0.031 units at 1\% level of significance. This relation is also against theory which states bigger firms are to pay more as compared to smaller firms. This may be due to the fact that bigger firms are stable and more preferred by employees as they have more chances of survival.\par
Further R square value is 0.4765 showing that 47.65\% of variance in labor intensity is predicted by independent variables in case where firms are in distress zone. Negative significant impact of leverage on Labor Intensity confirms theory that firms in bankrupt zone take on debt and use it as bargaining tool to reduce salaries of employees this also confirms our second hypothesis that firms labor intensity does not increase with increase in leverage in distress zone.\par
We further see that Pharmaceutical industry has the highest employee wage per asset among the firms in distress zone as shown in Regression Table  {\ref Bankrupt} across Industry (1). Pharmaceutical industry is followed by Travel Industry. Telecom industry has lowest employee wage per asset among the firms in distress zone as shown by Regression Table Bankrupt across Industry \hyperref[b1]{(2)}.\par
We further check moderating role of profitability, market to book ratio, physical capital intensity and size across the industries in our bankrupt sample. Regression Table Bankrupt across Industry with respect to Profitability shows the impact of profitability on Labor Intensity Industry wise. In Cement, Electric and Telecom Industry profitability has negative impact on labor intensity and only in Cement Industry profitability has significant negative impact on labor intensity. In Oil, Forestry, House Hold and Industrial mining the impact of profitability on labor intensity is positive but insignificant. In remaining five industries of Bankrupt Sample profitability significantly positively impacts labor intensity. 
\section[{e) Regression}]{e) Regression}\par
Regression Table Bankrupt across Industry with respect to M/B Ratio (market to book ratio) shows the impact of market to book ratio on Labor Intensity Industry wise. In Oil, Electric, Telecom and Industrial Mining Industry market to book ratio has negative impact on labor intensity. However the impact is insignificant. In Cement, Forestry, and House Hold the impact of market to book ratio on labor intensity is positive but insignificant. In remaining five industries of Bankrupt Sample market to book ratio significantly positively impacts labor intensity.\par
Regression Table Bankrupt across Industry with respect to Physical Capital Intensity shows the impact of Physical Capital Intensity on Labor Intensity Industry wise. In Media, Tobacco, Pharmaceutical and Travel Industry Physical Capital Intensity has positive impact on labor intensity. However the impact in travel industry is insignificant. In Oil, Forestry and House Hold Industry the impact of Physical Capital Intensity on labor intensity Regression Table Bankrupt across Industry with respect to Size shows the impact of Size on Labor Intensity Industry wise. In Media, Tobacco and Travel Industry Size has positive impact on labor intensity. However the impact is insignificant. In Pharmaceutical Industry the impact of Size on labor intensity is negative but insignificant. In remaining eight industries of Bankrupt Sample Size significantly negatively impacts labor intensity. 
\section[{X.}]{X.}\par
Conclusion  {\ref Titman (1984)} while exploring determinants of capital structure argued that firms don't reach optimal capital structure because of indirect costs associated with increase in leverage. According to  {\ref Titman (1984)} direct costs of debt do not truly and significantly explain why firms restrain from using debt thus the only answer for restraining firms from use of debt was the indirect cost borne by firms by incorporating debt in their capital structure.\par
Upon this argument Berk, Stanton, and Zechner (2010) developed a model stating that increase in salaries paid to employees with increase in leverage is a major indirect cost which refrains firms from using large amount of debt. As per BSZ (2010) as firms incorporate debt in their capital structure the employees feel high risks of bankruptcy of firms and further increased risk of unemployment. Thus to compensate the risk of unemployment employees demand a salary premium. This salary premium paid to employees offsets the tax benefits of debt thus a firm can only take up debt till the time this premium is below tax benefits of debt thereby enforcing firms to restrain from use of large amount of debt or even not letting firms to reach their optimal capital structure.\par
To statistically verify this model Chemmanu, Cheng, and Zhang (2012) for the first time explored the impact of increase leverage on salaries. As per results of Chemmanu, Cheng, and Zhang (2012) salaries rise with increase in leverage thus proving BSZ (2010) model and theory of  {\ref Titman (1984)}.\par
I also statistically checked the BSZ (2010) model with context of Pakistan. After analyzing sample data collected from listed companies from Pakistan I conclude that in overall results the theory of Titman and model of BSZ are not applicable in Pakistan. The main reason for this are the economic conditions of country and as well as the ownership structure of firms. There is a large workforce available in the country to work at any provided pay. Further the firms in the country are family held and thus the level of corporate governance is very low. Further these family held firms have small ownership structure thus it is easy for them to acquire leverage and keep employees at minimum wage. The same conclusion remains for observations of firms in safe zone.\par
The results of my observations of bankrupt firms or firms in distress zone support the theory that firms in distress zone will use debt as a bargaining tool to lower down the wages however the magnitude is quite small. Growth of firms in distress zone and profitability of these firms increase labor intensity significantly however size and physical capital Intensity of firm significantly decrease labor intensity. 
\section[{a) Direction for the Future Research}]{a) Direction for the Future Research}\par
This conclusion is drawn from a sample 84 listed companies from different sector of Pakistan covering a period of five years and can be further enhanced by collecting data of more firms for a longer period. Further to get a clear picture the data can be divided in two parts firms with specialized and non specialized employees. As firms with specialized employees will already be providing higher wages than firms with non specialized employees. Similarly technological and non technological firms can be separated to see the similar impact. Existing evidence suggests that employees in non technological firms are entrenched or are already paid higher and scholars expect that there is stronger effect of leverage on labor costs when employees are more entrenched. Further the BSZ (2010) model is of no use in cases where assets of firms are such that they support high leverage and highly paid employees giving a positive relationship between leverage and salary. Thus our conclusion is not final and is restricted to data, time period and the division of data. 
\section[{b) Recommendation}]{b) Recommendation}\par
The economic conditions of country, ownership structure of firms and the level of corporate governance in firms does not allow employees to bargain their rights. Thus leverage of firms has no significant impact on salary of employees of firm in Pakistan when they are in safe zone. Therefore the firms in Pakistan are free to take on leverage as the tax benefit of debt is not offset by any premium paid to employees to cover up their risk of unemployment.\begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-2.png}
\caption{\label{fig_0}Global}\end{figure}
  \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.41103533787972724\textwidth}P{0.3904835709857409\textwidth}P{0.04848109113453193\textwidth}}
Biafo Industries\tabcellsep Crescent Steel Ltd.\tabcellsep \\
Fauji Fert Bin\tabcellsep Dost Steels Ltd.\tabcellsep \\
Fauji Fertilizer\tabcellsep Siddiqsons Tin Plate\tabcellsep \\
Nimir Ind.Chemicals\tabcellsep TOBACCO\tabcellsep \\
Pak.P.V.C.\tabcellsep Pak Tobacco\tabcellsep \\
Sitara Chemical\tabcellsep Philip Morris Pak.\tabcellsep \\
Wah-Noble\tabcellsep PHARMACUETICAL\tabcellsep \\
ELECTRICITY\tabcellsep Ferozsons (Lab) Ltd.\tabcellsep \\
Hub Power Company\tabcellsep Highnoon (Lab) Ltd\tabcellsep \\
Japan Power\tabcellsep Sanofi-Aventis Pak\tabcellsep \\
Kot Addu Power K.E.S.C. Kohinoor Energy Ltd.\tabcellsep Wyeth Pak Limited GSK TRAVEL \& LEISURE\tabcellsep Year 2015\\
Nishat Chun Power\tabcellsep Dreamworld\tabcellsep \\
Southern Electric\tabcellsep P.I.A.C.(A)\tabcellsep \\
\tabcellsep INDUSTRIAL\tabcellsep \\
List of companies from sector is given below: AUTOMOBILE \& PARTS Sazgar Eng. PAK SUZUKI Atlas Battery Ltd. Bal.Wheels FORESTRY Century Paper Security Paper INDUSTRIAL TRANSPORTATION P.N.S.C.\tabcellsep CEMENT Al-Abbas Cement Attock Cement Bestway Cement Cherat Cement TRANSPORTATION P.N.S.C. MULTIUTILITIES Sui North Gas Sui South Gas\tabcellsep Volume XV Issue IX Version I\\
Exide (PAK) XD General Tyre\tabcellsep D.G.K.Cement Dandot Cement\tabcellsep ( ) C\\
ENGINEERING AL-Ghazi Tractor Bolan Casting Ghandhara Ind. Hinopak MotorXD Pak Engineering BEVERAGES Murree Brewery Shezan Inter. OIL \& GAS Attock Petroleum Attock Refinery Ltd Burshane LPG Byco Petroleum Mari Gas Company National Refinery Oil \& Gas Development Corp.\tabcellsep EMCO Industries Fauji Cement Fecto Cement Flying Cement Gharibwal Cement Kohat Cement Lafarge Cement lucky Cement Maple Cement Thatta Cement Frontier Creamics Pioneer Cement FIXED LINES TELECOMMUNICATIONS Pak Datacom Telecard Limited WorldCall Telecom HOUSEHOLD\tabcellsep Global Journal of Management and Business Research\\
Pak Petroleum\tabcellsep Singer Pakistan\tabcellsep \\
Pak Refinery\tabcellsep Tariq Glass Ind.\tabcellsep \\
P.S.O.\tabcellsep MEDIA\tabcellsep \\
Shell Pakistan Ltd.\tabcellsep Hum Network Ltd\tabcellsep \\
CHEMICALS\tabcellsep Media Times Ltd\tabcellsep \\
\tabcellsep INDUSTRIAL METAL \&\tabcellsep \\
Bawany Air Products\tabcellsep MINING\tabcellsep \end{longtable} \par
  {\small\itshape [Note: i. Variable Description a. Dependent variable: Labor Intensity (L.I)]} 
\caption{\label{tab_0}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.4561137071651091\textwidth}P{0.05163551401869159\textwidth}P{0.05693146417445482\textwidth}P{0.053621495327102794\textwidth}P{0.05097352024922118\textwidth}P{0.056269470404984416\textwidth}P{0.06222741433021806\textwidth}P{0.06222741433021806\textwidth}}
\tabcellsep LI\tabcellsep L\tabcellsep MB\tabcellsep P\tabcellsep PCI\tabcellsep S\tabcellsep \\
Mean\tabcellsep 0.07529\tabcellsep -1.8038\tabcellsep 1.84122\tabcellsep 0.09756\tabcellsep 0.49581\tabcellsep 6.87101\tabcellsep \\
Median\tabcellsep 0.04176\tabcellsep 0.4579\tabcellsep 0.81523\tabcellsep 0.08247\tabcellsep 0.47042\tabcellsep 6.85132\tabcellsep \\
Maximum\tabcellsep 1.88101\tabcellsep 132.563\tabcellsep 418.38\tabcellsep 0.65095\tabcellsep 0.99863\tabcellsep 8.54086\tabcellsep \\
Minimum\tabcellsep 0\tabcellsep -917.22\tabcellsep -479.29\tabcellsep -0.4693\tabcellsep 0.01679\tabcellsep 3.81585\tabcellsep \\
Std. Dev.\tabcellsep 0.13095\tabcellsep 46.6628\tabcellsep 31.669\tabcellsep 0.13265\tabcellsep 0.25802\tabcellsep 0.79066\tabcellsep \\
Skewness\tabcellsep 9.85987\tabcellsep -18.241\tabcellsep -2.8935\tabcellsep 0.53736\tabcellsep 0.05573\tabcellsep -0.304\tabcellsep \\
Kurtosis Jarque-Bera Probability Sum\tabcellsep 126.425 273395 0 31.6204\tabcellsep 355.725 2200555 0 -757.58\tabcellsep 199.124 673717 0 773.311\tabcellsep 5.45676 125.837 0 40.977\tabcellsep 1.98678 18.1831 0.00011 208.239\tabcellsep 3.76398 16.6837 0.00024 2885.82\tabcellsep Year 2015\\
Sum Sq. Dev.\tabcellsep 7.18458\tabcellsep 912337\tabcellsep 420227\tabcellsep 7.37299\tabcellsep 27.894\tabcellsep 261.936\tabcellsep \\
Observations\tabcellsep 420\tabcellsep 420\tabcellsep 420\tabcellsep 420\tabcellsep 420\tabcellsep 420\tabcellsep \\
\multicolumn{7}{l}{Z = 1.147 x X1 + 0.701 x X2 -0.732 x X3 Z = Z score Value Where: X1= sales to total assets ratio X2 = Earning before Interest \& taxes to Current Liability Ratio X3 = The FREQ Procedure Mean value of Labor Intensity is 0.0753 which means that on average employees earn PKR 0.0753 against every PKR 1 of assets. b) Corr elation overall sample Status Frequency Percent Cumulative Frequency Cumulative Percent LI L MB P PCI S LI 1 L 0.00568 1 MB 0.03414 0.7264 1 P 0.10329 0.05913 0.05619 1 PCI -0.1505 -0.029 -0.0117 -0.3531 1 S -0.2237 -0.0976 -0.1112 0.07006 -0.0797 1}\tabcellsep Volume XV Issue IX Version I ( ) C Global Journal of Management and Business Research\\
Bankrupt\tabcellsep 125\tabcellsep 29.76\tabcellsep 125\tabcellsep \tabcellsep \tabcellsep 29.76\tabcellsep \\
Non-Bankrupt\tabcellsep 295\tabcellsep 70.24\tabcellsep 420\tabcellsep \tabcellsep \tabcellsep 100.00\tabcellsep \end{longtable} \par
 
\caption{\label{tab_1}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.6880952380952381\textwidth}P{0.16190476190476188\textwidth}}
VIII.\tabcellsep Data Analysis\\
a) Overall Data Analysis (Total Sample)\tabcellsep negative relation between Labor Intensity and Physical\\
Descriptive Statistics overall sample\tabcellsep Capital Intensity.\\
c) Umeployment Rate\tabcellsep \\
Year\tabcellsep Unemployment Rate\\
2008\tabcellsep 5.20\%\\
2009\tabcellsep 5.50\%\\
2010\tabcellsep 5.60\%\\
2011\tabcellsep 6.00\%\\
2012\tabcellsep 6.20\%\\
\multicolumn{2}{l}{Thus after analyzing correlation matrix our regression equation comes in the following form}\\
\multicolumn{2}{l}{LI it = Intercept + B1 (L it ) -B2 (FS it ) + B3 (M/B it ) -B4 (PCI it ) + B5 (Pt it )}\\
With\tabcellsep \\
LI = Labor Intensity (Salaries/Total Assets)\tabcellsep \\
L = Leverage of firm (Total Debt/Total Equity)\tabcellsep \\
M/B = Market to book ratio\tabcellsep \\
PCI = Physical Capital Intensity\tabcellsep \\
\multicolumn{2}{l}{P = Earning per asset (Earnings before Interest \& Taxes / Total Assets)}\\
t = time series \& i = cross section\tabcellsep \end{longtable} \par
 
\caption{\label{tab_2}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.19744094488188976\textwidth}P{0.09872047244094488\textwidth}P{0.10708661417322834\textwidth}P{0.10541338582677165\textwidth}P{0.10541338582677165\textwidth}P{0.1187992125984252\textwidth}P{0.1171259842519685\textwidth}}
\multicolumn{2}{l}{f) Descriptive Statistic Non Bankrupt Sample}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep LI\tabcellsep L\tabcellsep MB\tabcellsep P\tabcellsep PCI\tabcellsep S\\
Mean\tabcellsep 0.08227\tabcellsep -3.4726\tabcellsep 1.13756\tabcellsep 0.0999\tabcellsep 0.47283\tabcellsep 6.88014\\
Median\tabcellsep 0.04624\tabcellsep 0.37106\tabcellsep 0.80582\tabcellsep 0.08353\tabcellsep 0.45194\tabcellsep 6.73822\\
Maximum\tabcellsep 1.88101\tabcellsep 11.8723\tabcellsep 418.38\tabcellsep 0.55364\tabcellsep 0.93604\tabcellsep 8.54086\\
Minimum\tabcellsep 0\tabcellsep -917.22\tabcellsep -479.29\tabcellsep -0.4693\tabcellsep 0.01679\tabcellsep 5.6878\\
Std. Dev.\tabcellsep 0.14975\tabcellsep 55.0484\tabcellsep 37.2452\tabcellsep 0.12156\tabcellsep 0.25107\tabcellsep 0.73048\\
Skewness\tabcellsep 9.23261\tabcellsep -15.802\tabcellsep -2.5177\tabcellsep 0.40539\tabcellsep 0.01072\tabcellsep 0.53971\\
Kurtosis\tabcellsep 103.651\tabcellsep 259.968\tabcellsep 148.241\tabcellsep 5.8231\tabcellsep 2.0335\tabcellsep 2.34968\\
Jarque-Bera\tabcellsep 128713\tabcellsep 823925\tabcellsep 259604\tabcellsep 106.044\tabcellsep 11.4877\tabcellsep 19.5202\\
Probability\tabcellsep 0\tabcellsep 0\tabcellsep 0\tabcellsep 0\tabcellsep 0.0032\tabcellsep 5.8E-05\\
Sum\tabcellsep 24.2695\tabcellsep -1024.4\tabcellsep 335.58\tabcellsep 29.4702\tabcellsep 139.485\tabcellsep 2029.64\end{longtable} \par
 
\caption{\label{tab_4}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.6160550458715596\textwidth}P{0.07408256880733945\textwidth}P{0.058486238532110095\textwidth}P{0.046788990825688076\textwidth}P{0.029243119266055047\textwidth}P{0.021444954128440368\textwidth}P{0.0038990825688073397\textwidth}}
With\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{2}{l}{LI = Labor Intensity (Salaries/Total Assets)}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{3}{l}{L = Leverage of firm (Total Debt/Total Equity)}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
M/B = Market to book ratio\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
PCI = Physical Capital Intensity\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{6}{l}{Earning per asset = Earning per asset (Earnings before Interest \& Taxes / Total Assets)}\tabcellsep \\
t = time series\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
i = cross section\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{2}{l}{h) Correlation Non Bankrupt Sample}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep LI\tabcellsep L\tabcellsep MB\tabcellsep P\tabcellsep PCI\tabcellsep S\\
LI\tabcellsep 1\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
L\tabcellsep 0.01171\tabcellsep 1\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
MB\tabcellsep 0.01788\tabcellsep 0.73567\tabcellsep 1\tabcellsep \tabcellsep \tabcellsep \\
P\tabcellsep 0.08982\tabcellsep 0.09499\tabcellsep 0.08387\tabcellsep 1\tabcellsep \tabcellsep \\
PCI\tabcellsep -0.1375\tabcellsep -0.0529\tabcellsep -0.0428\tabcellsep -0.3477\tabcellsep 1\tabcellsep \\
S\tabcellsep -0.2279\tabcellsep -0.1245\tabcellsep -0.1205\tabcellsep -0.087\tabcellsep 0.08724\tabcellsep 1\end{longtable} \par
 
\caption{\label{tab_5}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.022418101054969462\textwidth}P{0.2529705719044975\textwidth}P{0.03044142143253748\textwidth}P{0.0009439200444197668\textwidth}P{0.003775680177679067\textwidth}P{0.09627984453081621\textwidth}P{0.14583564686285397\textwidth}P{0.16966962798445306\textwidth}P{0.10430316490838423\textwidth}P{0.023362021099389226\textwidth}}
\tabcellsep \tabcellsep \multicolumn{6}{l}{Human Capital, Capital Structure, Employee Pay: Empirical Evidence from Pakistan}\\
\tabcellsep \multicolumn{2}{l}{AUTO*S}\tabcellsep PCI\tabcellsep -0.06397\tabcellsep 0.036208 0.01024\tabcellsep -1.76677 0.01763\tabcellsep 0.0783 0.580854\tabcellsep 0.5618\\
\tabcellsep \multicolumn{2}{l}{UTILITIES CEMENT *S}\tabcellsep S\tabcellsep -0.04477\tabcellsep 0.011801 -0.1527 -0.01136\tabcellsep -3.79352 0.057179 0.016971\tabcellsep 0.0002 -2.67058 -0.66956\tabcellsep 0.008 0.5037\\
\tabcellsep \multicolumn{2}{l}{R-squared CHEMICAL*S}\tabcellsep \tabcellsep \tabcellsep -0.01132\tabcellsep 0.017625\tabcellsep 0.211093 -0.64208\tabcellsep 0.5214\\
\tabcellsep \multicolumn{3}{l}{Adjusted R-squared OIL*S}\tabcellsep \tabcellsep -0.01916\tabcellsep 0.014676\tabcellsep 0.150408 -1.30577\tabcellsep 0.1927\\
\tabcellsep \multicolumn{2}{l}{F-statistic BEVERAGES*S}\tabcellsep \tabcellsep \tabcellsep -0.00845\tabcellsep 0.018683\tabcellsep 3.478498 -0.45226\tabcellsep 0.6514\\
\tabcellsep \multicolumn{3}{l}{Prob(F-statistic) ELECTRIC*S}\tabcellsep \tabcellsep -0.01525\tabcellsep 0.016452\tabcellsep 0.000001 -0.92676\tabcellsep 0.3549\\
\tabcellsep \multicolumn{4}{l}{k) Regression Table Non Bankrupt across Industry (2) ENGINEERING*S}\tabcellsep -0.01632\tabcellsep 0.017464\tabcellsep -0.93475\tabcellsep 0.3507\\
\tabcellsep \multicolumn{2}{l}{TELECOM*S}\tabcellsep \tabcellsep \tabcellsep -0.00345\tabcellsep 0.021271\tabcellsep -0.16197\tabcellsep 0.8715\\
Year 2015\tabcellsep \multicolumn{3}{l}{Variable INDUSTIRAL MINING *S C L MB FORESTRY*S HOUSE HOLD *S MEDIA *S}\tabcellsep \tabcellsep Coefficient 0.136977 8.21E-05 -0.00026 -0.01506 0.004218 -0.00925 -0.01942\tabcellsep Std. Error 0.1233 0.000222 0.000326 0.019376 0.020831 0.020893 0.017846\tabcellsep t-Statistic 1.110924 0.369705 -0.78491 -0.77698 0.202475 -0.44284 -1.08841\tabcellsep Prob. 0.2676 0.7119 0.4332 0.4378 0.8397 0.6582 0.2774\tabcellsep Year 2015\\
32\tabcellsep \multicolumn{2}{l}{P TOBACOO*S}\tabcellsep \tabcellsep \tabcellsep 0.043319 -0.00318\tabcellsep 0.077619 0.018027\tabcellsep 0.558094 -0.17613\tabcellsep 0.5772 0.8603\\
\tabcellsep \multicolumn{2}{l}{PCI PHARAMA *S}\tabcellsep \tabcellsep \tabcellsep -0.07587 0.00619\tabcellsep 0.047553 0.01827\tabcellsep -1.59546 0.338776\tabcellsep 0.1118 0.735\\
( ) C Volume XV Issue IX Version I\tabcellsep \multicolumn{3}{l}{Variable INDUSTRIAL TRANSPORTATION*S C HOUSE HOLD S AUTO CEMENT CHEMICAL OIL ELECTRIC ENGINEERING TELECOM FORESTRY TRAVEL *S UTILITIES*S R-squared Adjusted R-squared F-statistic Prob(F-statistic) IX.}\tabcellsep \tabcellsep Coefficient 0.332482 0.153321 -0.01299 0.195506 0.057028 0.048063 0.075886 0.026099 0.02366 0.102674 0.031678 -0.00296 -0.01299 -0.01326\tabcellsep Std. Error 0.105884 0.072541 0.016142 0.038536 0.037542 0.044887 0.053399 0.044318 0.039228 0.070881 0.067808 0.016179 0.017746 0.01491\tabcellsep t-Statistic 3.140058 2.113564 -0.80453 5.073259 1.519043 1.07076 1.421103 0.588908 0.603131 1.44855 0.467166 -0.18283 -0.73192 -0.88905 0.211085 0.150399 3.478333 0.000001\tabcellsep 0.0019 0.0355 Prob. 0.4218 0 0.1299 0.2852 0.1564 0.5564 0.5469 0.1486 0.6408 0.8551 0.4648 0.3748\tabcellsep C ( ) Volume XV Issue IX Version I\\
Global Journal of Management and Business Research\tabcellsep \multicolumn{5}{l}{L MB P PCI S CEMENT CHEMICAL OIL BEVERAGES ELECTRIC ENGINEERING TELECOM FORESTRY HOUSE HOLD MEDIA MEDIA INDUSTIRAL MINING TOBACOO PHARAMA TRAVEL INDUSTRIAL TRANSPORTATION UTILITIES R-squared Adjusted R-squared F-statistic Prob(F-statistic) l) Regression Table Non Bankrupt across Industry w.r.t Size 8.21E-05 -0.00026 0.043319 -0.07587 -0.01299 -0.13848 -0.14744 -0.19551 -0.11962 -0.16941 -0.17185 -0.09283 -0.16383 -0.04219 -0.12796 0.067547 0.003141 0.114533 0.16695 0.127093 0.044541 0.042804 Variable Coefficient C 0.18276}\tabcellsep 0.000222 0.000326 0.077619 0.047553 0.016142 0.034622 0.041584 0.038536 0.050867 0.044872 0.03774 0.067342 0.066896 0.068639 0.070323 0.074058 0.050914 0.066189 0.04339 0.072462 0.068702 0.049982 Std. Error 0.110698\tabcellsep 0.369705 -0.78491 0.558094 -1.59546 -0.80453 -3.99966 -3.54568 -5.07326 -2.35162 -3.77534 -4.55338 -1.3785 -2.449 -0.61459 -1.81957 0.912089 0.061685 1.730384 3.847706 1.753924 0.648322 0.856402 0.211093 0.150408 3.478498 0.000001 t-Statistic 1.65099\tabcellsep 0.7119 0.4332 0.5772 0.1118 0.4218 0.0001 0.0005 0 0.0194 0.0002 0 0.1692 0.015 0.5393 0.0699 0.3625 0.9509 0.0847 0.0001 0.0806 0.5173 0.3925 Prob. 0.0999\tabcellsep Global Journal of Management and Business Research\\
\tabcellsep L\tabcellsep \multicolumn{2}{l}{INDUSTIRAL MINING}\tabcellsep \tabcellsep -0.19237 7.08E-05\tabcellsep 0.05126 0.000223\tabcellsep -3.75274 0.31807\tabcellsep 0.0002 0.7507\\
\tabcellsep \multicolumn{2}{l}{TOBACOO MB}\tabcellsep \tabcellsep \tabcellsep -0.08097 -0.00023\tabcellsep 0.067765 0.000325\tabcellsep -1.1949 -0.71787\tabcellsep 0.2332 0.4735\\
\tabcellsep P\tabcellsep PHARAMA\tabcellsep \tabcellsep \tabcellsep -0.02856 0.047258\tabcellsep 0.04017 0.077416\tabcellsep -0.71086 0.610444\tabcellsep 0.4778 0.5421\\
\tabcellsep \multicolumn{2}{l}{TRAVEL PCI}\tabcellsep \tabcellsep \tabcellsep -0.06841 -0.07826\tabcellsep 0.075894 0.048807\tabcellsep -0.90142 -1.60339\tabcellsep 0.3682 0.11\\
\tabcellsep \tabcellsep \multicolumn{2}{l}{INDUSTRIAL TRANSPORTATION}\tabcellsep \tabcellsep -0.15096\tabcellsep 0.069649\tabcellsep -\tabcellsep 2.1675\tabcellsep 0.0311\\
\tabcellsep \multicolumn{2}{l}{© 2015 Global Journals Inc. (US) 1}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_7}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.0039050535987748855\textwidth}P{0.35319040326697293\textwidth}P{0.019959162838182744\textwidth}P{0.04555895865237366\textwidth}P{0.031240428790199084\textwidth}P{0.010847371107708014\textwidth}P{0.2685809086268504\textwidth}P{0.04252169474221542\textwidth}P{0.041220010209290454\textwidth}P{0.03297600816743236\textwidth}}
\tabcellsep \multicolumn{2}{l}{Jarque-Bera}\tabcellsep 54.8747\tabcellsep 55525\tabcellsep \tabcellsep 2477.43\tabcellsep 24.1035\tabcellsep 8.87856\tabcellsep 46.9503\\
\tabcellsep \multicolumn{2}{l}{Probability}\tabcellsep 0\tabcellsep 0\tabcellsep \tabcellsep 0\tabcellsep 6E-06\tabcellsep 0.0118\tabcellsep 0\\
\tabcellsep Sum\tabcellsep \tabcellsep 7.3509\tabcellsep 266.826\tabcellsep \tabcellsep 437.732\tabcellsep 11.5068\tabcellsep 68.7545\tabcellsep 856.18\\
\tabcellsep \multicolumn{2}{l}{Sum Sq. Dev.}\tabcellsep 0.54294\tabcellsep 18661.6\tabcellsep \tabcellsep 11896.9\tabcellsep 3.02293\tabcellsep 8.83755\tabcellsep 104.973\\
\tabcellsep \multicolumn{2}{l}{Observations}\tabcellsep 125\tabcellsep 125\tabcellsep \tabcellsep 125\tabcellsep 125\tabcellsep 125\tabcellsep 125\\
\tabcellsep \multicolumn{5}{l}{Correlation Non Bankrupt Sample table shows}\tabcellsep \multicolumn{2}{l}{intensity than any other variable stating that as firms}\\
\tabcellsep \multicolumn{5}{l}{the correlation matrix of the variables in bankrupt}\tabcellsep \multicolumn{2}{l}{become more mechanized the labor intensity decreases}\\
\tabcellsep \multicolumn{5}{l}{sample. The results state that there is positive}\tabcellsep \multicolumn{2}{l}{and also increased firm size decreases labor Intensity.}\\
\tabcellsep \multicolumn{5}{l}{correlation of Labor Intensity with Market to Book Ratio}\tabcellsep \multicolumn{2}{l}{Further as expected in literature leverage of firms in}\\
\tabcellsep \multicolumn{5}{l}{and Earning per asset. All other independent variables}\tabcellsep \multicolumn{2}{l}{bankrupt zone is negatively correlated with labor}\\
Year\tabcellsep \multicolumn{5}{l}{(leverage, physical capital intensity and firm size) are negatively correlated to Labor Intensity. Labor Intensity has higher value of positive correlation with market to}\tabcellsep \multicolumn{2}{l}{intensity as firm use debt as bargaining tool to lower salaries of employees. This relation is however very less. Further Physical Capital Intensity is negatively}\\
\tabcellsep \multicolumn{5}{l}{book ratio of the firm showing that with increase in}\tabcellsep \multicolumn{2}{l}{correlated with Earning per assets and firm size.}\\
\tabcellsep \multicolumn{5}{l}{market value of firm average pay will also increase.}\tabcellsep \multicolumn{2}{l}{Leverage is positively correlated with Market to Book}\\
\tabcellsep \multicolumn{5}{l}{Physical Capital Intensity and firm size however have a}\tabcellsep \multicolumn{2}{l}{ratio of firm.}\\
\tabcellsep \multicolumn{5}{l}{negative correlation with labor Intensity with a higher}\tabcellsep \\
\tabcellsep \multicolumn{2}{l}{c) Correlation Bankrupt Sample}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep \tabcellsep LI\tabcellsep L\tabcellsep \tabcellsep \tabcellsep MB\tabcellsep P\tabcellsep PCI\tabcellsep S\\
\tabcellsep LI\tabcellsep 1\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep L\tabcellsep -0.022652\tabcellsep 1\tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep MB\tabcellsep 0.433759\tabcellsep 0.346376\tabcellsep \tabcellsep 1\tabcellsep \\
\tabcellsep P\tabcellsep 0.198948\tabcellsep \multicolumn{2}{l}{-0.124945}\tabcellsep \multicolumn{2}{l}{-0.0586}\tabcellsep 1\\
( ) C\tabcellsep PCI\tabcellsep -0.206161\tabcellsep 0.075271\tabcellsep \tabcellsep \multicolumn{2}{l}{0.190246}\tabcellsep -0.365769\tabcellsep 1\\
\tabcellsep S\tabcellsep -0.322994\tabcellsep \multicolumn{2}{l}{-0.016132}\tabcellsep \multicolumn{2}{l}{-0.175592}\tabcellsep 0.299102\tabcellsep -0.371148\tabcellsep 1\\
\tabcellsep \multicolumn{3}{l}{b) Descriptive Statistic Bankrupt Sample}\tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep \tabcellsep \tabcellsep LI\tabcellsep L\tabcellsep \tabcellsep MB\tabcellsep P\tabcellsep PCI\tabcellsep S\\
\tabcellsep Mean\tabcellsep \tabcellsep 0.05881\tabcellsep 2.13461\tabcellsep \tabcellsep 3.50185\tabcellsep 0.09206\tabcellsep 0.55004\tabcellsep 6.84944\\
\tabcellsep \multicolumn{2}{l}{Median}\tabcellsep 0.03132\tabcellsep 0.71357\tabcellsep \tabcellsep 1.0046\tabcellsep 0.08142\tabcellsep 0.51751\tabcellsep 7.10994\\
\tabcellsep \multicolumn{2}{l}{Maximum}\tabcellsep 0.28133\tabcellsep 132.563\tabcellsep \tabcellsep 61.6848\tabcellsep 0.65095\tabcellsep 0.99863\tabcellsep 8.34585\\
\tabcellsep \multicolumn{2}{l}{Minimum}\tabcellsep 0\tabcellsep -6.1262\tabcellsep \tabcellsep -1.4477\tabcellsep -0.2738\tabcellsep 0.10993\tabcellsep 3.81585\\
\tabcellsep \multicolumn{2}{l}{Std. Dev.}\tabcellsep 0.06617\tabcellsep 12.2677\tabcellsep \tabcellsep 9.79501\tabcellsep 0.15614\tabcellsep 0.26697\tabcellsep 0.92009\\
\tabcellsep \multicolumn{2}{l}{Skewness}\tabcellsep 1.49985\tabcellsep 9.8913\tabcellsep \tabcellsep 4.47924\tabcellsep 0.70356\tabcellsep 0.07416\tabcellsep -1.2697\\
\tabcellsep \multicolumn{2}{l}{Kurtosis}\tabcellsep 4.24003\tabcellsep 104.338\tabcellsep \tabcellsep 22.885\tabcellsep 4.62723\tabcellsep 1.70282\tabcellsep 4.60193\end{longtable} \par
 
\caption{\label{tab_8}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.85\textwidth}}
With\\
LI = Labor Intensity (Salaries/Total Assets)\\
L = Leverage of firm (Total Debt/Total Equity)\\
M/B = Market to book ratio\\
PCI = Physical Capital Intensity\\
Earning per asset = Earning per asset (Earnings before Interest \& Taxes / Total Assets)\\
t = time series \& i = cross section\\
Panel Least Square Regression Bankrupt\\
Sample table shows panel results of model. The results\\
shows that the constant value of dependent variable\\
(Labor Intensity) is 0.309 which shows the change in\\
non-financial\end{longtable} \par
 
\caption{\label{tab_9}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.02813017098731384\textwidth}P{0.6341790770362199\textwidth}P{0.03938223938223938\textwidth}P{0.048133848133848134\textwidth}P{0.06454311454311455\textwidth}P{0.0029692958264386834\textwidth}P{0.013596249310535024\textwidth}P{0.019066004780290493\textwidth}}
\tabcellsep OIL MEDIA *P ELECTRIC*PCI ELECTRIC TELECOM INDUSTIRAL MINING *P TELECOM*P FORESTRY TOBACCO *P FORESTRY*PCI HOUSE HOLD PHARAMA *P HOUSE HOLD *PCI\tabcellsep -0.17659 0.424413 -0.08487 -0.19357 -0.1976 2.98408 -0.12355 -0.15801 0.532146 -0.03917 -0.16788 0.529907 -0.02699\tabcellsep 0.015353 0.064227 0.020755 0.01467 0.016023 1.935959 0.039724 0.017819 0.058607 0.021121 0.018234 0.089303 0.070234\tabcellsep \multicolumn{2}{l}{-11.5023 6.608043 -4.08891 -13.1944 -12.3317 1.541396 -3.11005 -8.86777 9.079904 -1.85448 -9.20707 5.933809 -0.38424}\tabcellsep 0 0 0 0.1261 0.0001 0.0024 0 0 0.0664 0 0 0 0.7016\\
\tabcellsep MEDIA TRAVEL *P MEDIA *PCI\tabcellsep -0.05681 3.330852 0.708774\tabcellsep 0.018599 1.439251 0.110691\tabcellsep \multicolumn{2}{l}{-3.05434 2.314295 6.403171}\tabcellsep 0\tabcellsep 0.0028 0.0225\\
\tabcellsep INDUSTIRAL MINING TOBACCO R-squared INDUSTIRAL MINING *PCI TRAVEL Adjusted R-squared TOBACCO *PCI\tabcellsep -0.18859 -0.07138 -0.07826 -0.05313 0.300155\tabcellsep 0.018864 0.017387 0.016483 0.034048 0.032819\tabcellsep \multicolumn{3}{l}{-9.9975 -4.10565 0.779489 -4.74773 0 -1.56041 0.746821 9.145834 0}\tabcellsep 0 0.0001 0.1216\\
\tabcellsep R-squared F-statistic PHARAMA *PCI\tabcellsep 0.298003\tabcellsep 0.053047\tabcellsep 5.617687\tabcellsep \multicolumn{2}{l}{0.860142 23.86077 0}\\
Year Year\tabcellsep \multicolumn{2}{l}{Adjusted R-squared F-statistic Prob(F-statistic) f) Regression Table Non Bankrupt across Industry (2) Variable Coefficient C 0.162703 Prob(F-statistic) h) Regression Table Bankrupt across Industry w.r.t M/B Ratio TRAVEL *PCI 0.060164 R-squared Variable Coefficient C 0.203088 Adjusted R-squared F-statistic L -9.08E-05 Prob(F-statistic)}\tabcellsep Std. Error 0.028106 0.041841 Std. Error 0.031495 0.000639\tabcellsep \multicolumn{3}{l}{0.839422 41.51311 0 t-Statistic 5.788792 0 1.437924 0.1533 Prob. 0 0.843669 t-Statistic Prob. 6.448319 0.820509 36.4277 0 -0.14215 0.8872 0}\tabcellsep Year 2015\\
Global Journal of Management and Business Research Volume XV Issue IX Version I ( ) C Volume XV Issue IX Version I C ( ) Global Journal of Management and Business Research\tabcellsep \multicolumn{6}{l}{Variable CEMENT *PCI ELECTRIC*P C L MB P PCI S S OIL*P L MB P PCI S CEMENT CHEMICAL OIL ELECTRIC FORESTRY HOUSE HOLD P PCI S CEMENT *MB CHEMICAL*MB OIL*MB ELECTRIC*MB TELECOM*MB FORESTRY*MB j) Regression Table Bankrupt across Industry w.r.t Size Coefficient 0.360299 -8.12E-05 0.000894 0.00386 -0.06375 -0.01696 -0.11872 -0.05049 0.058557 -0.01951 -8.12E-05 0.000894 0.00386 -0.06375 -0.01696 0.033308 0.031659 0.021005 0.00403 0.039584 0.029716 -0.01166 -0.05572 -0.01933 0.000875 0.007846 -0.003 -0.01244 -0.0423 0.01134 Variable Coefficient C 0.193037 L -7.41E-05 MB 0.00089 P 0.00063 PCI -0.06289 CEMENT *S -0.01676 CHEMICAL*S -0.01642 OIL*S -0.01828 ELECTRIC*S -0.02043 MEDIA 0.140789 INDUSTIRAL MINING 0.009005 TOBACCO 0.197596 PHARAMA 0.126212 TRAVEL 0.144468 R-squared Adjusted R-squared F-statistic Prob(F-statistic) g) Regression Table Bankrupt across Industry w.r.t Profitability Variable Coefficient C 0.203031 L -0.00056 MB 0.002284 PCI -0.04554 S -0.02055 CEMENT *P -0.07396 CHEMICAL*P 0.071685 P 0.007107 HOUSE HOLD *MB 0.002059 MEDIA *MB 0.159175 TELECOM*S -0.02115 FORESTRY*S -0.01573 INDUSTIRAL MINING *MB -0.0155 TOBACCO *MB 0.026279 PHARAMA *MB 0.042641 TRAVEL *MB 0.003133 R-squared Adjusted R-squared F-statistic Prob(F-statistic) i) Regression Table Bankrupt across Industry w.r.t Physical Capital Intensity Std. Error 0.029488 0.000296 0.000719 0.019096 0.016275 0.003479 0.072322 0.014217 0.053171 0.003629 0.000296 0.000719 0.019096 0.016275 0.003479 0.011887 0.011706 0.012047 0.011447 0.016092 0.015211 0.023698 0.017505 0.00403 0.001947 0.003234 0.005986 0.006822 0.025723 0.013626 Std. Error 0.027927 0.000294 0.000714 0.019257 0.016969 0.003481 0.003648 0.00357 0.003292 0.016351 0.017959 0.016023 0.014907 0.037291 Std. Error 0.033053 0.000287 0.000509 0.016454 0.0041 0.034086 0.033672 0.01988 0.011341 0.024336 0.003636 0.00362 0.013333 0.002333 0.007017 0.00032 Variable Coefficient Std. Error C 0.201963 0.028775 L -0.00016 0.000311 MB 0.001159 0.00075 HOUSE HOLD *S -0.01705 0.004545 MEDIA *S 0.001148 0.004905 INDUSTIRAL MINING *S -0.02049 0.003988 TOBACCO *S 0.006672 0.003985 PHARAMA *S -0.00346 0.004023 TRAVEL *S 0.003069 0.007412 R-squared 0.861138 t-Statistic 12.2183 -0.27411 1.244526 0.202147 -3.91677 -4.87685 -1.64159 -3.55166 1.101302 -5.37581 -0.27411 1.244526 0.202147 -3.91677 -4.87685 2.801983 2.7044 1.743581 0.352042 2.459845 1.953531 -0.49207 -3.1831 -4.79691 0.449322 2.425802 -0.50061 -1.82364 -1.64432 0.83219 t-Statistic 6.912131 -0.25172 1.246061 0.032704 -3.70599 -4.8133 -4.49968 -5.1201 -6.20635 8.610629 0.501439 12.33165 8.466453 3.874078 0.860142 0.839422 41.51311 0 t-Statistic 6.142612 -1.95564 4.48442 -2.76803 -5.01227 -2.16964 2.128933 0.357498 0.181516 6.540635 -5.81769 -4.34507 -1.16285 11.26333 6.076647 9.79572 0.809104 0.0006 Prob. 0 0.7845 0.216 0 0.1036 0.0002 0.2732 0 0.8402 0.7845 0.216 0.8402 0.0002 0 0.006 0.008 0.0841 0.7255 0.0155 0.6237 0.0019 0 0.6541 0.0169 0.6177 0.071 0.103 0.4071 Prob. 0 0.8017 0.2154 0.974 0.0003 0 0 0 0 0.0533 0 0.6171 0 0 0.0002 Prob. 0 0.0531 0 0.0066 0 0.0322 0.7214 0.0355 0.8563 0 0 0 0.2475 0 0 0 0.780823 28.60959 0 t-Statistic Prob. 7.01873 0 -0.514 0.6083 1.544964 0.1253 -3.74984 0.0003 0.233962 0.8155 -5.13795 0 1.674244 0.097 -0.86125 0.391 0.414011 0.6797 Adjusted R-squared 0.840565 F-statistic 41.8593 Prob(F-statistic) 0}\tabcellsep Volume XV Issue IX Version I Global Journal of Management and Business Research ( ) C\\
\tabcellsep CEMENT TELECOM*P CHEMICAL*PCI\tabcellsep -0.16429 -0.08778 -0.06057\tabcellsep 0.013986 0.151225 0.01853\tabcellsep \multicolumn{2}{l}{-11.7464 -0.58043 -3.26864}\tabcellsep 0 0.5628 0.0014\\
\tabcellsep CHEMICAL FORESTRY*P OIL*PCI\tabcellsep -0.16594 0.126712 -0.04448\tabcellsep 0.01358 0.173723 0.027176\tabcellsep \multicolumn{2}{l}{-12.2193 0.72939 -1.63667}\tabcellsep 0 0.4673 0.1046\\
\tabcellsep HOUSE HOLD *P\tabcellsep 0.088646\tabcellsep 0.177157\tabcellsep \multicolumn{2}{l}{0.500379}\tabcellsep 0.6178\end{longtable} \par
 
\caption{\label{tab_11}}\end{figure}
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