Leverage In Textile Sector

Published Date: 02 Nov 2017

Chapter 1

In this chapter, the background was present the concept of influence of Leverage, Interest Rate, Asset Turnover , and Taxation in Textile. A problem discussion will then follow describing the Participation of these variables in effecting the Leverage. A consistently declining investment and economic growth rate is the major problem that the Pakistani economy has been facing for the last decade. An in-depth analysis of the determinants of private investment in different sectors of the economy is quite helpful in designing a plan for the economy. Interest rates emerge as the significant determinants of investment in all the sectors. Nominal interest rates and infrastructure are important in the case of agriculture only, while relative prices of imported machinery and real interest rates are significant in the manufacturing and services sectors. Unexplained variation in private investment is observed in the entire sector, which might be due to the different external and internal shocks to the economy. The proposed economic package will not only be helpful in increasing private investment but will also play an important role in restoring investor confidence that has been eroded due to the shocks. Therefore, the present study is aimed to describing the , Interest Rate, Asset Turnover , and Taxation affects have a strong relationship with Leverage in textile sector.

With all these issues in mind the aim of this research is to develop and test a model for comparing electronic media and print media. The model will analyze the effect of sales through electronic and print media. Too few studies have focused on electronic media and print media but there is no sufficient study about the comparison of electronic media with print media. Therefore, the present study is aimed to describing the comparison between electronic media and print media.

Purpose statement

Leverage has extensive effect on any organization. High leverage often demolished organization. Many factors enhance leverage. I am trying to analysis those factors that enhance leverage in textile sector in Pakistan. Therefore, purpose of this study will be, find the financial reports of selected textile company and search and calculate the values of variables.

Objectives of the study

To determine the relationship between leverage and Interest Rate, Asset Turnover, Taxation in Textile sector of Pakistan.

Significance of study

Leverage has significant role in textile sector.

Govt. strict policy about the textile sector.

But on the others side Govt. is also providing lot of incentives to the exporters.

Also manage to decrease interest rates on the loans.

Cheap labor and Cotton is available.

Interest rates emerge as the significant determinants of investment in all the sectors.

Nominal interest rates and infrastructure are important in the case of agriculture only, while relative prices of imported machinery and real interest rates are significant in the manufacturing and services sectors.

Unexplained variation in private investment is observed in the entire sector, which might be due to the different external and internal shocks to the economy.

The proposed economic package will not only be helpful in increasing private investment but will also play an important role in restoring investor confidence that has been eroded due to the shocks.

Interest is paid from net income it means more debt change to more interest and more interest mean low income.

Research Question:

What are the factors responsible for high leverage in textile sector?

Theoretical Foundation

Theoretical framework focuses on the relationship between the dependent and independent variables. The distinction between dependent and independent variables is an important in a comparative study as in a regression analysis. Dependent variable in case of a comparative study is the one, which we aim to predict, and independent variable here ate the ones who are used to predict the dependent variable.

A theoretical framework provides a diagrammatic illustration of the variables selected to conduct a study. The following diagram represents the theoretical framework of this study. The independent variables are listed on the right side and left side shows the leverage that is dependent on these variables.

Theoretical Model:

Interest Rate

Firm size

Leverage in Textile Sector

Taxation

Dependent Variable

Asset Turnover

Independent Variables

Key terms Defined

Leverage In textile

The textile sector accounts for 8-9 per cent of the total GDP, and generates 51 per cent of the export revenues for Pakistan, which is a huge number making it a very important sector. This sector is subject to high operating and financial leverage. Leverage plays a vital role in textile unit.

Interest Rate

Interest rates are the main determinants of investment and have an inverse relationship with investment. Lower real interest rates will encourage new investment by reducing the cost of capital. More debt means a higher level of interest payment each year, which is paid from net income.

Asset Turnover:

The past policies of developing the capital-intensive industrial sector under heavy protection gave rise to a manufacturing sector, which is saddled with many negative value-adding activities. The textile units shift from Pakistan to Bangladesh or other countries like China, India. It is not diversified and is generally not able to compete either at home, without heavy protection, or in the competitive international market place. New investment in the sector is minimal, skill developments are negligible and induction of modern technology rather infrequent and weak. The development of Small and Medium Enterprises was not encouraged and as a result small industries that employ about 70 per cent of the labor force never got the attention and support they deserved. Consequently both the large and small-scale segments of the industrial sector remain vulnerable especially under the new, global sing environment.

Taxation:

The textile Industry is the backbone of the Country but current situation of the textile sector is not good . Taxation, Import and Export duties or charges in Pakistan play a vital role in decline of textile sector. Pakistan’s economy decreasing rapidly due to Govt. Policies and operation in FATA and becomes the reason of terrorism, so FDI hesitate to came. 6% R&D allowed by government is closed.

Hypothesis

For checking the relationship we will make two hypotheses: null (H0) and alternative (H1). We interpret the findings on the acceptance or rejection of the hypothesis. We used correlation matrix to check the mutual relationship of different variables. The hypothesis which we developed are given below

Hypothesis 1:

H1: there is relationship between Leverage and Interest Rates

H0: there is no relationship between Leverage and Interest rates

Hypothesis 2:

H1: there is relationship between Leverage and Firm size

H0: there is no relationship between Leverage and Firm size

Hypothesis 3:

H1: there is relationship between Leverage and Asset Turnover

H0: there is no relationship between Leverage and Asset Turnover

Hypothesis 4:

H1: there is relationship between Leverage and Taxation

H0: there is no relationship between Leverage and Taxation

LITERATURE REVIEW

Introduction:

The textile sector accounts for 8-9 per cent of the total GDP, and generates 51 per cent of the export revenues for Pakistan, which is a huge number making it a very important sector. This sector is subject to high operating and financial leverage.

Leverage plays a vital role in textile unit. Interest rates are the main determinants of investment and have an inverse relationship with investment. Lower real interest rates will encourage new investment by reducing the cost of capital.

More debt means a higher level of interest payment each year, which is paid from net income. Interest is the major factor which is responsible for leverage. As compare to the others countries like Indonesia s Korea Malaysia is 5.5 and 4%.

The situation with regard to textile industry is very serious. While interest as percentage of sales was 8.58%, interest as a share of value added was a high 12.9% for textiles. Garments is one sector which seems not be as adversely affected on this account.

A consistently declining investment and economic growth rate is the major problem that the Pakistani economy has been facing for the last decade. An in-depth analysis of the determinants of private investment in different sectors of the economy is quite helpful in designing a plan for the economy. Interest rates emerge as the significant determinants of investment in all the sectors.

Nominal interest rates and infrastructure are important in the case of agriculture only, while relative prices of imported machinery and real interest rates are significant in the manufacturing and services sectors. Unexplained variation in private investment is observed in the entire sector, which might be due to the different external and internal shocks to the economy.

According to (MAHMUD, 2003) showed that economy is not good in Pakistan. Pakistan market capitalization and GDP growth are very low they have undeveloped equity market that is the reason of very high leverage ratio in Pakistan. A high proportion of fixed cost means that very high risk belongs to company. Government attention is not positively towards the textile sector. A high risk involved in the company so very low investment is carried out in the manufacturing sector and also high risk involvement means taking loans to the bank with high interest rate. Good economic policies requires for Pakistan and Japan textile sector (MAHMUD, 2003).

The researcher (Denis, 2001) have searched that several academic studies have documented significant shareholder gains and operating improvements following highly leveraged transactions. These gains are generally attributed to changes in the incentive, monitoring, and governance structures of the firms. The results suggest that while high leverage is important in giving managers the incentive to generate cash, high managerial ownership of shares and improved monitoring from the board of directors are important in ensuring that cash is generated in a way that maximizes returns to shareholders (Denis, 2001).

According to (V.O Boadu, 2002) the U.S. textile complex has experienced overcapacity of production, global financial crisis, changes in fashion trends and demand; and cheap imports from Asia. To become more competitive and profitable, U.S. textile manufacturers have focused on achieving greater speed, efficiency, and high quality production by investing heavily in automated technology Exports to Mexico and Canada were $9.5 billion, which constituted 51% of total exports. (V.O Boadu, 2002)

Low-priced Asian imports believed to have been caused by the currency devaluation of major textile exporters such as Hong Kong, India, Indonesia, Japan, Pakistan, Philippines, South Korea, Sri Lanka, Taiwan, and Thailand Asian currencies stabilized through 2000, and resumed their downward path.

(V.O Boadu, 2002) U.S. apparel manufacturers seem to have benefited from the cheaper Asian imports of textiles by the U.S. Global sourcing strategies by the industry in locating manufacturing. Sourcing is explained by the cost of investing in facilities and equipment, production costs, labor costs and availability, quality control, timing, risks which involved language, culture, political, etc. and reliability of product supply in the international market.

Firm size relation with leverage:

(shah & Hijazi, 2004) took the test whose showed that tangibility, profitability, growth and size of the firm effect leverage in textile. There was positive relationship between the size of the firm and board size, high board size means number of directors, larger board means high’s leverage. Debt is taken more and more, that will affect the company equity. Leverage board size showed that more out siders, which possibly reflects debt, can act as a monitoring device and also showed that leverage was lower when the CEO had a long tenure in office. (shah & Hijazi, 2004) analyzed size of firms and profitability was negatively correlated with leverage. Hence this rejects the static trade off theory, which showed a positive relation between size of the firm and profitability. This shows that firms in cement industry use more equity and less debt. Tangibility of Assets and growth found to be positively correlated with leverage. All the results were Significant except the size of the firm. Their results with Shah and Hijazi (2005) were found to be different in terms of growth and size of the firm. They concluded that in developing countries like Pakistan, cement industry usage of short term financing is high than long term financing.

(Spuma, 2000)concerned with different variables that indicate the level of leverage in firm. It shows that there is a negative relation among growth and leverage of the firm. Size of the firm is negatively correlated with the leverage of the firm.

Interest rate relation with leverage:

(N.Majluf, 2004) showed that there is a relationship between managerial operation and high leverage ratio; external investor not has enough information about the country policies, their environment, and firms operations. Inside investor can easily handle that situations comparison with external investor. (N.Majluf, 2004) Present share holder prefer debt financing because of firms need to issue debt when information is larger, stock price decrease etc. that could avoid under pricing and also show that the managerial share holder and long-term debt have a negative relationship. Interest is paid from net income it means more debt change to more interest and more interest means low income.

(Chhibber & K. Majumdar, 1998)The size of a firm is known to affect a firm’s performance in many ways. Key features of a large firm are its diverse capabilities, the ability to exploit economies of scale, and the formalization of procedures. These characteristics make the implementation of operations more effective and allow larger firms not only to generate greater returns on assets and sales but also to capture more value as a proportion of the value of production than is possible for smaller firms. Alternatively, larger firms could be less efficient than smaller firms because of the loss of control by top managers over strategic and operational activities within the firm. (Chhibber & K. Majumdar, 1998) SIZE is an important control variable for another reason. While our data are cross-section ally extensive, we do not have the ability to measure a firm’s market power or the level of concentration in the industries in which the firms in our sample operate. This is a major limitation of the data, and we cannot include controls for market-structure factors that are important determinants of economic performance. SIZE reflects the ability of firms to attain economies of scale as well as market power.35 Finally, the inclusion of SIZE allows us to avoid the criticism directed against much empirical work in this area. H. Short notes that ‘‘a major criticism that can be levied at the majority of the empirical studies is that they tend to concentrate on large firm samples, rather than taking a broad cross-section of firms of different sizes.’’

(chen, 2008) argued that high leverage ratio would increase the possibility of a firm’s bankruptcy. More debt means a higher level of interest payment each year, which is paid from net income. Once the operation of a firm goes into trouble and net income is not enough to pay the interest, the firm has to face the threat of bankruptcy. This is one of the main reasons why firms cannot employ debt financing as much as they want and keep high leverage ratios. Static trade-off is exactly a trade-off between marginal tax saving from debt and marginal expected bankruptcy cost. Later literature tends to replace the bankruptcy cost with financial stress. Too much interest payment would reduce the cash retained in the firm.(CHEN, 2008) Consequently the firm will not have enough budgets to hire capable workers and executives, to undertake positive NPV projects, to cope with emergencies, etc. Furthermore, a higher leverage ratio would reduce the credit level and increase the operation risk of the firm. When facing new financing needs, the firm would be unable to use debt financing anymore, or unable to collect enough capital, or suffer a higher interest rate when borrowing. Even using equity financing, due to the low credit level and high risk, the firm would have to pay a higher price. Larger firms have larger amount of fixed assets and this amount directly reflects the ability of using collateral debt. Thus larger firms could borrow more than smaller firms and could get a more favorable price- lower interest rate(CHEN, 2008).

According to the (Verma, 2002) India’s international competitors have as high an interest cost as in India 70. Its respective ratios were 2.05% and 3.3%. One important reason for this, according to some entrepreneurs, is the fact of predominant decentralized nature of garment sector in India. In Product Specific Cost- Supply Chain Management contain Factor cost (Cost of raw material), In Government Policy (Excise Policy, Technology Up gradation Fund, Strict labor laws), (Verma, 2002) IN Economy-wide costs (Economy-Wide Costs, Transaction costs, Transportation, interest rate).One important reason for this, according to some entrepreneurs, is the fact of predominant decentralized nature of garment sector in India. Also discussed Non-Price Factors in which included (Allow Foreign Direct Investment, Reduce the import duty on textile, Promote fair competition, Remove policy-bias against synthetic fiber, modify Labor related Provisions, Collaborating to Compete- Policies on Investing Abroad). Furthermore, under the era of managed trade, too many textile.

Taxation relation with leverage:

(wang, jiebing, & yao, 2001) the global financial crisis has led to a rising number of unemployed textile and clothing workers in China. The global financial crisis has had a negative impact on economic growth in China. The orders received by textile and clothing companies at the China Import and Export Fair declined by 30 per cent in the autumn. The Ministry of Finance increased tax rebate rates on some textile and clothing exports from 11 per cent to 13 per cent. The global financial crisis has seriously affected the textile and clothing industry in China. (wang, jiebing, & yao, 2001) Some of those firms have gone bankrupt as a result of the global financial crisis More and more textile and clothing factories have been forced to relocate to the middle and western regions of China or to Asia-Pacific developing countries such as Bangladesh, Cambodia, Thailand and Viet Nam. China continues to maintain their unique competitive advantages arising from local textile and clothing industrial clusters with a comprehensive production chain, a pool of skilled labor, innovative fabric technology, sound infrastructure and economies of scale within the textile and clothing industry. The Government of China should continue to encourage the domestic large-scale textile and clothing enterprises to establish textile industrial parks in other developing countries. Provide a better financial package to support foreign direct investment by Chinese textile and clothing firms Improve infrastructure facilities and government efficiency in the least developed countries. (wang, jiebing, & yao, 2001)

Asset Turnover relation with leverage:

(fama, 2009) mentioned in firm size, the proportion of tangible assets would probably play a role in debt or equity financing. he discussed that assets with a substantial and stable liquidation value would be a good guarantee for the firm’s investors. Compared with intangible assets, tangible assets are easier to be valuated and information is less asymmetric. In case of default and bankruptcy, tangible assets are easily to be changed into cash to pay for debt. Thus a firm with larger proportion of tangible assets tends to use more debt. Moreover, the guarantee effect of tangible assets depends on whether resale market is easily accessed. First, plants, machines and other properties that could be adopted by other firms would generally sale at a good bargain and thus are better guarantees for collateral debt. (fama, 2009) Assets that are unique and could not be directly used by other firms would not. Second, removable assets or assets that are close to market or to potential buyers would easily be resold for cash and thus would be better as collateral. Not only proportion of tangible assets, but also characters of assets would play a role in leverage ratio.

This researcher (J Ilyas , 2008) use proportion of tangible assets in total assets as a proxy for assets composition. Due to availability of data, characters of assets will not be precisely analyzed. the guarantee effect of tangible assets depends on whether resale market is easily accessed. First, plants, machines and other properties that could be adopted by other firms would generally sale at a good bargain and thus are better guarantees for collateral debt. Assets that are unique and could not be directly used by other firms would not. Second, removable assets or assets that are close to market or to potential buyers would easily be resold for cash and thus would be better as collateral. Not only proportion of tangible assets, but also characters of assets would play a role in leverage ratio.

(J Ilyas , 2008) in firm size firm size’s influence on leverage ratio is not necessarily positive. Due to asymmetry information, small firms are more likely to be underpriced by investors than large firms and could not get favorable price when financing through equity. While using debt with a fixed interest rate, small firms could suffer less loss from mispricing. Thus small firms should tend to consider using more debt, compared to large firms..earnings plays more important role in firm’s leverage decisions as compared to other determinants of the capital structure.Tangibility of the firm is found to be negatively related to the leverage of the firm(J Ilyas , 2008) .

(Miao,2005) provides a competitive equilibrium model of capital structure and industry dynamics. In the model, firms make financing, investment, entry, and exit decisions subject to idiosyncratic technology shocks. The capital structure choice reflects the tradeoff between the tax benefits of debt and associated bankruptcy and agency costs. The interaction between financing and production decisions influences the stationary distribution of firms and their survival probabilities. The analysis demonstrates that the "equilibrium" output price has an important feedback effect. (Miao,2005) This effect has a number of testable implications. For example it implies that high growth industries have relatively lower leverage and turnover rates. the higher the difference between ROA and cost of capital the higher is the return on equity because of the leverage effects. Similarly the higher turnover of assets results in higher return on assets, which in turn results in higher return on equity. Thus the assets tangibility ratio i.e., ratio between fixed assets and total assets becomes important as capital structure determinant.

(Spuma, Waters, and Payne, 2010) hence smaller firms are accepted to increase the profitability of going private, concluded that firms with less investment opportunities apply more leverage that is in accordance to both theories and leverage has a direct relation with the tangibility of assets. They also suggest that more profitable firms use less leverage.

(Thornhill & P, 2010)find that firms with higher financial deficits, i.e., firms that raise more external capital, tend to increase their leverage. They examine the tendency of managers to time the equity markets by interacting the market-to-book ratio with the amount of capital that a firm raises (i.e., its financial deficit). Their evidence suggests that firms tend to reduce their leverage ratios when they raise substantial amounts of capital when the equity market is perceived to be more favorable, (i.e., when market-to-book ratios are higher). There seems to be a consensus in the literature that suggests that these variables affect capital structures, at least temporarily.

(Rajan,r,g & zingales, 2002)compared leverage and its determinates across G-7 Countries that are united states, Germany, Canada, Italy, France, Japan and united Kingdom. They analyzed there was a positive relationship of leverage and profitability. Tangibility is positively correlated in all countries. Size is positively correlated with leverage except Germany. Investigated determinants of capital structure and leverage ratio of French, German and British firms with the help of penal data. Their results suggested that size of the firm positively affect the leverage ratio. They analyze relation of profitability, size of firms, fixed assets. (Rajan,r,g & zingales, 2002) This study identifies a positive impact on firm’s size on leverage. While the relationship between fixed asset ratio and level of leverage was mixed means positive in Germany but negative in France and UK. This shows that tangibility of assets is more significant in bank borrowing in Germany. The effect of all these factors on leverage depends on financial environment and tradition of the country in which firm operates investigated that there are a large number of variables that appear to be related to debt ratio of the firm but only few factors have significant effect on debt ratio. They found that relation between leverage and size of firm is positive. For tangibility of assets Empirical results showed a positive relation among leverage and tangibility of assets of firm.

(Harris, 2007) a high leverage ratio would decrease the value of a firm’s equity. This provides opportunity for managers to buy more shares with the same amount of fortune. Meanwhile, external investors might be reluctant to invest in such firms, as high leverage is often linked with high risk. They also argued that managerial ownership is determined endogenously. Thus it is not safe and proper to assume an exogenous ownership structure and a dependent capital structure. (Harris, 2007) They try to use lagged control variables to get rid of endogenously. One way to address this issue is to use lagged variable. As there is no reason a priori that historical ownership structure would be correlated with current leverage ratio, we try to include historical ownership concentration in the regression. The variable they use ownership concentration during the year of the first listing. It could also be considered as an instrument of current year ownership concentration, if ownership is determined endogenously indeed.

(Fatehi ,2003) 30 to 50 percent of all the expatriate placements do not work out as anticipated. Besides the direct financial costs involved with a failed expatriate assignment, the firm may incur other costs, including voided business deals, loss of valuable employees, the break up of joint ventures, and poor relations with the host Government. Fortunately, many MNCs have now realized the importance of cross-cultural training and the number of organizations involved in making preparations and arranging training prior to the departure of managers in foreign countries has increased lately(Fatehi ,2003)

Chapter 3

Data and Methdology

3.1 Research Paradigm :

Positivism is seen in quantitative method in which we are generalizing the theory for common understanding and the theory to be verified changed.

A paradigm is a set of beliefs and assumptions about how something works. In research, there are several different paradigms that people ascribe to when trying to understand how things work in their field. These paradigms guide how people ask questions and what they consider to be the truth. Paradigms can also be seen as a framework for how we interpret things we observe or learn. There are several different ways of describing and categorizing paradigms depending on the field of research, including Interpretive.

3.2 Research Approach

Quanitative approach:

A textile sector describes a population by providing "a quantitative or numeric description of some fraction of the population – the sample – through the data analysis process of annual report of textile mills this enables a researcher to generalize the findings from a sample of responses to a population". Adding together, data collected with the help of annual report of textile mills useful for data analyses.

3.3 Population and sample

When we think of the term "population," we usually think of people in our town, region, state or country and their respective characteristics such as gender, age, marital status, ethnic membership, religion and so forth. In statistics the term "population" takes on a slightly different meaning. The "population" in statistics includes all members of a defined group that we are studying or collecting information on for data driven decisions.

The Sample of listed 5 textile mills annual report is drawn for the purpose of analysis. Five Mills are selected only because of the limited time . The company of textile s sectors in Pakistan is a population. Sample size is five company annual report of textile sector.

The results will be compiled on the basis of information collected from all sources and shall be analyzed for the purpose of final interpretation. I choose annual report tool to collect data on four variables including ,Interest rates, firm size , Asset turnover and taxation on leverage in textile sector of Pakistan.

3.4 Data and Instrumentation:

There are data collection tool for secondary of textile mills and management, and Finance research widely used technique for getting secondary data. A textile sector describes a population by providing "a quantitative or numeric description of some fraction of the population – the sample – through the data analysis process of annual report of textile mills this enables a researcher to generalize the findings from a sample of responses to a population". Adding together, data collected with the help of annual report of textile mills useful for data analyses.

Therefore, by using analyses method, the detail analyses of annual report, compare and contrast responses amid the samples seem for relationships between variables, and facilitate generalizations from a sample of responses. More specifically, the present study will use a "self-completion analysis.

3.5 Data analysis

All the definations:

3.6 Etical consideration:

This data is use only for the research purpose.

The data use only which is assessable

All information provided would be held in strict confidence.

All the data is also kept in secret.

In this research no body take harm.

The purpose of this study is that make a reason able research on the High leverage in textile not is that to discourage any investor or textile sector.

3.7 Limitation:

The study is limited to 5 years’ data only; therefore a detailed trend covering a lengthy period is not possible.

The study is based on secondary data, which are collected from the annual reports of the textile, hence the quality of the study depends purely upon the accuracy, reliability and quality of the secondary data.

The study is confined to only 5 firms of the textile industry, which are listed in the Karachi shock exchange.

Chapter 4:

Results and Analysis

Table Number 1.

AT?

IR?

L?

S?

T?

 Mean

 1.321748

 93481127

 0.500680

 2.35E+08

 18132383

 Median

 1.246500

 1446796.

 0.343400

 3515999.

 370608.0

 Maximum

 2.833100

 3.26E+08

 2.093500

 1.00E+09

 97804871

 Minimum

 0.382000

 91643.00

 0.035400

 86400.00

 12501.00

 Std. Dev.

 0.662769

 1.19E+08

 0.452149

 3.96E+08

 27923683

 Observations

25

25

25

25

25

 Cross sections

5

5

5

5

5

Interpretation:

The table above illustrates the depended variable is Leverage and the independent interest rates, Firm size, Asset turnover and Taxation. The means value of the dependent variable is 0.50.

The mean values of independent variables are interest rates, Firm size, Asset turnover and Taxation 93481127, 2.35, 1.32and 18132383 also given on the table separately.

The value of median of each dependent and independent variable are also given in the table.

Leverage median value is 0.343 The independent variable interest rates, Firm size, Asset turnover and Taxation Median values are 1446796, 3515999, 1.24, and 370608 respectively.

The maximum and minimum value of the data has been given for each variable separately. There are total 5 companies for each of the variable and the total observation for each dependent and independent variables are 25.

Table Number 2.

Pool unit root test: Summary 

Date: 03/29/13 Time: 23:43

Sample: 2008 2012

Series: AT_C1, AT_C2, AT_C3, AT_C4, AT_C5

Exogenous variables: Individual effects

Automatic selection of maximum lags

Automatic selection of lags based on SIC: 0

Newey-West bandwidth selection using Bartlett kernel

Balanced observations for each test 

Cross-

Method

Statistic

Prob.**

sections

Obs

Null: Unit root (assumes common unit root process) 

Levin, Lin & Chu t*

-2.75358

 0.0029

 5

 20

Breitung t-stat

-1.09258

 0.1373

 5

 15

Null: Unit root (assumes individual unit root process) 

Im, Pesaran and Shin W-stat 

-0.05311

 0.4788

 5

 20

ADF - Fisher Chi-square

 8.76142

 0.5549

 5

 20

PP - Fisher Chi-square

 13.3259

 0.2060

 5

 20

Null: No unit root (assumes common unit root process) 

Hadri Z-stat

 4.33419

 0.0000

 5

 25

** Probabilities for Fisher tests are computed using an asympotic Chi

        -square distribution. All other tests assume asymptotic normality.

Interpretation:

The above table shows the variables of Asset turnover data of 5 companies from 2008 to 2012 which tells the data of size is stationery or not.

In Levin, Lin & Chu t* test the probability is 0.0029 which is less than 0.05 which show that the data is stationery or normal.

In Breitung t-stat test the value is 0.1373 which is greater than 0.05, it means that the data is not normal according to this test. According to Im, Pesaran and Shin W-stat the date is not normal because the value is greater than 0.05.

In ADF - Fisher Chi-square the value is 0.5549 which show that the date is not normal because the value of probability is not less than 0.05.

According to PP - Fisher Chi-square the value of the probability is 0.20 which is more then the 0.05 which show that the data is not normal. According to Hadri Z-stat the data is normal because the value of probability is less than 0.05.

Table Number 3.

Pool unit root test: Summary 

Date: 03/29/13 Time: 23:44

Sample: 2008 2012

Series: IR_C1, IR_C2, IR_C3, IR_C4, IR_C5

Exogenous variables: Individual effects

Automatic selection of maximum lags

Automatic selection of lags based on SIC: 0

Newey-West bandwidth selection using Bartlett kernel

Balanced observations for each test 

Cross-

Method

Statistic

Prob.**

sections

Obs

Null: Unit root (assumes common unit root process) 

Levin, Lin & Chu t*

-7.91027

 0.0000

 5

 20

Breitung t-stat

-1.96440

 0.0247

 5

 15

Null: Unit root (assumes individual unit root process) 

Im, Pesaran and Shin W-stat 

-2.09676

 0.0180

 5

 20

ADF - Fisher Chi-square

 17.8862

 0.0569

 5

 20

PP - Fisher Chi-square

 17.8862

 0.0569

 5

 20

Null: No unit root (assumes common unit root process) 

Hadri Z-stat

 2.85348

 0.0022

 5

 25

** Probabilities for Fisher tests are computed using an asympotic Chi

        -square distribution. All other tests assume asymptotic normality.

Interpretation:

The above table shows about the Interest Rate. The above table shows the variables of Interest Rate data of 5 companies from 2008 to 2012 which tells the data of size is stationery or not. In Levin, Lin & Chu t* test the probability is 0.0029 which is less than 0.05 which show that the data is stationery or normal.

In Breitung t-stat test the value is 0.0247 which is less than 0.05 which show that the data is stationery or normal. According to Im, Pesaran and Shin W-stat the value is 0.0180 which is less than 0.05 which show that the data is stationery or normal.

In ADF - Fisher Chi-square the value is 0.569 which show that the date is not normal because the value of probability is not less than 0.05. According to PP - Fisher Chi-square the value of the probability is 0.0569 which is more then the 0.05 which show that the data is not normal. According to Hadri Z-stat the data is normal because the value of probability is less than 0.05.

Table Number 4.

Pool unit root test: Summary 

Date: 03/29/13 Time: 23:44

Sample: 2008 2012

Series: L_C1, L_C2, L_C3, L_C4, L_C5

Exogenous variables: Individual effects

Automatic selection of maximum lags

Automatic selection of lags based on SIC: 0

Newey-West bandwidth selection using Bartlett kernel

Balanced observations for each test 

Cross-

Method

Statistic

Prob.**

sections

Obs

Null: Unit root (assumes common unit root process) 

Levin, Lin & Chu t*

-1.94565

 0.0258

 5

 20

Breitung t-stat

 0.60549

 0.7276

 5

 15

Null: Unit root (assumes individual unit root process) 

Im, Pesaran and Shin W-stat 

 0.14524

 0.5577

 5

 20

ADF - Fisher Chi-square

 5.99441

 0.8157

 5

 20

PP - Fisher Chi-square

 7.29489

 0.6973

 5

 20

Null: No unit root (assumes common unit root process) 

Hadri Z-stat

 2.19590

 0.0140

 5

 25

** Probabilities for Fisher tests are computed using an asympotic Chi

        -square distribution. All other tests assume asymptotic normality.

Interpretation:

The above table shows about the Leverage.

The above table shows the variables of Interest Rate data of 5 companies from 2008 to 2012 which tells the data of size is stationery or not. In Levin, Lin & Chu t* test the probability is 0.0258 which is less than 0.05 which show that the data is stationery or normal.

In Breitung t-stat test the value is 0.7276 which is more than 0.05 which show that the data is not normal.

According to Im, Pesaran and Shin W-stat the value is 0.5577 which show that the date is not normal because the value of probability is Greater than 0.05..

In ADF - Fisher Chi-square the value is 0.8157 which show that the date is not normal because the value of probability is Greater than 0.05.

According to PP - Fisher Chi-square the value of the probability is 0.6973 which is more then the 0.05 which show that the data is not normal.

According to Hadri Z-stat the data is normal because the value of probability is less than 0.05.

Table Number 5.

Pool unit root test: Summary 

Date: 03/29/13 Time: 23:44

Sample: 2008 2012

Series: S_C1, S_C2, S_C3, S_C4, S_C5

Exogenous variables: Individual effects

Automatic selection of maximum lags

Automatic selection of lags based on SIC: 0

Newey-West bandwidth selection using Bartlett kernel

Balanced observations for each test 

Cross-

Method

Statistic

Prob.**

sections

Obs

Null: Unit root (assumes common unit root process) 

Levin, Lin & Chu t*

-2.25090

 0.0122

 2

 8

Breitung t-stat

 0.46186

 0.6779

 2

 6

Null: Unit root (assumes individual unit root process) 

Im, Pesaran and Shin W-stat 

 0.27044

 0.6066

 2

 8

ADF - Fisher Chi-square

 3.18543

 0.5273

 2

 8

PP - Fisher Chi-square

 6.71052

 0.1520

 2

 8

Null: No unit root (assumes common unit root process) 

Hadri Z-stat

 4.02038

 0.0000

 5

 25

** Probabilities for Fisher tests are computed using an asympotic Chi

        -square distribution. All other tests assume asymptotic normality.

Interpretation:

The above table shows about the size. The above table shows the variables of size data of 5 companies from 2008 to 2012 which tells the data of size is stationery or not.

In Levin, Lin & Chu t* test the probability is 0.0122 which is less than 0.05 which show that the data is stationery or normal.

In Breitung t-stat test the value is 0.7276 which is more than 0.05 which show that the data is not normal.

According to Im, Pesaran and Shin W-stat the value is 0.6066 which show that the date is not normal because the value of probability is Greater than 0.05..

In ADF - Fisher Chi-square the value is 0.5273 which show that the date is not normal because the value of probability is Greater than 0.05.

According to PP - Fisher Chi-square the value of the probability is 0.1520 which is less then the 0.05 which show that the data is normal.

According to Hadri Z-stat the data is normal because the value of probability is less than 0.05.

Table Number 6

Pool unit root test: Summary 

Date: 03/29/13 Time: 23:44

Sample: 2008 2012

Series: T_C1, T_C2, T_C3, T_C4, T_C5

Exogenous variables: Individual effects

Automatic selection of maximum lags

Automatic selection of lags based on SIC: 0

Newey-West bandwidth selection using Bartlett kernel

Balanced observations for each test 

Cross-

Method

Statistic

Prob.**

sections

Obs

Null: Unit root (assumes common unit root process) 

Levin, Lin & Chu t*

-4.82815

 0.0000

 5

 20

Breitung t-stat

 0.51889

 0.6981

 5

 15

Null: Unit root (assumes individual unit root process) 

Im, Pesaran and Shin W-stat 

-0.46922

 0.3195

 5

 20

ADF - Fisher Chi-square

 9.56606

 0.4794

 5

 20

PP - Fisher Chi-square

 9.35662

 0.4986

 5

 20

Null: No unit root (assumes common unit root process) 

Hadri Z-stat

 2.03342

 0.0210

 5

 25

** Probabilities for Fisher tests are computed using an asympotic Chi

        -square distribution. All other tests assume asymptotic normality.

Interpretation:

The above table shows about the Taxation. The above table shows the variables of Taxation data of 5 companies from 2008 to 2012 which tells the data of taxation is stationery or not.

In Levin, Lin & Chu t* test the probability is 0.0000 which is less than 0.05 which show that the data is stationery or normal. In Breitung t-stat test the value is 0.6981 which is greater than the 0.05 which show that the data is not normal.

According to Im, Pesaran and Shin W- the value is 0.3195 which is greater than the 0.05 which show that the data is not normal.

In ADF - Fisher Chi-square the value is 0.4794 which show that the date is not normal because the value of probability is Greater than 0.05.

According to PP - Fisher Chi- the value is 0.4986 which is greater than the 0.05 which show that the data is not normal. According to Hadri Z-stat the data is normal because the value of probability is less than 0.05.

Table Number 7:

Dependent Variable: L?

Method: Pooled Least Squares

Date: 03/29/13 Time: 23:45

Sample: 2008 2012

Included observations: 5

Cross-sections included: 5

Total pool (balanced) observations: 25

Variable

Coefficient

Std. Error

t-Statistic

Prob.  

C

0.919134

0.185563

4.953212

0.0001

AT?

-0.316592

0.126006

-2.512511

0.0194

R-squared

0.215358

    Mean dependent var

0.500680

Adjusted R-squared

0.181243

    S.D. dependent var

0.452149

S.E. of regression

0.409128

    Akaike info criterion

1.127040

Sum squared resid

3.849867

    Schwarz criterion

1.224550

Log likelihood

-12.08800

    F-statistic

6.312713

Durbin-Watson stat

1.091032

    Prob(F-statistic)

0.019449

Interpretation:

The model summary table given above shows the value of Adjusted R Square which is 0.215358. This means that the independent variable Asset turnover have 18.12% affect on the dependent variable. The data is total 5 observations and cross-section observations are 5.

Table Number 8.

Dependent Variable: L?

Method: Pooled Least Squares

Date: 03/29/13 Time: 23:45

Sample: 2008 2012

Included observations: 5

Cross-sections included: 5

Total pool (balanced) observations: 25

Variable

Coefficient

Std. Error

t-Statistic

Prob.  

C

0.402746

0.113674

3.542985

0.0017

IR?

1.05E-09

7.59E-10

1.379395

0.1810

R-squared

0.076407

    Mean dependent var

0.500680

Adjusted R-squared

0.036250

    S.D. dependent var

0.452149

S.E. of regression

0.443878

    Akaike info criterion

1.290084

Sum squared resid

4.531633

    Schwarz criterion

1.387594

Log likelihood

-14.12605

    F-statistic

1.902731

Durbin-Watson stat

0.871542

    Prob(F-statistic)

0.181039

Interpretation:

The model summary table given above shows the value of Adjusted R Square which is 0.076407. This means that the independent variable Interest Rate have 3.62 % affect on the dependent variable. The data is total 5 observations and cross-section observations are 5.

Table Number 9.

Dependent Variable: L?

Method: Pooled Least Squares

Date: 03/29/13 Time: 23:46

Sample: 2008 2012

Included observations: 5

Cross-sections included: 5

Total pool (balanced) observations: 25

Variable

Coefficient

Std. Error

t-Statistic

Prob.  

C

0.546667

0.106448

5.135539

0.0000

S?

-1.95E-10

2.35E-10

-0.832971

0.4134

R-squared

0.029284

    Mean dependent var

0.500680

Adjusted R-squared

-0.012921

    S.D. dependent var

0.452149

S.E. of regression

0.455061

    Akaike info criterion

1.339846

Sum squared resid

4.762843

    Schwarz criterion

1.437356

Log likelihood

-14.74807

    F-statistic

0.693841

Durbin-Watson stat

0.925114

    Prob(F-statistic)

0.413428

Interpretation:

The model summary table given above shows the value of Adjusted R Square which is 0.029284. This means that the independent variable size have negative-1.92% affect on the dependent variable. The data is total 5 observations and cross-section observations are 5.

Table Number 10.

Dependent Variable: L?

Method: Pooled Least Squares

Date: 03/29/13 Time: 23:46

Sample: 2008 2012

Included observations: 5

Cross-sections included: 5

Total pool (balanced) observations: 25

Variable

Coefficient

Std. Error

t-Statistic

Prob.  

C

0.557008

0.108761

5.121378

0.0000

T?

-3.11E-09

3.31E-09

-0.937501

0.3582

R-squared

0.036807

    Mean dependent var

0.500680

Adjusted R-squared

-0.005071

    S.D. dependent var

0.452149

S.E. of regression

0.453294

    Akaike info criterion

1.332066

Sum squared resid

4.725930

    Schwarz criterion

1.429576

Log likelihood

-14.65082

    F-statistic

0.878908

Durbin-Watson stat

0.938472

    Prob(F-statistic)

0.358242

Interpretation:

The model summary table given above shows the value of Adjusted R Square which is 0.03. This means that the independent variable Taxation have 0.5% affect on the dependent variable. The data is total 5 observations and cross-section observations are 5.

Table Number 11

Dependent Variable: L?

Method: Pooled Least Squares

Date: 03/29/13 Time: 23:46

Sample: 2008 2012

Included observations: 5

Cross-sections included: 5

Total pool (balanced) observations: 25

Variable

Coefficient

Std. Error

t-Statistic

Prob.  

C

0.679440

0.186639

3.640387

0.0016

T?

-7.31E-09

5.02E-09

-1.456037

0.1609

AT?

-0.184493

0.123553

-1.493226

0.1510

IR?

2.77E-09

9.51E-10

2.912327

0.0086

S?

-2.61E-10

3.24E-10

-0.805788

0.4298

R-squared

0.453289

    Mean dependent var

0.500680

Adjusted R-squared

0.343947

    S.D. dependent var

0.452149

S.E. of regression

0.366227

    Akaike info criterion

1.005732

Sum squared resid

2.682451

    Schwarz criterion

1.249507

Log likelihood

-7.571653

    F-statistic

4.145596

Durbin-Watson stat

1.405007

    Prob(F-statistic)

0.013199

Interpretation:

The model summary table given above shows the value of Adjusted R Square which is 0.453289. This means that the independent variables interest rates, Firm size, Asset turnover and Taxation have 34.39 % affect on the dependent variable

Chapter 5

5.1 Discussion:

Summary:

The primary purpose of the present study is to investigate the Asset Turnover (AT), Interest Rate (IR), Firm Size (S), and Taxation (T) on leverage of textile sector of Pakistan. Quantitative method is used through the data collection by using five textile companies and than by applying tests using . This research will be very useful for the researcher which can help to understand the effects of Asset Turnover (AT), Interest Rate (IR), Firm Size (S), and Taxation (T) on leverage of textile sector of Pakistan and it will be helpful for the companies to make their marketing strategies according to the outcome of the research. Using a quantitative method that is to check the proposed model in the context of Pakistan and to see the generalize ability of the research to the large population with the sample size of 110. The overarching purpose is to guide both researches and policy makers, to develop and implementation of such policies that improve leverage that considered being the main actors in textile sector.

Through an extensive literature review on initial model is proposed which encompass that how leverage mediates the relationship with Power Crisis (PC), Interest Rate (IR), Global Competition (GC), Current political Affect in Textile (CP). The literature review has provided the basic theoretical evidence with regard to the link between leverage and Power Crisis (PC), Interest Rate (IR), Global Competition (GC), Current political Affect in Textile (CP). Questionnaire was developed for data collection purpose on four variables (Power Crisis (PC), Interest Rate (IR), Global Competition (GC), and Current political affect in Textile (CP). The data from the questionnaires is first checked for its reliability and after being reliable the data is used for the descriptive statistics of the whole project and descriptive statistics of the three variables is presented through the help of table from SPSS. After the descriptive statistics the scatter plot is used to show the relationship between the variables, correlation is also used to shows how much of a relation these selected variables posses. After checking the correlation the regression has been used for the analysis of the whole data.

Descriptive Analysis shows the complete picture of the data and afterwards scatter plots shows the relationship between independent and dependent variables. The regression analysis shows that the effect is there because the significance level of T-test and F-test is less than 0.05 so there is an effect but the value of R Square is 0.24 which means there is 24% change in dependent variable because of independent variable In the study we found that Interest rate and Power Crisis have a strong relationship with Leverage in textile sector

5.2 Conclusion:

This study advances knowledge of leverage in textile several ways. Political instability is a major threat and Govt. strict policy about the textile sector. But on the others side Govt. is also providing lot of incentives to the exporters. Also manage to decrease interest rates on the loans. Cheap labor and Cotton is available. In the study we found that Interest rate and Power crisis have a strong relationship with Leverage in textile sector. These two findings support the generalization and external validity of earlier experimental findings in the perceived organizational support literature. The Government of Pakistan should continue to provide a better financial package to support foreign direct investment by Pakistani textile and clothing firms Improve infrastructure facilities. For the load shedding of electricity Pakistan should made dam to overcome the problem. Allow Foreign Direct Investment (FDI) in garment retailing to enable large, modern retail showrooms to set up shops in Pakistan, Reduce the import duty on textile.

5.3 Suggegtions

The textile sector of Pakistan is going through a tough time in due to the current recession and the energy crisis. So,

the firms are finding it hard to survive and compete with the world. These drastic times demand for drastic measures

to get the industry going. In such situation the firms need to manage the resources optimally in order to maximize

profits and sustain. Hence the careful decisions ought to be taken in terms of capital structure changes, keeping in

view the impact of profitability, size and capital intensity of the firms. The managers must know how their decision

of increasing capital intensity, size and the changes in profitability would affect the debt financing level.