“The moderating role of firm size and interest rate in capital structure of the firms: selected sample from sugar sector of Pakistan”

The selection of financing is a top priority for businesses, particularly in short-and long-term investment decisions. Mixing debt and equity leads to decisions on the financial structure for businesses. This research analyzes the moderate position of company size and the interest rate in the capital structure over six years (2013–2018) for 29 listed Pakistani enterprises operating in the sugar market. This research employed static panel analysis and dynamic panel analysis on linear and nonlinear regression methods. The capital structure included debt to capital ratio, non-current liabilities, plus current liabilities to capital as a dependent variable. Independent variables were profitability, firm size, tangibility, Non-Debt Tax Shield, liquidity


INTRODUCTION
The sugar industry of Pakistan participates in a significant portion of the overall economy.Sugarcane is Pakistan's fourth largest cultivated cash crop.An agriculture-based industry provides employment for the rural landless population and greatly impacts the country's economy.There has been a renewed interest worldwide in identifying the factors affecting optimum capital structure decisions in manufacturing sectors.The main goal of enterprises is to maximize shareholders` wealth using mixed financing sources, including equity capital, retained profits, issuance of ordinary shares, preferred shares, and debt capital.Banks, individuals, financial institutions, and insurance firms have issued debt capital.Borrowing companies may take advantage of the tax shield using debt resources if they have operating profits, but it raises bankruptcy risks.Direct and indirect costs include the risk of bankruptcy.Indirect costs emerged due to shifts in corporate practices concerning long-term investments.Consequently, the potential advantages of leverage are minimized due to bankrupt costs, which are deemed highly leveraged companies to be an incredible risk.Modigliani and Miller (1958) claimed that a company's investment strategy should be focused solely on those factors which would improve a company's net worth or profitability.They also described a more sustainable capital structure and indicated that leverage and firm value were negligible.Ibrahim and Lau (2019) studied the determinants of financial leverage and suggested that tangibility is a significant positive association to debt ratio while liquidity and profitability observed a significant negative association.In the Pakistani context, which comprises a growing sector of sugar, the key objectives of this study are to contribute and extend the literature in exploring the relationship between macroeconomic factors and capital structure.This study is designed as follows.The following segment will review the literature.Thereafter, the methodology and the proposed theoretical model analyze the empirical results and originate a conclusion based on the findings.Modigliani and Miller (1958) clarified the capital structure value; although this assumption is only effective in perfect market conditions where all shareholders have free access to the financial data.There is no tax difference between zero transaction costs and profits and capital gains.Although several studies have been conducted on the determinants that define the capital structure, Sari and Sedana (2020) interred the effect of profitability and capital structure.They revealed a clear positive association between the variables of profitability and the capital structure of samples taken.Chen and Duchin (2019) noted that operating leverage showed a negative association between profitability and leverage statically.Operating leverage decrease optimal financial leverage and enhance profitability.They demonstrated outcomes using the capital-labor ratio of US enterprises.An.Chakrabarti and Ah.Chakrabarti (2019) revealed a significant negative association between debt ratio and profitability.Shah and Khan (2017) noted that the leverage ratio is inversely associated with the current ratio and profitability.However, the leverage ratio is favorably influenced by tangibility, firm size, and Non-Debt Tax Shield.The profitability effect is substantially poor, while the impact of tangibility, liquidity, Non-Debt Tax Shield, and size is highly significant.As suggested by Nasution, Siregar, and Panggabean (2017), tangible assets have a positive effect on the capital structure, while Non-Debt Tax Shield and profitability have a negative impact on the capital structure.Besides, these factors together have a major impact on the capital structure.Almendros and Mira (2016) revealed that financial distress has a significant and positive association with Non-Debt Tax Shield.Goh, Tai, Rasli, Tan, and Zakuan (2018) performed research on the capital structure and its factors in Malaysian firms from 2011 to 2014 and revealed that firm's Non-Debt Tax Shield and profitability are negatively related to firm debt.Lei (2020) also disclosed the important positive relationship between corporate capital structure and Non-Debt Tax Shield.

LITERATURE REVIEW
Vo (2017) suggested the coefficients are significant and negative in the short-term firm leverage.According to Eysimkele and Koori (2019), the debt financing and efficiency of the Nairobi securities exchange-listed agricultural companies, Kenya, has revealed a negative relationship between longterm debt and profits while being stable in the short and medium term.A further negative association is also observed in size, liquidity, and shortterm debt.Ibrahim (2017) provides evidence that liquidity, size, profitability, and leverage have a significant negative impact on firm value.
Céspedes, Chang, and Velasco (2017) suggested that the real exchange rate could affect credit constraints, and a novel leverage ratio also affects.As per the study, uncertainty in the exchange rate influences foreign trade in the lengthy period and seems to have no impact in the short term (Nguyen & Do, 2020).Submitter, Sari, Siska, and Sulastri (2019) studied the moderating effect of size and revealed that size offers a moderating influence on the link between profitability, tangibility, liquidity, and capital structure efficiency, and this moderation is significant in large corporations.L. Chen and S. Chen (2011) suggested that firm size is the moderator variable and affects the relationship between leverage and profitability.In the first stage, the moderating effect happens.Mirza (2015) noted that firm size positively affects firm leverage.Muigai and Muriithi (2017) study the capital structure and indicated that firm size has a major moderating impact on the combination of financial instability and corporate capital structure.
Al-Hunnayan (2020) found that the leverage relates positively to the company's size and is negatively linked to its competitiveness and tangibility.Li, Krause, Qin, Zhang, Zhu, Lin, and Xu (2018) examined interest rate regulations and accomplishing transparency.The finding of the study indicates that transparency of earnings increases firm leverage and the additional research indicated that such shock occurs as a means of raising the cost of debt financing.Although information disclosure can reduce the effect of the interest rate on the capital structure, Guo and Zhao (2017) examined the capital structure determinants and showed that size and tangibility are positively related.In contrast, Non-Debt Tax Shield and profitability have a negative impact on the determinants.Yazdanfar, Öhman, and Homayoun (2019) noted that profitability, tangibility, size, and financial crises explained the changes from the perspective of debt ratio.Rao, Khursheed, and Mustafa (2020) also explained that borrowing showed significant tangibility and firm size is negatively associated with debt ratio.Iqbal and Usman (2018) suggested that a high amount of debt and interest rates decrease equity value.Leland (1994) examined capital structure debt values and revealed that the debt ratio is explicitly linked with interest rate.Staking and Babbel (1995) focused on studying the role of capital structure and interest rate and noted that interest rate and debt have opposite effects.Bokpin (2009) examines the effect of macroeconomics variables and capital structure using a panel date unrelated regression approach of 34 emerging market countries.He indicated that the interest rate has a beneficial impact on businesses to replace long-term debt with short-term debt shows that a significant result is not obtained.

HYPOTHESES OF THE STUDY
Based on the previously discussed aims, the following hypotheses concerning the sugar sector are described: There is a positive relationship between debt to capital ratio and profitability of the Pakistani sugar sector.
The interest rate has a significant moderating influence on the relationship between debt to capital ratio and profitability of sugar firms.
H 3 : The firm size has a significant moderating influence on the relationship between debt to capital ratio and profitability of sugar firms.
H 4 : There is a positive correlation between debt to capital ratio and liquidity of the Pakistani sugar sector.
H 5 : There is a significant relationship between debt to capital ratio and the Non-Debt Tax Shield of the Pakistani sugar sector.
H 6 : There is a positive correlation between the debt to capital ratio and the exchange rate of the Pakistani sugar sector.

METHODOLOGY
This section of the study describes analytical techniques for examining patterns, variables, the development of research assumptions, and the interdependence of interest rate and firm size on its capital structure.

Data and sample
The study sample included 29 registered Pakistani businesses working in the sugar sector.The first sugar sector was undertaken to avoid specious findings or some situations, such as the impact of interest rate on the firms' capital structure formation.The major focus of the study here is the moderating effect of the firm size and interest rate on capital structure, the net decision on profitability and tangibility, and the focus of macroeconomic variables (exchange rate and interest rate) on debt to capital ratio.They tend to be influenced and Source: Authors.

Tools and techniques
For assessing the impact of interest rate and firm size as moderate with debt to capital ratio, mean, standard deviation, and coefficient of variance are used.The coefficient of correlation is applied to get the association between firm size and debts to capital ratio and interest rate with debt to capital ratio.In the case of a static panel, to manage the robust standard error, a PCSE technique is used, where it covers the problem of autocorrelation and a heteroscedasticity problem after applying the correlation(ar1).During the analysis with linear and nonlinear regression analysis, to test the regression T-test results instead of the Z-value, the "small" option is used in system GMM regression.
For "robustness," PCSE helps manage the heteroscedasticity and autocorrelation consistency (HAC) problem as well.The no-diff Sargan command is used to prevent the recording of a certain difference in Sargan statistics.An orthogonal option is used for transmitting orthogonal variations transform rather than the first difference.

Variables
An experimental variable counts in the investigation and it is considered during the experiment.

Empirical model
The paper explores how variables impact the company's debt to capital ratio (DCR) using the panel data analysis of cross-sectional time-series data ended in 2013-2018.DCR will be used as a response variable with a combination of variables; hence, DCR can be interpreted as follows: , Tangibility, Non-Debt Tax Shield, .

Static panel model
A simple linear regression equation is as follows: Static linear models stand accessible in the subsequent empirical equations ( 3) and ( 4): where i ( ) is the intercept for every firm, t ( )

Dynamic panel model
Many businesses, banking, economics, and finance matters are character-driven and use panel data arrangements to agree with adjustments.It is essential to allow dynamics in the primary pro-cess for the constant estimation of other parameters.The dynamic connections are described by the carriage of a lagged dependent variable with the regressors, i.e. ,1 ,  (Bowsher, 2002).Rodman (2009) explained that the source of these difficulties is device expansion, an answer that cuts the measurement of the adjustable instrumental combination.Blundell and Bond (1998) and Alonso-Borrego and Arellano (1999) show that if the dependent and explanatory variables determined and running continuously over time or almost behaving a random walk, the variance of these components, in differences is performing as a weak instrument for regression (Nyblom, 1989).This is either due to the autoregressive approximation of the parameter union or the variability of the separate impact rises, increasing when idiosyncratic error varies.Therefore, to reduce the potential error and barriers related to difference estimators, Blundell and Bond (1998) projected a GMM method by merging differences and regressions crosswise levels.
In calculating the regression of differences, the means on behalf of regression in levels are lagged differences (transformed), in which the reliability of GMM estimation is contingent on double descriptive diagnostics tests.

Correlations
The correlation analysis Results are presented in Table 3 where debt to capital ratio is a dependent variable and independent variable, are as follows: profitability, size, tangibility, NDTS, liquidity, exchange rate, and interest rate.To explore the correlation between DCR and profitability, it has a positive and significant impact, while liquidity has a significant and negative association with DCR.
Overall variables significantly correlated with DCR.Tangibility, NDTS, and interest rates are positively correlated with DCR, while exchange rate, liquidity, and size are negatively and significantly correlated.(Singla, 2020).The initial reports of different independent variables are the results in the first column for Pooled Ordinary Least Squares (OLS), second column Random Effects (RE), then in the third column, the Fixed Effects (FE) regressions at the second stage.One uses the techniques to robust the standard error with the techniques of autocorrelation parameter is high, and the standard errors are large than for model exclusive of serial correlation, which is to be possible if there is a serial correlation.Column 4 (Hambuckers & Ulm, 2020) makes a case in contradiction of estimating panel exact AR parameters instead of one autocorrelation (AR) parameter for all panels.Outcomes from the two-step system GMM regression are included in the last column.The coefficient of determination, known as adjusted R-squared, suggests that different explanatory variables best explain the statistical models, and the model is best fit to data, and there are no multicollinearity problems in all the sample data as indicated by the variance inflation factor (VIF) values.Profitability, size, NDTS, liquidity, ExR, have a negative influence on debt to capital ratio, while tangibility and interest rate has a positive effect on debt to the capital ratio in case of a fixed-effects model.Using the PCSE technique to manage the problem of serial correlation, it was reported through the Wooldridge test and heteroscedasticity test as significant.It was then adjusted with PCSE in static panel data and reported that profitability and NDTS had changed their signs from negative to positive.It shows that PCSE effectively covers the problem of serial correlation and heteroscedasticity.In the case of system GMM, the value profitability rotates position and becomes positive, which means that one can infer after applying system GMM with a positive influence on debt to capital ratio.

RESULTS AND DISCUSSION
The OLS model explains profitability, size, tangibility, NTDS, liquidity, exchange rate, and interest rates to explain the disparity in debt to capital ratio.The fixed-effect model revealed that profitability, size, and NDTS are negative, while tenability and interest rates significantly positively affect the debt to capital ratio as it is the best choice.PCSE is always a good technique to overcome the problem with heteroscedasticity and serial corre-  (Bokpin, 2009).The regression findings with adjusted R-squared values show that for all models, the specified independent variables have meaningfully explained the variance in debt to capital ratios (Mulyadi & Sihabudin, 2020).AR (1) and AR (2) are insignificant, whereas the Sargan test also has a consistent value.The selection of system GMM is the best fit for the selected sample data to infer the outcomes (Zhang & Wang, 2020).This model is tested using the Sargan / Hansen method for over-identification restrictions (Chatterjee, 2020).The AR (1) estimates were insignificant, whereas those for AR (2) were insignificant.The Sargan test results were insignificant, suggesting that the null hypothesis of jointly valid instrumental variables has not been ignored (Ma & Fu, 2020).

CONCLUSION
Researchers have conducted several experiments to determine what defines a firm's capital structure.Similarly, one examined the moderating effect of firm size and the interest rate on the firm's capital structure using panel data from the sugar sector of Pakistan.One has adopted a static and dynamic data panel approach.Interactive data panel models are anticipated to serial connection challenges, heteroscedasticity, and independent variable endogeneity.In this regard, applying static data panels, one uses PCSE, and for dynamic panel models, GMM estimation yields highly accurate regression results and is widely applied in research-based finance sectors.The results showed that firm size and interest rate have a strong and negative effect on its capital structure.Due to the high interest rates offered by commercial banks, large-size firms have enough relationships with consumers.They can manage their funds for loans and capital structure ratios in the firm's best interest.Higher short-term loans can accumulate more money because they lower the risk of liquidity, and it is found that moderator role interest rates affect liquidity.They can set up their funds.The Non-Debt Tax Shield is adversely linked to corporate debt ratios, and the higher Non-Debt Tax Shield is followed by lower levels of debt, thereby creating a certain replacement effect on corporate capital structure.The study findings affirm the effect of Non-Debt Tax Shield on the fundamental hypothesis.A favorable correlation is found between debt to capital ratio and tangibility, where the business collects debt to purchase tangible assets.The sample data from Pakistan is subject to a correlation test, which indicates no high correlations between the independent variables; therefore, no multicollinearity problem exists.Afterwards, it is checked with the command of VIF and found its value is less than 10, which means no multicollinearity in the model.The paper indicates that different influences, including the size, interest rate, profitability, liquidity position, influence the debt to capital ratio of the company.Managers will be considering the interest rate and the proportion of their total assets to debts of the company and other considerations in their debt finance decisions.

Table 1 . Empirical literature review Author(s) Sample Dependent variable(s) Independent variable(s) Empirical methodology
Several abbreviations were used to save space in creating a table of studies in the literature.PA = profitability, TB = tangibility, NDTS = Non-Debt Tax Shield, LQ = liquidity, REER = exchange rate.The positive sign (+) in the table indicates a positive association here between variables and the response variable, whereas the negative sign shows a negative relationship between the dependent variable(s) and the variables.The IS abbreviation (Insignificant) regarding debt management and other decisions regarding capital structure, which can fluctuate around different manufacturing sectors.All selected firms are listed on the Karachi Stock Exchange (KSE).The selected sample describes six years from 2013 to 2018, and the data were collected from the State Bank of Pakistan Department of Statistics.

Table 4 .
Linear regression model Standard errors in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1.The dependent variable is DCR representing debt to capital ratio, profitability means firm financial performance, measured by net profit before tax / total assets; size represents the log of total assets of the firms; tangibility represents fixed assets after depreciation / total assets; Non-Debt Tax Shield (NDTS) represents the output of depreciation expenses of fixed assets/total assets; liquidity represents a firm's liquid position, measured by the ratio between current assets to current liability; EeR represents a Pakistani rupee vs. USD exchange rate real effective exchange rate (REER); Irate is the interest rate (KIBOR) offered by commercial bank calculated by State Bank of Pakistan and beta represents a firm's systematic risk.The numbers presented in Table3for each variable are coefficients.Column 3 shows the main effect of DCR; column 4 tests PCSE for the interaction effect of size and Irate; column 5 shows the main effect of the two-step system GMM. Notes:

Table 5 .
(Youn, Hua, & Lee, 2015)interaction method is applied to check the moderator effect of interest rate and firm size on the debt to capital ratio(Youn, Hua, & Lee, 2015).One found understanding of interactions in a nonlinear model is more complicated than in a linear model, where the interaction term marginal effect is approximately equal to the interaction term coefficient.As emphasized in Ai and Norton (2003), the model is nonlinear; the interaction effect cannot be re-evaluated simply by looking at the symbol, significance, or statistical relevance of the interaction term coefficient.The interaction effect may have different signs with different covariate values, and therefore the sign does not necessarily indicate the interaction effect.The interaction term is included in the model.Irate*Size is expected to capture the joint effects of firm size with interest rate and debt to capital ratio.Its alpha value is compared to the linear model, and here some explanatory variable coefficient value also gets changed, for example, size has a negative value in the linear model, but in the nonlinear, it gets rotate its position become positive.Similarly, the coefficient value of liquidity and exchange rate has changed very severely.Through empirical analysis about the selected sample, it was found that interest rate with firm size have an interaction effect with debt to capital ratio.It was observed from the outputs, and it infers abnormal variation in the coefficient value of different variables, which approves the moderate effect.
liability; EeR represents a Pakistani rupee vs. USD exchange rate real effective exchange rate (REER); Irate is the interest rate (KIBOR) offered by commercial bank calculated by State Bank of Pakistan and beta represents a firm's systematic risk.The numbers presented in Table4for each variable are coefficients.Column 3 shows the main effect of DCR; column 4 tests PCSE the interaction effect of size and Irate; column 5 shows the main effect of two-step system GMM.