




































BANGLADESH JOURNAL OF MULTIDISCIPLINARY SCIENTIFIC RESEARCH 9(4) (2024), 1-15 

1 

        MULTIDISCIPLINARY SCIENTIFIC RESEARCH 
          BJMSR VOL 9 NO 4 (2024) P-ISSN 2687-850X E-ISSN 2687-8518 

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

     Journal homepage: https://www.cribfb.com/journal/index.php/BJMSR 

                                                                                                                                                                                                    Published by CRIBFB, USA 
                                                                                                                                     

A PANEL DATA EXPLORATION OF MACROECONOMIC 

FACTORS INFLUENCING THE OPTIMAL CAPITAL STRUCTURE 

OF THE INDIAN AUTOMOTIVE SECTOR      
 

 Tapas Kumar Sahoo (a)1    Awungshi Yarso (b )    Arijeet Das (c)    Navajit Borah (d)    Happyson Gachuiwo (e)      
 

(a) Assistant Professor, Department of Commerce, SRM University, Sikkim, India; E-mail: tapassahoo7537@gmail.com 
(b) Associate Professor, Department of Commerce, Assam University, Silchar, India; E-mail: asyarso@gmail.com 
(c) Assistant professor, Department of Commerce,  NERIM Commerce College, Guwahati, India; E-mail: arijeetdas.aus@gmail.com   
(d) Research Scholar, Department of Commerce, Assam University, Silchar, India; E-mail: navajitcool@gmail.com   
(e) Assistant Professor, Department of Business Administration, SRM University, Sikkim, India; E-mail: happygachui@gmail.com  
                     

 
A R T I C L E I N F O 

 
 

Article History: 
 

Received: 14th May 2024 

Reviewed & Revised: 14th May 

to 25th July 2024 

Accepted: 27th July 2024 

Published: 8th August 2024 

 
Keywords: 

 

Macroeconomic Determinants, Optimal 

Capital Structure, Indian Automotive  

Industry, Debt-to-Equity ratio, Wealth 
Maximisation. 

 
JEL Classification Codes: 

 

G32, C58, E01 
 

      Peer-Review Model:  

 

      External peer review was done through  

      double-blind method.        

 
A B S T R A C T      
 
Economic instability in emerging economies presents substantial challenges for firms, particularly in 

accessing debt funding, due to heightened perceived risk. This often results in a less favorable debt-to-

equity ratio and complicates the overall composition of capital structure. Macroeconomic conditions 

play a pivotal role in influencing investor sentiment and risk perceptions, which in turn complicate 

capital structure decisions. This study aims to investigate the impact of various macroeconomic 
variables on the capital structure decisions of firms within the Indian automobile and automobile 

ancillary sectors over a comprehensive 17-year period from 2004 to 2020. They are utilizing secondary 

data collected from reputable sources like ProwessIQ, the Reserve Bank of India, and financial reports. 

The study employs various statistical tools, including descriptive statistics, correlation analysis, and 

dynamic panel data regression models, to analyze the data. The findings indicate that macroeconomic 

variables significantly shape the optimal capital structure decisions in the Indian automotive sector. Key 

variables such as the bank rate, GDP growth rate, inflation rate, and public debt substantially impact 
leverage ratios. For instance, an increase in the bank rate or public debt levels correlates with higher 

leverage ratios, suggesting that firms adjust their capital structures in response to changes in these 

macroeconomic indicators. This study provides valuable insights into the complex interplay between 

macroeconomic conditions and capital structure financing decisions. By highlighting the significant 

influence of these broader economic factors, the research underscores the necessity for firms, especially 

in emerging economies like India, to consider these determinants when making financial decisions. The 

findings thus contribute to a deeper understanding of capital structure dynamics in the face of 

macroeconomic challenges within the Indian automotive sector. 
 

 

© 2024 by the authors. Licensee CRIBFB, USA. This open-access article is distributed under the 
terms and conditions of the Creative Commons Attribution (CC BY) license 
(http://creativecommons.org/licenses/by/4.0).  

            

 

INTRODUCTION 

The manufacturing sector is integral to the growth and development of the Indian economy, underpinning the advancement 

of various industries (Chadha & Sharma, 2016). It is pivotal in fostering equitable growth through modernization and 

increased output. Beyond profit generation, this sector contributes to capital accumulation, job creation, infrastructure 

development, and overall economic progress, thereby enhancing the standard of living (Huong, 2018). The automotive 

industry, a cornerstone of this sector, has been vital since the first car appeared in Mumbai in 1898. Today, India is the 

fourth-largest automobile producer and the third-largest market by sales, with passenger vehicle sales reaching 3.89 million 

units in 2023. This industry's GDP contribution has grown from 2.77% in 1992-1993 to approximately 7.1%, employing 

around 19 million people. Government policies promoting foreign direct investments (FDIs) underscore its significance, 

attracting US$ 34.74 billion in FDI equity inflows between April 2000 and March 2023. 

In emerging economies, macroeconomic factors significantly influence capital structure financing decisions 

(Bokpin, 2009; Zafar et al., 2019). Given the global economic integration, these factors have profound implications at both 

national and global levels (Amjed & Shah, 2016). Capital structure has garnered considerable attention from finance 

researchers, reflecting its critical importance to stakeholders (Nha et al., 2016). Financial managers must strategically 

determine the optimal capital structure to maximize organizational wealth, considering both external and internal factors 

(Altman, 1984). The complexities of capital structure decisions, influenced by macroeconomic conditions, affect a firm's 

                                                      
1Corresponding author: ORCID ID: http://orcid.org/0000-0002-3820-1786 
© 2024 by the authors. Hosting by CRIBFB. Peer review is the responsibility of CRIBFB, USA.  

https://doi.org/10.46281/bjmsr.v9i4.2239 

 
To cite this article: Sahoo, T. K., Yarso, A., Das, A., Borah, N., & Gachuiwo, H. (2024). A PANEL DATA EXPLORATION OF MACROECONOMIC 

FACTORS INFLUENCING THE OPTIMAL CAPITAL STRUCTURE OF THE INDIAN AUTOMOTIVE SECTOR. Bangladesh Journal of 

Multidisciplinary Scientific Research, 9(4), 1-15. https://doi.org/10.46281/bjmsr.v9i4.2239 

mailto:asyarso@gmail.com
http://creativecommons.org/licenses/by/4.0/)
http://creativecommons.org/licenses/by/4.0/)
https://www.openaccess.nl/en
https://doi.org/10.46281/bjmsr.v9i4.2239
http://orcid.org/0000-0002-3820-1786
http://orcid.org/0009-0004-7517-3771
https://orcid.org/0000-0002-8810-2532
http://orcid.org/0000-0002-5967-6879
https://orcid.org/0009-0005-7209-0556


Sahoo et al., Bangladesh Journal of Multidisciplinary Scientific Research 9(4) (2024), 1-15

 

2 

risk profile and profitability, necessitating a comprehensive analysis by financial managers (Zafar et al., 2019). 

Despite the evolution of capital structure theories, there needs to be a definitive solution (Myers & Majluf, 1984; 

Zafar et al., 2019). Research has predominantly focused on developed economies, leaving a gap in understanding for 

developing countries like India (Chakrabarti & Chakrabarti, 2019). Existing studies on Indian capital structure are 

insufficient, highlighting the need for further exploration. The expansion of financial markets due to globalization offers 

companies easier access to funds, necessitating well-informed capital structure decisions (Handoo & Sharma, 2012). 

Maintaining a robust capital structure in the automotive industry is crucial for sustaining success and gaining global investor 

trust. Recognizing factors influencing capital structure choices enables managers to make informed decisions (Haron, 2014). 

This research aims to address the literature gap by examining macroeconomic variables' impact on capital structure 

in the Indian automotive sector. Economic instability and macroeconomic fluctuations affect investor sentiment and risk 

perceptions, complicating capital structure decisions (Sheikh & Wang, 2011). To optimize capital structures, businesses 

must analyze macroeconomic conditions comprehensively. This study uses secondary data from 2004 to 2020, employing 

statistical tools like descriptive statistics, correlation analysis, and dynamic panel data regression models. The findings 

reveal that macroeconomic variables significantly influence capital structure decisions, with notable impacts from bank 

rates, GDP growth rates, inflation rates, and public debt. This research contributes to understanding the complex dynamics 

of capital structure financing in the Indian automotive sector, providing valuable insights for financial decision-makers. 

The study, comprising five chapters, offers a comprehensive examination of the capital structure of Indian 

automotive firms. Chapter 1 introduces the significance of the manufacturing sector, the automotive industry's role, and the 

importance of capital structure decisions, highlighting the problem and objectives. Chapter 2 reviews the literature on capital 

structure, examines firm-specific and macroeconomic factors, and identifies gaps. Chapter 3 outlines the research design, 

data collection, and the use of a dynamic panel data regression model to explore the impact of macroeconomic variables on 

capital structure. Chapter 4 presents and discusses the findings, showing the effects of various factors on capital structure 

with statistical support. Chapter 5 concludes with key findings, implications for financial decision-makers, and suggestions 

for future research on capital structure determinants in emerging markets. 

 

LITERATURE REVIEW 

The mystery surrounding capital structure, as noted by Myers (2001), has endured for more than five decades. Initially 

proposed by Modigliani and Miller (1958) in a hypothetical perfect capital market where debt and equity are deemed 

interchangeable, the notion that firm value is unaffected by capital structure choices has been challenged by subsequent 

research since 1963. Factors such as bankruptcy costs, transaction costs, agency costs, and taxes have led to the rejection of 

the perfect capital market assumption, prompting the development of alternative capital structure theories to maximize firm 

value globally. The trade-off theory (Kraus & Litzenberger, 1973), the pecking order theory (Myers & Majluf, 1984), the 

agency theory (Jensen & Meckling, 1976), and the market timing theory (Baker & Wurgler, 2002) are among the theories 

that elucidate how various factors influence capital structure decisions under different macroeconomic and microeconomic 

conditions (Frank & Goyal, 2009; Haron, 2014). The Trade-off theory posits capital structure as a trade-off between the 

costs of financial distress and the benefits of tax shields. Literature on this theory discusses concerns regarding tax-shield 

advantages, financial distress costs, cash flow volatility, and potential bankruptcy costs, extensively discussed in DeAngelo 

and Masulis (1980) and Myers (2001). Pecking order theory suggests that firms prioritize retained earnings as an internal 

funding source over external funding. When internal funds are inadequate to cover capital expenditures, firms opt for 

external borrowing over equity issuance (Myers & Majluf, 1984). Kayhan and Titman (2007) introduce a modified pecking 

order theory, proposing that trade-off theory guides long-term capital structure decisions while pecking order theory 

influences short-term decisions. According to agency cost-based theory, using debt in capital structures results in agency 

costs, as outlined by Haider et al. (2018) and Jensen and Meckling (1976). Jensen and Meckling (1976) identify two primary 

types of conflicts arising from agency costs: conflicts between managers and shareholders and conflicts between 

shareholders and bondholders (Myers, 2001). 

Research has increasingly focused on emerging countries, aiming to elucidate potential deviations in factors 

stemming from events like the 1997 Asian financial crisis. Early investigations, such as those by Rajan and Zingales (1995), 

examined the notable firm-specific determinants impacting capital structures across various countries alongside the country-

specific factors influencing leverage choices within those same countries. Initial empirical inquiries into leverage decisions 

primarily scrutinized U.S. firms (Titman & Wessels, 1988), while subsequent studies by Rajan and Zingales (1995), Zafar 

et al. (2019), Deesomsak, Paudyal, and Pescetto (2014) delved into cross-country comparative analyses. Numerous 

researchers have extensively explored this phenomenon across diverse nations, industries, and temporal periods. 

Investigations conducted within the Indian context by scholars like Mukherjee and Mahakud (2012) have underscored the 

influence of factors such as asset structure, profitability, taxes, and growth rates (Hussain & Rahman, 2022).  on companies' 

capital structures. Analyses undertaken by academics such as Tomschik (2015) in E7 and G7 countries and Haron (2014) 

in the Asian Pacific region have focused on the impact of macroeconomic factors such as GDP, inflation, and interest rates. 

These studies underscore the importance of institutional and country-specific factors in shaping decisions regarding capital 

structure. 

In addition, studies focusing on particular industries, such as the automobile business (Haider et al., 2018) and 

leasing companies in Pakistan, have provided detailed insights into the factors that influence these industries, including their 

scale, profitability, liquidity, and tax implications. Ramli et al. (2019) and Lim (2012) researched the Chinese contexts and 

discovered intricate relationships among capital structure, financial performance, and factors such as growth rate, tax shield, 

profitability, and liquidity. The literature review encompasses studies employing sophisticated methodologies, such as fixed 

effect models, system GMM, and seemingly unrelated regression. This demonstrates the diverse range of approaches that 



Sahoo et al., Bangladesh Journal of Multidisciplinary Scientific Research 9(4) (2024), 1-15

 

3 

can be used to examine capital structure dynamics. Furthermore, the studies include different timeframes, ranging from 

specific years to long periods, demonstrating a thorough comprehension of the development of capital structure theories. In 

summary, this comprehensive study offers a wide-ranging examination of the various factors that influence a company's 

capital structure. These factors include individual characteristics of the firm, broader economic conditions, institutional 

effects, and industry-specific dynamics. 

Despite the extensive literature review on the factors influencing capital structure choices and their association with 

firm value across diverse contexts, there are notable research gaps. By and large, the above literature review was focused 

on developed countries, specific industries, or timeframes, limiting the generalizability of the findings; only a few studies 

were found in developing countries like India but not in the automobile and ancillaries sector. Additionally, most studies 

emphasise the impact of internal firm-specific factors and industry-specific dynamics. Still, there needs to be more 

comprehensive research considering the macroeconomic indicators or the interplay of these factors. Also, the literature uses 

many different methods, such as fixed effect models, random effect models, and seemingly unrelated regression that seem 

like they need to be more robust in the case of panel data. In contrast, the dynamic panel data regression model can give a 

more reliable result for the panel cause-and-effect relationship. Therefore, this study examined how firm-specific factors, 

as well as macroeconomic factors, determine the capital structure of the Indian automobile and ancillary sector by using a 

dynamic panel approach, as per the requirements of the data set in the study. 

 

Variables and Hypotheses of the Study 

Key variables used in this research are divided into three categories: Macroeconomic Variables used as independent 

variables, Firm-Specific Variables used as control variables, and Capital Structure Variables used as dependent variables. 

A detailed description of variables with a hypothesis is presented below: 

 

Macroeconomic Variables 

In the macroeconomic case, the variables GDP growth rate, Inflation rate, corporate tax, bank rate, market size, business 

confidence index, foreign direct investment, and public debt were chosen for the study. 

 

GDP Growth Rate 

A country's gross domestic product (GDP) growth rate indicates the percentage change in economic output, serving as a 

measure of the nation's growth. The available research suggests that firms with higher growth rates avoid external financing, 

which is anticipated to be negatively correlated with leverage ratios. This is because economic growth leads to increased 

profitability for companies, prompting them to utilize internally generated profits for further investments instead of seeking 

external financing (Bokpin, 2009; Magwai, 2014; Ayanle et al., 2022; Rehman, 2016; Zafar et al., 2019).  

 

Hypothesis 1: GDP growth rate negatively affects capital structure variables.  

 

Inflation Rate 

The inflation rate refers to the overall rise in the costs of goods and services, accompanied by a decrease in the currency's 

value in purchasing power measured by utilisation of the GDP deflator. The GDP deflator serves as a metric for gauging 

fluctuations in the price level within an economy. Existing literature suggests that inflation has a varied effect on capital 

structure leverage ratios. Rising inflation leads to higher interest rates, providing tax advantages for companies. However, 

it also increases the cost of bankruptcy. Several studies (Bokpin, 2009; Magwai, 2014; Camara, 2012; Rehman, 2016; Zafar 

et al., 2019) have examined this relationship.  

 

Hypothesis 2: Inflation rate has a mixed association with capital structure variables.  

 

Corporate Tax 

Modigliani and Miller (1963) initially introduced corporate tax incorporation in their research, owing to its significant role 

in influencing capital structure determinations. Based on the Modigliani-Miller theory of capital structure, when corporate 

taxes are taken into account, a corporation's valuation is predicted to rise due to tax benefits associated with debt. 

Consequently, a positive correlation is anticipated between leverage ratios and firm value. Several researchers, including 

Rehman (2016) and Tomschik (2015), have employed corporate tax to influence capital structure. 

 

Hypothesis 3: Corporate Tax has a positive association with capital structure variables. 

 

Bank Rate 

The bank rate refers to the interest rate at which the Reserve Bank of India (RBI) provides loans to commercial banks for 

an extended duration. The rise in the bank rate is anticipated, at times, to result in an elevation of debt financing for numerous 

organizations since firms can expect to generate more significant tax advantages from the utilization of debt. 

Simultaneously, specific organizations may decrease their borrowing to mitigate the potential escalation of bankruptcy costs. 

Multiple studies have been conducted to examine the effects of bank rates on leverage ratios, and the findings have been 

varied (Amjed & Shah, 2016). 

 

Hypothesis 4: Bank rate has a mixed association with capital structure variables.  

 



Sahoo et al., Bangladesh Journal of Multidisciplinary Scientific Research 9(4) (2024), 1-15

 

4 

Market Size 

Security markets are of utmost importance in fulfilling the financial needs of any business. A robust financial market 

mitigates the cost of financing and facilitates the acquisition of accurate information, thereby diminishing the overall risk 

exposure of a company. Market size is commonly measured through stock market capitalization, and it will favorably impact 

the degree of leverage at the company level. Higher capitalization enables enterprises to obtain leverage financing at a lower 

cost (Rehman, 2016; Bokpin, 2009). Thus, it can be hypothesized as: - 

 

Hypothesis 5: Market Size has a positive association with capital structure variables. 

 

Business Confidence Index 

The Business Confidence Index (BCI) is a metric that gauges the degree of optimism businesses exhibit towards the 

prevailing economic conditions. A high Business Confidence Index (BCI) indicates a positive outlook among businesses 

regarding the state of the economy and tends to show a greater propensity to choose debt financing. This is attributed to 

their high confidence level in their capacity to create financial gains and fulfil their debt obligations. Hence, this suggests a 

positive correlation with leverage ratios.  

 

Hypothesis 6: BCI has a positive association with capital structure variables 

 

Foreign Direct Investment 

In a thriving economic climate, Foreign Direct Investment (FDI) assumes a significant role in capital structure financing. 

As a result of an abundant influx of foreign direct investment (FDI), domestic enterprises may opt to transition to either debt 

or equity financing, depending on the prevailing economic conditions. When a nation possesses a conducive climate for 

debt financing, foreign direct investment (FDI) contributes to an increase in the level of debt inside the economy. 

Conversely, equity financing is deemed more favourable during economic expansion.  

 

Hypothesis 7: FDI has a mixed association with capital structure variables 

 

Public Debt 

Public debt encompasses the aggregate amount of funds borrowed by the government. When a government engages in debt 

issuance, it garners a substantial share of the capital available in the market due to the perception that government debt 

offerings are relatively secure investments. Consequently, there will be a rise in the interest rate, which could encourage the 

corporation to pursue equity financing.  Hence, this study posits a negative correlation between public debt and leverage 

ratios. Mokhova and Zinecker (2014) discovered an inverse correlation between governmental debt and capital structure in 

their study. This study hypothesized the association of public debt with leverage ratios as: - 

 

Hypothesis 8: Public Debt has a negative association with capital structure variables. 

 

Firm Specific Factors (Control Variables):  

The study considered firm size, profitability, assets tangibility, short-term solvency, risk of bankruptcy, non-debt tax shield, 

growth rate, and earning volatility. 

 

Firm Size 

A firm's size indicates its overall value, as measured by its natural logarithm of the total assets of the firm. The Pecking 

Order Theory postulated a negative correlation, suggesting that larger firms exhibit a reduced adverse selection rate and 

possess greater ease in issuing stock than smaller firms. Nevertheless, a majority of empirical investigations have indicated 

a negative correlation between leverage and firm size, as demonstrated by Lim (2012), Handoo and Sharma (2012), Zafar 

et al. (2019), and Chakrabarti and Chakrabarti (2019).  

 

Hypothesis 9: Firm Size has a negative association with capital structure variables. 

 

Profitability 

A firm's profitability refers to the degree of efficiency with which the company converts its business operations into financial 

gains. The Pecking Order Theory presents an alternative perspective, suggesting that an entity with significant profitability 

can fund its investments internally by retaining earnings, diminishing the necessity for external debt. This study assesses 

profitability by measuring return on assets (ROA), which evaluates the capacity of assets to generate income. The proxy has 

been extensively used by numerous researchers (Doan, 2019; Li & Islam, 2019; Deesomsak et al., 2014; Jaworski & 

Czerwonka, 2021; Sheikh & Wang, 2011; Zafar et al., 2019; Chakrabarti & Chakrabarti, 2019) as a determinant of capital.  

 

Hypothesis 10: Profitability has a negative association with capital structure variables. 

 

Assets Tangibility 

Tangibility is a proxy for tangible assets in the asset structure. Trade-off theory has a positive association with leverage 

ratios because tangible assets can be considered collateral against the creditor's default risk. Some empirical studies also 

found a positive relationship between asset tangibility and leverage (Li & Islam, 2019; Nha et al., 2016; Raza et al., 2021; 



Sahoo et al., Bangladesh Journal of Multidisciplinary Scientific Research 9(4) (2024), 1-15

 

5 

Chaklader & Chawla, 2016; Ghani et al., 2023).  

 

Hypothesis 11: Assets Tangibility has a positive association with capital structure variables. 

 

Short-term Solvency 

Short-term solvency refers to the ability of a business to fulfill its immediate financial obligations measured through the 

current ratio divided by current liabilities, as discussed by Doan (2019), Jaworski and Czerwonka (2021), Chaklader and 

Chawla (2016), and Chakrabarti and Chakrabarti (2019). Nevertheless, by the pecking order theory, this proxy favors 

leverage ratios because financially stable enterprises, which are often more liquid, tend to favor higher leverage levels in 

their capital structure.  

 

Hypothesis 12: Short-term Solvency has a positive association with capital structure variables. 

 

Risk of Bankruptcy 

The measure of bankruptcy risk indicates a company's capacity to fulfil its financial obligations. When a firm cannot fulfil 

its financial obligations to its creditors, it declares bankruptcy. The bankruptcy was measured in this study by the utilization 

of the Altman Z score. The outcome of a credit strength test assesses the probability of insolvency for a manufacturing 

company (Bandyopadhyay & Barua, 2016). The model employs five critical criteria, profitability, leverage, liquidity, 

solvency, and activity, to forecast the likelihood of a company experiencing insolvency. It is anticipated that the possibility 

of bankruptcy will exhibit a negative correlation with the capital structure because enterprises facing a high risk of 

bankruptcy choose to employ less debt, a more expensive form of financing (Rehman, 2016). 

 

Hypothesis 13: The Risk of Bankruptcy has a negative association with capital structure variables. 

 

Non-Debt Tax Shield 

DeAngelo and Masulis (1980) were the pioneering researchers who introduced the notion of a non-debt tax shield in their 

research work. A non-debt tax shield refers to the tax advantage derived from fixed expenses not associated with debt 

financing. There is a negative correlation between the leverage ratio and non-debt tax shielding. The measurement of the 

non-debt tax shield was based on the depreciation divided by operating profit, as indicated by previous research conducted 

by Nha et al. (2016), Deesomsak et al. (2014), Lim (2012), Chaklader and Chawla (2016), Chakrabarti and Chakrabarti 

(2019) and Zafar et al. (2019). 

 

Hypothesis 14: Non-debt tax shield has a negative association with capital structure variables.  

 

Growth Rate 

The growth rate is commonly employed as a surrogate measure for the percentage change in sales; this measure has been 

adopted by numerous researchers (Nha et al., 2016; Malinic et al., 2013; Raza et al., 2021; Chakrabarti & Chakrabarti, 2019; 

Jaworski & Czerwonka, 2021). According to the Pecking Order Theory, a positive correlation exists between sales growth 

rate and debt ratios. This is primarily because growth-oriented enterprises often need more retained earnings, restricting 

their ability to finance additional investments.  

 

Hypothesis 15: Growth rate has a positive association with capital structure variables. 

 

Earning Volatility 

Based on the findings made by both Trade-off Theory and Pecking Order Theory, an inverse relationship exists between 

earning volatility and leverage. According to theoretical assertions, economic distress is positively correlated with the degree 

of earnings volatility. Consequently, lenders will impose elevated interest rates, thereby increasing the expense of 

borrowing. A majority of the researchers observed identical correlations per the theory. The measurement of earning 

volatility in this study is conducted by the utilization of the standard deviation of operating profit, as indicated by previous 

research conducted by Malinic et al. (2013) and Sheikh and Wang (2011). 

 

Hypothesis 16: Earning volatility has a negative association with capital structure variables. 

 

Capital Structure Variables 

The capital structure variables selected for the study are long-term debt ratio, short-term debt ratio, and total debt ratio. 

 

Long-term Debt Ratio 

The long-term debt ratio is a financial indicator adopted to evaluate a corporation's financial solvency. This ratio quantifies 

the relative amount of long-term debt in relation to the company's overall assets. A higher ratio signifies a more significant 

proportion of the company's assets being financed through debt, implying an elevated level of financial risk. The variable 

in question holds significance within the capital structure framework as it indicates a company's long-term economic 

stability. 

 

 



Sahoo et al., Bangladesh Journal of Multidisciplinary Scientific Research 9(4) (2024), 1-15

 

6 

Short-Term Debt Ratio 

Short-term debt refers to a company's financial liability that must be repaid within one financial year. This financial indicator 

quantifies the ratio of a corporation's short-term liabilities to its overall assets. This ratio serves as a means of evaluating a 

company's imminent financial solvency and its capacity to fulfil its immediate financial obligations. The utilisation of short-

term debt exposes a company to several risks that can impact its financial and economic health. Considering short-term debt 

to equity as a metric for assessing capital structure is essential. 

 

Total Debt Ratio 

The total debt ratio is an indicator of a company's total leverage over its total assets. This ratio assesses a company's financial 

leverage and risk. It tells us the percentage of the company's total assets financed by creditors. In other words, it is the total 

amount of a firm's total debt divided by total assets. This proxy serves as an essential measure of capital structure. 

 

MATERIALS AND METHODS 

Data 

This study adopts the analytical research design. The relevant data for the study were collected from secondary sources. Out 

of the population of 148 listed automobile and automobile ancillary firms, a sample of 118 firms was selected based on data 

available for at least 10 years. The sample comprises 15 automobile and 103 automobile ancillary firms. Data from 17 years, 

from 2004 to 2020, were collected according to availability to make the data panel. Within the enormous automotive 

industry, our research sample is limited to two specific sub-sectors: automobile and automobile ancillaries. The selection of 

these sub-sectors is based on their homogeneity. The homogeneity pertains to the uniformity observed in both input and 

output aspects and the technological commonalities encompassing robots and automation, IoT technology, nanotechnology, 

cloud computing, and artificial intelligence (source: Investopedia.com and Sharpmeg.com). In addition, it is worth noting 

that these two industries operate inside comparable business contexts characterised by both backward and forward linkages. 

The data utilized in this research were categorized into two main groups: country-level data encompassing 

macroeconomic factors and industry-specific data comprising microeconomic factors. Macro-level data were sourced from 

various repositories such as the World Bank Data Base, Reserve Bank of India (RBI) Database, and the Bombay Stock 

Exchange (BSE) database, depending on data availability and research requirements. On the other hand, industry-specific 

data were extracted from the financial reports of relevant companies and retrieved from the PROWESSIQ database. Various 

statistical techniques were employed for data analysis, including descriptive statistics, panel data assumption tests, and panel 

data regression analysis. Descriptive statistics involve measures such as covariance analysis and correlation analysis. 

Assumption tests included checks for stationarity, autocorrelation, multicollinearity, and heteroskedasticity. To achieve the 

research objectives, the Generalised Method of Moment (GMM) model was employed in panel data regression. Data was 

analyzed using statistical software tools, including MS Excel, E-Views, STATA, and R-Studio. 

Based on the literature, a summary of the description of key variables used in the study is presented in Table 1 below. 

 

Table 1. Measurement of determinants and the expected relationship with capital structure 

 
Variables Measurement Expected Relationship for This Study 

  
  
 F

ir
m

-s
p

ec
if

ic
 

V
a

r
ia

b
le

s 

Assets Tangibility Total Tangible Assets / Total Assets + VE 

Profitability Return on Assets - VE 

Size Natural Log of Total Assets - VE 

Risk of Bankruptcy Altman Z Score - VE 

Short Term Solvency Current Ratio + VE 

Growth Rate Percentage Change in Sales + VE 

Earning Volatility Standard Deviation of Operating Profit - VE 

Non-Debt Tax Shield Depreciation / EBIT -VE 

M
a

cr
o
e
co

n
o
m

ic
 

V
a

r
ia

b
le

s 
 

GDP Growth Rate Annual % Change in GDP -VE 

Inflation Rate GDP deflator Mixed 

Corporate Tax Corporate tax rate +VE 

Bank Rate REPO Rate Mixed 

Market Size Stock Market Capitalisation +VE 

FDI FDI Inflow Mixed 

BCI Business Confidence Index Value + VE 

Public Debt Govt. Debt/ GDP - VE 

C
a

p
it

a
l 

S
tr

u
c
tu

r
e
 

V
a

r
ia

b
le

s 

Lon-Term debt ratio Long-Term Debt / Total Assets NA 

Short-Term Debt Ratio Short-Term Debt / Total Assets  NA 

Total Debt Ratio Total Debt / Total Assets NA 

 

Empirical Model Selection 
A panel data regression model is used to study the impact of macroeconomic variables on the capital structure of Indian 

automobile and automobile ancillary firms. However, several assumption tests were performed to choose the appropriate 

model. Assumption tests include covariance and correlation analysis, multicollinearity check, Stationarity test, a test of 

cross-section and time effect, autocorrelation, and heteroskedasticity test. According to the outcome of the assumption test, 



Sahoo et al., Bangladesh Journal of Multidisciplinary Scientific Research 9(4) (2024), 1-15

 

7 

the Generalised Method of Moment (GMM) model is used to examine the impact of macroeconomic variables on capital 

structure financing. The detailed model of the study is presented hereunder: 

 

Dynamic Panel Model Specification 

The dynamic panel regression model is used when the data exhibit both cross-sectional and time-series dimensions and the 

problem of autocorrelation and heteroskedasticity is present. The basic model specification behind dynamic panel regression 

is to include lagged dependent variables as explanatory variables in the regression model. This helps to capture the dynamic 

relationship between the variables and accounting for potential endogeneity issues. This study used the Generalised Method 

of Moment (GMM) to address the potential inconsistency caused by autocorrelation and heteroskedasticity by using a 

system of equations and instruments to estimate the parameters, providing more consistent estimates. The basic specification 

of the dynamic model is as follows: 

 

𝒀𝒊𝒕 =  𝜷𝟏𝒀𝒊𝒕 − 𝟏 +  𝜷𝟏𝒀𝒊𝒕 − 𝟐 +  𝜷𝟑𝑴𝑬𝑭𝒊𝒕 +  𝜷𝟒𝑭𝑺𝑭𝒊𝒕 + 𝝁𝒊𝒕 + €𝒊𝒕 
 

The above-shown model is the specification of difference Generalised Method of Moments (Difference GMM) 

Where Yit represents three leverage measures (Short-Term Debt ratio, Long-Term Debt ratio, and Total Debt ratio) for firm 

i and in year t and Yit-1 and Yit-2 are used as first-order lag and second order lag for three leverage measures (Short-Term 

Debt ratio, Long-Term Debt ratio, and Total Debt ratio). MEFit is the vector for the macroeconomic factors (Bank Rate, 

BCI, GDP Growth Rate, Inflation Rate, Corporate Tax, Public Debt, FDI, and Market Size), and FSFit is the vector for the 

firm-specific factors (Earning Volatility, Size, Non-debt Tax Shield, Risk of Bankruptcy, Profitability, Solvency, 

Tangibility, and Growth Rate).  μit represents time-invariant random heterogeneity, and €it is the error term of the model. 

 

RESULTS 

Checking of Covariance and Correlation 
Both a covariance matrix and a correlation matrix are utilized to assess the statistical relationship between dependent and 

independent variables. As depicted in Table 2, the covariance matrix indicates that the covariance between dependent and 

independent variables was statistically significant across all variables with p-values less than 0.05. A similar conclusion is 

derived from the correlation matrix presented in Table 3.  

 

Checking of Multicollinearity 

Once again, the correlation matrix and Variance Inflation Factor (VIF) were utilized to examine the relationship between 

the independent variables in the study and determine whether any correlations existed. Table 4 displays the VIF values for 

all independent variables. According to theory, a VIF exceeding 10 indicates significant multicollinearity. The results 

indicate no evidence of multicollinearity among the macroeconomic and firm-specific variables, as all VIF values fall below 

the threshold of 10. 

 

Table 2. Covariance Matrix of Key Variables 

 

B
. 

R
A

T
E

 

1
.2

 

                     

B
. 

S
IZ

E
 

-1
.5

9
 

(0
.0

0
) 

2
4

.9
 

                    

B
C

I 

0
.4

3
 

(0
.0

0
) 

-2
.8

 

(0
.0

0
) 

1
.1

6
 

                   

T
A

X
 

0
.0

2
 

(0
.0

0

) -0
.1

 

(0
.0

0

) 0
.0

1
 

(0
.0

0

) 0
.0

0
1

 

                  

C
U

R
. 

S
T

G
. 

-4
.1

2
 

(0
.0

0
) 

2
0
.7

 

(0
.0

0
) 

0
.6

7
 

(0
.0

1
) 

-0
.1

5
 

(0
.0

) 

1
1
4
.9

 

                 

G
D

P
 

0
.0

2
 

(0
.0

0
) 

-0
.1

4
 

(0
.0

0
) 

0
.0

2
 

(0
.0

0
) 

0
.0

0
 

(0
.0

0
) 

-0
.1

5
 

(0
.0

0
) 

0
 

                

IN
F

L
 

0
.3

5
 

(0
.0

0
) 

0
.1

4
 

(0
.6

) 

-0
.4

4
 

(0
.0

0
) 

0
.0

1
 

(0
.0

0
) 

-2
1

.4
 

(0
.0

0
) 

-0
1
 

(0
.8

6
) 

6
.2

7
 

               

M
K

T
 

S
IZ

E
 

-1
3

0
7

1
9

 

(0
.0

0
) 

6
9

2
7

6
2
 

(0
.0

0
) 

9
3

9
1

0
.7

 

(0
.0

0
) 

-6
2

4
7

1
 

(0
.0

0
) 

3
8

1
3

7
4

7
4
 

(0
.0

0
) 

- 5
1

5
9

8
.4

2
 

(0
.0

0
) 

- 6
4

4
0

9
4

2
 

(0
.0

0
) 

               



Sahoo et al., Bangladesh Journal of Multidisciplinary Scientific Research 9(4) (2024), 1-15

 

8 

P
U

B
. 

D
B

T
 

-0
.0

1
 

0
.0

0
 

-0
.0

7
 

0
.0

0
 

-0
.0

1
 

0
.0

0
 

0
.0

0
 

0
.0

0
 

-0
.1

2
 

0
.0

0
 

0
.0

0
 

0
.0

0
 

0
.0

0
4

 

0
.0

3
 

-6
0
9
1
4

 

0
.0

0
 

0
.0

0
1

 

             

F
D

I 

-5
.7

 

0
.0

0
 

4
8
.1

 

0
.0

0
 

-4
.1

8
 

0
.0

0
 

-0
.2

6
 

0
.0

0
 

9
8
.7

 

0
.0

0
 

-0
.3

3
 

0
.0

0
 

-1
3
.1

5
 

0
.0

0
 

3
8
2
8
5
5

6
8
 

0
.0

0
 

-0
.1

1
 

0
.0

0
 

1
6
8
 

             

E
R

N
 V

O
L

 

-0
.0

4
 

0
.2

4
 

0
.4

5
 

0
.0

1
 

0
.1

 

0
.0

1
 

-0
.0

1
 

0
.0

0
 

3
.4

2
 

0
.0

0
 

-0
.0

0
3

 

0
.0

1
 

-0
.5

5
 

0
.0

0
 

1
5
2
0
5
2
7

 

0
.0

0
 

-0
.0

0
7

 

0
.0

0
0

 

3
.3

5
 

0
.0

0
 

2
.6

1
 

           

G
. 

R
A

T
E

 

0
.0

5
 

0
.0

0
 

-0
.3

6
 

0
.0

0
 

0
.0

5
 

0
.0

0
 

0
.0

0
1

 

0
.0

0
 

-0
.8

3
 

0
.0

0
 

0
.0

0
2

 

0
.0

0
 

0
.1

2
 

0
.0

0
 

-1
9
7
0
7
2

 

0
.0

0
 

0
.0

0
 

0
.5

4
 

-1
.0

5
 

0
.0

0
 

0
.0

7
 

0
.0

0
 

0
.1

4
 

          

N
.D

. 
T

A
X

 

S
L

D
. 

0
.0

1
 

0
.9

7
 

-1
.6

5
 

0
.0

3
 

0
.2

1
 

0
.2

2
 

0
.0

0
8

 

0
.1

2
 

1
.5

7
 

0
.3

6
 

0
.0

1
 

0
.3

4
 

-0
.4

7
 

0
.2

4
 

2
0
1
6
6
2

 

0
.7

5
 

0
.0

1
 

0
.1

6
 

-1
.6

9
 

0
.4

1
 

-0
.2

8
 

0
.2

8
 

0
.0

1
 

0
.8

7
 

4
8
.2

2
 

         

P
R

O
F

. 

0
.0

1
 

0
.0

0
 

-0
.0

6
 

0
.0

0
 

0
.0

1
 

0
.0

1
 

0
.0

0
 

0
.0

0
 

-0
.1

3
 

0
.0

0
 

0
.0

0
 

0
.0

0
 

0
.0

2
 

0
.0

1
 

-3
8
8
2
9
.2

9
 

(0
.0

0
) 

0
.0

0
 

0
.7

8
 

-0
.1

8
 

0
.0

0
 

0
.0

9
 

0
.0

0
 

0
.0

1
 

0
.0

0
 

-0
.0

2
 

0
.2

7
 

0
.0

1
4

 

        

R
IS

K
 O

F
 B

R
. 

0
.1

9
 

0
.6

5
 

-2
.6

 

0
.1

7
 

1
.6

3
 

0
.0

0
 

0
.0

1
 

0
.4

1
 

1
6

.3
1

 

0
.0

0
 

0
.0

2
 

0
.1

1
 

-3
.6

9
 

0
.0

0
 

6
5

4
8

5
5

4
 

(0
.0

0
) 

-0
.0

3
 

0
.0

1
 

7
.3

4
 

0
.1

3
 

2
.9

5
 

0
.0

0
 

0
.3

4
 

0
.0

1
 

-0
.4

2
 

0
.8

7
 

0
.6

2
 

0
.0

0
 

2
6

6
.2

 

        

S
IZ

E
 

-4
3

8
 

0
0

1
 

2
4

6
8
 

0
.0

0
 

7
4

.5
 

0
.6

5
 

-1
8

.5
2

 

0
.0

0
 

1
0

1
4

0
 

0
.0

0
 

-1
8

.3
2

 

0
.0

0
 

-1
6

2
9

 

0
.0

0
 

3
.7

7
E

 

(0
.0

0
) 

-1
4

.5
 

0
.0

1
 

1
0

0
6

2
 

0
.0

0
 

9
2

2
.5

 

0
.0

0
 

-3
9

.3
 

0
.4

9
 

4
7

0
1
 

0
.0

0
 

6
3

.0
3

 

0
.0

0
 

4
0

9
7

3
3
 

0
.0

0
 

4
3

0
5

5
1

6
2

 

      

S
O

L
V

. 

-0
.0

6
 

0
.1

9
 

0
.1

6
 

0
.4

2
 

-0
.0

2
 

0
.5

7
 

-0
.0

0
1

 

0
.2

6
 

0
.0

9
 

0
.8

3
 

-0
.0

0
1

 

0
.3

4
 

0
.0

5
 

0
.6

 

4
8

4
3

9
.9

7
 

(0
.7

5
) 

0
.0

0
 

0
.9

6
 

0
.1

9
 

0
.7

 

0
.3

9
 

0
.0

0
 

-0
.0

1
 

0
.5

5
 

-0
.2

9
 

0
.2

7
 

0
.0

2
 

0
.0

0
 

-1
.0

6
 

0
.0

9
 

-6
5

2
.1

 

0
.0

1
 

2
.8

7
 

     

T
A

N
G

. 

-0
.0

1
 

0
.1

2
 

0
.0

2
 

0
.2

7
 

-0
.0

0
2

 

0
.5

5
 

0
.0

0
 

0
.7

2
 

0
.0

4
 

0
.2

6
 

0
.0

0
 

0
.6

1
 

-0
.0

0
9

 

0
.3

4
 

5
6

5
2

.7
 

(0
.7

0
) 

0
.0

0
 

0
.9

4
 

0
.0

4
 

0
.4

4
 

-0
.0

2
 

0
.0

0
 

-0
.0

0
1

 

0
.4

3
 

0
.0

7
 

0
.0

1
 

-0
.0

0
3

 

0
.0

0
 

-0
.3

6
 

0
.0

0
 

-1
3

2
 

0
.0

0
 

-0
.0

8
 

0
.0

0
 

0
.0

3
 

    

T
D

 R
A

T
IO

 

-0
.0

0
1

 

0
.8

8
 

-0
.0

6
 

0
.0

4
 

0
.0

1
 

0
.0

2
 

0
.0

0
 

0
.3

4
 

-0
.3

9
 

0
.0

0
 

0
.0

0
 

0
.8

9
 

0
.0

7
 

0
.0

0
 

-1
3

9
8

6
9

.5
 

(0
.0

0
) 

0
.0

0
1

 

0
.0

0
 

-0
.2

8
 

0
.0

0
 

-0
.1

2
 

0
.0

0
 

-0
.0

0
3

 

0
.2

2
 

-0
.0

1
 

0
.7

6
 

-0
.0

2
 

0
.0

0
 

-0
.8

5
 

0
.0

0
 

-1
7

7
.2

 

0
.0

0
 

-0
.0

9
 

0
.0

0
 

0
.0

1
 

0
.0

0
 

0
.0

8
 

    

S
T

D
 R

A
T

IO
 

-0
.0

0
7

 

0
.1

8
 

0
.0

5
 

0
.0

4
 

0
.0

0
2

 

0
.6

9
 

-0
.0

0
 

0
.0

0
 

0
.1

5
 

0
.0

1
 

-0
.0

0
1

 

0
.0

0
 

-0
.0

2
 

0
.0

6
 

5
6
0
5
8
.2

6
 

(0
.0

0
) 

0
.0

0
 

0
.7

5
 

0
.1

9
 

0
.0

0
 

-0
.0

6
 

0
.0

0
 

-0
.0

0
8

 

0
.0

0
 

0
.0

2
 

0
.5

4
 

-0
.0

1
 

0
.0

0
 

-0
.5

1
 

0
.0

0
 

-1
2
9

 

0
.0

0
 

-0
.0

7
 

0
.0

0
 

0
.0

0
3

 

0
.0

0
 

0
.0

5
 

0
.0

0
 

0
.0

4
9

 

  

L
T

D
 R

A
T

IO
 

0
.0

0
6

 

0
.1

1
 

-0
.1

2
 

0
.0

0
 

-0
.0

1
 

0
.0

0
 

0
.0

0
1

 

0
.0

0
 

-0
.5

5
 

0
.0

0
 

0
.0

0
1

 

0
..

0
 

0
.1

 

0
.0

0
 

-1
9
5
9
9
0
.2

 

(0
.0

0
) 

0
.0

1
 

0
.0

0
 

-0
.4

7
 

0
.0

0
 

-0
.0

6
 

0
.0

0
 

0
.0

0
5

 

0
.0

0
 

-0
.0

3
 

0
.1

7
 

-0
.0

1
 

0
.0

0
 

-0
.3

3
 

0
.0

0
 

-4
7
.3

 

0
.0

6
 

-0
.0

3
 

0
.0

0
 

0
.0

1
 

0
.0

0
 

0
.0

3
 

0
.0

0
 

0
.0

0
1

 

0
.5

5
 

0
.0

3
 

 

D
E

 R
A

T
IO

 

0
.4

8
 

0
.0

8
 

-0
.3

8
 

0
.7

8
 

0
.1

7
 

0
.5

3
 

0
.0

1
 

0
.3

7
 

-2
.1

5
 

0
.4

3
 

0
.0

1
 

0
.4

2
 

-0
.1

3
 

0
.8

4
 

-1
2
1
9
9
1

2
 

(0
.2

2
) 

-0
.0

0
5

 

0
.6

1
 

-3
.6

4
 

0
.2

7
 

-0
.7

5
 

0
.0

7
 

-0
.1

9
 

0
.0

5
 

0
.2

2
 

0
.8

9
 

-0
.0

6
 

0
.0

5
 

-3
.7

5
 

0
.3

7
 

-8
6
7

 

0
.6

 

-0
.3

1
 

0
.4

6
 

-0
.0

5
 

0
.2

2
 

0
.0

4
 

0
.5

7
 

0
.0

4
 

0
.4

5
 

-0
.0

0
2

 

0
.9

5
 

1
2
1
.9

 

 

B
. 

R
A

T
E

 

B
. 

S
IZ

E
 

B
C

I 

T
A

X
 

C
U

R
. 

S
T

G
. 

G
D

P
 

IN
F

L
. 

M
K

T
. 

S
IZ

E
 

P
U

B
. 

D
E

B
T

. 

F
D

I 

E
R

N
 

V
O

L
 

G
. 

R
A

T
E

 

N
.D

. 
T

A
X

 

S
L

D
. 

P
R

O
F

. 

R
IS

K
 O

F
 

B
R

. 

S
IZ

E
 

S
O

L
V

. 

T
A

N
G

. 

T
D

 R
A

T
IO

 

S
T

D
 

R
A

T
IO

 

L
T

D
 R

A
T

IO
 

D
E

 R
A

T
IO

 

Source: Calculated authors by using E-Views 10 software 

 

 

 



Sahoo et al., Bangladesh Journal of Multidisciplinary Scientific Research 9(4) (2024), 1-15

 

9 

Table 3. Correlation Matrix of Key Variables 

 
B

. 

R
A

T
E

 

1
.0

0
 

                     

B
. 

S
IZ

E
 

-0
.2

9
 

0
.0

0
 

1
.0

0
 

                    

B
C

I 

0
.3

6
 

0
.0

0
 

-0
.5

2
 

0
.0

0
 

1
.0

0
 

                   

T
A

X
 

0
.4

6
 

0
.0

0
 

-0
.5

9
 

0
.0

0
 

0
.2

9
 

0
.0

0
 

1
.0

0
 

                  

C
U

R
. 

S
T

G
. 

-0
.3

5
 

0
.0

0
 

0
.3

9
 

0
.0

0
 

0
.0

6
 

0
.0

1
 

-0
.4

7
 

0
.0

0
 

1
.0

0
 

                 

G
D

P
 

0
.4

2
 

0
.0

0
 

-0
.7

0
 

0
.0

0
 

0
.3

8
 

0
.0

0
 

0
.7

7
 

0
.0

0
 

-0
.3

6
 

0
.0

0
 

1
.0

0
 

                

IN
F

L
 

0
.1

3
 

0
.0

0
 

0
.0

1
 

0
.3

6
 

-0
.1

6
 

0
.0

0
 

0
.1

6
 

0
.0

0
 

0
.7

9
 

0
.0

0
 

-0
.0

0
4

 

0
.8

6
 

1
.0

0
 

               

M
K

T
 

S
IZ

E
 

-0
.3

0
 

0
.0

0
 

0
.3

5
 

0
.0

0
 

0
.2

1
 

0
.0

0
 

-0
.5

1
 

0
.0

0
 

0
.8

9
 

0
.0

0
 

-0
.3

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Sahoo et al., Bangladesh Journal of Multidisciplinary Scientific Research 9(4) (2024), 1-15

 

10 

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Source: Calculated authors by using E-Views 10 software 

 

Table 4. Variance Inflation Factor of Independent Variables after removing Bank Size and Currency Strength 

 

Source: Calculated authors by using E-Views 10 software 

 

Test of Stationarity of Key Variables 

The concept of unit root is normally used in the case of time series analysis, but nowadays, it is also of similar importance 

in panel data analysis. There are several tests to check the unit root in the data set. This research uses the Levin, Lin, and 

Chu tests, which give a better approximation in the case of panel data analysis. The results of the Levin, Lin, and Chu tests 

presented in Table 5 indicate that all the firm-specific and capital structure variables are stationary at level except firm size, 

which is stationary at first difference. On the other hand, macroeconomic variables such as bank rate, BCI, market size, and 

public debt are stationary at this level. In contrast, other macroeconomic variables are stationary at the first difference. 

 

Table 5. Test of Stationarity of Key Variables 

 
Variables Levin, Lin & Chu test Statistic (P-Value) Result 

Bank Rate -3.264 (0.0005) Stationary at Level 

BCI -30.910 (0.0000) Stationary at Level 

Corporate Tax 44.732 (1.0000) Stationary at First Difference 

GDP Growth Rate 81.658 (1.0000) Stationary at First Difference 

Inflation 6.045 (1.0000) Stationary at First Difference 

Market Size -3.424 (0.0003) Stationary at Level 

Public Debt -20.882 (0.0000) Stationary at Level 

FDI -0.251 (0.4009) Stationary at First Difference 

Earning Volatility -3.273 (0.0005) Stationary at Level 

Growth Rate -12.712 (0.0000) Stationary at Level 

Non-Debt Tax Shield -21.162 (0.0000) Stationary at Level 

Profitability -11.345 (0.0000) Stationary at Level 

Risk of Bankruptcy -3.595 (0.0002) Stationary at Level 

Size -0.235 (0.4069) Stationary at First Difference 

Solvency -3.430 (0.0003) Stationary at Level 

Tangibility -3.977 (0.0000) Stationary at Level 

Long-term Debt Ratio -32.355 (0.0000) Stationary at Level 

Short-term Debt Ratio -6.304 (0.0000) Stationary at Level 

Total Debt Ratio -9.940 (0.0000) Stationary at Level 

Source: Calculated authors by using E-Views 10 software 

 

Autocorrelation and Heteroskedasticity Test 
The Durbin-Watson (D-W) test was utilized to examine autocorrelation or serial correlation, while the Likelihood statistics 

were employed to assess heteroskedasticity. Table 6 presents the results of these assumption tests for all three models 

employed in this study. The D-W statistics yield values of 0.704, 0.445, and 0.577 for the long-term debt ratio, short-term 

debt ratio, and total debt ratio models, respectively, significantly lower than the standard value of 2.00. This indicates the 

presence of serial correlation issues across all models. Additionally, the Likelihood test rejects the null hypothesis of 

homoscedasticity at a 1 percent significance level with a probability value of 0.00 for all three models, indicating the 

presence of heteroskedasticity problems. Given the presence of serial correlation and heteroskedasticity issues in the models, 

relying on the static panel regression model may yield non-robust results. To address these concerns, this study transitions 

to a dynamic panel regression model capable of mitigating these issues. 

 

 

 

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Sahoo et al., Bangladesh Journal of Multidisciplinary Scientific Research 9(4) (2024), 1-15

 

11 

Table 6. Durbin Watson test for Serial Correlation and Likelihood ratio test for Heteroskedasticity 

 
Model D-W test Statistics for 

Serial Correlation 

Likelihood Ratio Test Statistics for Heteroskedasticity 

Cross-section LR Value  

(P-Value) 

Period LR Value  

(P-Value) 

Model 1: Long-Term Debt Ratio 0.704 2011.084 
(0.000) 

558.339 
(0.000) 

Model 2: Short-Term Debt Ratio 0.445 3806.43 

(0.000) 

1598.42 

(0.000) 

Model 3: Total Debt Ratio 0.577 2459.77 
(0.000) 

723.24 
(0.000) 

Source: Calculated authors by using E-Views 10 software 

 

The Difference GMM Model for Long-term Debt Ratio 

Table 7 highlights the result of the difference in the GMM model for long-term debt ratio and Sargan J statistics for model 

fitness. The long-term debt ratio model shows that the lag1 and lag2 of the dependent variable have a significant impact on 

the long-term debt ratio. In macroeconomic variables, all the variables significantly impact the long-term debt ratio except 

market size. More specifically, corporate tax, business confidence index, and FDI have negative influences, while bank rate, 

GDP, inflation, and public debt positively impact long-term leverage. Likewise, firm-specific variables such as earning 

volatility, size, solvency, and tangibility have a significant positive impact. Still, growth rate, profitability, and risk of 

bankruptcy have a significant negative impact on long-term leverage. The result also found an insignificant impact of the 

non-dent tax shield on the leverage ratio. Sargan J statistics were used to check the instruments' robustness, and the result 

indicates that all the instruments are exogenous with a high p-value of 0.391. 

 

Table 7. Difference GMM Model for Long-term Debt Ratio 

 
Variables Coefficient t-Statistics P-Value 

L-T Debt Ratio Lag 1 0.786 208.00 0.000** 

L-T Debt Ratio Lag 2 0.076 43.11 0.000** 

Bank Rate 0.005 19.827 0.000** 

BCI -0.004 -9.33 0.000** 

Corporate Tax -0.072 -5.58 0.000** 

GDP Growth Rate 0.120 9.133 0.000** 

Inflation 0.003 18.602 0.000** 

Market Size -0.000 -0.206 0.795 

Public Debt 0.442 27.416 0.000** 

FDI -0.0004 -10.98 0.000** 

Earning Volatility 0.002 4.321 0.000** 

Growth Rate -0.007 -8.303 0.000** 

Non-Debt Tax Shield -0.0001 -1.420 0.155 

Profitability -0.045 -10.036 0.000** 

Risk of Bankruptcy -0.0002 -3.062 0.002** 

Size 0.000 1.687 0.091* 

Solvency 0.012 10.976 0.000** 

Tangibility 0.087 13.351 0.000** 

J-Statistics: 104.30 

P-Value:0.391 

Instruments Used: 119 

Source: Calculated authors by using E-Views 10 software 

 

The Difference GMM Model for Short-term Debt Ratio 

Table 8 shows the difference in the GMM model for the short-term debt ratio. The results indicate that the lag values of 

dependent variables significantly impact the short-term debt ratio. In macroeconomic variables, bank rate, BCI, market size, 

and FDI have a significant positive impact, whereas GDP growth rate, inflation rate, and public debt have a significant 

negative impact on the short-term debt ratio. Firm-specific factors such as growth rate, non-debt tax shield risk, and 

tangibility significantly positively impact the short-term debt ratio. On the other hand, profitability, size, and solvency 

significantly negatively impact the short-term leverage ratio. The J statistics indicate that all the instruments used in the 

models were exogenous, with a p-value of 0.219. 

 

Table 8. Difference GMM Model for Short-term Debt Ratio 

 
Variables Coefficient t-Statistics P-Value 

S-T Debt Ratio Lag 1 0.817 187.29 0.000** 

S-T Debt Ratio Lag 2 0.302 87.26 0.000** 

Bank Rate 0.004 9.548 0.000** 

BCI 0.004 8.542 0.000** 

Corporate Tax -0.183 -8.694 0.000** 

GDP Growth Rate -0.219 -8.694 0.000** 

Inflation -0.001 -4.901 0.000** 

Market Size 0.000 10.489 0.000** 

Public Debt -0.024 -1.180 0.238 



Sahoo et al., Bangladesh Journal of Multidisciplinary Scientific Research 9(4) (2024), 1-15

 

12 

FDI -0.0004 -3.609 0.000** 

Earning Volatility 0.001 1.725 0.085 

Growth Rate 0.012 9.358 0.000** 

Non-debt Tax Shield 0.0004 3.571 0.000** 

Profitability -0.250 -26.198 0.000** 

Risk of Bankruptcy 0.0001 0.944 0.345 

Size 0.000 -3.271 0.001** 

Solvency -0.011 -8.807 0.000** 

Tangibility 0.019 2.480 0.013** 

J-Statistics: 110.66 

P-Value:0.219 

Instruments Used: 118 

Source: Calculated authors by using E-Views 10 software 

 

The Difference GMM Model for Total Debt Ratio 

The outcome of model 3 presented in the following Table 9 indicates that, like the first two models, the lag value of the 

dependent variable has a significant positive impact on the total debt ratio. Likewise, public debt, bank rate, and market size 

have a significant positive impact on the dependent variable, whereas corporate tax, GDP growth rate, inflation rate, and 

FDI have a significant negative impact on the total debt ratio. In fir-specific factors, earning volatility, non-debt tax shield, 

and tangibility have a significant positive impact on the total leverage, while growth rate in sales, size of the firm, and 

profitability have a negative impact on the total leverage. The overall exogeneity of the instruments used in the model was 

tested using Sargan J statistics and found to be exogenous with a p-value of 0.413. 

 

Table 9. Difference GMM Model for Total Debt Ratio 

 
Variables Coefficient t-Statistics P-Value 

T Debt Ratio Lag 1 0.844 121.91 0.000** 

T Debt Ratio Lag 2 0.259 54.436 0.000** 

Bank Rate 0.002 5.327 0.000** 

BCI 0.000 -0.111 0.911** 

Corporate Tax -0.113 -4.175 0.000** 

GDP Growth Rate -0.329 -8.885 0.000** 

Inflation -0.003 -8.286 0.000** 

Market Size 0.000 9.915 0.000** 

Public Debt 0.330 12.90 0.000** 

FDI -0.001 -8.277 0.000** 

Earning Volatility 0.003 3.286 0.001** 

Growth Rate -0.001 -0.731 0.000** 

Non-Debt Tax Shield 0.0003 3.192 0.001** 

Profitability -0.322 -20.401 0.000** 

Risk of Bankruptcy 0.000 0.278 0.781 

Size 0.000 -2.724 0.006** 

Solvency -0.001 -1.325 0.185 

Tangibility 0.058 5.468 0.000** 

J-Statistics: 102.45 

P-Value:0.413 

Instruments Used: 118 

Source: Calculated authors by using E-Views 10 software 

 

DISCUSSIONS 

The findings of the GMM model, which examines the drivers of the capital structure of Indian automobile and automobile 

ancillary industries, indicate that both macroeconomic factors and firm-specific factors significantly influence the capital 

structure of these sectors. Table 10 highlights the overall outcome of the different GMM models for long-term, short-term, 

and total debt ratios. The analysis reveals that the long-term leverage ratio is positively influenced by macroeconomic 

drivers, namely the bank rate, GDP growth rate, inflation rate, and public debt. These results align with the studies conducted 

by Bokpin (2009) and Tomschik (2015), reinforcing the assertion that favorable macroeconomic conditions encourage firms 

to increase their long-term borrowing. Conversely, the GDP growth rate, inflation rate, and public debt negatively influence 

the short-term leverage ratio. These findings corroborate with prior research, suggesting that during periods of economic 

expansion and high inflation, firms prefer to reduce short-term debt to mitigate financial risk (Bokpin, 2009). Moreover, the 

study finds that both corporation tax and foreign direct investment (FDI) exert a noteworthy adverse influence on the 

leverage ratio, which aligns with the conclusions given by Rehman (2016) and Mokhova and Zinecker (2014). This supports 

the widely held notion that a country's macroeconomic variables influence decisions about capital structure. 

When considering firm-specific factors, the profitability of the industries above has a significant and negative 

impact on the capital structure. This finding is consistent with the Pecking Order Theory, where profitable firms prefer 

internal financing over debt (Deesomsak et al., 2014; Jaworski & Czerwonka, 2021; Chakrabarti & Chakrabarti, 2019). On 

the other hand, asset tangibility has a beneficial effect on the capital structure, supporting the Trade-Off Theory, which 

posits that firms with tangible assets have greater borrowing capacity (Chakrabarti & Chakrabarti, 2019). Furthermore, firm 

size and sales growth rate have a negative impact on Indian automobile and ancillary firms' capital structure decisions. These 

results are in line with previous studies by Chakrabarti and Chakrabarti (2019), Chadha and Sharma (2016), Nha et al. 



Sahoo et al., Bangladesh Journal of Multidisciplinary Scientific Research 9(4) (2024), 1-15

 

13 

(2016), Malinic et al. (2013), and Raza et al. (2021), which found that larger firms with higher growth prospects tend to rely 

less on debt financing due to better access to equity markets and internal funds. 

The findings of this study are consistent with existing literature on capital structure determinants. For instance, 

Bokpin (2009) found similar positive impacts of macroeconomic conditions on long-term debt. The negative relationship 

between profitability and leverage observed in this study mirrors the results of Deesomsak et al. (2014) and Jaworski and 

Czerwonka (2021), emphasizing the preference for internal financing in profitable firms. Moreover, the adverse influence 

of corporation tax and FDI on leverage aligns with Mokhova and Zinecker (2014), who highlighted the negative impact of 

taxation and foreign investments on debt levels. The beneficial effect of asset tangibility on leverage corroborates with 

Chakrabarti and Chakrabarti (2019), supporting the trade-off theory. 

 

Table 10. Overall Outcome of Difference GMM Model 

 
Variables Model 1: L-T Debt Ratio Model 2: S-T Debt Ratio Model 3: TD Ratio 

Direction of 

Impact 

Sig. at 5% 

LOS 

Direction of 

Impact 

Sig. at 5% 

LOS 

Direction of 

Impact 

Sig. at 5% 

LOS 

 Lag 1 of Dependent Variable +Ve Sig. +Ve Sig. +Ve Sig. 

Lag 2 of Dependent Variable +Ve Sig. +Ve Sig. +Ve Sig. 

Bank Rate +Ve Sig. +Ve Sig. +Ve Sig. 

BCI -Ve Sig. +Ve Sig. -Ve Not Sig. 

Corporate Tax -Ve Sig. -Ve Sig. -Ve Sig. 

GDP Growth Rate +Ve Sig. -Ve Sig. -Ve Sig. 

Inflation +Ve Sig. -Ve Sig. -Ve Sig. 

Market Size -Ve Not Sig. +Ve Sig. +Ve Sig. 

Public Debt +Ve Sig. -Ve Not Sig. +Ve Sig. 

FDI -Ve Sig. -Ve Sig. -Ve Sig. 

Earning Volatility +Ve Sig. +Ve Not Sig. +Ve Sig. 

Growth Rate -Ve Sig. +Ve Sig. -Ve Sig. 

Non-Debt Tax Shield -Ve Not Sig. +Ve Sig. +Ve Sig. 

Profitability -Ve Sig. -Ve Sig. -Ve Sig. 

Risk of Bankruptcy -Ve Sig. +Ve Not Sig. +Ve Not Sig. 

Size +Ve Not Sig. -Ve Sig. -Ve Sig. 

Solvency +Ve Sig. -Ve Sig. -Ve Not Sig. 

Tangibility +Ve Sig. +Ve Sig. +Ve Sig. 

Source: author’s own representation based on the above results 

 

The study suggests that both firm-specific and broader economic factors influence the capital structure choices 

made by Indian Automobile and Auxiliary Companies. Additionally, the prior financing patterns of these companies also 

play a role in shaping their capital structure decisions. Financial decision-makers operating within these sectors should 

consider the magnitude and nature of the association between macroeconomic variables (interest rates, GDP, inflation, FDI, 

and stock market development) and firm-specific factors (profitability, asset tangibility, size, risk of bankruptcy, and past 

financing patterns) when formulating capital structure decisions. The positive influence of macroeconomic factors on long-

term leverage can be attributed to their stability and growth potential, encouraging firms to take on more debt for expansion. 

Conversely, the negative impact of macroeconomic conditions on short-term leverage may be due to firms' preference for 

reducing short-term obligations during economic volatility to maintain liquidity and mitigate risk. 

Future research could expand on this study by exploring the impact of additional macroeconomic variables, such 

as exchange rates and commodity prices, on capital structure decisions. Additionally, a comparative analysis between 

different sectors within the Indian economy could provide deeper insights into sector-specific capital structure dynamics. 

Longitudinal studies examining the impact of economic cycles on capital structure choices also contribute to a more 

comprehensive understanding of the determinants of capital structure in emerging markets. 

 

CONCLUSIONS 
In conclusion, capital structure determinants are multifaceted and vary from sector to sector, country to country, and time 

to time. Hence, the capital structure of Indian automobile and automobile ancillary firms is affected by firm-specific and 

macroeconomic factors. In the dynamic business environment, where uncertainties are around while designing the capital 

structure of these two sectors, the financial manager must consider these factors to achieve the wealth maximization 

objective of the firm. Moreover, the optimal capital structure decision is important for the Indian automobile and automobile 

ancillary firms because the debt structure has a non-linear relationship. Studying capital structure and a firm's value is a 

dynamic and complicated phenomenon that demands a nuanced approach.  Striking the right equilibrium requires a thorough 

understanding of the firm-specific as well as macroeconomic circumstances. In navigating these complexities, financial 

managers are pivotal in steering the firm towards optimal capital structure decisions that are positive for market value and 

the long-term sustainability of Indian automobile and automobile ancillary firms. In the future, this type of research can be 

conducted by taking the whole manufacturing sector and world-level data. Secondly, more macroeconomic variables like 

the political environment can be used for more robust findings. Besides, the perceptions of finance managers of different 

firms collected through primary sources can be used to make the findings more applicable in a practical field. 

This study found that the capital structure of Indian automobile and automobile ancillary firms is affected by both 

firm-specific as well as macroeconomic factors of the country. Therefore, it is suggested to the financial decision-makers of 

these two sectors to consider the degree and direction of association of the macroeconomic determinants like interest rates, 



Sahoo et al., Bangladesh Journal of Multidisciplinary Scientific Research 9(4) (2024), 1-15

 

14 

Corporate tax, GDP, inflation, FDI, and the stock market development and firm-specific determinants like Profitability, 

assets tangibility, Size, risk of bankruptcy while making capital structure decisions. The financial managers may consider a 

conservative or aggressive approach to financing to find a balance between short-term leverage, long-term leverage, and 

equity financing depending on different economic conditions like interest rate, GDP growth rate, inflation rate, FDI inflow, 

and stock market development, which helps the firm achieve its wealth maximization objective. 

Despite the valuable insights provided by this study, several limitations warrant consideration. Firstly, the study's 

focus on the Indian automotive industry may limit the generalizability of its findings to other industries or regions. Future 

research could broaden the scope to include a more diverse range of sectors or conduct comparative studies across different 

countries to explore variations in capital structure determinants. Secondly, the study predominantly utilizes secondary data, 

which may need to fully capture the qualitative aspects of firms' financial decision-making processes. Incorporating primary 

data, such as surveys or interviews with financial managers, could provide a more nuanced understanding of the factors 

influencing capital structure choices. Lastly, while this study considers a set of macroeconomic variables, it does not account 

for other potentially influential factors such as political stability, regulatory changes, or technological advancements. Future 

research should consider these elements to offer a more comprehensive analysis. 

 

Author Contributions: Conceptualization, T.K.S., A.Y., N.B. and H.G.; Methodology, T.K.S., A.Y. and N.B.; Software, T.K.S. and N.B.; Validation, 

T.K.S., A.Y. and N.B.; Formal Analysis, T.K.S., A.Y. and N.B.; Investigation, T.K.S., A.Y. and N.B.; Resources, T.K.S.; Data Curation, T.K.S. and N.B., 

Writing – Original Draft Preparation, T.K.S. and H.G.; Writing – Review & Editing, N.B. and H.G.; Visualization, T.K.S.; Supervision, T.K.S. and A.Y.; 
Project Administration, N.B. and H.G.; Funding Acquisition, T.K.S. Author has read and agreed to the published version of the manuscript.  

Institutional Review Board Statement: Ethical review and approval were waived for this study because the research does not involve vulnerable groups 

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

Acknowledgments: Not Applicable. 
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. 

Data Availability Statement: The data presented in this study are available upon request from the corresponding author. Due to restrictions, they are not 

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

 

REFERENCES 
Amjed, S., & Shah, S. A. (2016). The impact of volatile economic conditions on corporate capital structure adjustment towards 

dynamic target in Pakistan. Pakistan Journal of Commerce and Social Sciences, 10(2), 296-315. Retrieved from 

https://hdl.handle.net/10419/188254 

Altman, E. I. (1984). A further empirical investigation of the bankruptcy cost question. Journal of Finance, 39(4), 1067-1089. 

https://doi.org/10.1111/j.1540-6261.1984.tb03893.x 

Ayanle, A. D., Chowdhury, M. S., Imran, M. A.-, & Rahman, S. (2022). The role of microcredit and micro savings for raising 

microfinance sustainability in somali. Bangladesh Journal of Multidisciplinary Scientific Research, 6(1), 30-39. 

https://doi.org/10.46281/bjmsr.v6i1.1925 

Baker, M., & Wurgler, J. (2002). Market timing and capital structure. The journal of finance, 57(1), 1-32. 

https://doi.org/10.1111/1540-6261.00414 

Bandyopadhyay, A., & Barua, N. M. (2016). Factors determining capital structure and corporate performance in India: Studying 

the business cycle effects. The Quarterly Review of Economics and Finance, 61, 160–172. 
http://dx.doi.org/10.1016/j.qref.2016.01.004 

Bokpin, G. A. (2009). Macroeconomic development and capital structure decisions of firms: Evidence from emerging market 

economies. Studies in economics and finance, 26(2), 129-142. https://doi.org/10.1108/10867370910963055 

Camara, O. (2012). Capital structure dynamics of US-based multinationals (MNCs) and domestic (DCs) firms (Doctoral 

dissertation, University of St Andrews). Retrieved from https://hdl.handle.net/10023/3147 

Chadha, S., & Sharma, A. K. (2016). An Empirical Study on Capital Structure in Indian Manufacturing Sector. Global Business 

Review, 17(2), 411–424. https://doi.org/10.1177/0972150915619817 

Chaklader, B., & Chawla, D. (2016). A study of determinants of capital structure through panel data analysis of firms listed in 

NSE CNX 500. Vision, 20(4), 267-277. https://doi.org/10.1177/0972262916668700 

Chakrabarti, A., & Chakrabarti, A. (2019). The capital structure puzzle–evidence from Indian energy sector. International Journal 

of Energy Sector Management, 13(1), 2-23. https://doi.org/10.1108/IJESM-03-2018-0001 

DeAngelo, H., & Masulis, R. (1980). Optimal Capital Structure under Corporate and Personal Taxation”, Journal of Financial 

Economics, 8(1), 3-29. https://doi.org/10.1016/0304-405X(80)90019-7 

Deesomsak, R., Paudyal, K., & Pescetto, G. (2014). The determinants of capital structure: evidence from the Asia Pacific 

region. Journal of Multinational Financial Management, 14(4-5), 387-405. 

https://doi.org/10.1016/j.mulfin.2004.03.001 

Doan, T. T. T. (2019). Firm-specific factors as determinants of capital structure: Evidence from an emerging country. 

International Journal of Economics and Management Systems, 4, 298-304. 

Frank, M. Z., & Goyal, V. K. (2009). Capital structure decisions: which factors are reliably important?. Financial 

management, 38(1), 1-37. https://doi.org/10.1111/j.1755-053X.2009.01026.x 

Ghani, E. K., Rehan, R., Salahuddin, S., & Hye, Q. M. A. (2023). Discovering Capital Structure Determinants for SAARC Energy 

Firms. International Journal of Energy Economics and Policy, 13(1), 135–143. https://doi.org/10.32479/ijeep.13938 

Haider, S., Anjum, N., Sufyan, M., Khan, F., & Ullah, A. (2018). Impact of Macroeconomic Variables on Financial Performance: 

Evidence of Automobile Assembling Sector of Pakistan Stock Exchange. Sarhad Journal of Management Sciences, 

4(2), 202-213. https://doi.org/10.31529/sjms.2018.4.2.6 

Handoo, P. A., & Sharma, D. K. (2012). Comparison of capital structure determinants of public sector and private sector 

https://doi.org/10.1111/1540-6261.00414
http://dx.doi.org/10.1016/j.qref.2016.01.004
https://doi.org/10.1108/10867370910963055
https://doi.org/10.1177/0972150915619817
https://doi.org/10.1177/0972262916668700
https://doi.org/10.1108/IJESM-03-2018-0001
https://doi.org/10.1016/0304-405X(80)90019-7
https://doi.org/10.1016/j.mulfin.2004.03.001
https://doi.org/10.32479/ijeep.13938
https://doi.org/10.31529/sjms.2018.4.2.6


Sahoo et al., Bangladesh Journal of Multidisciplinary Scientific Research 9(4) (2024), 1-15

 

15 

companies in India. Paradigm, 16(2), 13-23. https://doi.org/10.1177/0971890720120203 

Haron, R. (2014). Capital structure inconclusiveness: evidence from Malaysia, Thailand and Singapore. International Journal of 

Managerial Finance, 10(1), 23–38. https://doi.org/10.1108/IJMF-03-2012-0025 

Huong, P. T. Q. (2018). Macroeconomic factors and corporate capital structure: Evidence from listed joint stock companies in 

Vietnam. International Journal of Financial Research, 9(1), 31-40. https://doi.org/10.5430/ijfr.v9n1p31 

Hussain, S., & Rahman, M. H. (2022). Are foreign aid and economic growth positively related? Empirical evidence from 

Bangladesh. Bangladesh Journal of Multidisciplinary Scientific Research, 5(1), 1-13. 

https://doi.org/10.46281/bjmsr.v5i1.1611 

Jaworski, J., & Czerwonka, L. (2021). Determinants of enterprises’ capital structure in energy industry: Evidence from European 

Union. Energies, 14(7), 1871. https://doi.org/10.3390/en14071871 

Jensen, M. C., & Meckling, W. H. (1976). Theory of the Firm: Managerial Behavior, Agency Costs and Ownership Structure. 

Journal of Financial Economics, 3(4), 305-360. https://doi.org/10.1016/0304-405X(76)90026-X 

Kayhan, A., & Titman, S. (2007). Firms’ histories and their capital structures. Journal of financial Economics, 83(1), 1-32. 

https://doi.org/10.1016/j.jfineco.2005.10.007 

Kraus, A., & Litzenberger, R. H. (1973). A State-Preference Model of Optimal Financial Leverage. The Journal of Finance, 28(4), 

911–922. https://doi.org/10.2307/2978343 

Li, L., & Islam, S. Z. (2019). Firm and industry specific determinants of capital structure: Evidence from the Australian market. 

International Review of Economics & Finance, 59, 425-437. https://doi.org/10.1016/j.iref.2018.10.007 

Lim, T. C. (2012). Determinants of capital structure empirical evidence from financial services listed firms in China. International 

journal of economics and finance, 4(3), 191–203. http://dx.doi.org/10.5539/ijef.v4n3p191 

Magwai, M. K. (2014). Effect of macroeconomic conditions on capital structure choice for listed South African firms (Doctoral 

dissertation, University of Pretoria). Retrieved from http://hdl.handle.net/2263/45234 

Malinic, D., Dencic-Mihajlov, K., & Ljubenovic, E. (2013). The determinants of capital structure in emerging capital markets: 

evidence from Serbia. European Research Studies Journal, 16(2), 98-119. Retrieved from 

https://www.um.edu.mt/library/oar/handle/123456789/30868 

Modigliani, F., & Miller, M. H. (1958). The Cost of Capital, Corporation Finance and the Theory of Investment. The American 

Economic Review, 48(3), 261–297. Retrieved from http://www.jstor.org/stable/1809766 

Modigliani, F., & Miller, M. H. (1963). Corporate Income Taxes and the Cost of Capital: A Correction. The American Economic 

Review, 53(3), 433–443. Retrieved from http://www.jstor.org/stable/1809167 

Mokhova, N., & Zinecker, M. (2014). Macroeconomic factors and corporate capital structure. Procedia-Social and Behavioral 

Sciences, 110, 530-540. https://doi.org/10.1016/j.sbspro.2013.12.897 

Mukherjee, S., & Mahakud, J. (2012). Historical Market-to-Book Ratio and Corporate Capital Structure: Evidence from India. 

Global Business Review, 13(2), 339-350. https://doi.org/10.1177/097215091201300211 

Myers, S. C., & Majluf, N. S. (1984). Corporate financing and investment decisions when firms have information that investors 

do not have. Journal of financial economics, 13(2), 187–221. https://doi.org/10.1016/0304-405X(84)90023-0 

Myers, S. C. (2001). Capital structure. Journal of Economic perspectives, 15(2), 81–102. https://doi.org/10.1257/jep.15.2.81 

Nha, B. D., Bich Loan, N. T., & Nhung, N. T. T. (2016). Determinants of capital structure choice: Empirical evidence from 

Vietnamese listed companies, Society and Economy, 38(1), 29-45. https://doi.org/10.1556/204.2016.38.1.3 

Rajan, R. G., & Zingales, L. (1995). What do we know about capital structure? Some evidence from international data. The 

journal of Finance, 50(5), 1421–1460. https://doi.org/10.1111/j.1540-6261.1995.tb05184.x 

Ramli, N. A., Latan, H., & Solovida, G. T. (2019). Determinants of capital structure and firm financial performance—A PLS-

SEM approach: Evidence from Malaysia and Indonesia. The Quarterly Review of Economics and Finance, 71, 148-160. 

https://doi.org/10.1016/j.qref.2018.07.001 

Raza, H., Hamid, Z., Shah, S. A., & Khan, S. (2021). Firm and industry specific determinants of capital structure: Empirical 

evidence from the listed industrial sectors of Pakistan. Indian Journal of Economics and Business, 20(2), 397-414. 

Rehman, O. U. (2016). Impact of capital structure and dividend policy on firm value. Journal of Poverty, Investment and 

Development, 21(1), 40-57.  

Sheikh, N. A., & Wang, Z. (2011). Determinants of capital structure: An empirical study of firms in manufacturing industry of 

Pakistan. Managerial finance, 37(2), 117–133. https://doi.org/10.1108/03074351111103668 

Titman, S., & Wessels, R. (1988). The Determinants of Capital Structure Choice. The Journal of Finance, 43(1), 1–19. 

https://doi.org/10.2307/2328319 

Tomschik, D. (2015). The impact of macroeconomic variables on capital structure: A comparison between companies in E7 and 

G7 countries (Bachelor's thesis, University of Twente). Retrieved from https://purl.utwente.nl/essays/67363 

Zafar, Q., Wongsurawat, W., Camino, D., & Elgammal, M. M. (2019). The determinants of leverage decisions: Evidence from 

Asian emerging markets. Cogent Economics & Finance, 7(1), 1-28. https://doi.org/10.1080/23322039.2019.1598836 

 

 

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https://doi.org/10.1177/0971890720120203
https://doi.org/10.1108/IJMF-03-2012-0025
https://doi.org/10.5430/ijfr.v9n1p31
https://doi.org/10.3390/en14071871
https://doi.org/10.1016/0304-405X(76)90026-X
https://doi.org/10.1016/j.iref.2018.10.007
http://dx.doi.org/10.5539/ijef.v4n3p191
http://hdl.handle.net/2263/45234
https://www.um.edu.mt/library/oar/handle/123456789/30868
http://www.jstor.org/stable/1809766
http://www.jstor.org/stable/1809167
https://doi.org/10.1016/j.sbspro.2013.12.897
https://doi.org/10.1177/097215091201300211
https://doi.org/10.1016/0304-405X(84)90023-0
https://doi.org/10.1257/jep.15.2.81
https://doi.org/10.1111/j.1540-6261.1995.tb05184.x
https://doi.org/10.1016/j.qref.2018.07.001
https://doi.org/10.1108/03074351111103668
https://doi.org/10.2307/2328319
https://purl.utwente.nl/essays/67363
https://doi.org/10.1080/23322039.2019.1598836
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