Capital Structure Dynamics in Indian MSMEs 9789813342750, 9789813342767

Citation preview

Capital Structure Dynamics in Indian MSMEs Nufazil Altaf Farooq Ahmad Shah

Capital Structure Dynamics in Indian MSMEs

Nufazil Altaf · Farooq Ahmad Shah

Capital Structure Dynamics in Indian MSMEs

Nufazil Altaf School of Business Studies Central University of Kashmir Ganderbal, Jammu and Kashmir, India

Farooq Ahmad Shah School of Business Studies Central University of Kashmir Ganderbal, Jammu and Kashmir, India

ISBN 978-981-33-4275-0 ISBN 978-981-33-4276-7 (eBook) https://doi.org/10.1007/978-981-33-4276-7 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Cover illustration: © Melisa Hasan This Palgrave Macmillan imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Preface

Capital structure is regarded as one of the most ticklish areas of corporate finance. As rightly put by Myers, capital structure is a multidimensional riddle that is simultaneously affected by numerous inherent factors like scale of operations, macroeconomic states and debt maturity structure. As this multidimensional riddle enters the empirical arena, academics and practitioners are found engaged in defending their favourite approach/es. The prominent researchers in the field include the Nobel laureates Franco Modigliani, Merton Miller and Joseph Stiglitz. Moreover, the persistent research on the theme of capital structure dynamics over the last five to sixdecades has led to the development of a robust theory on the subject followed by a strong practical support by the corporate practitioners. Though capital structure dynamics is gaining acceptance in the corporate world, yet the focus of researchers has generally been on its interplay in the large enterprises, thereby ignoring some fundamental and unresolved issues as to how the capital structure dynamics interact in the contexts of micro, small and medium enterprises (MSMEs). The role of MSMEs in the economic development of India can hardly be overlooked. As a matter of fact, MSMEs are a crucial driver of India’s economic growth which contribute massively to the country’s Gross Domestic Product (GDP) and employment generation. In fact, it is in acknowledgement of this crucial role that the present book is dedicated to explore various key issues related to the capital structure dynamics in the Indian MSMEs. v

vi

PREFACE

Specifically, Chapter-1 of the book provides an overview of the Indian MSME sector. The chapter deliberates upon the MSMEs forming part of the sample and illustrates upon the distribution of firms by size, geographical spread, sector of operation, industry of operation and type of ownership. Additionally, the chapter brings forth the key aspects of capital structure planning and policy in general and the capital structure practices followed by the Indian MSMEs in particular. The chapter concludes by highlighting the contribution made by the book to the field. Chapter-2 explores the financing pattern in the Indian MSMEs with regard to the use of debt to finance total assets. Additionally, financing pattern across diverse firms categorized on the basis of size, geographical spread, sector of operation and ownership type is also explored. Similarly, Chapter-3 attempts to understand how the capital structure adjustment speed, the firm-specific and macroeconomic variables vis-avis capital structure determination behave across different macroeconomic states and for debts of different maturities. Likewise, Chapter-4 is devoted to the investigation of how the working capital and its components affect capital structure decisions of the Indian MSMEs. Additionally, this chapter highlights the importance of cross-sectional heterogeneity in determining the relationship between working capital and leverage. Chapter-5 of the book dissects the impact of cash flow volatility on debt of different maturities for the Indian MSMEs and lastly, Chapter-6 adds on to the debate of capital structure and firm performance by testing whether credit risk has an impact on capital structure and firm performance among the Indian MSMEs. It is worth noting that the present book is perhaps the first attempt on exploring the capital structure dynamics in the Indian MSMEs. Moreover, the experience of authors in teaching various finance courses including the course on capital structure at the universities has been a source of inspiration for compiling this book. Ganderbal, India

Nufazil Altaf Farooq Ahmad Shah

Acknowledgements

Any academic project of this nature and magnitude is obviously not possible without the divine support of God Almighty, and the guidance and good wishes of people all around. Thus, we owe a sincere gratitude to all the colleagues and peers whom we interacted with during the course of writing this book. With great reverence, the authors express heartiest thanks to their parents and other members of family for their unfailing blessings and unparalleled support. Our sincere thanks are due to the fellow researchers and academics for their comments and suggestions in structuring and shaping this book and making it a readable piece for all the relevant practitioners and policymakers. We are particularly grateful to all our colleagues at the Department of Management Studies, Central University of Kashmir for their academic support and scholarly insights. The authors feel obliged to the University Librarian Dr. A M Baba and Assistant Librarian Dr. T A Shah for providing access to the relevant literature and the material needed for this book. Thanks are also due to the anonymous reviewers for their suggestions on the earlier drafts of this work as their suggestions have substantially improved the book. Last but not the least, we are thankful to the entire editorial team at Palgrave

vii

viii

ACKNOWLEDGEMENTS

Macmillan especially Ms. Sandeep Kaur and Ms. Aishwarya Balachandar for successfully guiding this project to the logical end. Nufazil Altaf Farooq Ahmad Shah

Contents

1

1

Introduction

2

Financing Pattern of Indian MSMEs

19

3

Response of Capital Structure Determinants in Different Macroeconomic States

35

4

Working Capital and Capital Structure

61

5

Cash Flow Volatility and Capital Structure

75

6

Does Credit Risk Affect Capital Structure and Firm Performance Link?

91

ix

List of Tables

Table Table Table Table

1.1 1.2 1.3 2.1

Table Table Table Table

2.2 2.3 2.4 2.5

Table 2.6 Table 2.7 Table 3.1

Table 3.2 Table 3.3

Table 3.4 Table 3.5 Table 4.1

Definition of MSMEs Sample selection procedure Sample description Sample selection procedure for analyzing financing pattern in Indian MSMEs Indicators of firm financing and their constituents Descriptive statistics of financing pattern across time Descriptive statistics of financing patterns across firm size Descriptive statistics of financing pattern across the geographic spread Descriptive statistics of financing pattern across sectors Descriptive statistics of financing pattern across ownership type Sample selection procedure for estimating the response of capital structure determinants in different macroeconomic states Descriptive statistics of firm-specific variables Target leverage, determinants of leverage across macroeconomic states and debt of different maturities Wald statistics for joint significance of firm and macro multiplicative dummies Robustness test results Sample selection procedure for investigating the impact of working capital on capital structure decisions

3 5 7 23 24 25 27 29 30 31

47 47

49 54 54 67

xi

xii

LIST OF TABLES

Table 4.2 Table 4.3 Table 4.4 Table 5.1 Table 5.2 Table 5.3 Table 5.4 Table 6.1

Table 6.2 Table 6.3 Table 6.4

Descriptive statistics of working capital, capital structure and control variables Working capital and leverage choices Cross-sectional heterogeneity: Impact of profit margin Sample selection procedure for testing the impact of cash flow volatility on capital structure Descriptive statistics of cash flow volatility, leverage and control variables Cash flow volatility and leverage Robustness test results of cash flow volatility and leverage Sample selection procedure for examining the impact of credit risk on capital structure and firm performance link Descriptive statistics of leverage, firm performance and control variables Estimation results of capital structure-firm performance relation Impact of credit risk on capital structure and firm performance relationship

68 69 72 82 83 84 86

100 100 101 103

CHAPTER 1

Introduction

1.1

Overview of Indian MSMEs

In India, Micro, Small and Medium-sized Enterprises (MSMEs) are recognized as pillars of economic development since they contribute about 37% to the country’s gross domestic product (GDP) and this contribution is likely to become 42% by 2021.1 In addition, Indian MSMEs represent about 90% of India’s industrial tissue and has registered a stable growth rate of 5%.2 According to World Bank’s Financing India’s Micro, Small and Medium enterprise report (2018), there are approximately 55.8 million3 MSME in India is employing about 124 million people. Among these, 55.8 million MSMEs, 53 million (94.9%) are micro-enterprises, 2.7 million (4.9%) are small enterprises and 0.1 million (0.2%) are medium enterprises. Additionally, only 8.2 million (14.6%) MSMEs are registered while as 47.6 million (85.4%) MSMEs are unregistered4 making the MSME sector of India, one of the largest unregistered 1 Vision 2020: ‘Implications for MSMEs 2011’. 2 The Indian SME Survey ‘Analysing Indian SME Perceptions Around Union Budget

2014–15’. 3 Among these, 7.812 million (14%) enterprises are women-led. 4 Unregistered SMEs do not file business information with the respective District

Industry centres (DICs) of the State/Union Territory. Therefore, it is difficult to ascertain the performance of these SMEs adequately.

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 N. Altaf and F. A. Shah, Capital Structure Dynamics in Indian MSMEs, https://doi.org/10.1007/978-981-33-4276-7_1

1

2

N. ALTAF AND F. A. SHAH

sector across the globe. Further, 11.7 million (21%) MSMEs are operational in the manufacturing sector and 44.1 million (79%) MSMEs are functional in the service sector. Additionally, as per the report, these MSMEs are operational in five major industries like Retail industry with 25.66 million (46%), Food products and beverages industry with 3.34 million (6%), Wearing apparels industry with 2.8 million (5%), Repair and Maintenance of Motor Vehicles industry with 2.23 million (4%) and Textiles industry with 1.67 million (3%) MSMEs.5 Furthermore, MSMEs offer a broad spectrum with regard to the ownership structure and geographic spread, for instance, the total MSMEs with proprietary ownership are 52.35 million (93.83%), ownership as a partnership are 0.85 million (1.53%), private firms 0.128 million (0.23%), public firms 0.022 million (0.04%), cooperative societies 0.072 million (0.13%) and others 2.36 million (4.24%). Further, with regard to the geographical spread, the World Bank’s financing Indian MSMEs report (2018) mentions that about 1.9 million (3.4%) MSMEs belong to the northern states of India. Further, 23.6 million (42.3%) MSMEs belong to low-income states and 30.3 million (54.3%) MSMEs belong to India’s rest. For promoting the growth and development of Indian MSMEs, the MSMED Act, 2006 was enacted. However, prior to enacting this act, tiny, cottage, traditional, village enterprises and MSMEs were called Small Scale Industries (SSIs) and were regulated by the Industrial Development and Regulation (IDR) Act, 1951. The enactment of the MSMED Act, 2006 edifice the way forward for enabling a robust legal and regulatory framework, government support and financial infrastructure support for MSMEs. Through these measures, MSMEs were empowered to function and sustain in a highly competitive environment. The legal and regulatory support was provided by enacting financial regulation for inclusion of MSMEs under the purview of priority sector lending (PSL) and boosting the supply of finance through Securitisation and Reconstruction of Financial Assets Enforcement of Security Interest Act (SARFAESI), 2002. Further, government support was provided in the form of policies that promote skill development and technology adoption. Additionally, the government also provided schemes to support

5 Rest of MSMEs operational in other industries are in small numbers, hence not mentioned.

1

INTRODUCTION

3

credit guarantee and enhance unsecured financing. The financial infrastructure support was provided by way of credit bureaus that were enabled to keep track of enterprises’ credit history. Additionally, credit rating agencies were also impanelled for providing rating services to MSMEs and asset reconstruction companies were allowed to facilitate MSMEs in paying for bad loans to banks and other financial institutions.

1.2

Definition of MSME

There is no universal definition of MSME across the globe. Different parameters like the number of employees, sales turnover, asset base, etc., are used to classify firms into Micro, Small and Medium enterprises. Earlier in India IDR act 1951 used the number of employees as a parameter to define small industries. However, the non-availability of data on the number of employees did not allow such a parameter to be useful for identifying MSMEs. Following, IDR act 1951, investments in plant, machinery and equipment was used as a proxy by MSMED Act, 2006 for defining MSMEs. Table 1.1 provides the threshold limits on the investment in plant, machinery and equipment (as per MSMED act 2006) applicable for classifying firms into Micro, Small and Medium enterprises in manufacturing and service sectors, respectively. According to this act, firms are defined as Micro manufacturing firms, if their investment in plant, machinery and equipment is up to 2.5 million. Further, firms are defined as small manufacturing firms if their investment in plant, machinery and equipment is between 2.5 million and 50 million and medium manufacturing firms if such investment is between 50 million and 100 million. However, for being classified as micro firms in services sector the total investment in plant, machinery and equipment Table 1.1 Definition of MSMEs Micro Small Medium

Manufacturing (Amount in Rs.)

Services (Amount in Rs.)

2.5 million 2.5 million to 50 million 50 million to 100 million

one million one million to 20 million 20 million to 50 million

Source MSMED Act (2006)

4

N. ALTAF AND F. A. SHAH

should be up to one million, for small service firms the investment in plant, machinery and equipment shall be between one million and 20 million and for medium service firms this investment should be between 20 million and 50 million.

1.3

Constraints to MSME Development in India

Like other developing countries, the development of Indian MSMEs is hampered by several factors included in them are lack of availability and accessibility to an adequate amount of credit (Rao et al. 2019; Biswas 2014), high cost of credit (Mukherjee 2018); lack of advanced technology (Biswas 2014); inadequate infrastructure facilities, lack of skilled manpower for manufacturing, services, marketing (Mukherjee 2018). The banking sector in India is not robust enough to supply an adequate amount to credit to MSMEs. Additionally, the process of providing loans is very long and formalistic, in fact, MSME owners are required to produce extensive documentation to prove their creditworthiness (Biswas 2014). Further, the availability of an adequate amount of credit is not timely and not reasonable, thereby creating credit constraints for Indian MSMEs (Mukherjee 2018). Additionally, these credit constraints have been attributed to the high-risk perception of the Indian banks about the MSME sector and high transaction costs for loan appraisal. In addition, these credit constraints are also attributed to lack of collateral availability by MSME for availing loans from banks (Nagpal et al. 2009; Das 2008; Seshasayee 2012; Basu 2004). Further, it is asserted that owners of MSMEs are not aware of the available advanced technologies of production, thereby hampering their production efficiency (Biswas 2014). It is also suggested that even if owners are aware of the innovative production methods, the skill development schemes conducted by the government are not sufficient (Ali and Husain 2014). Further, MSME owners are still using older production methods that result in low-quality products (Biswas 2014). In India, MSMEs are located in industrial estates set up and functioning mostly within rural areas. However, these estates have been set up in an unorganized manner. Due to this fact, the state of infrastructure is poor and unreliable (Mukherjee 2018). Further, it is contended that inadequate infrastructure facilities like lack of power, water and roads negatively affect the productivity and profitability of MSMEs (Doe and Emmanuel 2014; Akinwale 2010) and thereby the availability of infrastructure and technology is essential for ensuring the competitiveness of

1

INTRODUCTION

5

Table 1.2 Sample selection procedure Total number of firms after applying the limit of investment as per the MSMED Act 2006 Less: Firms operating in the financial industry Remaining non-financial firms Less: Firms that are not conferring with the definition of MSMEs as per the MSMED Act 2006 during the whole analysis period Less: Firms with incomplete data for the period under chapter Firms forming part of the sample

3468 (618) 2850 (772) (397) 1681

Source CMIE Prowess

the MSMEs. Additionally, lack of skilled manpower for manufacturing, services and marketing are the root cause of MSMEs’ failure in India (Mukherjee 2018). Although human resources and manpower are available in an adequate amount in India, but they lack the skills required to perform the job (Mukherjee 2018).

1.4

MSMEs Forming Part of the Sample

Before we proceed on explaining the sampling procedures adopted, it is worth to note that we have used an electronic database PROWESS of Centre for Monitoring Indian Economy (CMIE) to extract firm-level information on the firms forming part of the sample for the period 2006– 2017.6 It is worth noting that the total number of MSME firms available in CMIE PROWESS after applying the limits prescribed by MSMED act 2006, mentioned in Sect. 1.2, is equal to 3468. Among these 3468 firms, 618 belonged to the finance industry. These firms have been excluded from the sample because these firms’ financial decisions are guided by different schemes (Altaf and Shah 2018; Altaf and Ahmad 2019). Having dropped financial firms from the sample, we were left with 2850 non-financial firms. Further, we found that certain firms in the PROWESS database did not adhere to the investment criteria of the MSMED Act 2006 throughout the period of study. Specifically, 772 firms’ investment exceeds the limit of SME in between the period of study. Accordingly, these firms were dropped from the sample. In addition, 397

6 The sample selection procedure is given in Table 1.2.

6

N. ALTAF AND F. A. SHAH

firms reported incomplete information for the period under consideration, hence dropped from the sample, leaving us with the final sample of 1681 firms across a period of 12 years (2006–2017), making the total number of observations equal to 20,172.

1.5

Sample Description

Table 1.3 shows the distribution of firms by size, geographical spread, sector of operation, the industry of operation and type of ownership. From the figures presented in Table 1.3 it can be ascertained that among the 1681 MSMEs, majority of the firms are small-sized (60%) followed by micro enterprises (25%) and the remaining 15% are medium enterprises. These results imply that more than half of the firms sampled are smallsized, one-fourth are micro-sized enterprises and rest are medium-sized, across India. These results are the same as those reported by (Kumar and Rao 2016). Further, with regard to the geographical spread, we find that most of the firms belong to western states with a proportion of (49%). It may be because Western states like Gujarat, Maharashtra, Madhya Pradesh and Chhattisgarh are considered industrially developed states. Further, the northern states own a 22% proportional shares of MSMEs, it may again be because some northern states like Punjab, Haryana, Delhi and Uttar Pradesh are industrially advanced states. Additionally, only 19% share belongs to southern states, which may be due to the fact the among southern states like Tamil Nadu, Andhra Pradesh, Karnataka, Kerala and only Tamil Nadu is industrially advanced. Further, the least proportion of firms (10%) belong to eastern states, these include the industrially backward states of India like Meghalaya, Nagaland, Mizoram, Manipur, Arunachal Pradesh and Sikkim. Further, in terms of sector of operation, we find that 60% of MSMEs are operational in manufacturing and 40% belong to the service sector. However, small-sized firms dominate micro and medium-sized enterprises in both manufacturing and services industries. Additionally, we find that MSMEs are spread across all the 19 industries of economy like Chemicals and chemical products, Communication services, Construction and real estate, Construction materials, Consumer goods, Electricity, Food and agro-based products, Hotel and tourism, Information technology, Machinery, Metal and metal products, Mining, Miscellaneous manufacturing, Miscellaneous services, Plastic Products, Textiles, Trading, Transport equipment and Transport service.

1

INTRODUCTION

7

Table 1.3 Sample description

Distribution of firms Geographic spread Eastern states Northern states Southern states Western states Sector of operation Manufacturing Services Industry of operation Chemicals and chemical products Communication services Construction and real estate Construction materials Consumer goods Electricity Food and agro-based products Hotel and tourism Information technology Machinery Metal and metal products

Micro

Small

Medium

Total

420 (25)

1008 (60)

253 (15)

1681

42 (25) 85 (23) 64 (20) 190 (23)

101 (60) 237 (64) 198 (62) 453 (55)

25 (15) 48 (13) 58 (18) 180 (22)

168 (10) 370 (22) 320 (19) 823 (49)

202 (20) 168 (25)

645 (64) 404 (60)

161 (16) 101 (15)

1008 (60) 673 (40)

17 (10) 3 (18) 60 (40) 7 (20) 11 (17) 7 (40) 17 (17) 8 (24) 18 (18) 20 (13) 9 (11)

101 (60) 10 (58) 67 (44) 19 (56) 37 (55) 7 (40) 71 (70) 18 (52) 71 (70) 106 (70) 60 (70)

50 (30) 4 (24) 24 (16) 8 (24) 19 (28) 3 (20) 13 (13) 8 (24) 12 (12) 25 (17) 15 (19)

168 (10) 17 (1) 151 (9) 34 (2) 67 (4) 17 (1) 101 (6) 34 (2) 101 (6) 151 (9) 84 (5)

(continued)

8

N. ALTAF AND F. A. SHAH

Table 1.3 (continued)

Mining Miscellaneous manufacturing Miscellaneous services Plastic Products Textiles Trading Transport equipment Transport service Type of ownership Affiliated Government Private

Micro

Small

Medium

Total

10 (20) 10 (30) 70 (30) 4 (8) 40 (40) 70 (30) 0 (0) 10 (30)

35 (70) 18 (52) 129 (55) 41 (82) 42 (42) 129 (55) 11 (65) 24 (70)

5 (10) 6 (18) 36 (15) 5 (10) 19 (18) 36 (15) 6 (35) 0 (0)

50 (3) 34 (2) 235 (14) 50 (3) 101 (6) 235 (14) 17 (1) 34 (2)

170 (24) 10 (20) 259 (28)

438 (62) 35 (70) 564 (61)

98 (14) 5 (10) 102 (11)

706 (42) 50 (3) 925 (55)

Source CMIE Prowess and Authors calculations Figures in parenthesis are percentage

Lastly, concerning the type of ownership, the majority of the firms (55%) in the sample are private Indian firms followed by group affiliated firms with a proportion of 42%, while the rest of the sample constitutes central/state-government-held firms. These results are in conformity to the results of (Kumar and Rao 2016).

1

1.6

INTRODUCTION

9

Capital Structure Planning and Policy

The term ‘capital’ can be defined in many ways. Its definition depends on the context in which it is used. However, in economics and finance literature, capital is referred to as capital goods like machines, plant, tools, etc. (Samuelson 2005). This form of capital has been referred to as ‘financial capital’ by contemporary schools of thought (see, for example, Flaherty 2008; Roemer 2008). In modern times, the entire capital of firms’ remains invested in fixed and current assets, financed by debt or equity or the combination of both. How the firm combines, different forms of capital is called the capital structure of the firm. Specifically, capital structure refers to a mix of debt and equity or any ‘other long-term funding sources’ used to finance the firm’s investment (Kapil 2013). In theory, the thinking of an optimum capital structure is easy, but is quite difficult to design the one in practice. It is rightfully put by Myers (1984) that capital structure is a multidimensional puzzle that is simultaneously affected by the inherent specificities of various contrasting pairs like large enterprises and small and medium-sized enterprises (SMEs); short-term and long-term debt, and growth and recessionary states of the economy. Given the peculiar specificities, the finance manager must go beyond the theory and attempt to design an optimal capital structure, the one that maximizes the value of the firm. This can be done by taking into consideration all the relatable factors that have a bearing on the firm’s capital structure, keeping in view the interests of the equity shareholders and financial requirements of the company. Further, an optimal capital structure level is determined, vis-à-vis the balance between benefits and costs related to capital structure. For instance, the static trade-off theory of capital structure by Kraus and Litzenberger (1973) suggests that the optimal level of capital structure is determined by means of the balance between tax benefits from debt financing and the cost of financial distress. Further, Jensen and Meckling’s (1976) agency theory of capital structure approaches capital structure by considering agency costs that may arise based on principal–agent conflicts. According to this theory, optimal capital structure is determined by the agency costs and firm performance will be maximized when the firm achieves optimal capital structure. A more recent and well-established capital structure theory is credited to Myers (1984) as a dynamic tradeoff theory or target adjustment theory. This theory states that firms adjust to the target capital structure or optimal capital structure by following a

10

N. ALTAF AND F. A. SHAH

dynamic adjustment process. Any deviation from the target will result in lower performance and hence adjustment towards the target is desirable. Based on the literature mentioned above, it can be ascertained that capital structure is a multidimensional puzzle and for this reason, this area has been recognized as a broiling topic for research. Graham and Harvey (2001) assert that a wide gap exists between the theory and practice of capital structure. They suggest that theories offer descriptions of what finance mangers in firms should do, but many corporations ignore the theoretical advice. Additionally, with regard to the theory, researchers have different opinions on which theory explains the firm’s capital structure decisions in the best possible manner. For instance, Bulan and Yan (2009, 2011), Lemmon and Zender (2010) suggest that pecking order theory is better able to explain capital structure decisions of the firms. On the other hand, Frank and Goyal (2009) and Singh and Kumar (2008) suggest that the trade-off theory is more robust for capital structure decisions. These compelling theories and difficulty in the exact measurements of capital structure make this area even more interesting for research.

1.7

Capital Structure Practices in Indian MSMEs

Although Indian MSMEs have massive potential, their performance lags expectation due to many challenges. Among these challenges, financing and credit risk are the major issues reported in the prior literature (Rao et al. 2019). It may be because MSMEs exhibit considerable differences in financing pattern compared to large enterprises (Daskalakis et al. 2017; Kumar and Rao 2016). In addition, availability and accessibility to funds by Indian MSMEs could possibly be another reason for heightened financing and credit risk. The easy accessibility to finance for Indian SMEs is majorly hindered by high information asymmetry, which is because Indian MSMEs are not obliged to publicly broadcast their financial statements (Rao et al. 2019). Further, the sustained existence of Indian MSMEs is threatened by a lack of accessibility to appropriate and timely credit (Abe et al. 2015). In addition, a large credit gap is also witnessed among Indian MSMEs, which may be due to the lack of creditworthiness of Indian MSMEs among banks. Moreover, a large credit gap is also witnessed due to the absence of requisite skills among financial institutions for assessing borrowers’ creditworthiness and due to tedious and complicated procedures for accessing credit (Thampy 2010).

1

INTRODUCTION

11

Keeping into consideration the challenges of financing, one of the important factors to be taken into consideration by Indian MSMEs is the availability of funds at competitive rates (see IBEF report, 2013, ‘MSME and the growing role of industrial clusters’). In addition, banks are the main source of external finance for MSMEs in India (Baker et al. 2019). As Biswas (2014) notes that, external finance, although costly and limited in availability, is necessary to finance long-term projects/assets in Indian MSMEs. However, the poor regulatory environment in emerging economies like India discourages MSMEs financing from formal sources, thereby impending gap exists between formal and informal financing sources (Lucey et al. 2016). In addition, switching between formal and informal financing is often uneconomical for MSMEs. Therefore, Indian MSMEs largely rely on informal financing sources because of the substantial barriers in financing from formal sources (Allen et al. 2012). Furthermore, Indian MSMEs are typically family-owned and so ownership concentration is also high in these firms. High concentration of ownership forces MSMEs to look for risk-averse financing sources (Lappalainen and Niskanen 2012). Another reason for the popularity of risk-averse sources of finance is the likeliness among owners to generate more profits. Moreover, the owners of MSMEs do not want to lose control, therefore maintaining control is also one of the key factors taken into consideration while determining the capital structure of MSMEs. It is worth to note that according to ‘Financing India’s MSMEs’ report (2018) by World Bank, the total demand of finance (debt and equity finance) in Indian MSMEs was estimated to be INR 87.7 trillion (USD 1.35 trillion).7 Among the estimated finance demand, 80% (INR 69.3 trillion) was demanded through debt and the remaining 20% (INR 18.4 trillion) was demanded through equity. It must be noted that World Banks report on MSMEs in India (2013) reported the total finance demand of INR 32.5 trillion, implying that there was an increase of 170% in the demand for finance from 2013 to 2018. Further, among the INR 69.3 trillion debt demand in the year 2018, about 70% (INR 48.5 trillion) was demanded for working capital needs and the rest 30% (INR 20.8 trillion) was required to meet Capital expenditures (CAPEX) demand.

7 The average capital demand has been defined as the sum of the capital expenditure and working capital demand of an enterprise required for operational expenses and for investments in fixed assets, it includes demand from both formal and informal sources.

12

N. ALTAF AND F. A. SHAH

However, from the total debt demand, we only consider the addressable debt demand by the MSME Sector’.8 Among the INR 69.3 trillion debt demand, only INR 36.74 trillion (53%) debt demand of MSMEs was considered addressable during the year 2018 and INR 27.9 Trillion during the year 2013. It is worth to note that among the INR 36.74 trillion addressable debt demand during 2018, INR 12 trillion (32%) belonged to micro-enterprises, INR 21.65 trillion (59%) to small enterprises and the remaining INR 3.22 trillion (9%) to medium enterprises. Further, with regard to the supply of finance, the formal sources could supply only INR 10.9 trillion of the MSME debt demand in the year 2018, which is much higher than INR 7 trillion supplied in the year 2013. During the year 2018, banking institutions supplied INR 9.4 trillion while as non-banking and government financial institutions supplied the remaining INR 1.5 trillion. Further, among the INR 9.4 trillion debt requirement supplied by banking institutions, INR 5.4 trillion was supplied by public banks, private banks supplied INR 3.1 trillion and foreign banks supplied INR 0.3 trillion. Additionally, the total supply from formal sources (INR 10.9 trillion) was channelized to micro, small and medium enterprises in different ratios, the micro-enterprises received INR 3.9 trillion (35%), small enterprises received INR 4.8 trillion (45%) and medium enterprises received INR 2.2 trillion (20%) of the debt supplied. Considering the addressable debt demand and the supply of debt, we can find the credit gap in the MSME sector is estimated to be INR 25.8 trillion for the year 2018 and INR 20.9 trillion for the year 2013. It is worth to note that if we divide the credit gap by the addressable debt in their respective years, we find that during the year 2013 credit gap as a percentage of addressable debt demand was 74% and the same was 70% during the year 2018, implying the fall in credit gap. However, the credit gap still remains high. Keeping in view the literature and the statistics mentioned above and the interplay of both firm and macroeconomic factors, it is interesting to determine how capital structure dynamics play in Indian MSMEs.

8 Due to data unavailability, Addressable Debt demand estimates exclude finance sought from informal sources.

1

1.8

INTRODUCTION

13

What Is This Book All About?

The research on the capital structure has so far majorly examined the issues like determinants of capital structure choices (see for example; Rao et al. 2019; Balios et al. 2016; Frank and Goyal 2009; Daskalakis and Psillaki 2008), speed of adjustment towards the target capital structure (see for example; He and Kyaw 2018; Yang et al. 2018; Devos et al. 2017; Ghose 2017), capital structure and firm performance (see for example; Ahmed and Afza 2019; Detthamrong et al. 2017; Le and Phan 2017). The results of these studies have identified a number of factors instrumental in explaining capital structure. In addition, many studies have provided evidence in support of the existence of leveragetargeting behaviour of firms and the speed of adjustments towards the target thereof. However, many other studies have refuted the claims of the existence of such phenomena. Regarding the impact of capital structure on firm performance, the study findings have remained ambiguous; some studies have suggested a positive relationship between capital structure and firm performance, while others have suggested a negative relationship between the two variables. Having highlighted the aspects of capital structure investigated by some previous studies, this book endeavours to enrich the understanding of capital structure puzzle by answering some relevant questions that have not been answered before. Notably, these aspects have been explored empirically by analyzing the data on a sample of the Indian MSMEs. The book attempts to answer the following specific questions: 1. What type of financing pattern is followed by Indian MSMEs? Is there any difference in the financing pattern across the cross-section of firms? 2. How did capital structure determinants respond to different macroeconomic states and use of debt of different maturities? 3. How does working capital affect the capital structure? 4. How does cash flow volatility affect capital structure? 5. Does credit risk have an impact on capital structure and firm performance relationship? As noted above, MSMEs are a crucial component of the Indian economy’s growth engine, a sample of Indian MSMEs has been used for scientific exploration of these aspects. Moreover, prior literature has not

14

N. ALTAF AND F. A. SHAH

given considerable attention to MSMEs. In fact, to the authors’ best knowledge, these issues have never been reported earlier, particularly vis-a-vis the Indian MSMEs. In addition, it must be noted that some peculiarities are specifically signaled by MSMEs, such as knowing that small businesses are not ‘scaled-down versions’ of large businesses (Cressy and Olofsson 1997) and thus need to be vetted differently. Further, Ang (1991) asserts that finance theory was not developed with the small business in mind. Accordingly, large firms’ financial aspects have limited applicability to SMEs, including the theory of capital structure. Therefore, this book entreaties the audience looking to understanding newer dynamics of capital structure and its interplay in the Indian MSMEs.

1.9

Organization of the Book

Apart from this chapter (Chapter 1), the book is organized into six broad chapters. In Chapter 2, we attempt to analyze the financing pattern in Indian MSMEs. Specifically, we examine the proportion of debt (shortterm and long-term) used to finance total assets. Additionally, in this chapter, we examine the main constituents of short-term and long-term debt in Indian MSMEs. Further, in this chapter, we explore whether the considerable differences exist in the financing pattern across the crosssection of firms classified on the basis of size, geographical spread, sector of operation and ownership type. Chapter 3 makes an inceptive attempt to understand how the capital structure adjustment speed, the firm-specific and macroeconomic variables in an abreast of capital structure determination behave across different forms of debt and macroeconomic states for Indian SMEs. This chapter makes affirmations regarding the distinct speed of adjustment. Specifically, this chapter’s results affirm that the speed of adjustment for long-term debt slows down during bad states while it remains the same for short-term debt. Further, we also present the diverse impact of firmspecific and macroeconomic variables across the macroeconomic states and debt with different maturity. Chapter 4 is among the preliminary attempts to investigate how working capital and its components affect capital structure decisions of the Indian MSMEs. This chapter makes affirmation on the substantial impact of working capital and its components on both long-term and

1

INTRODUCTION

15

short-term debt use. This chapter also highlights the importance of crosssectional heterogeneity in terms of profit margin on the relationship between working capital and leverage. Chapter 5 makes a humble attempt to dissect the impact of cash flow volatility on a debt of different maturities for the Indian SMEs. This chapter makes affirmation regarding the robust negative impact of cash flow volatility on both long and short-term debt. The chapter concludes that this negative relationship is more pronounced for long-term debt. Chapter 6 adds on to the pronounced, yet ambiguous debate of capital structure and firm performance by testing whether credit risk has an impact on capital structure and firm performance among the Indian SMEs. This chapter abet that capital structure and firm performance are negatively related but positively related in firms facing high credit risk.

References Abe, M., Troilo, M., & Batsaikhan, O. (2015). Financing small and medium enterprises in Asia and the Pacific. Journal of Entrepreneurship and Public Policy, 4(1), 2–32. Ahmed, N., & Afza, T. (2019). Capital structure, competitive intensity and firm performance: Evidence from Pakistan. Journal of Advances in Management Research, 16(5), 796–813. Akinwale, A. A. (2010). The menace of inadequate infrastructure in Nigeria. African Journal of Science, Technology, Innovation and Development, 2(3), 207–228. Ali, A., & Husain, F. (2014). MSMEs in India-problems, solutions and prospectus in present scenario. International Journal of Engineering and Management Sciences, 5(2), 109–115. Allen, F., Chakrabarti, R., De, S., & Qian, M. (2012). Financing firms in India. Journal of Financial Intermediation, 21(3), 409–445. Altaf, N., & Ahmad, F. (2019). Working capital financing, firm performance and financial constraints. International Journal of Managerial Finance, 15(4), 464–477. Altaf, N., & Shah, F. A. (2018). How does working capital management affect the profitability of Indian companies? Journal of Advances in Management Research, 15(3), 347–366. Ang, J. S. (1991). Small business uniqueness and the theory of financial management. Journal of Small Business Finance, 1(1), 1–13. Baker, H. K., Kumar, S., & Singh, H. P. (2019). Working capital management: Evidence from Indian SMEs. Small Enterprise Research, 26(2), 143–163.

16

N. ALTAF AND F. A. SHAH

Balios, D., Daskalakis, N., Eriotis, N., & Vasiliou, D. (2016). SMEs capital structure determinants during severe economic crisis: The case of Greece. Cogent Economics & Finance, 4(1), 1145535. Basu, P. (2004). Providing better access to finance for SMEs in India: New bank project addresses financing constraints with innovative tools (No. 38847, pp. 1– 4). The World Bank. Biswas, A. (2014). Financing constraints for MSME sector. International Journal of Interdisciplinary and Multidisciplinary Studies, 1(5), 60–68. Cressy, R., & Olofsson, C. (1997). European SME financing: An overview. Small Business Economics, 9(2), 87–96. Das, K. (2008). SMEs in India: Issues and possibilities in times of globalisation. In H. Lim (Ed.), SME in Asia and Globalization, ERIA Research Project Report 2007-5 (pp. 69–97). Available at: http://www.eria.org/SMEs%20in%20India_Issues%20and%20Possibi lities%20in%20Times%20of%20Globalisation.pdf. Daskalakis, N., & Psillaki, M. (2008). Do country or firm factors explain capital structure? Evidence from SMEs in France and Greece. Applied Financial Economics, 18(2), 87–97. Daskalakis, N., Balios, D., & Dalla, V. (2017). The behaviour of SMEs’ capital structure determinants in different macroeconomic states. Journal of Corporate Finance, 46, 248–260. Detthamrong, U., Chancharat, N., & Vithessonthi, C. (2017). Corporate governance, capital structure and firm performance: Evidence from Thailand. Research in International Business and Finance, 42, 689–709. Devos, E., Rahman, S., & Tsang, D. (2017). Debt covenants and the speed of capital structure adjustment. Journal of Corporate Finance, 45, 1–18. Doe, F., & Emmanuel, S. E. (2014). The effect of electric power fluctuations on the profitability and competitiveness of SMEs: A study of SMEs within the Accra Business District of Ghana. Journal of Competitiveness, 6(3), 32–48. Flaherty, D. (2008). Radical Economics. In S. N. Durlauf & L. E. Blume (Eds.), The new Palgrave dictionary of economics (2nd ed., pp. 835–840). Basingstoke, UK: Palgrave Macmillan. Frank, M. Z., & Goyal, V. K. (2009). Capital structure decisions: Which factors are reliably important? Financial Management, 38(1), 1–37. Ghose, B. (2017). Impact of business group affiliation on capital structure adjustment speed: Evidence from Indian manufacturing sector. Emerging Economy Studies, 3(1), 54–67. Graham, J. R., & Harvey, C. R. (2001). The theory and practice of corporate finance: Evidence from the field. Journal of Financial Economics, 60(2–3), 187–243.

1

INTRODUCTION

17

He, W., & Kyaw, N. A. (2018). Capital structure adjustment behaviors of Chinese listed companies: Evidence from the Split Share Structure Reform in China. Global Finance Journal, 36, 14–22. 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. Kapil, S. (2013). Fundamental of financial management. Pearson Education India. Kraus, A., & Litzenberger, R. H. (1973). A state-preference model of optimal financial leverage. The Journal of Finance, 28(4), 911–922. Kumar, S., & Rao, P. (2016). Financing patterns of SMEs in India during 2006 to 2013—An empirical analysis. Journal of Small Business & Entrepreneurship, 28(2), 97–131. Lappalainen, J., & Niskanen, M. (2012). Financial performance of SMEs: Impact of ownership structure and board composition. Management Research Review, 35(11), 1088–1108. Le, T. P. V., & Phan, T. B. N. (2017). Capital structure and firm performance: Empirical evidence from a small transition country. Research in International Business and Finance, 42, 710–726. Lemmon, M. L., & Zender, J. F. (2010). Debt capacity and tests of capital structure theories. Journal of Financial and Quantitative Analysis, 45(5), 1161–1187. Lucey, B., Macan Bhaird, C., & Vidal, J. S. (2016). Discouraged borrowers: Evidence for Eurozone SMEs (Working Paper), Trinity Business School. Mukherjee, S. (2018). Challenges to Indian micro small scale and medium enterprises in the era of globalization. Journal of Global Entrepreneurship Research, 8(1), 1–19. Myers, S. C. (1984). The capital structure puzzle. The Journal of Finance, 39(3), 574–592. Nagpal, V. P., Saini, M., & Gupta, S. (2009). Problems faced by small and medium enterprises. In SME in transitional economics-challenges and opportunities (pp. 566–577). Deep and Deep Publications. Rao, P., Kumar, S., & Madhavan, V. (2019). A study on factors driving the capital structure decisions of small and medium enterprises (SMEs) in India. IIMB Management Review, 31(1), 37–50. Roemer, J. E. (2008). Socialism (new perspectives). The new Palgrave dictionary of economics (2nd ed.). Palgrave Macmillan. http://www.Dictionaryofeconom ics.com/article. Samuelson, L. (2005). Economic theory and experimental economics. Journal of Economic Literature, 43(1), 65–107. Seshasayee, R. (2012). Financing SMEs: An industry perspective. Business World.

18

N. ALTAF AND F. A. SHAH

Singh, P., & Kumar, B. (2008). Trade off theory or pecking order theory: What explains the behavior of the Indian firms? Available at SSRN 1263226. Thampy, A. (2010). Financing of SME firms in India: Interview with Ranjana Kumar, former CMD, Indian bank; vigilance commissioner, Central Vigilance Commission. IIMB Management Review, 22(3), 93–101. Yang, S., He, F., Zhu, Q., & Li, S. (2018). How does corporate social responsibility change capital structure? Asia-Pacific Journal of Accounting & Economics, 25(3–4), 352–387.

CHAPTER 2

Financing Pattern of Indian MSMEs

2.1

Introduction

In emerging economies, MSME financing is considered a critical element for developing and harnessing the potential of MSMEs (Cook 2001). In fact, obstacles in access to finance are considered a major growth-limiting factor for MSMEs (Kumar and Rao 2016). Although Indian MSMEs have enormous potential, the lack of availability and accessibility to funds may create impediments in the growth and sustenance of Indian MSMEs. It is worth noting that according to the Indian MSME census (2007), 92% of the MSMEs had no access to the formal sources of financing and in fact, these firms were largely self-financed or highly dependent on informal sources of financing. Additionally, the recent ‘Financing India’s MSMEs’ report (2018) by World Bank suggests that the credit gap in the Indian MSME sector was estimated to be INR 25.8 trillion in 2018 compared to INR 20.9 trillion for the year 2013. The huge credit gap is attributed to the lesser developed Indian financial sector that has failed to supply an adequate amount of capital at regular intervals, thereby creating a barrier for growth and development. Further, prior literature has documented a significant relationship between the availability of finance and growth at the macro level (see for example Cecchetti and Kharroubi 2012; Levine 2005; Beck and Demirguc-Kunt 2006; Beck et al. 2008). However, recent evidences suggest that such relationship exists at the firm level as well (see for © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 N. Altaf and F. A. Shah, Capital Structure Dynamics in Indian MSMEs, https://doi.org/10.1007/978-981-33-4276-7_2

19

20

N. ALTAF AND F. A. SHAH

example Didier et al. 2015; Shane 2012; Wilson 2011). In fact, some studies suggest that MSMEs that are financially constrained tend to experience slower growth (Quartey et al. 2017). It is well known that generally, MSMEs are not publicly traded and thereby have less access to the capital markets. Furthermore, information asymmetry is also a peculiarity common to MSMEs, making them riskier and less preferred for financing by banks (Liu and Yu 2008). The above-mentioned peculiarities have made MSMEs a demanding and key area for research, particularly in developing economies (Wu et al. 2008). Thereby adding to this debate, we analyze 1681 Indian MSMEs’ financing patterns for the period 2006–2017. More specifically, in this chapter, we analyze the proportion of debt (short-term and long-term) used to finance total assets, the composition of short-term and long-term debt and the proportion of total borrowings financed by short-term and long-term bank loans. It is worth mentioning that such analysis is carried over time (2006–2017) and across firms classified on the basis of size, geographical spread, sector of operation and ownership type. This exercise helps us to track whether the considerable differences exist in the financing pattern when firms are classified according to the above-mentioned classification. Univariate t-tests are used to investigate such differences in the mean financing ratios across firm types. The results of the study suggest that Indian MSMEs are heavily dependent on the short-term debt for their financing needs. Additionally, we find that the major constituents of short-term debt are bank loans, payables and inter-corporate borrowings and among these payables are mostly used for financing short-term debt. Further, we find that long-term debt is mostly financed by bank loans, financial institution borrowings, foreign currency borrowings and other borrowings. Among these sources, long-term bank loans are mostly used for financing longterm debt. It is worth to note such pattern has been witnessed across the time period as well as firm type. Additionally, we do not find considerable differences across the cross-section of firms except than that of firms with different sizes. The remained of the chapter is organized as follows. Section 2.2 discusses the methodology adopted, Sect. 2.3 presents the statistics of financing patterns in Indian MSMEs, Sect. 2.4 presents the financing patterns across the cross-section of firms and Sect. 2.5 concludes

2

2.2

FINANCING PATTERN OF INDIAN MSMES

21

Methodology

This section is dedicated to describing methodology applied for analyzing the financial patterns of MSMEs and examining the divergences in financial patterns across firms of different sizes, geographic spread, sector of operation and ownership type. 2.2.1

MSMEs Forming Part of Sample

As mentioned in Sect. 1.4 of Chapter 1, investments in plant, machinery and equipment are used as a proxy by MSMED Act, 2006 for defining MSMEs in India. According to this act, firms are defined as Micro manufacturing firms, if their investment in plant, machinery and equipment is up to 2.5 million. Further, firms are defined as small manufacturing firms if their investment in plant, machinery and equipment is between 2.5 million to 50 million and medium manufacturing firms if such investment is between 50 million and 100 million. However, for being classified as micro firms in services sector the total investment in plant, machinery and equipment should be up to one million, for small service firms the investment in plant, machinery and equipment shall be between one million and 20 million and for medium service firms this investment should be between 20 million and 50 million. We first applied the investment limit filters as per MSMED act on CMIE PROWESS database to choose the sample. The total number of firms available in CMIE PROWESS after applying these limits were found to be 3468. Among these 3468 firms, 618 belonged to the finance industry. These firms have been excluded from the sample because these firms’ financial decisions are guided by different schemes (Altaf and Shah 2018; Altaf and Ahmad 2019). Having dropped financial firms from the sample, we were left with 2850 non-financial firms. Further, we found that certain firms in the PROWESS database did not adhere to the investment criteria of the MSMED Act 2006 throughout the period of the study. Specifically, for 772 firms, investment exceeds the limit of SME in between the period of the study. Accordingly, these firms were dropped from the sample. In addition, 397 firms reported incomplete information for the period under chapter. Hence, dropped from the sample

22

N. ALTAF AND F. A. SHAH

leaving us with the final sample of 1681 firms across a period of 12 years (2006–2017) making the total number of observations equal to 20,172.1 2.2.2

Indicators of Firm Financing and Their Constituents

It is worth to mention that we have used an electronic database PROWESS of Centre for Monitoring Indian Economy (CMIE) PROWESS to extract firm-level information on the firm financing indicators of Indian MSMEs. The name, acronym and description of firm financing indicators and their constituents is given in Table 2.2. Following, Kumar and Rao (2016) and Love and Martinez (2005), we investigate the financing pattern of Indian MSMEs by analyzing the use of debt in financing total assets. More specifically, for the use of debt in financing total assets, we use the ratio of long-term debt to total assets (LTDR), short-term debt to total assets (STDR) and total debt to total assets (TDR). LTDR is defined as long-term debt to total assets. Long-term debt includes ‘loans/borrowings from banks (both secured and unsecured), financial institutions, central and state government, loans in foreign currency, and borrowings debentures and bonds, fixed deposits, and hire purchase loan’. Besides, short-term debt includes ‘loans/borrowings from banks (both secured and unsecured), inter-corporate loans and trade credit’. Further, total debt is equal to the sum of both short-term and long-term debt and total assets include both fixed and current assets. These indicators are presented in panel A of Table 2.2. In addition, to the indicators mentioned above, we also analyze the major constituents of short-term and long-term debts (presented in panel B of Table 2.2) and bank loans as a percentage of total borrowings (presented in Panel C of Table 2.2). It is worth to mention that from data we find that short-term debt is composed of bank loans (both secured and unsecured), payables and inter-corporate loans, while as long-term debt is composed of bank loans (both secured and unsecured), borrowings from financial institutions, foreign currency borrowings and other borrowings.

1 The sample selection procedure for analyzing financing pattern in Indian MSMEs is given in Table 2.1.

2

FINANCING PATTERN OF INDIAN MSMES

23

Table 2.1 Sample selection procedure for analyzing financing pattern in Indian MSMEs Total number of firms after applying the limit of investment as per the MSMED Act 2006 Less: Firms operating in the financial industry Remaining non-financial firms Less: Firms that are not conferring with the definition of SMEs as per the MSMED Act 2006 during the whole analysis period Less: Firms with incomplete data for the period under chapter Firms forming part of the sample

3468 (618) 2850 (772) (397) 1681

2.3 Statistics of Financing Patterns in Indian MSMEs We begin by providing insights into the financing pattern of Indian MSMEs, specifically, Table 2.3, provides the descriptive statistics of the indicators highlighting the financing pattern adopted by Indian MSMEs from 2006 to 2017. First, the time series behaviour of STDR shows that short-term debt is used in greater proportion among the Indian MSMEs when compared to LTDR. It is thereby asserted that short-term is the most favoured source of financing for Indian MSMEs, in fact, the figures presented in Panel D of Table 2.3 suggest that short-term bank loans constitute more or less 45% of the total borrowing. The dependence of MSMEs on short-term borrowing may be due to the conservative nature of Indian banks in providing long-term loans to MSMEs (Altaf and Shah 2017; Altaf and Ahmad 2019). As short-term loans constitute the major chunk of total borrowing, in Panel B of Table 2.3, we present a more detailed description of the short-term debt structure in Indian MSMEs. It is evident that the major constituents of short-term debt are payables emphasizing that MSMEs remain largely dependent on trade credit as a source of short-term debt. In fact, payable constitutes about 40% of the short-term debt financing among the sampled firms, followed by short-term bank loans that remain 18% throughout the sample period. These results assert that despite the short-term bank loan being the major constituent of the total borrowings, it does not occupy the major proportion of short-term debt financing in Indian MSMEs.

24

N. ALTAF AND F. A. SHAH

Table 2.2 Indicators of firm financing and their constituents Name

Acronym

Description

Panel A: Financing pattern indicators Long-term debt to total assets LTDR

Long-term debt to total assets; Long-term debt includes ‘loans/borrowings from banks (both secured and unsecured), financial institutions, central and state government, loans in foreign currency, borrowings through debentures and bonds, fixed deposits, and hire purchase loan’ Total assets include both fixed and current assets Short-term debt to total assets STDR Short-term debt includes ‘loans/borrowings from banks (both secured and unsecured), inter-corporate loans and trade credit’ Total assets include both fixed and current assets Total debt to total assets TDR Total debt is the sum of long and short-term debt and total assets include both fixed and current assets Panel B: Major constituents of short-term and long-term debts Bank loans BL Both short-term and long-term, secured and unsecured as available in PROWESS Payables PAY As available in PROWESS Inter-corporate loans ICL As available in PROWESS Borrowings from financial FIB As available in PROWESS institutions Foreign currency borrowings FCB As available in PROWESS Other borrowings OB As available in PROWESS Panel C: Bank loans as a percentage of total borrowings Short-term bank loans STB Secured and unsecured short-term bank borrowings Long-term bank loans LTB Secured and unsecured long-term bank borrowings

Further, with regard to LTDR, we find that long-term debt is not employed in major proportion by Indian MSMEs. Long-term debt as a proportion of total assets remains approximately 30–35% during the period 2006–2017. Contrary to the constituents of short-term debt, the

2009

2010

2011

Panel A: Financing pattern of Indian MSMEs STDR 0.55 0.57 0.53 0.56 0.58 0.56 LTDR 0.32 0.31 0.34 0.33 0.30 0.32 TDR 0.86 0.87 0.87 0.89 0.88 0.88 Panel B: Major constituents of short-term debt (in percentage terms) BL 18.14 18.49 17.23 17.91 18.07 17.93 PAY 40.12 40.86 39.12 40.10 42.14 40.09 ICL 8.12 10.16 9.08 9.67 10.73 9.54 Panel C: Major constituents of long-term debt (in percentage terms) BL 21.02 20.17 22.14 22.04 20.18 23.14 FIB 5.12 5.10 6.07 5.81 5.77 5.99 FCB 2.12 2.11 2.14 2.10 2.08 2.18 OB 8.11 8.08 9.07 9.01 8.97 9.35 Panel D: Bank loans as a percentage of total borrowings STB 44.12 45.03 44.08 44.17 45.05 44.76 LTB 9.86 9.80 10.15 10.06 9.99 11.28

2008

2006

Year

2007

Descriptive statistics of financing pattern across time

Table 2.3

0.52 0.39 0.91 17.27 39.01 9.21 27.18 6.56 2.47 9.53 44.07 12.51

17.84 39.81 9.39 26.12 6.34 2.35 9.47 44.32 11.33

2013

0.53 0.34 0.86

2012

46.13 11.94

26.99 6.41 2.40 9.51

17.55 39.87 11.77

0.57 0.36 0.93

2014

46.08 12.30

30.67 6.89 2.59 10.07

17.21 39.80 10.52

0.56 0.36 0.92

2015

46.81 12.28

30.34 6.80 2.51 9.83

17.82 40.16 12.91

0.61 0.35 0.95

2016

47.01 12.21

30.01 6.71 2.50 9.56

18.13 41.45 15.07

0.62 0.33 0.94

2017

2 FINANCING PATTERN OF INDIAN MSMES

25

26

N. ALTAF AND F. A. SHAH

figures presented in Panel C of the Table reveal that long-term bank loans are the major constituents of long-term bank loans. These results are indicative of the fact that even if obtaining long-term bank loans is a burdensome process in India yet it is mostly preferred for long-term debt because other sources of long-term funds like loans from financial institutions and foreign currency borrowings are mostly absent in the Indian economy. The absence of other alternative sources of financing long-term debt may be because procurement of funds from alternative institutions requires the filing of credit ratings and Indian MSMEs are not generally rated. Hence, the only way left for financing long-term debt is by filing collaterals with banks. Additionally, despite the fact that long-term bank loans dominate the overall financing of long-term debt, it occupies a much lower proportion in the total borrowings than short-term bank loans. Therefore, based on these results, it can be concluded that shortterm debt occupies a significant proportion of financing in Indian MSMEs as compared to long-term debt.

2.4

Financing Patterns in Indian MSMEs Across the Cross-Section of Firms

In this section, we elaborate upon the financing pattern of Indian MSMEs by analyzing the financing pattern of across cross-section of firms classified based on size, geographical spread, sector of operation and ownership type. 2.4.1

Across Firm Size

Table 2.4 presents the descriptive statistics of financing pattern across Micro, Small and Medium enterprises. The figures presented in the Table reveal that short-term debt is a major financing source across firms of all sizes. Additionally, we find that as firms size increase (micro to medium), we find that use of both short-term and long-term debt decreases. A similar pattern is seen for the total debt ratio as well. Further, the mean t-test for Micro vs. Small, Small vs. Medium and Micro vs. Medium reveal significant differences in financing patterns across Micro, Small and Medium enterprises both in terms of short and long-term debt. Further, these results are supported by the figures presented in Panel D of Table 2.4, as we find that short-term bank loans are major constituents of total borrowings across the firms of all sizes and there are significant

2

FINANCING PATTERN OF INDIAN MSMES

27

Table 2.4 Descriptive statistics of financing patterns across firm size Micro

Small

Medium

Micro vs. Small

Small vs. Medium

Panel A: Financing pattern of Indian MSMEs STDR 0.5823 0.4715 0.3614 *** *** LTDR 0.4210 0.3641 0.2179 ** * TDR 0.9213 0.8266 0.7901 *** ** Panel B: Major constituents of short-term debt (in percentage terms) BL 10.87 29.12 37.51 *** NS PAY 34.23 43.13 44.96 ** NS ICL 9.14 11.63 12.88 ** NS Panel C: Major constituents of long-term debt (in percentage terms) BL 20.12 33.46 42.93 *** ** FIB 3.47 4.82 5.01 * NS FCB 0.98 1.25 2.86 ** *** OB 6.192 7.888 8.015 NS NS Panel D: Bank loans as a percentage of total borrowings STB 24.11 53.28 41.04 *** ** LTB 7.62 8.72 11.17 *** ***

Micro vs. Medium

** *** *** ** ** NS *** ** *** NS ** ***

Note Asterisks indicate significance at 1% (*) 5% (**) and 10% (***). NS means not significant. Sample size: Micro = 420; Small = 1008; Medium = 253

differences in the composition of short-term bank loans across Micro, Small and Medium enterprises. Further, similar to the full sample results, we find payables to be the major constituents of short-term debt for Micro, Small and Medium enterprises. In spite of being a major constituent of across firms of all sizes, we find a significant difference in the use of payables as a source of short-term debt. Additionally, we also find significant differences among the use of bank loans and inter-corporate lending across firms of all sizes. In line with the results of the full sample, we find that bank loans constitute the highest proportion of long-term debts for the firms of all sizes and such use of long-term loans is also significantly different among the Micro, Small and Medium-sized firms. We also find significant differences among other long-term debt constituents like borrowings from financial institutions and foreign currency borrowings. However, we do not find a significant difference among other borrowings. Based on the results mentioned above, it can be concluded that firms size plays a key role in determining the financing pattern of Indian

28

N. ALTAF AND F. A. SHAH

MSMEs. These results are in line with the notion that as firm size increases the dependence on bank financing tends to reduce, and thereby non-bank, non-market sources constitute the most vital form of external finance in the growing economies like India (Allen et al. 2012). Further, bank size has also been found to be a significant determinant of capital structure decisions of MSMEs by a number of studies (see for example Rao et al. 2019; Daskalakis et al. 2017; Sogorb-Mira 2005; Hall et al. 2004). 2.4.2

Across Geographic Spread

To understand the financing pattern of Indian MSMEs across the country, we divide the country into four main regions: eastern, northern, southern and western states. The results of the financing pattern across geographic locations are given in Table 2.5. Perusing Table, we again find that short-term debt is used more than long-term debt in firms categorized across the geographic regions, implying that short-term debt is the most favoured source of financing for MSMEs throughout India. Additionally, similar to prior results we find that payables occupy the highest proportion of short-term debt and bank loans make up the largest chunk of long-term debt. These results remain robust across geographical regions. Further, consistent with the previous results, we find that short-term bank loans hold a greater proportion of total borrowings as compared to long-term bank loans. Contrary to the previous results of the significant difference in financing patterns across firm size, we find no significant difference in firms’ financing patterns categorized across geographic locations of India. These results may be due to the fact that technological advances have made location irrelevant for obtaining finance and may not be a factor considered for accessing finance (Kumar and Rao 2016). 2.4.3

Across Sector of Operation

In order to analyze the financing pattern of firms across the sector, we have classified firms operating in the manufacturing and services sector based on the criterion of the MSME Act 2006. These results are presented in Table 2.6. In line with the prior results, we find short-term debt is employed in greater proportion in manufacturing and service firms than

West

East vs. North

NS NS NS NS NS *** ** NS NS ara> NS NS NS

NS NS NS NS NS NS NS NS ***

East vs. West

NS NS NS

East vs. South

NS NS

NS NS NS NS

** NS NS

NS NS NS

North vs. South

NS NS

NS NS NS NS

NS NS NS

NS NS NS

North vs. West

** NS

NS NS NS NS

NS *** NS

NS NS NS

South vs. West

Note Asterisks indicate significance at 1% (*) 5% (**) and 10% (***). NS means not significant. Sample size: East = 168; North = 370; South = 320; West = 823

Panel A: Financing pattern of Indian MSMEs STDR 0.5018 0.5216 0.4973 0.5184 NS LTDR 0.3101 0.2914 0.3133 0.3018 NS TDR 0.8715 0.7986 0.7343 0.8219 NS Panel B: Major constituents of short-term debt (in percentage terms) BL 17.29 19.03 17.32 18.03 NS PAY 32.10 36.95 40.19 41.83 NS ICL 10.07 12.15 11.87 13.94 NS Panel C: Major constituents of long-term debt (in percentage terms) BL 29.78 30.21 32.91 31.14 NS FIB 3.92 4.12 5.94 6.26 NS FCB 1.81 2.02 3.14 2.19 NS OB 7.198 8.982 9.118 10.01 NS Panel D: Bank loans as a percentage of total borrowings STB 37.87 35.23 43.17 41.07 NS LTB 9.33 11.02 10.33 11.82 NS

South

East

Year

North

Descriptive statistics of financing pattern across the geographic spread

Table 2.5

2 FINANCING PATTERN OF INDIAN MSMES

29

30

N. ALTAF AND F. A. SHAH

Table 2.6 Descriptive statistics of financing pattern across sectors Year

Manufacturing

Services

Manufacturing vs. services

Panel A: Financing pattern of Indian MSMEs STDR 0.5593 0.5817 NS LTDR 0.3101 0.3594 NS TDR 0.8832 0.8914 NS Panel B: Major constituents of short-term debt (in percentage terms) BL 17.64 18.11 *** PAY 35.96 41.82 *** ICL 12.83 11.28 ** Panel C: Major constituents of long-term debt (in percentage terms) BL 29.18 30.71 *** FIB 5.17 6.21 ** FCB 1.80 2.13 NS OB 7.271 8.111 NS Panel D: Bank loans as a percentage of total borrowings STB 47.21 45.10 ** LTB 9.84 12.43 *** Note Asterisks indicate significance at 1% (*) 5% (**) and 10% (***). NS means not significant. Sample size: Manufacturing = 1008; Services = 673

long-term debt. However, there is no significant difference among the firms in these two sectors with regard to the use of debt. Further, with regard to the composition of short-term debt, we find that payables are mostly used to finance short-term debt in Indian MSMEs in both manufacturing and service firms. Also, there are significant differences in the composition of payables in the firms of these two sectors. Additionally, we also find significant differences in other constituents of short-term debt in the firms of the manufacturing and services sector. With regard to the composition of long-term debt, we find that bank loan is mostly used to finance long-term debt in both the manufacturing and services firms and this composition is also significantly different. We also find a significant difference in the composition of financial institution borrowings among the manufacturing and services firms. However, we did not find a significant difference among other long-term finance sources like foreign currency and other borrowings. These results are indicative of the fact that the sector of operation plays an important role in explaining the financing pattern of Indian MSMEs.

2

2.4.4

FINANCING PATTERN OF INDIAN MSMES

31

Across Ownership Type

We have classified firms into group affiliated firms, private-owned firms and government-owned firms for analyzing financing patterns across ownership type. The results of such an analysis are presented in Table 2.7. In line with the previous results, we find that short-term debt is used in a greater proportion than long-term debt when firms are categorized according to the ownership type. Further, we find that governmentowned firms have high debt (both short and long-term) when compared to affiliated and private firms. There might be the possibility of overleverage in these firms, which might be because government firms have easy access to loans compared to other firms. These results may also be due to the fact that private equity ownership prefers to hold a lesser amount of debt in the wake of the fear of insolvency (Abor 2008). With regard to the difference in the use of short-term debt, we find a significant difference between private and government firms and affiliate and government firms. However, we do not find any difference between Table 2.7 Descriptive statistics of financing pattern across ownership type Year

Affiliated

Private

Government

Affiliated vs. Private

Private vs. Government

Panel A: Financing pattern of Indian MSMEs STDR 0.4417 0.3217 0.6914 NS ** LTDR 0.2527 0.2977 0.4283 NS NS TDR 0.7216 0.7416 1.8314 NS NS Panel B: Major constituents of short-term debt (in percentage terms) BL 7.66 27.24 15.99 ** * PAY 41.10 40.16 42.84 NS NS ICL 11.39 13.33 12.17 *** NS Panel C: Major constituents of long-term debt (in percentage terms) BL 10.84 33.46 22.17 *** ** FIB 3.49 4.92 5.87 NS NS FCB 1.94 2.88 3.10 NS NS OB 7.140 8.912 9.110 NS NS Panel D: Bank loans as a percentage of total borrowings STB 21.31 45.10 23.26 ** * LTB 9.12 12.06 8.01 ** NS

Affiliated vs. Government

* NS ** NS NS NS *** NS ** NS *** NS

Note Asterisks indicate significance at 1% (*) 5% (**) and 10% (***). NS means not significant. Sample size: Affiliated = 706; Government = 50; Private = 925

32

N. ALTAF AND F. A. SHAH

affiliate and private firms. Additionally, we find no statistical difference in the use of long-term debt across the firms of different ownership types. Further, with regard to the composition of short-term and long-term debt, we find that payable dominates the composition of short-term debt across the ownership types while bank loans dominate the composition of long-term debt. In line with the previous findings, short-term loans continue to dominate long-term bank loans in the composition of total borrowings.

2.5

Conclusions

This chapter analyzed the financial pattern of Indian MSMEs for the period 2006–2017. Specifically, we looked into the use of debt in financing total assets and the composition of short-term and long-term debt. Additionally, we also analyzed the differences in the financing pattern across the firms classified on the basis of size, geographic spread, sector of operation and ownership type. We find that debt is the major financing source in Indian MSMEs with short-term debt being most favoured over long-term debt. Additionally, we find that payables are the major constituents of short-term debt, while long-term bank loans are the major constituents of long-term debt. Further, we find that these results remain robust when firms are classified according to size, geographic spread, sector of operation and ownership type. It is worth noting that we find a significant difference in the financing pattern when firms are classified according to size. We also find a significant difference in short-term and long-term debt constituents when firms are classified according to size, sector of operation, and ownership type. However, we do not find any significant difference in financing and composition of short-term and long-term debt when firms are classified as per geographic spread. From the results, it is clear that newer and flexible avenues of financing must be made available to MSMEs to harness their growth and development potential. These avenues must address the issue of reducing the dependence on debt and devise innovative instruments that are suited for MSME finance needs.

2

FINANCING PATTERN OF INDIAN MSMES

33

References Abor, J. (2008). Agency theoretic determinants of debt levels: Evidence from Ghana. Review of Accounting and finance, 7 (2), 183–192. Allen, F., Chakrabarti, R., De, S., & Qian, M. (2012). Financing firms in India. Journal of Financial Intermediation, 21(3), 409–445. Altaf, N., & Ahmad, F. (2019). Working capital financing, firm performance and financial constraints. International Journal of Managerial Finance, 15(4), 464–477. Altaf, N., & Shah, F. A. (2017). Working capital management, firm performance and financial constraints. Asia-Pacific Journal of Business Administration, 9(3), 206–219. Altaf, N., & Shah, F. A. (2018). How does working capital management affect the profitability of Indian companies? Journal of Advances in Management Research, 15(3), 347–366. Beck, T., & Demirguc-Kunt, A. (2006). Small and medium-size enterprises: Access to finance as a growth constraint. Journal of Banking & Finance, 30(11), 2931–2943. Beck, T., Demirgüç-Kunt, A., & Maksimovic, V. (2008). Financing patterns around the world: Are small firms different? Journal of Financial Economics, 89(3), 467–487. Cecchetti, S. G., & Kharroubi, E. (2012). Reassessing the impact of finance on growth. Available at: http://cuffelinks.com.au/wp-content/uploads/4cecch etti.pdf. Cook, P. (2001). Finance and small and medium-sized enterprise in developing countries. Journal of Developmental Entrepreneurship, 6(1), 17. Daskalakis, N., Balios, D., & Dalla, V. (2017). The behaviour of SMEs’ capital structure determinants in different macroeconomic states. Journal of Corporate Finance, 46, 248–260. Didier, T., Levine, R., & Schmukler, S. L. (2015). Capital market financing, firm growth, and firm size distribution. The World Bank. Hall, G. C., Hutchinson, P. J., & Michaelas, N. (2004). Determinants of the capital structures of European SMEs. Journal of Business Finance & Accounting, 31(5–6), 711–728. Kumar, S., & Rao, P. (2016). A conceptual framework for identifying financing preferences of SMEs. Small Enterprise Research, 22(1), 99–112. Levine, R. (2005). Finance and growth: Theory and evidence. Handbook of Economic Growth, 1, 865–934. Liu, M., & Yu, J. (2008). Financial structure, development of small and medium enterprises, and income distribution in the People’s Republic of China. Asian Development Review, 25(1), 2.

34

N. ALTAF AND F. A. SHAH

Quartey, P., Turkson, E., Abor, J. Y., & Iddrisu, A. M. (2017). Financing the growth of SMEs in Africa: What are the constraints to SME financing within ECOWAS? Review of Development Finance, 7 (1), 18–28. Rao, P., Kumar, S., & Madhavan, V. (2019). A study on factors driving the capital structure decisions of small and medium enterprises (SMEs) in India. IIMB Management Review, 31(1), 37–50. Shane, S. (2012). The importance of angel investing in financing the growth of entrepreneurial ventures. The Quarterly Journal of Finance, 2(02), 1250009. Sogorb-Mira, F. (2005). How SME uniqueness affects capital structure: Evidence from a 1994–1998 Spanish data panel. Small Business Economics, 25(5), 447– 457. Wilson, K. E. (2011). Financing high-growth firms: The role of angel investors. Available at SSRN 1983115. Wu, J., Song, J., & Zeng, C. (2008). An empirical evidence of small business financing in China. Management Research News, 31(12), 959975.

CHAPTER 3

Response of Capital Structure Determinants in Different Macroeconomic States

3.1

Introduction

Micro, Small and Medium-sized Enterprises (MSMEs) are recognized as pillars of economic development in both developing and developed economies for their contribution to gross domestic product (GDP), job creation, alleviating poverty and diverse economic activities (Baker et al. 2019; Daskalakis et al. 2017; Kumar and Rao 2015). In addition, MSMEs exhibit considerable differences in financing patterns compared to large enterprises (Daskalakis et al. 2017). In India, MSMEs alone contributes about 37% to the country’s GDP and this contribution is likely to become 42% by 2020.1 Currently, MSMEs represent about 90% of India’s industrial tissue and growing at a stable growth rate of 5%.2 Although MSMEs have massive potential their performance lags expectation in many developing countries (Arinaitwe 2006) and India is no exception to it. Among the many challenges faced by Indian MSMEs, financing and credit risk are the major issues reported in the prior literature (Rao et al. 2019). For that reason, financing start-ups majorly depend on the availability and accessibility to funds (Cook 2001). While the MSMEs in developed nations have many financing options, the MSMEs in developing countries, including 1 Vision 2020: ‘Implications for MSMEs 2011’. 2 The Indian SME Survey ‘Analysing Indian SME Perceptions Around Union Budget

2014–15’.

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 N. Altaf and F. A. Shah, Capital Structure Dynamics in Indian MSMEs, https://doi.org/10.1007/978-981-33-4276-7_3

35

36

N. ALTAF AND F. A. SHAH

India, struggle to find adequate and appropriate financial resources. Further, the quick information diffusion process in developed nations reduces information asymmetry, thereby helping MSMEs obtain finances easily compared to MSMEs in developing nations, including India. The easy accessibility to finance for Indian MSMEs is majorly hindered by high information asymmetry, which is because Indian MSMEs are not obliged to bring out their financial statements publicly (Rao et al. 2019). Recognizing the fact that MSMEs are important parts of the economy’s engine, researchers deviated their attention towards understanding the peculiarities of MSMEs, such as knowing that small businesses are not ‘scaled-down versions’ of large businesses (Cressy and Olofsson 1997) and thus need to be vetted differently. Nevertheless, with regard to finance theory, Ang (1991) asserts that finance theory was not developed with the small business in mind. Accordingly, the researchers in the finance domain recognized that financial aspects of large firms extend limited applicability to MSMEs and capital structure theory is not an exemption to this wide recognition. Given the importance of capital structure decisions to a firm, a series of research emerged, debating on the factors that determine firms’ capital structure (Rao et al. 2019; Balios et al. 2016; Frank and Goyal 2009; Daskalakis and Psillaki 2008). However, the substantial development of the capital structure literature has been along the lines of large business and very little attention has been paid to MSMEs. Only a few studies like (Rao et al. 2019; Daskalakis et al. 2017; Balios et al. 2016; Kulkarni and Chirputkar 2014; Sogorb-Mira 2005) diverted their attention on the capital structure aspects of MSMEs. Moreover, the search of literature could identify only a few studies like (Pan et al. 2019; Daskalakis et al. 2017; Baum et al. 2017; Akhtar 2012; Cook and Tang 2010; among other) that paid attention towards the effects of macroeconomic conditions on the capital structure adjustment speed. Among them, only Daskalakis et al. (2017), explored the effects of macroeconomic states on the determinants (firm-specific and macroeconomic) and adjustment speed of capital structure for MSMEs and to the best knowledge of the authors, no such study has been reported in the Indian context, particularly on Indian MSMEs. The prior works of Daskalakis et al. (2017) and Baum et al. (2017) generate this paper’s idea. Specifically, we combine capital structure adjustment speed, firm-specific and macroeconomic determinants of leverage in an emerging economies MSMEs. We explore this issue in different economic states and across debt with different maturities. The

3

RESPONSE OF CAPITAL STRUCTURE DETERMINANTS …

37

results of the chapter confirm the distinct speed of adjustment, distinct impact of firm-specific and macroeconomic variables across macroeconomic states and also across debt with different maturity. In view of that, this chapter’s broad contribution is to enhance the capital structure debate with regard to nascent research area of Indian MSMEs. Specifically, we show the impact of macroeconomic states on the speed of capital structure adjustment and the determinants of leverage and also how these relationships behave with the debt of different maturity in Indian MSMEs. The rest of the chapter is as follows. Section 3.2 presents the literature review, Sect. 3.3 describes the methodology adopted, Sect. 3.4 presents the empirical results, Sect. 3.5 presents robustness test results and Sect. 3.6 concludes the chapter.

3.2

Related Literature

There is no united agreement among the capital structure theories regarding the existence of the target leverage ratio. However, the empirical evidence supports the existence of a target leverage ratio (see, for example, Rao et al. 2019; Daskalakis et al. 2017; Cook and Tang 2010). In fact, corporate CFOs encourage having a target for the debt–equity ratio that balances the benefits and costs (Graham and Harvey 2001). An underleveraged or overleveraged firm would impair its firm value and therefore, they must take steps to offset deviations away from target leverage, keeping in mind the adjustment costs (Cook and Tang 2010). Given the importance of target leverage for a firm, Hackbarth et al. (2006) emphasized that despite the development of capital structure literature very little attention has been paid to test the effects of macroeconomic conditions on the choice of capital structure. Accordingly, a series of research emerged that explained how the firms financing choices changed in abreast to the macroeconomic conditions, and how firms adjust their leverage towards the target in different macroeconomic states (see, for example, Pan et al. 2019; Daskalakis et al. 2017; Öztekin and Flannery 2012; Cook and Tang 2010; among others). Notably, these studies were conducted on large firm samples and only Daskalakis et al. (2017), investigated how the firm-specific and macroeconomic determinants of leverage and adjustment speed of capital structure change through good and bad states. Additionally, using three forms of leverage (total, long-term and short-term leverage ratio) and sample of non-financial MSMEs in Greece, Daskalakis et al. (2017), provided

38

N. ALTAF AND F. A. SHAH

evidence for differential effect and contribution of the firm-specific and the macroeconomic variables for both short-term and long-term leverage ratio in growth and recessionary macroeconomic states. Further, the study concluded that firm-specific variables are more important determinants of short-term leverage ratio in growth states when compared to macro variables. However, macro variables are important determinants of the short-term leverage ratio in recessionary states. In addition, macro variables are more important determinants of long-term leverage throughout the sample period and become additionally important in recessionary states. Further, Baum et al. (2017) found a quick adjustment of leverage under the low firm-specific and high macroeconomic risk conditions in firms with financial surplus and above-target leverage. However, they also found quick adjustment of leverage when both firm-specific and macroeconomic risks are low in firms with financial deficit and below-target leverage. Other studies like (Öztekin and Flannery 2012; Akhtar 2012; Cook and Tang 2010; Korajczyk and Levy 2003) conclude that firms financing choices are affected by macroeconomic conditions. More specifically, firms adjust their leverage relative to macroeconomic states with faster adjustment seen in good macroeconomic states compared to bad states. Given the strand in literature, we expect differential speed of capital structure adjustment through macroeconomic states and debt of different maturity. In addition, we find that firm-specific and macroeconomic variables that determine capital structure change through macroeconomic states and also across debt of different maturities.

3.3

Methodology and Data

This section is dedicated to the description of the empirical models, the estimation approach and data used for the purpose of this study. 3.3.1

The Model

Following the rationale of Daskalakis et al. (2017) and Baum et al. (2017), we use the partial adjustment model which assumes that the target debt ratio of firm i at time t is given by: ∗ Di,t = α ∗ + αi∗ + β ∗ X i,t−1 + γ ∗ Mt−1

(3.1)

3

RESPONSE OF CAPITAL STRUCTURE DETERMINANTS …

39

where α ∗ is the constant term, αi∗ is the unobserved heterogeneity of firm i, X i,t−1 represents lagged values of firm-specific variables of firm i at time t and Mt−1 represent lagged values macroeconomic variables at time t. Accordingly, β ∗ represents the coefficients of the firm-specific variables and γ ∗ represent the macroeconomic variables’ coefficients. The debt ratio Di,t adjusts to its target according to the rule:  ∗  − Di,t−1 + εi,t ; 0 < δ < 1 (3.2) Di,t − Di,t−1 = δ ∗ Di,t ∗ is the firm where Di,t is the firm i’s debt ratio at the end of year t; Di,t   ∗ −D i’s target debt ratio at the end of year t, Di,t i,t−1 is the adjustment ∗ required to reach the target leverage; δ takes the value between zero and one and measures the speed of adjustment. If δ ∗ = 1, then Di,t = ∗ , implying that adjustment costs are so low that the firm immediately Di,t adjusts their current level of leverage to reach the target level. However, if δ ∗ = 0, then Di,t = Di,t−1 , implying that adjustment costs are so high that the firm chooses to remain at the same level in spite of adjusting. Incorporating Eq. (3.1) in the partial adjustment specification, i.e. Eq. (3.2), the current debt ratio is determined by:

Di,t = a + ai + δ Di,t−1 + β X i,t−1 + γ Mt−1 + εi,t

(3.3)

where a = δ ∗ · α ∗ , ai = δ ∗ · αi∗ , δ = 1 − δ ∗ , β = δ ∗ · β ∗ and γ = δ ∗ · γ ∗ Baum et al. (2017) and Cook and Tang (2010) extended the Eq. (3.3) to incorporate a dummy variable that interacts with a lagged debt ratio in order to find the difference in the speed of adjustment through the good (growth) and bad (recession) states of the economy. More recently, Daskalakis et al. (2017) extended Eq. (3.3) to incorporate a dummy variable that interacts with lagged debt ratio and lagged firm-specific and macroeconomic variables to investigate the effect of these variables on leverage ratios in different macroeconomic states. Given the possibility that the adjustment speed, the coefficients on firm-specific and macroeconomic variables are likely to be different in two states, Eq. (3.3) is extended by incorporating a dummy variable (Dum t ) takes the value 1 if t is the crisis period (and 0 otherwise) After incorporating dummies Eq. (3.3) can be rewritten as follows: Di,t = a + ai + a dDum t + δ Di,t−1 + δ d Di,t−1 Dum t + β X i,t−1 + β d X i,t−1 Dum t + +γ Mt−1 + γ d Mt−1 Dum t + εi,t

(3.4)

40

N. ALTAF AND F. A. SHAH

By construction, the coefficient on the lagged debt ratio, the lagged firm-specific variables and the lagged macroeconomic variables in good (growth) state is given by δ, β and γ respectively. Further, the coefficient on the lagged debt ratio, the lagged firm-specific variables and the lagged macroeconomic variables in bad (recession) state is given by (δ + δ d ),   d β + β and (γ + γ d ). Whereas, δ d , β d and γ d represent the coefficient on these variables for the difference between the two periods. Having estimated Eq. (3.4), we follow Daskalakis et al. (2017) and examine the effect of firm and macro variables on three form debt ratios in different states by performing hypothesis testing (H for each hypothesis) on the significance of the multiplicative dummies. Specifically, the following hypothesis is tested: H0 : δ d = 0; βnd = 0, n = 1, . . . , n; γkd = 0, k = 1, . . . , k,

(H1)

as well as the joint significance of the multiplicative firm and macro dummies, H0 : β1d = . . . = βnd = γ1d = . . . = γ jd = 0,

(H2)

of the multiplicative firm dummies H0 : β1d = . . . = βnd = 0,

(H3)

and of the multiplicative macro dummies H0 : γ1d = . . . = γ jd = 0

3.3.2

(H4)

Defining Good and Bad Macroeconomic States

Since the chapter aims to examine the impact of good (growth) and bad (recession) macroeconomic states on leverage adjustment speed, firmspecific and macroeconomic variables, it is necessary to identify good and bad states of the economy. For this purpose, we used OECD identification of good and bad states for Indian economy during the period of the chapter (2006–2017).3 Based on OECD identification years 2008, 3 OECD identification of macroeconomic states for Indian economy https://fred.stloui sfed.org/series/INDRECP#0.

3

RESPONSE OF CAPITAL STRUCTURE DETERMINANTS …

STINT 9.5

LTINT

9

TINT

41

Credit supply

0.3

GDPGR

0.25 0.2

8.5

0.15

8

0.1 7.5

0

6.5

-0.05

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

7

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

0.05

-0.1

Fig. 3.1 Annual data on macroeconomic variables

2011, 2012, 2013 and 2015 among the sample period are classified as bad (recession) states and other as good (growth) states. We further follow the mapping scheme and intersect this identification with our macroeconomic variables. Since the recession is characterized by a fall in GDP, low credit supply and a fall in interest rates. Hence, we check the behaviour of these variables across the sample period (see Fig. 3.1). From Fig. 3.1 it can be seen that for the years (2008, 2011, 2012, 2013 and 2015), Gross domestic product growth (GDPGR), annual growth in credit supply (CS), short-term debt interest rate (STINT), long-term debt interest rate (LTINT) and total debt interest rate (TINT) takes a dip downward, hence these years can be classified as bad states for Indian economy. 3.3.3

Estimation Approach

We have used panel data methodology to estimate the models specified above. It is worth to mention that panel data offers a number of advantages like controlling for unobservable heterogeneity (see, for example Hsiao 2003; Klevmarken 1989; Moulton 1986, 1987), contains more information, produces more efficiency and less collinearity among variables (Hsiao 2003), and also helps to model technical efficiency better (Koop and Steel 2001). Further, to deal with the possible problems of endogeneity and other panel data models’ inconsistency, generalized method of moments (GMM) has been suggested by the econometric literature. Accordingly, we perform Arellano and Bover (1995) and Blundell

42

N. ALTAF AND F. A. SHAH

and Bond (1998) two-step GMM as an estimation technique for all the models. This technique is designed for dynamic short panels, where N is big and T is small. Further, we use one lag of the variables as instruments in the GMM estimation. In addition, we evaluate Arellano and Bond (1991) statistic for autocorrelation in the first-differenced residuals, Hansen (1982) statistics for overidentifying restrictions. We use Wald statistic for checking goodness of fit and also like Daskalakis et al. (2017) we examine the fit of the model by evaluating the coefficient of determination R 2 of Eq. (3.4) for the whole period and the two sub-periods with and without estimated fixed effects. 3.3.4

Description of Variables Used in the Chapter

This section is devoted to describing and defining variables used in this chapter and deliberate upon the data used in this chapter. 3.3.4.1 Measurement of Leverage Following the strand in the literature (see, for example, Daskalakis et al. 2017; Diamond and He 2014; Mateev et al. 2013; Koeter-Kant and Hernandez-Canovas 2011; García-Teruel and Martinez-Solano 2010; Cassar and Holmes 2003) we use three forms of leverage, total debt ratio (TDR), the short-term debt ratio (STDR) and the long-term debt ratio (LTDR). Where TDR is defined as sum of short and long-term debt to total assets. Similarly, STDR is defined as short-term debt to total assets and LTDR as long-term debt to total assets. It is worth mentioning that these three forms of leverage serve as a proxy for different debt maturities and help us examine influence of debt maturity structure on leverage across macroeconomic states (Daskalakis et al. 2017). 3.3.4.2 Firm-Specific Variables Following the string of literature, we use following variables as a set of firm-specific capital structure determinants. Growth (GR): Growth is measured as an annual rate of change in sales. Prior literature provides conflicting findings with regard to the impact of growth on leverage. It is contended that MSMEs are overenthusiastic about their growth aspirations that may result in moral hazard consequences (Myers 1977). Accordingly, growth may have uncertain effects on firms’ financing. Myers (1977) asserts that growth causes large variations in the value of the firm and accordingly, firms with high growth

3

RESPONSE OF CAPITAL STRUCTURE DETERMINANTS …

43

potential are considered riskier and will tend to employ less debt in their financial structure. From this point of view, growth is expected to have a negative impact on leverage. On the other hand, firms with high growth are likely to exhaust internal funds and require external financing for additional capital requirements (Michaelas et al. 1999). For that reason, a positive relationship may be expected between growth and leverage. Profitability (PRO): The relationship between profitability and leverage can be projected in the pecking order theory framework. It is generally contended that MSMEs follow a hierarchical pattern of financing with a preference for internal over external financing and then for debt over equity (Daskalakis and Psillaki 2008; Michaelas et al. 1999). In addition, MSMEs show control aversion that is demonstrated by preference to sell the firm rather than relinquish equity (Bayrakdaroglu et al. 2013). Therefore, a negative relationship is expected between profitability and leverage. Profitability is measured as the ratio of profit before depreciation interest tax and amortization (PBDITA) to total assets. Firm size (SIZE): Firm size is measured as the natural logarithm of total sales and is assumed to be positively associated with leverage. It is generally believed that large-sized have very fewer chances of going bankrupt because of being well-diversified (Pettit and Singer 1985). In addition, the cost of acquiring funds is lower for large firms when compared to small firms. Hence a positive relationship is expected between firm size and leverage as found by many studies like (Rao et al. 2019; Daskalakis et al. 2017; Sogorb-Mira 2005; Hall et al. 2004). Asset Tangibility (TAN): Asset tangibility is defined as the ratio of fixed assets to total assets. Generally, banks demand collaterals from MSMEs to grant loans. In addition, MSMEs suffer from information asymmetry because they are not obliged to publish financial statements, which makes collateralized lending even more important for them (Rao et al. 2019). Therefore more tangible assets available, a higher amount of loan can be procured. Accordingly, a positive relationship is expected between tangibility and leverage. Trade credit (TC): Trade credit is considered as an important source of short-term financing for MSMEs (Guariglia and Mateut 2006). In fact short-term liabilities occupy a higher proportion in the capital structure of MSMEs when compared to large firms (Mateev et al. 2013). The relationship between trade credit and leverage is explained by the substitution hypothesis and the complementarity hypothesis. Prior literature asserts

44

N. ALTAF AND F. A. SHAH

that during periods of tight monetary policy, trade credit acts as a complement rather than a substitute to bank credit (Psillaki and Eleftheriou 2015; Casey and O’Toole 2014). In view of that, we expect a positive relationship between trade credit and leverage during the crisis and a positive or a negative relationship before the crisis. Following Daskalakis et al. (2017) and Love et al. (2007), we calculate trade credit, first subtracting trade payables from trade receivables and then dividing by total sales. Non-debt tax shields (NDTS): The non-debt tax shields is measured as the ratio of depreciation expenses to total assets. The available literature asserts that NDTS is negatively related to leverage. It may be due to the fact that MSMEs find it difficult to access debt financing and accordingly the use of non-debt tax shields (NDTS) could be viewed as their main alternative to reduce any tax burdens (Daskalakis et al. 2017). Cash flow (CASH): Cash flow is calculated as the ratio of profit after tax plus depreciation divided by total assets. Jensen and Meckling (1976) contended that free cash flows allow managers to engage in selfish attitude and thereby invest in unprofitable projects. This creates agency problem and to reduce agency problems debt is employed. However, this view is true for large firms and not for small firms because small firms are governed by owners and the possibility of agency problems is very rare. As a result, small firms would accumulate cash to avoid underinvestment issues in future (Rao et al. 2019; Daskalakis et al. 2017; Mateev et al. 2013). Accordingly, negative relationship is expected between cash flows and leverage. Financial expenses (FINEXP): This ratio is defined as financial expenses to sales. FINEXP is introduced in the model to capture the effects of interest burden on leverage. The increase in leverage will increase the financial expenses for the company, thereby a negative relationship between the lagged value of FINEXP and leverage. 3.3.4.3 Macroeconomic Variables Following the global financial crisis, various studies emerged, debating external (macroeconomic) determinants of the corporate capital structure. Given the strand in the literature, we have used Gross domestic product growth (GDPGR), annual growth rate in credit supply (CS) and Interest rate (INT) as macroeconomic capital structure determinants. Gross domestic product growth (GDPGR): the increase in the country’s GDP leads to an increase in companies’ profits (Mokhova and Zinecker 2014). Further, as contended that MSMEs prefer internal over

3

RESPONSE OF CAPITAL STRUCTURE DETERMINANTS …

45

external financing and then debt over equity (Daskalakis and Psillaki 2008; Michaelas et al. 1999), therefore, with increased profits MSMEs would prefer financing with internal sources as compared to other sources. Hence a negative relationship is expected between GDP and leverage. Gross domestic product growth is measured as the annual growth rate of GDP. Annual growth rate in credit supply (CS): Given that the most common source of financing for MSMEs is commercial banks (De Bettignies and Brander 2007). Therefore, credit supply is the most important macroeconomic variable that may have an effect on a firm’s capital structure. Prior literature emphasizes a positive relationship between credit supply (expansion during the good/growth stage or contraction during the bad/recessionary stage) and leverage Daskalakis et al. (2017). Credit supply is measured as the annual growth rate of total credit expansion. Interest rate (INT): The findings assert that interest rate is positively related to leverage. For example, Daskalakis et al. (2017) found a significant positive relationship between the interest rate and leverage during the crisis and a weak and negative relationship before the crisis. These results confirm that during periods of recession, interest rates tend to be lower, but firms also lower their demand for external financing (Daskalakis et al. 2017). Hence, a positive relationship between the interest rate and leverage is quite justified. Like Daskalakis et al. (2017), we adjust the interest rate variable to each type of leverage. More specifically, we use short-term interest rates for short-term debt, the long-term interest rate for long-term debt and the average of the two for total debt. 3.3.5

The Data

In India, MSMEs are promoted by Micro, Small and Medium Enterprises Development (MSMED) Act 2006. It states that micro manufacturing firms are those that have made investment in plant, machinery and equipment is up to 2.5 million. Further, firms are defined as small manufacturing firms if their investment in plant, machinery and equipment is between 2.5 million to 50 million and medium manufacturing firms if such investment is between 50 million and 100 million. However, for being classified as micro firms in services sector the total investment in plant, machinery and equipment should be up to one million, for small service firms the investment in plant, machinery and equipment shall be

46

N. ALTAF AND F. A. SHAH

between one million and 20 million and for medium service firms, this investment should be between 20 million and 50 million. It must be noted that for the purpose of this chapter, we have followed MSMED Act 2006 and choose the sample given the criteria of this act. Further, we have used an electronic database PROWESS of Centre for Monitoring Indian Economy (CMIE) PROWESS to extract firm-level information on the variables of the chapter for the period 2006–2017.4 The data on macroeconomic variables is extracted from the World Bank database and database for the Indian economy (DBIE) by Reserve bank of India. It is worth to note that the total number of firms available in CMIE PROWESS after applying the investment limits as per MSMED act 2006 o is equal to 3468. Among these 3468 firms, 618 belonged to the finance industry. These firms have been excluded from the sample because these firms’ financial decisions are guided by different schemes (Altaf and Shah 2018b, 2019). Having dropped financial firms from the sample, we were left with 2850 non-financial firms. Further, we found that certain firms in the PROWESS database did not adhere to the investment criteria of MSMED Act 2006 throughout the period of study. Specifically, for 772 firms’ investment exceeds the limit of MSME in between the period of study. Accordingly, these firms were dropped from the sample. In addition, 397 firms reported incomplete information for the period under chapter. Hence, dropped from the sample, leaving us with the final sample of 1681 firms across 12 years (2006–2017), making the total number of observations equal to 20,172 (Table 3.1). The descriptive statistics of the three forms of leverage ratios (shortterm, long-term and total debt) and the firm-specific variables are given in Table 3.2, whereas the data on macroeconomic variables is given in Fig. 3.1. Perusing Table 3.2, it can be construed that debt is most favoured, in fact, the primary source of financing for Indian MSMEs. In addition, short-term debt is most favoured among the two forms of debt (short and long-term) compared to long-term debt. It is in line with this that the standard deviation of STDR is more than LTDR. These results are consistent with those reported by Rao et al. (2019). Further, other interesting annotations by means of leverage are that during bad states,

4 The sample selection procedure for estimating the response of capital structure determinants in different macroeconomic states is given in Table 3.1.

3

RESPONSE OF CAPITAL STRUCTURE DETERMINANTS …

47

Table 3.1 Sample selection procedure for estimating the response of capital structure determinants in different macroeconomic states Total number of firms after applying the limit of investment as per MMSMED Act 2006 Less: Firms operating in the financial industry Remaining non-financial firms Less: Firms that are not conferring with the definition of MSMEs as per MSMED Act 2006 during the whole analysis period Less: Firms with incomplete data for the period under chapter Firms forming part of the sample

3468 (618) 2850 (772) (397) 1681

Table 3.2 Descriptive statistics of firm-specific variables Year

STDR LTDR TDR

GR

PRO

SIZE TAN TC

NDTS CASH

FINS

Panel 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Panel 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

A: Mean 0.55 0.32 0.86 0.10 0.09 5.82 0.35 0.12 0.012 0.08 0.012 0.57 0.30 0.87 0.11 0.12 5.87 0.34 0.13 0.012 0.09 0.014 0.53 0.34 0.87 −0.018 −0.03 5.86 0.34 0.10 0.013 −0.021 0.014 0.56 0.33 0.89 −0.021 −0.09 5.85 0.32 0.17 0.091 −0.024 0.018 0.58 0.30 0.88 0.05 0.07 5.87 0.33 0.19 0.011 0.04 0.017 0.56 0.32 0.88 0.08 0.10 5.91 0.32 0.15 0.014 0.06 0.017 0.53 0.34 0.86 0.10 0.11 5.96 0.37 0.15 0.015 0.09 0.016 0.52 0.39 0.91 0.12 0.12 6.02 0.35 0.14 0.013 0.11 0.021 0.57 0.36 0.93 0.13 0.13 6.08 0.34 0.21 0.012 0.12 0.022 0.56 0.36 0.92 0.11 0.10 6.13 0.36 0.18 0.017 0.06 0.023 0.61 0.35 0.95 0.14 0.13 6.20 0.35 0.24 0.017 0.10 0.024 0.61 0.33 0.94 0.15 0.15 6.27 0.38 0.26 0.015 0.13 0.021 B: Standard deviation 1.58 1.28 1.92 0.37 0.28 1.02 0.24 0.39 0.02 0.38 0.001 1.62 1.18 1.93 0.39 0.31 1.04 0.21 0.43 0.02 0.35 0.002 1.56 1.21 1.93 0.42 0.34 1.03 0.24 0.40 0.03 0.36 0.002 1.58 1.17 1.95 0.41 0.36 1.02 0.26 0.48 0.05 0.37 0.002 1.60 1.15 1.94 0.36 0.29 1.01 0.27 0.46 0.01 0.39 0.001 1.61 1.14 1.93 0.34 0.27 1.05 0.27 0.44 0.04 0.37 0.003 1.58 1.18 1.97 0.37 0.30 1.03 0.32 0.42 0.07 0.33 0.004 1.53 1.23 1.98 0.39 0.32 1.09 0.29 0.49 0.05 0.31 0.004 1.57 1.23 1.97 0.40 0.34 1.08 0.24 0.38 0.06 0.33 0.005 1.55 1.21 1.89 0.41 0.28 1.08 0.27 0.39 0.04 0.29 0.003 1.63 1.20 1.95 0.37 0.27 1.09 0.23 0.47 0.08 0.29 0.003 1.66 1.19 1.93 0.38 0.29 1.10 0.24 0.42 0.09 0.34 0.002

48

N. ALTAF AND F. A. SHAH

LTDR increases, whereas STDR decreases; however, STDR continues to be more than LTDR through the bad states. Contrary to this, the STDR increases and LTDR decreases in good states; however, STDR continues to be more than LTDR. The relative preference of Indian MSMEs towards short-term debt can be attributable to the lesser developed banking sector, lack of financial literacy among entrepreneurs, lack in the availability of collaterals, etc. Another significant observation is with regard to growth, profitability and cash flow variables. These ratios turn out to be negative during the period 2008–2009. It is due to the fact that in 2008 India witnessed a heavy downfall in GDP (see Fig. 3.1) and the economy turned into contraction. This might have resulted in a fall in sales and by these means a fall in profits. The data of the macroeconomic variables are presented in Fig. 3.1. Figure 3.1 shows that GDP and CS dropped sharply during the years 2008, 2011 and 2012. In addition, these variables further show a trivial decrease during the years 2013 and 2015. In addition, STINT, LTINT and TINT also show a dip downward during these years. For the rest of years, all the macroeconomic variables have shown a significant increase.

3.4

Empirical Results

Table 2.3 presents the estimated coefficients of Eq. (3.4). Columns (2), (5) and (8) present the results of good (growth) state by taking TDR, STDR and LTDR as a dependent variable with coefficients for lagged debt ratio, the lagged firm-specific variables and the lagged macroeconomic variables given by δ, β and γ . Further, columns (3), (6) and (9) present the results of bad (recession)  with TDR, STDR and LTDR as dependent variables with (δ + δ d ), β + β d and (γ + γ d ) presenting the coefficients for lagged debt ratio, the lagged firm-specific variables and the lagged macroeconomic variables respectively. In addition, columns (4), (7) and (10) present the coefficients for the difference between the two periods by way of δ d , β d and γ d representing the coefficients for lagged debt ratio, the lagged firm-specific variables and the lagged macroeconomic variables. All these results are presented in Panel A of Table 3.3. It can be inferred from the results presented in the table that all the coefficients on lagged debt ratio are highly significant, implying that

(2)

(3) 0.8444* (5.59) 0.0460** (1.98) −0.3108 (−1.14) 0.0110 (0.70) −0.5318* (−3.81) −0.0131** (−1.97)

(5)

Good

G VS B (H1) (4)

Good

Bad

STDR

TDR

0.8321* (4.98) 0.0458** (2.04) −0.304*** (−1.86) 0.0174 (1.07) −0.437* (−2.89) 0.0148** (2.33)

(6)

Bad

0.0123 (1.43) 0.0002** (2.25) 0.006*** (1.83) 0.006 (1.34) −0.094* (−2.97) 0.0279** (2.13)

G VS B (H1) (7) 0.6503* (4.22) 0.0355 (1.08) 0.0930*** (1.93) −0.0077** (−2.30) 0.0287 (0.53) −0.0170 (−0.58)

(8)

Good

LTDR

0.7321* (5.29) 0.0421 (1.03) 0.0873* (2.77) 0.0087* (2.84) 0.0347 (0.97) −0.0186 (−0.85)

(9)

Bad

(continued)

0.0819* (5.87) 0.006 (1.01) 0.0057* (2.89) 0.0174* (2.99) −0.006 (−1.02) 0.0016 (0.67)

G VS B (H1) (10)

Target leverage, determinants of leverage across macroeconomic states and debt of different maturities

Panel A: Estimates of coefficients and their z-statistics 0.7238* 0.043 LLEV 0.7658* (4.52) (4.02) (1.01) LGR 0.0093 0.0081 0.002 (0.21) (0.34) (0.44) −0.012** LPRO −0.433*** −0.421** (−1.79) (2.04) (−1.99) LSIZE 0.0046 0.0086 −0.004 (0.32) (0.21) (−0.24) LTAN −0.502* 0.607** 1.01** (−3.76) (2.03) (2.34) LTC 0.0222 0.0234 −0.001 (0.31) (0.56) (−0.34)

(1)

Table 3.3

3 RESPONSE OF CAPITAL STRUCTURE DETERMINANTS …

49

5.23** (2.14) −0.4528** (−1.99) −0.130 (−0.42) −0.0014 (−0.17)

6.614* (3.90) 0.3593** (2.44) −0.121 (−0.43) −0.0098 (−0.12)

LNDTS

LGDPGR

LFINS

LCASH

(3)

(2) 1.39* (3.04) 0.823** (2.07) 0.009 (0.45) 0.0084 (0.76)

3.873*** (1.86) 0.2856*** (1.80) −0.0205 (−0.07) 0.0181** (2.22)

(5)

Good

G VS B (H1) (4)

Good

Bad

STDR

TDR

(continued)

(1)

Table 3.3

3.678* (2.84) 0.2962** (2.34) 0.3067 (0.97) 0.0157* (2.84)

(6)

Bad

0.20* (3.94) 0.0106 (1.43) −0.327 (−1.23) 0.003* (2.56)

G VS B (H1) (7) 0.8802** (2.05) 0.01615 (0.24) −0.092 (−0.88) 0.0495** (1.99)

(8)

Good

LTDR

0.9561** (2.54) 0.01476 (0.67) −0.087 (−0.54) 0.0321** (2.03)

(9)

Bad

−0.0759** (−1.96) 0.0013 (0.65) −0.179 (−0.67) 0.0816** (2.53)

G VS B (H1) (10)

50 N. ALTAF AND F. A. SHAH

75.3% 66,184 (0.00) 11.60 (0.81) 1.02 (0.27) 1.06 (0.29)

66.7%

67.9%

0.1001*** (1.82) 0.0309 (1.10)

(5)

65.0%

67.2%

0.1202** (2.01) 0.0431 (0.93)

(6)

Bad

68.4%

70.3%

0.020 (1.46) 0.0122 (1.21)

G VS B (H1) (7)

31,932 (0.00) 14.62 (0.77) 1.62 (0.10) 1.20 (0.25)

59.7%

60.1%

0.0263*** (1.75) −0.0011** (−2.28)

(8)

Good

LTDR

61.7%

62.3%

0.0242** (1.99) −0.0010** (−2.27)

(9)

Bad

65.3%

66.9%

0.023** (2.07) −0.0001** (−1.97)

G VS B (H1) (10)

Note This Table reports empirical results after estimating Eq. (3.4). Specifically, the results presented in this Table are obtained from the two-step GMM approach. Asterisks indicate significance at 1% (*) 5% (**) and 10% (***). The variables are the same as defined in Sect. 3.3.4. Z-Statistics of 2 the two-step GMM model are reported in parentheses and based on robust standard errors. Coefficients of determination R 2 and RnoF E with and without the estimated fixed effects. L denotes the first lag. Wald statistic, Hansen statistic, refers to p values for over-identifying restrictions distributed asymptotically under the null hypothesis of the instruments’ validity and their p-values in parenthesis. AR(2) refers to p-values of serial correlation test of second-order using residuals of first differences, asymptotically distributed as N(0,1) under the null hypothesis of no serial correlation

72.0%

74.4%

78.2%

73.3%

2 69.8% RnoF E Panel C: Diagnostic tests Wald 218,326 (0.00) Hansen 11.63 (0.81) AR (1) 1.53 (0.11) AR (2) 1.55 (0.11)

LCS

R2

(3) 0.011*** (1.86) 0.0617* (4.76)

(2)

0.0605*** 0.0503** (1.77) (1.97) LINT −0.0126* −0.0743* (−3.03) (−4.03) Panel B: Coefficient of determination

(1)

Good

G VS B (H1) (4)

Good

Bad

STDR

TDR

3 RESPONSE OF CAPITAL STRUCTURE DETERMINANTS …

51

52

N. ALTAF AND F. A. SHAH

debt ratio exhibits very strong persistence5 and also demonstrates the importance of lagged leverage in determining the present capital structure of the firm. Further, with regard to speed of adjustment towards the target, results reveal varying speeds for TDR, STDR and LTDR. In addition, the speed of adjustment also varies across states. More explicitly, the adjustment speed is 0.2342 and 0.2762 for TDR in good and bad states, respectively, but not significantly different. Further, the speed of adjustment for STDR is 0.1556 and 0.1659 in good and bad states, respectively, but not significantly different. Lastly, LTDR adjusts with a speed of 0.3497 in good state and 0.2679 in a bad state and is also significantly different. These results reveal that in Indian MSMEs, adjustment speed lowers down for LTDR during the crisis but remains more or less the same for STDR and is therefore not affected by states change. This is in line with the financing pattern of Indian MSMEs since these firms employ short-term debt in a major proportion than long-term debt in both the states. For that reason, adjustment speed for STDR may not be affected by states. Further, with regard to firm-specific variables, we find that profitability and non-debt tax shields maintain a constant and persistent effect across macroeconomic states and all forms of debt, whereas the remaining firm-specific variables turn out to be either non-significant or not stable through different forms of debt and macroeconomic states. Likewise, among macroeconomic variables, only credit supply shows a stable positive effect across macroeconomic states and all the forms of debt. In addition, GDPGR turns out to be a significant positive determinant for STDR and LTDR through both good and bad economy states. However, INT shows a significant negative relationship with TDR and LTDR in both the economy’s states. Having ascertained the above results, we are now in the position to test hypothesis H1 (see G VS B in Table 3.3). H0 : δ d = 0 for LTDR is rejected, though not rejected for STDR and TDR, implying that the speed of adjustment for LTDR decreases during bad states but remains more or less the same for STDR and TDR. Further, as mentioned earlier, that Indian MSMEs mostly employ short-term debt, it may, for this

5 Given the persistence of lagged debt ratios, we performed unit root test on all forms of debt. The results rejected the presence of unit root. These results are not reported but are available on request.

3

RESPONSE OF CAPITAL STRUCTURE DETERMINANTS …

53

reason, be that adjustment speed of TDR may be influenced by STDR and hence above results for TDR are expected. Panel B of Table 3.3 presents the coefficients of determination for all the forms of debt and across macroeconomic states. The high values on the coefficient of determination complement the high values of the autoregressive parameter. They further confirm the goodness of fit for the model. In addition, we do not witness much difference between R 2 2 (with) and RnoF E (without fixed effects), by this means suggesting weak unobserved heterogeneity. Lastly, Panel C of Table 2.3 presents the results of diagnostic tests. The Wald statistic is statistically significant at 1% level of significance, which confirms the overall fit and well-specification of the system GMM model. Further, we empirically check the validity of the GMM estimator using the Hansen test of overidentification. The Hansen test yields the p-values of (0.81), (0.81) and (0.77), thus confirming the validity of the system GMM model. In addition, the p-value for the AR(2) statistic is a serial correlation test of second-order using residuals of first differences, asymptotically distributed as N(0,1) under the null hypothesis of no serial correlation. The p-value of AR (2) statistic is non-significant, implying no second-order serial correlation. Hence, the results from the diagnostic tests are satisfactory. 3.4.1

Significance of Firm and Macro Multiplicative Dummies

Having found the difference in regressors’ behaviour in two states and the persistence of lagged debt ratio, the effect of the firm and macro regressors cannot be easily found (Altaf and Shah 2018a). Subsequently, in this section, we perform hypothesis testing to analyze the differences between firm-specific and macroeconomic variables across different forms of leverage and among the macroeconomic states. More specifically, we perform hypothesis testing on the individual multiplicative dummy and also group these dummies across the firm and macro type. Regarding H0 : β1d = . . . = βnd = 0 and γ1d = . . . = γ jd = 0, we reject for n = PRO, TAN, CASH; j = CS, INT for TDR, for STDR we reject for n = GR, PRO, TAN, TC, NDTS, CASH; j = GDPGR, for LTDR we reject for n = PRO, SIZE, NDTS; j = GDPGR, CS, INT (see Table 3.3 column (4) for TDR; (7) for STDR; (10) for LTDR). Further, Table 3.4 reports the results of H2, H3 and H4. All the test statistics for three forms of leverage are significant at 1% level of significance, hence all hypotheses

54

N. ALTAF AND F. A. SHAH

Table 3.4 Wald statistics for joint significance of firm and macro multiplicative dummies TDR

STDR

LTDR

H2

H3

H4

H2

H3

H4

H2

H3

H4

77.28 (00.00)

76.12 (0.00)

70.13 (0.00)

57.39 (0.00)

56.29 (0.00)

60.45 (0.00)

64.12 (0.00)

66.38 (0.00)

67.37 (0.00)

Note “Wald statistics p-values in parenthesis”

Table 3.5 Robustness test results TDR H1 Check 1 Check 2 Check 3

STDR H2

H3

H4

H1

LTDR H2

H3

H4

– – – * – – * – – – – – – – – – The z-statistics for each coefficient remain mostly unaffected

H1

H2

H3

H4

– –

– **

– –

* –

Note * denotes the marginal difference in the p-value ** denotes a critical difference in p-values

stand rejected. These results imply that the impact of firm-specific and macroeconomic variables on three forms of leverage differ significantly through good and bad states.

3.5

Robustness Check

This section is devoted to discussing the results of the robustness check. Specifically, Table 3.5 shows the difference between the p-values of Eq. (3.4) and the respective checks (1–3).6 It must be noted that (*) symbolizes the marginal difference in the results, i.e. the p-value was