Does Competition Affect Financial Distress of Non-Financial Firms in India: A Comparison Using the Lerner Index and Boone Indicator

Abstract

Firms’ financial distress (FD) is a major issue for smooth business activities. Timely recognition of FD should be a prime concern; otherwise, it may cause a nasty bankruptcy situation. The FD issue is paramount to researchers, policymakers, and investors. Several factors, whether they are financial or non-financial, may be responsible for financial distress. Such aspects specific to the firms have been explored. Exogenous factors such as competition can also be responsible for a firm’s FD situation. In view of this, this study proposes to determine competition’s impact on financial distress in the Indian context. BSE 100 (“Bombay Stock Exchange”)-listed non-financial firms (NFFs) in India, over a timeframe of 2016–2020, are incorporated in this study. Panel data econometrics is performed for hypothesis testing. This study is novel in its approach, employing multi-technique analysis for measuring financial distress. FD is measured using Altman Z-scores, BOS, and AC distress scores variants. The Boone index (BI) and Lerner index (LI) are undertaken for the competition assessment of NFFs in India. The findings have contrasting views based on BI and LI; BI is positively connected to Z-scores; however, LI negatively connects to Z-scores. The findings suggest that competition (reverse of BI) positively affects financial distress (reverse of Z-score), while competition (reverse of LI) has an adverse effect on FD. It is also found that competition as BI affects FD non-linearly (inverted U shape connection). This means that competition (or market power) initially increases financial distress (or financial stability), and after a specific limit, it reduces financial distress. It can also be said that market power improves financial soundness to a specific limit, and after that, it starts decreasing financial stability. The study’s findings provide fresh and exciting evidence for the connectivity of competition and financial distress. This situation has noticeable implications for all stakeholders and policymakers concerned with the survival of Indian listed firms. The significant connection of competition with financial distress implies that all stakeholders should consider competition an essential element for a firm’s financial distress.

1. Introduction

Competition and financial distress are critical elements in any business activity. If they is not handled correctly and in time, then they may lead to business failure. Although FD does not always lead to bankruptcy or business failure, it is the key indicator of such an undesirable situation in the future (Gordon 1971). Traditionally, a firm’s financial distress and market competition are studied in isolation. However, financial experts and industrial economists mostly admit that a firm’s finance and the market’s competitive behavior may be interconnected (Brander and Lewis 1986; Showalter 1995).

The existing literature explicates several causes responsible for firms’ FD, as Habib et al. (2020) described. The majority of the literature on financial distress subscribes to three broad reasons (Lizal 2002; Habib et al. 2020) for a firm’s financial distress: (1) financial causes; (2) governance affairs; and (3) exogenous determinants. Among these three causes, the last category, the exogenous factors, is not given much attention as these factors are beyond the boundaries of a firm’s domain (Habib et al. 2020). However, these factors and their connection with financial distress must be examined (Ikpesu 2019; Zhang et al. 2016; Habib et al. 2020; Amendola et al. 2015).

Furthermore, many factors are included in the big basket of exogenous variables (Lizal 2002; Habib et al. 2020). Competition is among the most critical factors which may be responsible for a firm’s FD. This situation has become an intensified subject of interest since the global financial crisis (GFC) (Reinhart and Rogoff 2011), as many questions arise about whether to blame competition, regulations, or recapitalization for the failure of specific business activities. Many studies explore the connectivity of competition and FD (Habib et al. 2020; Ikpesu 2019). However, these studies have no common consensus regarding their association. One set of studies advocates that competition improves a firm’s financial stability (Obembe and Soetan 2015; Saha and Dutta 2020; Noman et al. 2018); other studies argue that competition harms a firm’s financial stability (Beck et al. 2013; Alam et al. 2019; Berger et al. 2009; Gyimah et al. 2021; Kanoujiya et al. 2022; Chen et al. 2023); and other studies indicate no association between competition and FD (Yazdanfar and Öhman 2020).

It is evident from the available studies that there are contrasting viewpoints on the connectivity of competition and a firm’s FD. This situation may be because corporate activities are not similar in all countries. These corporate activities might differ due to a country’s context-specific features such as culture, legal system, institutions, and developmental stages (La Porta et al. 2000a, 2000b; Gaud et al. 2007). The central portion of such studies is concentrated on the financial sector and developed economies. Therefore, fresh evidence of competition’s impact on financial distress must be delved into, particularly in NFFs in emerging economies such as India.

In the Indian set of affairs, the country is believed to be a greatly growing economy. It faced several business failure situations in the nation’s corporate world even after the reform period in 1991 (Kanoujiya et al. 2022). All efforts are being made by the government to lessen such unwanted conditions of business collapses. Therefore, the causes behind such issues should be identified to resolve such a situation. Competition can be an influential factor in a firm’s FD (Kanoujiya et al. 2022). However, the literature lacks empirical evidence for competition’s connectivity to FD, particularly for NFFs in India.

Moreover, the earlier pieces of evidence, which have mostly proxied competition as a concentration measure, are also contrasting. Fischer (2000) indicates that higher concentration leads to more financial strength, whereas Chong et al. (2013) contradict this claim. However, few studies question the adoption of concentration as a proxy for competition. Claessens and Laeven (2004) and Maudos and Fernandez de Guevara (2006) argue that the concentration measure leads to entry threat (or level of contestability). That concentration cannot sufficiently predict the competition. They advocate using non-structural measures such as LI and BI to assess the competition.

In the above discussion, it is observed that there exists an abundance of literature discussing competition and financial distress. However, studies on how competition relates to financial distress are significantly fewer. Therefore, in this context, two questions remain unanswered: (1) How does competition relate to a firm’s financial distress? (2) Do different measures of competition and distress scores have different outcomes for the competition’s impact on FD?

The study’s main focus is to find answers to the questions described above regarding the competition’s connectivity to FD and to provide fresh evidence of their connection. As discussed earlier, different views exist concerning competition’s impact on a firm’s FD due to countries’ differentiated context-specific features. Hence, fresh evidence is essential for a specific nation. There are some significant contributions made by this study. First, it gives competition’s measures of NFFs using popularly used non-structural competition measures LI (Lerner 1934) and BI (Boone 2008). Studies using both approaches to give the measure of the competition level of NFFs in India do not yet exist. Second, the current study finds fresh evidence for the external (non-financial) causes of firms’ FD from the perspective of competition. Investigating competition’s impact on FD is essential to delivering reliable evidence in emerging economies such as India. Third, the conflicting views on competition and FD connection also motivate us to look for non-linear relationships between the two. However, such investigations are not available in the literature. Thus, this study estimates both linear and non-linear relationships between the FD and competition. A significant impact of competition on FD is obtained. Hence, the study significantly contributes to the existing knowledge body on financial distress and competition of NFFs.

Furthermore, a firm’s FD follows the Altman Z-score (Altman 1968) multivariate models. Additionally, FD from investors’ viewpoints based on BOS and AC distress scores are undertaken (Al-Hadi et al. 2017). Hence, this study is a unique investigation using the multi-technique approach to provide comparable and robust outcomes.

The study’s critical implications are as follows: (1) policymakers would acquire insights into the competition issues for FD in the listed firms in India (Tripathy et al. 2016); (2) the recent beginning of a firm’s consolidation may augment it as a long-term policy, especially from a firm’s stability viewpoint (Jasrotia and Agarwal 2021); (3) existing different corporate activities may also become accepted as long-term policy (Misra 2006; Jayadev et al. 2017).

The paper is further segmented into five sections. Section 2 dives deep into the literature relevant to the current study to identify research gaps and in service to hypothesis formation. Section 3 discusses the data and methodology. The results are presented in Section 4. Section 5 discusses the current results compared to earlier relevant work. The paper marks its conclusion in Section 6.

2. Literature Review

The literature on financial distress is enormous. It is not a new challenge that a firm may face. Studies on FD issues have been a vital research interest since 1966 (Ward and Foster 1997). Our grip on this area has become more critical since the global financial crisis of 2008, which pushed the whole corporate world into the ring of financial challenges (Reinhart and Rogoff 2011). This paper presents a literature survey regarding financial distress in several categories to identify the research gaps and better comprehend the problem confronted in the present study.

2.1. Factors Specific to Firms and Firm’s Financial Distress

A firm’s FD and the factors specific to the firm are woven together. One of the essential types of firm-specific factors is financial factors, and these factors and FD are companions. The existing literature on financial distress is filled with studies mentioning only financial causes specific to the firm, and these factors are internal to the firm. However, financial distress may have other causes, such as non-financial and external causes (Waqas and Rus 2018; Balasubramanian et al. 2019).

In the studies regarding firm-specific factors (internal), Jahur and Quadir (2012) find financial factors to be a crucial cause of firms’ FD in SMEs (“Small and Medium Enterprises”) in Bangladesh. Similarly, Baimwera and Muriuki (2014), in their research on Nairobian listed firms, advocate for financial factors being responsible for firms’ financial distress. On a similar line, Jaafar et al. (2018) and Ikpesu (2019) present similar evidence on firms’ financial distress in Malaysia and Nairobi, respectively. Gebreslassie (2015) has explored many financial factors, such as NPAs (“non-performing assets”), CLR (“capital–loan ratio”), and NIM (“net interest margin”), and suggested that these are the essential factors of banks’ financial distress in Ethiopia.

Likewise, Farooq et al. (2018) indicated that issues of mild liquidity and depletion in profit provide early signals of a firm’s financial distress. In a recent study of 1742 firms listed in India for the period 2010–2016, Sehgal et al. (2021) developed a model incorporating five important financial ratios (i.e., cash–total liability ratio) for describing a firm’s financial distress. Shi and Li (2021) advocated that leverage, profitability, and liquidity are the significant causes of FD in European Airline firms. Rafatnia et al. (2020) also find that liquidity, profitability, and leverage are the prime causes of FD in Iranian firms. Waqas and Rus (2018) also indicate similar results for Pakistani listed firms.

Existing studies mainly focus on financial factors, as discussed earlier. However, non-financial factors may also be essential to financial distress, and they should be appropriately managed to overcome the issue of financial distress in a timely manner. Many studies, such as Balasubramanian et al. (2019), Agrawal (2015), Waqas and Rus (2018), and Obradović et al. (2018), have also advocated that combining financial and non-financial determinants provides better predictive ability with respect to financial distress. Jabeur and Fahmi (2018) used only financial information and suggested that non-financial parameters should be considered (Mselmi et al. 2017; Restianti and Agustina 2018).

Bakar and Noordin (2021) conducted a study on firms listed in Malaysia. They found that institutional and independent ownership improves a firm’s financial distress. Zeitun and Tian (2007), in a study on 59 listed firms in Jordon, found that ownership of the top five large shareholders and foreign and government ownership is negatively associated with a firm’s FD.

Yousaf et al. (2021) have explored the demographic attributes of the board as a non-financial determinant in a study of firms listed in China. They found that demographic attributes such as age, gender, education, competence, and liberty affect a firm’s financial distress and improve its health. Similarly, Kristanti et al. (2016) indicate the importance of corporate governance (CG) in improving financial distress in their study on the firms listed in Indonesia. Zeitun and Tian (2007), Manzaneque et al. (2016) and Udin et al. (2017), in their research on CG, have also indicated the importance of the ownership framework on financial performance. Likewise, Vosoughi et al. (2016) have found a positive association between institutional ownership and FD. In an exciting study by Whitaker (1999), the CG of firms did not relate to the financial distress issue. However, CG and managerial activities help firms exit from the early stages of FD.

The above discussion shows numerous causes for a firm’s FD. These causes can be internal or external to the firm. Among the several factors pertaining to a firm’s FD condition are financial and non-financial factors. It is observed that firms are more capable of dealing with internal factors; however, external (exogenous) factors are challenging for them to deal with. The current study mainly focused on finding competition’s impact (one of the essential exogenous factors) on a firm’s FD.

2.2. Incorporation of Exogenous Variables as the Determinants of Financial Distress

Apart from firm-specific financial or non-financial determinants of FD, exogenous factors also have equal importance to the causes of a firm’s FD. Hence, the macroeconomic signals, market structure, competition, and regulations are significant reasons for a firm’s financial distress (Kristanti and Herwany 2017; Ikpesu 2019). This is practically evident from the global crisis of 2008, which has shaken the whole world economy and affected many business activities, particularly in finance. Thus, it is logical and reasonable to contend that a mix of these three broad categories (firm-specific determinants (financial and non-financial) and exogenous determinants) might be the actual set of causes for a firm’s financial distress (Kristanti and Herwany 2017; Ikpesu 2019). However, exogenous factors such as non-financial factors are also given less attention in the existing literature.

According to Wanderi (2016), CG and macroeconomic factors such as IT and globalization have significantly affected the FD of Kenyan banks according to research from 2011–2015. Similarly, Zhang et al. (2016) studied 629 US listed banks from 2010–2013. They explored the reasons described above (i.e., financial, non-financial, and exogenous factors), and found non-performing assets, such as financial factors specific to a firm, ownership as a non-financial factor specific to a firm, and capital regulation and housing prices as exogenous macroeconomic factors. All are associated with banks’ financial distress in US banking. Providing evidence from 3865 listed firms (Swedish) for 2008–2015, Yazdanfar and Öhman (2020) advocate that exogenous and firm-specific factors affect a firm’s financial distress. Similarly, Elloumi and Gueyié (2001) find evidence from listed firms in Canada for all the reasons above—firm-specific and macroeconomic—being connected to a firm’s financial distress. Kristanti and Herwany (2017) and Ikpesu (2019) also advocate for the importance of macroeconomic factors. However, few studies explore the exogenous factors responsible for a firm’s FD. Hence, the current paper fills this particular gap.

2.3. Theoretical Framework on Competition and Financial Distress and Hypothesis Formation

Among exogenous variables, competition may be a vital issue causing the problem of financial distress. Beck et al. (2006) and Cetorelli (2001) have pointed this out in studies in the existing literature. In these studies, it is observed that competition as an exogenous determinant is not considered to be the sole the cause of financial distress; instead, it is one of the essential elements considered to be the cause of financial distress. There exists an abundance of literature discussing competition and financial distress. However, studies on how competition relates to financial distress are significantly fewer. The literature also fails to establish a connection between the two as no standard view on their relationship exists. One compendium advocates that competition significantly affects a firm’s FD, either positively or negatively. However, others indicate that competition has no link to a firm’s FD. With the contrasting views on the link between competition and FD, two hypotheses exist in the literature. One is the “competition-fragility hypothesis” (CFH), and the other is the “competition-stability hypothesis” (CSH). CFH proposes that competition leads to a higher risk of financial distress, while CSH assumes that competition lowers financial distress and improves financial stability (López-Penabad et al. 2021; Cuestas et al. 2020).

In support of CFH, Alam et al. (2019) conducted a study on banking in 10 Islamic countries, including 200 banks. They found that the CFH favors only conventional banking. Similarly, Cipollini and Fiordelisi (2012), in a study of 308 banks operating in Europe during 1996–2009, have also advocated that competition is an essential attribute of bank value for a bank in stressed conditions, supporting CFH. Berger et al. (2009) have also found evidence for CFH, considering 8235 banks of 23 economies. Similar support for CFH has also been found by Leroy and Lucotte (2017) and Hanggraeni (2018).

In support of CSH, Boyd and De Nicolo (2005) advocate that market power (or lower competition) reduces financial stability. Beck et al. (2013) provided blended evidence for CFH and CSH. CSH is accepted if regulations and supervision are effective; otherwise, CFH is followed. Similar outcomes were also identified by Alam et al. (2019), who explored the connection of competition with FD for the banking system in 10 leading Islamic nations during 2006–2016, including both Islamic and conventional banks. They have indicated that Islamic banks accept CSH while conventional banks reject it. In Indian banks, Kanoujiya et al. (2022) provide supportive evidence for CFH and indicate that competition enhances FD. Ugur et al. (2022) and Chen et al. (2023) also found a similar outcome in support of CFH. Hagen et al. (2019) and Anand and Venkataraman (2016) have also found evidence regarding the both the CFH and CSH views on a firm’s FD and competition. Hence, two essential theories provide contradictory conclusions on the connectivity of competition and a firm’s FD. First, the market power theory (in line with CSH) argues that competition reduces financial distress and improves financial stability by reducing financing costs and increasing the chances of credit availability (Boyd and De Nicolo 2005; Alam et al. 2019). However, the second, the information theory (in line with CFH), argues that competition deteriorates financial stability (increases financial distress) due to information asymmetry and agency costs (Petersen and Rajan 1995; Marquez 2006). Based on these theories, earlier studies have mixed empirical evidence regarding the connection between a firm’s competition and FD level.

Concerning the contrasting viewpoints on the connectivity of competition and financial distress, there is a requirement to further investigate the link between competition and financial distress to provide fresh evidence, especially in emerging economies such as India. Hence, competition’s connection to FD must be tested via different lenses. This study looks for the non-linear (quadratic) association of competition with FD. It is evident from the existing literature that both viewpoints mostly regard financial firms; non-financial firms are less investigated. Thus, the following hypothesis has been assumed:

Hypothesis 1a (H1a):

Competition non-linearly affects the financial distress of listed non-financial firms in India.

3. Data and Methodology

3.1. Data

The present paper utilizes the panel data of 78 listed NFFs in India (BSE 100) with a five-year (2015–16 to 2019–20) time dimension. In order to provide novel evidence on non-financial firms and to have a sufficient amount of all the required data, we have chosen this cross-section and timeframe. A few of the firms are avoided due to the absence of accurate data. The timeframe above is taken to have the most recent available data to provide fresh and reliable evidence. The data (secondary) are sourced mainly from the CMIE (“Centre for Monitoring Indian Economy”) Prowess database of Indian listed firms. Supplementary sources of data retrieval are the official websites of BSE, MoneyControl, and the firms’ annual reports. The list of variables by which the data are retrieved is mentioned in

Table 1. List of Variables.

SN	Variable (Type)	Code	Definition	Citations
1	ZS (original) (DV)	ZS1	It is the originally developed Altman Z-score by Altman (1968). It is an assessment of firms’ financial distress. Higher Z-score signifies lower FD (See Appendix A.1).	Altman (1968), Pradhan (2014)
2	ZS (without sales) (DV)	ZS2	It is an amended version of Altman Z-score to consider non-manufacturing firms (see Appendix A.1).	Altman and Hotchkiss (2006), Pradhan (2014)
3	ZS (for emerging economies) (DV)	ZS3	It is further improved version of existing Z-score particularly for developing economies (see Appendix A.1).	Heine and Altman (2011), Pradhan (2014)
4	Distress Score (from investor’s perspective) (DV)	BOS_DIS	It is another version of distress score having investor’s perspective (see Appendix A.2). Higher score signifies lower FD.	Al-Hadi et al. (2017), Berger et al. (1996)
5	Distress Score (from investor’s perspective)	AC_DIS	It is an amended version of distress score (BOS_DIS), including the investors’ perspective (see Appendix A.3).	Al-Hadi et al. (2017) Almeida and Campello (2007)
6	Learner’s Index (IV)	LI_comp	It is the computation of market power and competition (see Appendix A.4).	Paktinat and Javid (2015), Praveena and Samsai (2014), Lerner (1934).
7	Boone Index (IV)	BI_comp	It is another measure of competition and market power (see Appendix A.5).	Boone (2008), Tan (2018)
8	Market Capitalization (CV)	l_mcap	It is the representative of a firm’s value and computed as multiplying the number of a firm’s shares by current market price of the share.	Abdolmohammadi (2005), Dias (2013)
9	Sales (CV)	l_sales	Firms’ sales are the exchange of firms’ products or services for money. It is measured in INR (in crore).	Putra et al. (2021), Lee et al. (2020)
10	Operating margin (CV)	op margin	Operating margin is computed as the ratio of net income to total sales.	Park et al. (2013)

Note: DV, IV, and CV signify the dependent variables, independent variables, and control variables, respectively.

.

3.2. Methodology and Model Design

Panel data analysis (PDA) is undertaken as the regression analysis to examine the assumed hypothesis. The rationale behind applying PDA concerns its advantageous attributes over the ordinary time series or cross-sectional regression analysis. Baltagi (2008) and Hsiao (2007) state that PDA includes time series and cross-section information. Hence, PDA exhibits more information and variability comparatively. We have employed a static PDA model as no endogeneity exists (Wooldridge 2015). Having five dependent variables representing a firm’s financial distress and two exogenous variables representing competition, ten models (5 × 2) are developed by which to investigate the linkage of competition to financial distress. As this study looks for a quadratic connection between the competition and FD, the model specification follows the standard quadratic equation discussed in Baltagi (2008), Wooldridge (2015), and other authenticated studies. The following model specification is built to examine this competition’s impact on financial distress:

(Financial distress = f (competition, control variables))

$$Y_{it}=\alpha +{{\beta }_{1}X}_{it}+{{\beta }_{2}X\_2}_{it}+{\delta }_1{l\_mcap}_{it}+{\delta }_2{l\_sales}_{it}+{\delta }_3{opmargin}_{it}+u_{it}$$

(1)

where $Y_{it}$ is the representative of financial distress as the dependent variable, which can be ZS1, ZS2, ZS3 (Altman’s Z-score), BOS_score, or AC_score; and $X_{it}$ is the representative of exogenous variable competition, which can have LI_ comp or BI_comp. The competition is computed in the two most popular ways: Lerner’s index (LI_comp) and Boone index (BI_comp). Ten models are designed using different pairs of financial distress variables and exogenous variables. ${X\_2}_{it} \mathrm{c}\mathrm{a}\mathrm{n} \mathrm{h}\mathrm{a}\mathrm{v}\mathrm{e} BI\_2 \mathrm{o}\mathrm{r} LI\_2.$ “BI_2” and “LI_2” are the squares of BI_comp and LI_comp. The squared values are incorporated to test the non-linear connection between financial distress and competition. Moreover, also present are three control variables: l_mcap, l_sales (indicating, respectively, the natural-log values of market capitalization and sales), and op margin (operating margin). The control variables are kept in the model to ascertain the model’s good fit, and these variables are controlled to observe the sole impact of competition on financial distress (Wooldridge 2015). The $\alpha$ is constant. The $u_{it}$ is the error term, including μ and v, signifying the individual effect and regular error term. Moreover, in the fixed-effect (FE) model, the unobserved error is added to the constant term, while in the random-effect (RE) model, $u_{it}$ also incorporates the unobserved individual cross-section or time effect (Baltagi 2008).

3.3. Variables

As discussed in the model specification, the dependent variable is the distress scores representative of a firm’s financial distress level. Five variables in total are incorporated, including three variants of Altman Z-score (ZS1, ZS2, and ZS3) and two distress scores (BOS_DIS and AC_DIS). A detailed discussion of these variables is presented in the Appendix A.1, Appendix A.2 and Appendix A.3. The main exogenous variable is competition, with the two most acceptable competition measures: Boone Index (Boone 2008) and Lerner Index (Lerner 1934). These two variables are explained in detail in the Appendix A.4 and Appendix A.5. The control variables are market capitalization (l_mcap), sales (l_sales), and operating margin (op margin). Market capitalization is computed by multiplying the current market price of an equity share by a firm’s total number of outstanding shares (Abdolmohammadi 2005; Dias 2013). A firm’s sales are exchanging a firm’s products or services for money (Putra et al. 2021; Lee et al. 2020). The operating margin is the net income ratio to total sales (Park et al. 2013). These variables are kept controlled solely for observing the influence of competition. These variables are taken as a control to avoid endogeneity and to omit variable biasedness issues (Wooldridge 2015).

4. Results

4.1. Descriptive Statistics

Descriptive statistics of the applied variables in the regression models are presented in

Table 2. Descriptive statistics.

Variable	Mean	SD	Min	Max
ZS1	14.098	41.302	−38.898	449.491
ZS2	27.966	74.476	−65.693	790.090
ZS3	31.216	74.476	−62.443	793.340
BOS_DIS	0.550	2.196	0.003	24.537
AC_DIS	0.588	2.193	0.025	24.545
BI_comp	0.445	0.605	0	4.291
LI_comp	0.139	0.285	0	0.973
l_mcap	10.927	0.938	8.890	13.832
l_sales	9.598	1.336	5.303	13.330
Op margin	0.208	0.138	−0.061	1.211

Note: SD stands for standard deviation.

. The mean values of ZS1, ZS2, and ZS3 are 14.098, 27.966, and 31.2163, respectively. This result indicates that firms listed in India are not in the class of financially distressed firms in all cases of Altman Z-scores. BOS_DIS and AC_DIS have average scores of 0.5504 and 0.588. This result means that from the investors’ viewpoint, Indian listed firms are in a moderate situation regarding financial distress. Hence, all distress scores show a similar classification of the firms. The mean value of 0.445 for BI signals moderate competition, and 0.139 for LI indicates high competition in Indian listed firms. The firms’ market capital (l_mcap) averages amount to 10.927 (logarithmic). The firms’ sales (l_sales), on average, amount to 9.598 (logarithmic). Both are at a moderate level, having values in the middle of Min and Max. The “Op margin” is 20.83% on average (slightly close to Min), indicating a lower operating margin.

For the correlation, ZS1 positively correlates with other distress scores (ZS2, ZS3, BOS_DIS, and AC_DIS) (see

Table 3. Correlation matrix.

	ZS1	ZS2	ZS3	BOS_DIS	AC_DIS	BI_comp	LI_comp	l_mcap	l_sales	Op Margin
ZS1	1
ZS2	0.996 *	1
ZS3	0.996 *	1 *	1
BOS_DIS	0.273 *	0.352 *	0.352 *	1
AC_DIS	0.274 *	0.353 *	0.353 *	0.999 *	1
BI_comp	−0.017	−0. 018	−0. 018	0.097	0.094	1
LI_comp	0.097	0.154 *	0.154 *	0.647 *	0.646 *	−0. 025	1
l_mcap	0.060	0.064	0.064	0.064	0.064	0.100 *	0.030	1
l_sales	−0.046	−0.048	−0.048	0.055	0.051	0.570 *	−0. 082	0.527 *	1
Op margin	0.139 *	0.136 *	0.136 *	−0.084	−0.084	−0.270 *	0.261 *	0.078	−0.216 *	1

Note: * indicates the significant correlation coefficient at 0.05.

). It also positively correlates with op margin. ZS2 and ZS3 also correlate similarly with other distress scores, positively correlating with LI_comp and op margin. BOS_DIS and AC_DIS correlate significantly and positively with other distress scores and LI_comp. BI_comp positively correlates with l_mcap and l_sales but negatively correlates with op margin. However, the LI_comp only has a positive correlation with op margin. Furthermore, mcap positively correlates to sales, but sales negatively correlate to op margin. The correlation between the BI_comp and LI_comp is negative (−0.025) but insignificant at 5% significance. This result means that both competition measures do not correlate significantly.

4.2. Financial Distress and Competition as Boone Indicator

Table 4. Result of regression analysis (static panel data analysis) (financial distress vs. BI as competition).

	Model 1 DV: ZS_1 (RE)	Model 2 DV: ZS_2 (RE)	Model 3 DV: ZS_3 (RE)	Model 4 DV: BOS_DIS (RE)	Model 5 DV: AC_DIS (RE)
	Robust	Robust	Robust	Robust	Robust
BI_comp (exp_var)	3.726 ** (0.043)	6.220 *** (0.070)	6.220 ** (0.070)	0.212 (0.317)	0.168 (0.428)
BI_2 (Quadratic)	−0.906 *** (0.053)	−1.555 *** (0.081)	−1.555 *** (0.081)	−0.066 (0.331)	−0.056 (0.406)
l_mcap	5.312 * (0.004)	9.761 * (0.005)	9.761 * (0.005)	0.114 (0.357)	0.120 (0.333)
l_sales	−3.115 (0.250)	−5.301 (0.256)	−5.301 (0.256)	0.129 (0.282)	0.123 (0.305)
Op margin	16.3620 (0.273)	28.729 (0.278)	28.729 (0.278)	−0. 368 (0.348)	−0.415 (0.298)
Cons.	−17.59 (0.419)	−33.831 (0.402)	−33.831 (0.402)	−1.916 (0.392)	−1.860 (0.406)
F-test (Model)	11.80 ** (0.037)	11.48 ** (0.048)	11.48 ** (0.048)	3.02 (0.696)	2.94 (0.709)
F-test (FE)	44.07 * (0.000)	41.93 * (0.000)	41.93 * (0.000)	15.96 * (0.000)	15.90 * (0.000)
BP-test (RE)	622.03 * (0.000)	615.29 * (0.000)	615.29 * (0.000)	432.04 *(0.000)	440.5 * (0.000)
Hausman Test	1.94 (0.857)	2.04 (0.843)	2.04 (0.843)	4.16 (0.526)	4.35 (0.500)
Wald test for Heteroscedasticity 1	1.3 × 107 * (0.000)	1.6 × 107 * (0.000)	1.6 × 107 * (0.000)	5.4 × 107 * (0.000)	4.3 × 107 * (0.000)
Wooldridge Autocorrelation Test 2 AR (1)	89.522 * (0.000)	53.355 * (0.000)	53.355 * (0.000)	5.51 × 106 * (0.000)	6.49 × 106 * (0.000)
$\mathrm{Sigma}\_u_i$	39.555	71.061	71.061	1.914	1.911
$\mathrm{Sigma}\_v_i$	13.045	24.063	24.063	1.085	1.085
rho	0.901	0.897	0.897	0.756	0.756
R-Square	0.038	0.039	0.039	0.025	0.024

Note: ¹ Wald test of heteroscedasticity has the null hypothesis of no heteroscedasticity. ² Wooldridge test of autocorrelation in panel has the null hypothesis of no autocorrelation (with 1 lag). BP test refers to the Bruesch–Pagan test for random effect. RE and FE stand for random effect and fixed effect model, respectively. Sigma_$u_i$ and Sigma_$v_i$ are the variance of individual effect (firms in this case) and error term, respectively. The rho is the fraction of variance due to ui. Robust estimates are estimated due to significant heteroscedasticity and/or autocorrelation. Parenthesis has p-value. The exp_var signifies the explanatory variable and BI_comp is the measure of competition using the Boone index. BI_2 is the square of BI_comp, used to obtain non-linear (quadratic) relation. *, **, and *** are significant at 1%, 5%, and 10%.

demonstrates the results of Model 1, 2, 3, 4, and 5. These models are set to examine the connectivity of BI_comp (as competition) to ZS1, ZS2, ZS3, BOS_DIS, and AC_DIS (as financial distress), respectively. Regarding the suitability of the model, the random effect (RE) is well suited for all these models as the Hausman test confirms it (insignificant values with p-value > 0.05). The Hausman test is applied due to the significant outcome of the F-test (FE) and BP test (RE) (Baltagi 2008; Wooldridge 2015). Furthermore, both the Wald test (testing heteroscedasticity) and the Wooldridge test (testing autocorrelation) reject the null hypothesis of no heteroscedasticity and autocorrelation of account of theur having significant p-values (<0.05) in each model; hence, the robust estimates are also fetched concerning the findings.

In Model 1 (Table 4), the coefficient of BI_comp is 3.726, with p-value 0.043 (<0.05). The coefficient of BI_comp is significant at 5% sig. level. This result indicates a significant linear connection between ZS1 and BI_comp in the same direction (positive relationship). It implies that competition (reverse of BI) lowers financial stability and increases financial distress. At the same time, BI_2 (squared BI_comp) has a negative coefficient (−0.906), with a p-value 0.053 (<0.10 sig. level). This result indicated that there also exists a non-linear relationship between ZS1 and BI_comp, indicating that competition positively influences financial distress to a threshold, and then it reduces financial distress. Among the control variables (l_mcap, l_sales, and op margin), only l_mcap is a significant control variable, with a positive coefficient (5.312) and a p-value of 0.004.

Model 2 also exhibits the coefficient of BI_comp, with a positive value (6.220) significant at 10% (p-value = 0.070 < 0.10) (Table 4). Similarly, the coefficient of BI_2 is negative (−1.555) and significant at 10% (p-value = 0.081). This result again shows significant connectivity between ZS2 and BI_comp (both linear and non-linear), as found in Model 1. In this model, only l_mcap is positive and significant out of thecontrol variables (l_mcap, l_sales, and op margin).

In Model 3, the coefficient of the BI_comp is also positive and significant (6.220 with p-value = 0.07) (see Table 4). BI_2 also has a negative and significant coefficient (−1.555 with p-value = 0.081). Therefore, this model also exhibits that BI_ comp has significant linear and non-linear relations to ZS3. This result implies that competition has a significant impact on FD. Only l_mcap is a significant positive control variable in the model. Therefore, Model 3 presents similar results to previous models. Table 4 presents the results of Model 4. This model considers the value of distress scores BOS_DS (Berger et al. 1996) as the proxy for FD. The coefficient of BI_comp in this model is positive (0.212) but insignificant (p-value = 0.317 > 0.10). BI_2 has a negative coefficient value (−0.066), but this is also insignificant (p-value = 0.331 > 0.05). Hence, this model exhibits no linear or non-linear connection between FD and competition. Furthermore, no control variables have a significant coefficient.

The paper also puts the results of Model 5 in Table 4. This model takes the dependent variable of financial distress as the AC_DIS (Almeida and Campello 2007). The coefficient of BI_comp is 0.168 with p-value = 0.428 (positive but insignificant). The coefficient of BI_2 is -0.565 with p-value = 0.406 (negative but insignificant). Like the previous model, similar results are also seen in this model. This situation again signifies that neither a linear nor a non-linear connectivity exists between FD and competition. All three control variables are found to be insignificant in this model also. Here, all of the above five models (Models 1, 2, 3, 4, and 5) examining the connection between financial distress and BI_comp exhibit similar results, indicating a linear and a non-linear connection of BI_ comp to financial distress.

4.3. Financial Distress and Competition as Lerner’s Index

Table 5. Result of regression analysis (static panel data analysis) (financial distress vs. LI as competition).

	Model 6 DV: ZS_1 (RE)	Model 7 DV: ZS_2 (RE)	Model 8 DV: ZS_3 (RE)	Model 9 DV: BOS_DIS (FE)	Model 10 DV: AC_DIS (FE)
	Robust	Robust	Robust	Robust	Robust
LI_comp (exp_var)	−25.076 *** (0.065)	−52.324 *** (0.079)	−52.324 *** (0.079)	−3.757 (0.192)	−3.809 (0.185)
LI_2 (Quadratic)	28.110 (0.141)	63.154 (0.156)	63.154 (0.156)	4.493 (0.237)	4.511 (0.235)
l_mcap	5.145 * (0.004)	9.335 * (0.004)	9.335 * (0.004)	0.136 (0.215)	0.146 (0.187)
l_sales	−3.077 (0.250)	−5.351 (0.246)	−5.351 (0.246)	0.151 (0.345)	0.138 (0.388)
Op margin	25.256 (0.145)	48.759 (0.123)	48.759 (0.123)	1.425 (0.186)	1.402 (0.194)
Cons.	−15.449 (0.550)	−28.517 (0.383)	−28.517 (0.383)	−2.510 (0.322)	−2.426 (0.337)
F-test (Model)	27.71 * (0.000)	37.36 * (0.000)	37.36 * (0.000)	13.18 * (0.000)	13.21 * (0.000)
F-test (FE)	39.91 * (0.000)	37.08 * (0.000)	37.08 * (0.000)	2.24 * (0.000)	2.25 * (0.000)
BP-test (RE)	595.46 * (0.000)	583.57 * (0.000)	583.57 * (0.000)	0.06 (0.404)	0.05 (0.401)
Hausman Test	5.57 (0.350)	6.06 (0.300)	6.06 (0.300)	77.13 * (0.000)	77.33 * (0.000)
Wald test for Heteroscedasticity 1	8.9 × 106 * (0.000)	5.3 × 106 * (0.000)	5.3 × 106 * (0.000)	1.5 × 108 * (0.000)	1.7 × 108 * (0.000)
Wooldridge Autocorrelation Test 2 AR (1)	40.002 * (0.000)	22.575 * (0.000)	22.575 * (0.000)	89.038 * (0.000)	87.994 * (0.000)
$\mathrm{Sigma}\_u_i$	36.967	64.932	64.932	1.417	1.420
$\mathrm{Sigma}\_v_i$	12.860	23.482	23.482	0.983	0.984
Rho	0.892	0.884	0.884	0.674	0.675
R-Square	0.144	0.182	0.182	0.181	0.181

Note: As in Table 4, exp_var signifies the explanatory variable, and LI_comp is a measure of competition using the Lerner index. LI_2 is the square of LI_comp used to obtain non-linear (quadratic) relation. * and ***, respectively, show significance at 1% and 10%. ¹ Wald test of heteroscedasticity has the null hypothesis of no heteroscedasticity. ² Wooldridge test of autocorrelation in panel has the null hypothesis of no autocorrelation (with 1 lag).

presents the results of Models 6, 7, 8, 9, and 10. These models have used LI_comp as a competition proxy. The models estimate competition’s impact on financial distress. All the five models (Model 6, 7, 8, 9, and 10) use financial distress as ZS1, ZS2, ZS3, BOS_DIS, and AC_DIS, respectively. Among these five models, Models 6, 7, and 8 are consistent with RE, as the Hausman test indicates the suitability of RE in these models due to insignificant p-value at 5% significance. This test is required as both F-test for FE and BP test for RE are significant at 5% (see Table 5) (Baltagi 2008; Wooldridge 2015). However, Models 9 and 10 are suitable for financial distress as the F-test test for FE is significant, and the Hausman test also confirms that the FE in these two models has a significant p-value (<0.05). The availability of heteroscedasticity and autocorrelation (significant p-value of the Wald test for the heteroscedasticity and the Wooldridge test for autocorrelation) indicates the robust standard error estimates to be considered in all five models.

In Model 6, the coefficient of Ll_comp is negative and significant (−25.076 with p-value = 0.065) at 10%. Hence, Ll_comp negatively associates with ZS1 (see Table 5). This result implies that higher market power lowers the distress score (financial stability), or higher competition (reverse of LI) improves financial health. It further means that competition reduces FD. However, looking at the non-linear relationship, LI_2 (square of LI_ comp) has a positive value of coefficient (28.110 with p-value = 0.141). This result indicates an insignificant non-linear association between LI_comp and the financial distress score. Among the control variables (l_mcap, l_sales, and op margin), l_mcap is positive and significant.

Similarly, in Model 7, the coefficient of LI_comp is -52.324 with p-value = 0.079 (Table 5). The coefficient of LI_comp is significant at 10% sig. level. This result indicates a significant and negative linear connection between ZS2 and LI_comp. This result implies that higher market power lowers the distress score (financial stability), or that higher competition improves financial health. At the same time, LI_2 (squared LI_comp) has a positive coefficient (63.154) with p-value 0.156 (>0.10 sig. level). This result indicates that there does is no non-linear relationship between ZS2 and LI_comp. Among the control variables (l_mcap, l_sales, and op margin), only l_mcap is the significant control variable, with a positive coefficient (9.3, having a p-value of 0.004).

Like Models 6 and 7, in Model 8, the coefficient of LI_comp is negative and significant (−52.324 with p-value 0.079) (Table 5). Hence, an opposite linear connection exists between LI_comp and ZS3, indicating that higher market power lowers financial stability (increasing financial distress), or higher competition improves financial health. At the same time, LI_2 (squared LI_comp) has an insignificant coefficient. This result shows no significant non-linear relationship between ZS2 and LI_comp. The control variable l_mcap is positive and significant, as in Model 7.

Model 9 uses BOS_DIS as financial distress for the dependent variable, and its outcome is also presented in Table 5. The coefficient of LI_comp is −3.757, with a p-value of 0.192. Therefore, this exhibits a negative but insignificant linear link between BOS_DIS and LI_comp. This result indicates that increasing market power does not influence financial stability (or FD). Further, the coefficient of LI_2 (squared LI_comp) is 4.493, with a p-value of 0.237. As the coefficient of LI_2 is insignificant, this does not show a significant non-linear association between LI_comp and financial distress score. None of the control variables (l_mcap, l_sales, and op margin) are significant in this model.

Using AC_DIS as the dependent variable (the proxy for financial distress), Model 10 investigates the link between competition and financial distress (Table 5). LI_comp has a negative but insignificant coefficient (−3.809 with a p-value of 0.185). This result indicates no significant linear relation of LI_comp to AC_DIS. This result further implies that higher market power does not affect financial stability (or distress). Further, the coefficient of LI_2 (squared LI_comp) is 4.511, with a p-value of 0.235. As the coefficient of LI_2 is insignificant, this shows no non-linear association between LI_comp and financial distress score. These results are similar to the previous model. No control variable is found significant in the model. Thus, the answer to Question 1 is that competition exhibits linear and non-linear relations, indicating that competition affects a firm’s FD. However, comparing BI and LI, they do not exhibit similar outcomes. This result answers Question 2.

4.4. Endogeneity and Robustness Check

This study also tests for endogeneity issues due to the main explanatory variables in the models. The Wu–Hausman test and Durbin Chi-2 test are run to confirm it (Baltagi 2008; Wooldridge 2015). The “Instrumental regression” is applied with lag 3 values as instruments. The outcomes of these tests are presented in

Table 6. Endogeneity Test.

	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7	Model 8	Model 9	Model 10
Durbin Chi-2	0.3732 (0.5412)	0.4248 (0.5145)	0.4248 (0.5145)	0.5910 (0.4420)	0.6634 (0.4154)	0.8164 (0.3662)	1.2919 (0.2557)	1.2919 (0.2557)	1.2668 (0.1008)	1.4182 (0.1007)
Wu–Hausman Test	0.3593 (0.5498)	0.4092 (0.5234)	0.4092 (0.5234)	0.5699 (0.4515)	0.6400 (0.4250)	0.7884 (0.3760)	1.2516 (0.2651)	1.2516 (0.2651)	1.6884 (0.1008)	1.8581 (0.1007)

Note: all values are insignificant at 5% significance. Both tests have null hypotheses of exogenous variables.

. The insignificant values ensure that the explanatory variables are not endogenous. Hence, no biasedness comes through endogeneity. In addition, it also ensures that there is no omitted variable biasedness (Baltagi 2008; Wooldridge 2015).

This study’s novelty lies in the techniques applied to evaluate firms’ financial distress and competition. As the model specification mentions, five variables are employed to assess a firm’s financial distress score and two competition proxies (LI_comp and BI_comp). There are, overall, ten models developed using 5 × 2 combinations. There are thus two sets of outcomes: one with financial distress and BI_comp, and the other with financial distress and LI_comp. Models 1 to 5 (for financial distress vs. BI_comp) (Table 4) exhibit a significant linear relation and a non-linear association between financial distress and competition. Hence, this set of five models gives robust results. However, Models 6 to 10 (for financial distress vs. LI_comp) (Table 5) exhibit similar results but are opposite to the earlier five models (Models 1 to 5). Unlike the previous models, these models show a significant but inverse linear connection of competition to financial distress scores. However, the non-linear connection is insignificant in all these models (Models 6 to 10). The outcomes of the models of both sets exhibit robustness of results. However, as a whole, these two sets indicate two different viewpoints.

5. Discussion

5.1. Hypothesis Discussion

The current paper assumes the alternative hypothesis Ha: competition non-linearly affects the financial distress in Indian listed non-financial firms. In several models (Models 1, 2, and 3), the hypothesis assumed in the current study has enough evidence in its support. Hence, Ha cannot be refused. In most cases, it is found that competition somehow influences a firm’s FD. However, financial distress concerning competition as the Boone index has positive connectivity. It supports the CSH proposition. A firm’s FD concerning competition as the Lerner’s index has negative connectivity. It supports the CFH proposition. The quadratic association confirms that initially, competition enhances financial distress; beyond a threshold, it improves financial health by reducing distress.

5.2. Comparison with Previous Studies

The outcomes of the present study recognize that competition affects a firm’s FD, favoring the findings indicated by Beck et al. (2013), Alam et al. (2019), Berger et al. (2009), and Gyimah et al. (2021). Moreover, the outcomes also support the findings of Saha and Dutta (2020), Noman et al. (2018), and Obembe and Soetan (2015). They have indicated that competition influences FD. Furthermore, the findings do not support Yazdanfar and Öhman (2020), who argue that there is no connection between competition and a firm’s FD.

5.3. Contribution

The current study identifies the connection of competition to a firm’s FD. It has employed a multi-technique approach, measuring financial distress and competition using different methods to provide accurate and comparative results. The study finds answers to the framed questions in the present paper: (1) How does competition relate to a firm’s financial distress? (2) Do different measures of competition and distress scores have different outcomes for the competition’s impact on FD? The findings indicate that competition significantly influences a firm’s FD. However, from the investors’ viewpoint on financial distress, competition does not influence a firm’s FD. The authors believe that this study’s findings are novel and exciting; they contribute to the existing body of knowledge in the literature considering the link between competition and FD to be an important area of research.

5.4. Implications

Financial distress is not a desirable situation for the firm with respect to the proper execution of the business; it is not only limited to the firm itself; it may also disturb the whole economy in which it works (Freeman et al. 2010; Barney and Harrison 2020). Therefore, it is imperative to eliminate such a phenomenon in time so as to avoid it leading to business failure (Lizal 2002; Habib et al. 2020). Market competition is recognized as a critical exogenous factor for the firm. The study’s findings imply that competition significantly affects a firm’s FD. The study found that the firm with higher market power has greater capability to refine its profits by commanding the pricing. Hence, market gains enhance financial health and reduce the chance of default risk. However, a sustainable level of competition also encourages a firm’s financial stability. With the implementation and amendments of the IBC (“Insolvency and Bankruptcy Code”) 2016 Acts in India, many reforms were witnessed by firms listed in India. However, empirical evidence of competition’s impact on financial distress in Indian listed firms (BSE100) is unavailable. Therefore, the study’s findings have important implications for policymakers seeking to establish such corporate and economic policies to promote sustainable and healthy competition and thus to improve the financial health of the firms listed in India. Moreover, the necessary implication for the stakeholders concerns monitoring the management to ensure proper business activities, command pricing, and refine profits. This situation will result in a strong stand in tough competition. The findings also have important implications for investors, who will make their investment decisions in consideration of competition not being critical to a firm’s FD if seen from an investor’s viewpoint (BOS_DIS and AC_DIS). Hence, they must also consider other essential exogenous factors, including financial and non-financial determinants, which may influence a firm’s FD.

6. Conclusions

The current paper examines the influence of market competition on a firm’s FD (or financial health), controlling the worth (market cap), performance (op margin), and sales of listed non-financial firms in India. This study transforms the prevailing literature in many respects. First, it uses the extensive data of listed non-financial firms in India as the most recent available data (2016–2020). This timeframe allows us to account for economic changes due to the global financial crisis and IBC Act 2016 for Indian listed firms. Second, this is a unique study as it provides several pieces of evidence showing the influence of competition (based on two popular competition measures) on different financial distress profiles (based on Altman Z-scores and distress scores having investor’s viewpoint). Third, the paper specifies ten models using a panel data framework to establish linear and non-linear associations. The first five models (Model1, 2,3,4, and 5) use the Boone Index as a competition measure to find its influence on five different financial distress scores; the other five models (Models 6, 7, 8, 9 and 10) use the Lerner index as competition.

The upshots of these regression models are interesting as they indicate two different conclusions based on two different measures of market competition. Considering Boone Index as competition, it significantly and positively affects a firm’s financial distress. LI, as a competition measure, also influences a firm’s FD, signaling that higher competition promotes a firm’s financial stability via a linear association. However, in a non-linear relationship, competition adversely affects financial health, or the higher market power enhances financial stability (showing an inverted U curve for competition and financial distress connection). Hence, the current findings support both the CFH and CSH hypotheses (Cuestas et al. 2020; López-Penabad et al. 2021) via linear and non-linear relationships. This situation indicates that in the case of LI, in cases of a linear relationship, a higher level of competition improves a firm’s financial health (reducing FD). While considering BI, competition enhances financial distress; however, after a threshold in the quadratic establishment, it starts reducing the financial distress in cases of a non-linear relationship. This situation also means that higher market power enhances financial stability. Both indices (LI and BI) indicate that competition influences a firm’s financial distress. However, Lerner’s index and the Boone indicator present different outcomes with respect to competition’s connection to financial distress. Lerner’s index supports the “competition stability view” in line with Boyd and De Nicolo (2005) and Beck et al. (2013), but the Boone indicator supports the “competition fragility view”, in line with Kanoujiya et al. (2022), Chen et al. (2023), and Hagen et al. (2019). However, the current study is quite different in its approach.

The current findings have noticeable policy implications, as policymakers should understand the competition’s importance for a firm’s health. There should be a healthy competitive environment, and equal opportunities should be available to all participants in the market. Managers should understand that competition is crucial to participating in the market; hence, they should try their best to gain the advantage of competition and vigorously face it to survive.

This study should not be generalized because its scope is limited to NFFs listed in India. Financial firms or nations with different economic and market structures may have different outcomes. However, this study provides insights into other emerging economies comparable to India. Therefore, this study opens doors to the study’s future scope being extended in this direction for other economies. The current paper can be further extended to incorporate other measures of competition in order to provide more robust results. Other firms’ specific or exogenous determinants (financial or non-financial), such as corporate governance, market strategies, and political dominance, should also be explored in order to determine their influence on a firm’s FD.