Capital structure choice, information asymmetry,and debt capacity: evidence from India

Surenderrao Komera & Jijo Lukose P.J.

# Springer Science+Business Media New York 2014

Abstract We examine the relevance of the pecking order theory of capital structureamong emerging market firms in the light of their debt capacity concerns. We considerthe financing choices of all public listed Indian firms during 1992 to 2011 for theempirical analysis. The estimated annual pecking order coefficients range from 0.23 to0.56, rejecting the argument that sample firms follow the pecking order while makingtheir financing choices. We find that the pecking order theory fares poorly among firmsthat face higher asymmetric information costs. It is found to be performing relativelybetter among firms without debt capacity concerns. We also report an improvement inthe pecking order coefficient once the concave nature of the relationship between debtissuances and financial deficit is considered. However, the pecking order approachwhen nested in the conventional leverage regression model, adds abysmally smallamount of explanatory power. Overall, we argue that the pecking order theory failsto explain sample firms’ financing choices.

JEL Classification G32

Keywords Pecking order theory . Adverse selection costs . Debt capacity

1 Introduction

Do emerging market firms follow the pecking order while making their financingchoices? Pecking order theory argues that asymmetric information costs are the key

J Econ FinanDOI 10.1007/s12197-014-9285-3

S. Komera (*)Institute for Financial Management and Research, 24, Kothari road, Nungambakkam, Chennai 600 034,Indiae-mail: surender@ifmr.ac.in

S. Komerae-mail: surenderraogp@gmail.com

J. Lukose P.J.Indian Institute of Management Tiruchirappalli, Tiruchirappalli 620 015 Tamilnadu, Indiae-mail: jijo@iimtrichy.ac.in

drivers of firms’ financing choices (Myers 1984; Myers and Majluf 1984). Firms prefercapital that comes with lower asymmetric information costs. In a clear pecking order,they first prefer internal sources, and then they resort to external financing. Among theexternal sources, they prefer to issue debt securities and finally resort to equityissuances, which have higher asymmetric information costs. These asymmetric infor-mation costs are expected to be higher among firms operating in emerging markets(Stiglitz 1989; Khanna and Palepu 2010; Balasubramanian et al. 2010). With higherasymmetric information problems, emerging market firms are the natural candidates totest the validity of pecking order argument.

The theoretical challenge for the pecking order comes mainly from trade off theoryand market timing theory of capital structure. Trade off theory predicts that firmsoperate at their optimal capital structure, by trading off between costs (potential distresscosts) and benefits (tax savings) of debt financing1 (Kraus and Litzenberger 1973).Market timing theory argues that firms’ capital structure is the result of their cumulativeability to issue over-priced securities (Baker and Wurgler 2002). The empirical validityof the pecking order argument has been extensively tested among developed marketfirms. Shyam-Sunder and Myers (1999) suggested a basic pecking order test and foundstrong support for pecking order behavior among US firms. Though Shyam-Sundar andMyers’ test has been criticized for its potential bias arising from equity issuances(Chirinko and Singha 2000), it remains to be the preferred method of further studies.

Frank and Goyal (2003); Fama and French (2005) contradict Shyam-Sunder andMyers (1999), and report that equity issuances are far from rare. They report thatpecking order theory performs poorly among small and high growth firms where it isexpected to perform well as they face higher asymmetric information problems.Relating pecking order with market timing, Huang and Ritter (2009) report that firmscover major portion of their financial deficit by issuing equity, when the cost of theirequity is low. However, in their recent study, Lemmon and Zender (2010) report thatpecking order performs satisfactorily among firms that do not have debt capacityconcerns. Therefore, there has been an absence of consensus among previous studiesover the validity of pecking order argument among developed market firms.Apparently, there has been no previous study that has systematically examined thepecking order argument in the presence of debt capacity concerns in the context ofemerging market firms. In the present study, we make an attempt to empiricallyexamine whether Indian firms follow the pecking order while making their financingdecisions. A subsample analysis is carried out among small, high growth, as well asbusiness group affiliated firms that face varied degree of asymmetric informationproblems. Further, the debt capacity concerns and their influence on the relationshipbetween debt issuances and financing deficit are also examined.

From the empirical analysis, we find the annual pecking order coefficients in therange of 0.2376 to 0.5650, rejecting the argument that the sample firms follow peckingorder while making their financing choices. Evidence suggests that pecking order faresbadly among firms that face higher asymmetric information problems. We find animprovement in the pecking order coefficient after the sample firms’ debt capacityconcerns are considered. However, we report that the inclusion of financial deficit

1 Kraus and Litzenberger (1973) present classical trade-off theory. For dynamic version of the same refer toFischer et al. (1989).

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variable in conventional leverage regression model adds abysmally low explanatorypower. Overall, the empirical evidence suggests that debt financing does not dominateequity financing since Indian firms issue substantial amount of equity to cover theirfinancial deficit. The present study assumes significance for the following reasons.First, the study attempts to fill the research gap in literature that focuses on understand-ing the firms’ financing choices in the context of emerging markets. As mentionedearlier, there is no consensus on the validity of pecking order argument amongdeveloped market firms (Shyam-Sunder and Myers 1999; Frank and Goyal 2003;Fama and French 2005; Lemmon and Zender 2010). Apparently, there is no study thatsystematically investigated the pecking order argument in the context of emergingmarket firms, particularly Indian firms. In the present study, we attempt to fill the gapby empirically examining the Indian firms’ financing choices. Second, we examine thefirms’ financing choices in the light of their debt capacity concerns as attempted byLemmon and Zender (2010) in the case of US firms. Finally, the dataset employed forthis study spans over two decades following the liberalization episode. The dataavailability for 20 years permits rigorous empirical analysis. India, along with itsresembling characteristics of emerging markets, is a country with one of the largestnumber of listed firms in the world, provides an ideal testing ground for validating theimpact of asymmetric information costs on firms’ financing choices.

In the following section, we briefly present the existing literature. Section 3 providesan overview of Indian capital markets. The sample selection process and financingactivities of sample firms are analyzed in Section 4. The estimation procedure andresults are discussed in Section 5. Section 6 provides concluding remarks.

2 Review of previous studies

Pecking order theory argues that asymmetric information costs are the important driversof firms’ financing decisions (Myers 1984; Myers and Majluf 1984). Numerousattempts have been made to empirically validate this argument. Shyam-Sunder andMyers (1999) offered an empirical test to examine this argument. It has been the basicframework for many follow up studies (Frank and Goyal 2003; Fama and French 2005;Huang and Ritter 2009; Lemmon and Zender 2010) that investigated the pecking orderargument.

Shyam-Sunder and Myers (1999) find strong support among US firms for thepecking order theory. They examined 157 firms that traded continuously during 1971to 1989, and reported pecking order coefficient in the range of 0.75 to 0.85. It impliesthat an average US firm covers close to 85 % of their financing deficit by issuing debtsecurities. Chirinko and Singha (2000) questioned the interpretation of Shyam-Sunderand Myers’ test. They argue that equity issues can create negative bias in pecking ordercoefficient. Frank and Goyal (2003) examined an unbalanced panel of all publiclytraded US firms over the period of 1971 to 1998. Employing Shyam-Sunder andMyers’ test of pecking order, they reported that debt finance does not dominateequity financing in magnitude; equity issues are common and they track financingdeficit more closely than net debt issues. Frank and Goyal (2003) further reported thatfinancing deficit does not challenge the influence of traditional leverage factors on thechange in leverage. They also reported that pecking order theory performs poorly

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among small and high growth firms, where it is expected to perform better as they facehigher asymmetric information costs.

Fama and French (2005) reported that at least 54 % of US firms issue equity everyyear during their study period of 1973 to 2002. Rejecting the central argument of thepecking order theory, they reported that equity issues and repurchases are very far fromrare. Further, they reported that firms issue equity more frequently in various forms(issues to employees, rights issues, and so on) that seem to have lower asymmetricinformation costs. Huang and Ritter (2009) find the annual pecking order coefficient inthe range of 0.27 to 0.92 during their study period 1963 to 2001. They relate thepecking order coefficient to firms’ equity risk premium and report that firms financemajor portion of their financing deficit with equity issuance when the cost of equity islow. Huang and Ritter (2009) further examined whether to issue securities and whichsecurities to issue. Their findings suggest that consistent with the pecking orderhypothesis, firms with large cash balance and high profitability are less likely to resortto external capital markets. However, firms’ choice of debt versus equity is mainlydetermined by their equity risk premium.

Lemmon and Zender (2010) find support for the pecking order theory among firmsthat do not have debt capacity concerns. The study examined US firms traded during1971 to 2001. They reported that concerns over debt capacity explain the frequentequity issuances by small and high growth firms. Thus the study demonstrates theimportance of controlling for debt capacity when testing pecking order theory of capitalstructure. There is a limited work that examined the pecking order hypothesis amongemerging markets, particularly among Indian firms. Singh (1995) reported that firmsoperating in industrializing economies rely more on equity than on debt to meet theirfinancing requirements. Singh and Kumar (2012) found that Indian firms exhibit trade-off over pecking order behavior while making their financing choices. They examined1,448 Indian firms covering ten industries. However, these studies did not account fordebt capacity concerns while testing the pecking order hypothesis. The present studyemploys the basic pecking order test suggested by Shyam-Sunder and Myers (1999).Further, we implement the advancements over basic test of the pecking order in thecontext of an emerging market to incorporate debt capacity concerns and the concavenature of the relationship between debt issuances and financial deficit.

3 Overview of Indian capital markets

Indian capital markets are regulated by Securities and Exchange Board of India (SEBI)as per the SEBI act, 1992. In line with the Securities Exchange Commission (SEC) inUnited States, SEBI promotes, monitors, and regulates both equity and corporate debtmarkets in India. Indian equity market is one of the oldest in Asia. Presently, BombayStock Exchange (BSE, established in 1875) and National Stock Exchange(NSE, established in 1992 and operationalised in 1994) account for most ofthe trading in Indian equity markets.

Indian firms actively mobilize equity capital from the capital markets as they arewell developed in terms of liquidity and trade volumes. It is evident from the recentstatistics from World Federation of Exchanges. BSE continues to be the world’s largeststock exchange in terms of the number of listed companies with more than 5,000

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companies and NSE is the third largest in the world in terms of the number oftransactions. The number of public equity issues with Rs 1 billion or more have been55 in 2010–2011 and 34 in 2011–12.2 While Indian equity markets are well developed,the corporate debt market has remained relatively underdeveloped.

Indian companies have traditionally relied heavily on borrowings from banks andfinancial institutions to finance their investments. Commercial banks have been thesource of short term working capital, where as development financial institutions 3

mainly cater to the medium and the long term financing requirements. Private place-ments continued to dominate the public issues in terms of resources raised from debtmarkets. Of Rs 2,870 billion funds raised from debt market for the year 2011–12,private placements account for Rs 2,514 billion (87.6 %). With the inherent advantages(in terms of lower transaction costs) in privately placing debt issues, Indian firmscontinue to depend heavily on private debt capital. Hence, an average Indian firmdepends more on debt capital than an average firm from the US market (Oztekin andFlannery 2012). Since, Indian firms face relatively higher asymmetric information costsand predominantly borrow from the sources with lower asymmetric information costs;we expect clear pecking order behavior while making their financing choices.

4 Sample selection and summary statistics

This section discusses the characteristics of sample firms and their financing activities.

4.1 Data sources and sample characteristics

For the present study, we consider all the Indian listed firms for empirical analysis. Thestudy period is from 1992 to 2011. All the required financial information of the samplefirms has been sourced from CMIE—Prowess database. PROWESS provides informa-tion for over 21,000 firms belonging to manufacturing, services, and other utilities. Thefirms included in the database accounts for 70 % of the economic activity in theorganized industrial sector and 75 % of the corporate taxes (Goldberg et al. 2010).We exclude financial firms and firms with negative net worth. We also exclude firmscontrolled by state (public sector enterprises) as their capital structure choices mostoften are not driven by economic considerations.

The final sample is of 37,722 firm year observations, consisting of 3,832 firms withan average of 9.84 years each. It is an unbalanced panel dataset with gaps, as few firmswere observed to be de-listing and re-entering the market after a few years for variousreasons (acquisitions, bankruptcies, etc.). Of the sample firms, 38.24 % are businessgroup affiliated firms and remaining 61.76 % are stand-alone firms. Table 1 presents thesummary statistics of the relevant variables. All the variables included are winsorized at1 and 99 percentile to eliminate the effect of outliers. The average sample firm usesmuch higher debt financing than a typical firm from the US market.4 The average

2 Source: Indian Securities Market, A Review – Volume – 15, 2012. It is published by National StockExchange and available at http://www.nseindia.com/research/dynaContent/ismr.htm3 These are financial agencies (both state sponsored and privately managed) that provide medium and longterm financing assistance and engage in promotion and development of industry and other key sectors.4 Flannery and Rangan (2006) reports market debt ratio for a typical US firm as 0.2783.

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annual net debt of the sample firms has been 5.79 % of their assets; whereas the averageamount of net equity has been 2.78 % of their asset value. The description of thevariables is provided in Table 2. The sample firms’ financing activities and theirpatterns are described in the following subsection.

4.2 Financing activities

The financing activities of sample firms are summarized annually. Figure 1 illustratesannual financing activities of sample firms across the study period. We consider netdebt, net equity, and change in retained earnings as the sources of finance. Net debt hasbeen the change in debt and preferred stock, while net equity is the change in equityand convertible debt minus the change in retained earnings. Firms’ equity issuances arenot necessarily Follow on Public Offers (FPOs), they usually take the forms of issuing

Table 1 Summary statistics

Variable No. ofobservations

Mean Stdev Min Q1 Q2 Q3 Max

Net debt / TA 32,186 0.0579 0.1518 −0.2363 −0.0191 0.0209 0.1010 0.7745

Net equity /TA

32,186 0.0278 0.1023 −0.0729 0.0000 0.0000 0.0004 0.6997

MDR 37,722 0.4689 0.2948 0.0014 0.2076 0.4686 0.7253 0.9970

Total assets 37,722 324.8830 841.4328 2.9266 21.3019 65.2094 227.4141 6150.6360

Tangibility 37,578 0.3478 0.1996 0.0061 0.1950 0.3328 0.4874 0.8427

Profitability 37,451 0.0941 0.0825 −0.1290 0.0400 0.0941 0.1431 0.3311

MB 37,711 1.7047 2.5610 0.0597 0.3837 0.8384 1.8593 16.8209

Table presents the descriptive statistics of our sample. All the variables included in this table are winsorized at1 and 99 percentile. Total assets are expressed in rupee crores (one crore equals ten millions). The descriptionof the variables is provided in Table 2

Table 2 Variable description

Variables Definition

Net debt Change in debt (all interest bearing debt) and preferred stock

Net equity Change in net worth and convertible debt minus change in retained earnings

MDR Ratio of total debt to total market capital; where total debt is the sum debt andpreferred stock, total market capital is the sum of debt and market capitalization

Tangibility Ratio of net fixed assets to total assets

Size Log of total assets

Profitability Ratio of PBDITA to total assets

MB Ratio of market capitalization to net worth

DEF Change in total assets minus the change in retained earnings as a percentage ofbeginning of year assets.

Table describes the construction of variables

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equity in mergers, private placements, convertible debt, warrants, rights issues,and employee stock options. Following Huang and Ritter (2009), we define thefinancing deficit (DEF) as change in assets minus change in retained earningsas a percentage of beginning-of-year assets. DEF essentially captures the needfor external financing. As per the cash flow identity, it is expected that net debtand net equity to track the financing deficit. Following the pecking orderargument, net debt is expected to follow financial deficit more closely thannet equity. However, in the present case the estimates reveal that the correlationbetween financial deficit and net debt (0.9075) is not significantly differentfrom the correlation between financial deficit and net equity (0.8854) scaled bybeginning of the year assets. Further, the average net equity is higher than netdebt in 5 out of 19 years of the study period.

Table 3 reports the percentage of firms that are net issuers of securities.Firms are considered as issuing debt or equity in a given year, if their debt orequity increases by more than 5 % of the beginning-of-year assets (Hovakimianet al. 2004; Huang and Ritter 2009). The percentage of firms that issued netdebt to the extent of at least 5 % of assets during the sample period has neverbeen less than 21.90 %. According to the pecking order hypothesis, equityissuance has the highest adverse selection premium, and its issuances are veryrare. On the contrary, one finds that in the case of the current sample, thepercentage of equity issues of at least 5 % of the value of their total assetshave always been higher than 4.91 %, peaking 35.28 % in the year 1995. Thepercentage of equity issuances are more than 10 % in 10 out of 19 years of thestudy period. Based on the information reported in Table 3 and illustrated inFig. 1, it is evident that a substantial amount of external finance is raised in theform of equity. Therefore, the reported values clearly contradict the peckingorder theory, which argues that the debt dominates external financing andequity issuances are very rare (Myers 1984).

0.1

.2.3

.4

1993 1995 1997 1999 2001 2003 2005 2007 2009 2011Year

Average NetDebt/TA Average NetEquity/TAAverage financing deficit (DEF)

Fig. 1 Pattern of average financing deficit and various financing activities. Figure plots annual averages offinancing deficit (DEF); average net debt, and net equity issued as percentage of beginning-of-year assets. Thedescription of the variables is provided in Table 2

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5 Empirical analysis

This section presents the estimation procedure employed and the following findings.

5.1 Pecking order tests

We investigate whether the sample firms follow the pecking order while making theirfinancing decisions, by employing empirical model (Eq. 1) proposed by Shyam-Sunderand Myers (1999). The same model was employed by Frank and Goyal (2003); Huangand Ritter (2009).

ΔDit ¼ αt þ βPODEFit þ μit ð1ÞΔDit refers to i th firm’s net debt (scaled by beginning-of-year assets) in t th year.

DEF refers to the firm’s financing deficit. It captures a firm’s requirement of externalfinancing. If sample firms follow pecking order, βPO will be close to one and αt will beclose to zero. Chirinko and Singha (2000) argued that if firms issue equity afterexhausting their debt capacity, as predicted by pecking order theory, βPO will be lessthan one. Calibrating the model in accordance with actual debt and equity issuances, theyshow that βPO will be equal to 0.74 rather than one. We estimate Eq. 1 each year acrossthe study period to examine how pecking order coefficient has changed over time.5 The

5 We estimate βPO for each year across the study period 1993–2011. We lose 1 year (1992) data inconstructing required variables.

Table 3 Percent of firms issuingdifferent securities across time

Table presents percent of firmsthat issued debt and equity acrossthe sample period. A firm is con-sidered as issuing debt if its netdebt scaled by beginning-of-yearassets is at least 5 %, where netdebt is the change in debt andpreferred stock. A firm is consid-ered as issuing equity if its netequity scaled by beginning-of-year assets is at least 5 %, wherenet equity is the change in equityand cumulative debt minus thechange in retained earnings

Year Total Numberof firms

Debt issues Equity issues

1993 720 52.64 % 27.08 %

1994 880 44.00 % 30.63 %

1995 1,275 59.83 % 35.28 %

1996 1,810 56.26 % 15.41 %

1997 2,331 42.77 % 8.00 %

1998 2,208 42.60 % 5.58 %

1999 1,918 33.84 % 4.91 %

2000 1,803 32.15 % 10.22 %

2001 1,712 30.41 % 8.01 %

2002 1,647 21.90 % 6.02 %

2003 1,539 22.69 % 4.10 %

2004 1,487 26.94 % 5.79 %

2005 1,546 34.81 % 7.47 %

2006 1,635 41.47 % 16.99 %

2007 1,745 46.32 % 16.78 %

2008 1,889 44.79 % 17.43 %

2009 2,022 40.21 % 7.60 %

2010 2,058 34.16 % 11.73 %

2011 2,059 39.73 % 10.93 %

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estimated annual pecking order slope coefficients are plotted in Fig. 2. It is evident fromthe figure that annual pecking order coefficients are significantly less than what isexpected by the theory. They range from 0.2376 to 0.5650. The highest reported annualcoefficient (i.e., 0.5650) is considerably less than one as envisaged by the pecking ordertheory and is also less than 0.74 as suggested by Chirinko and Singha (2000). Theaverage pecking order coefficient estimated, using all firm year observations has been0.3828 (refer to Table 4). Such evidence implies that sample firms do not follow peckingorder while making financing decisions. They make a large portion of external financingin terms of equity issuance. These findings are similar to those reported by Frank andGoyal (2003); Huang and Ritter (2009) from US market.

5.1.1 Firm size

Pecking order theory of capital structure argues that firm’s financing decisions aredriven by adverse selection costs. The theory is expected to perform better among firmsthat face more adverse selection costs. These adverse selection costs tend to vary acrossfirms based on their size. Large firms face lower adverse selection costs as manyanalysts keep continued watch on them. Smaller firms particularly in the context ofemerging markets face higher informational costs. Hence, we expect pecking ordercoefficient to be higher for smaller firms when compared to that of their largercounterparts. To examine the same, we classify the sample firms into quartile groupsbased on the value of firms’ assets. We calculate the quartiles of sample firms for eachcalendar year, and then we check to which yearly quartile each firm belongs. Further,we compute the rounded average of quartile of all observations that belong to a firm,and assign all observations of the firm to this average quartile. This method ofclassifying sample into cross sectional groups not only uses time invariant groupingcriteria but also adequately addresses the potential misclassification of firms (as basevariable may potentially change over time). We consider firms that fall in the first

.2.3

.4.5

.6

1993 1995 1997 1999 2001 2003 2005 2007 2009 2011Year

Fig. 2 Pecking order slope coefficients. Figure plots pecking order slope coefficients estimated annually.ΔDit=αt+βPODEFit+μit is employed.ΔDit is the ratio of net debt to beginning-of-year assets of firm i in yeart. DEF is change in assets minus change in retained earnings divided by beginning-of-year assets. βPO,t is thepecking order coefficient estimated annually. They are found to be significant in every year at 1 % level.Variables included in the estimation are winsorized at 1 and 99th percentile

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quartile as small,6 and those that fall in fourth quartile as large firms. We separatelyestimate Eq. 1 using both the small and large firms. Table 4 presents the findings.Contrary to the argument, we find significantly lower βPO in the case of small firms(0.2605) when compared to their larger counterparts (0.4547). It implies that smallerfirms cover a substantial amount of their financing deficit through equity issuance.These findings are similar to those reported by Frank and Goyal (2003)7 from USmarket.

5.1.2 Growth opportunities

Market to book ratio is usually considered as the proxy for firm’s growth opportunities.Firms with higher growth opportunities face higher uncertainty and higher informationcosts. In a way, pecking order theory is expected to perform better among high growthfirms compared to their counterparts with fewer growth opportunities. To examine thesame, sample firms are classified into subsamples based on the value of their market tobook ratio. We follow a similar procedure as explained in previous section, in con-structing the subsample groups. Equation 1 is estimated separately for firms with lowergrowth opportunities (1st quartile) and firms with higher growth opportunities (4thquartile). We report in Table 4 that firms with higher growth opportunities (0.4011)have higher βPOwhen compared to the firms with fewer growth opportunities (0.3739).Such evidence complements the argument that high growth firms face higher asym-metric information costs, thereby issue a large amount of debt. However, the reportedestimates fail to explain the pecking order behavior sufficiently.

5.1.3 Business group affiliation

The dominance of business group ownership is one of the peculiar characteristics ofemerging markets. Business groups are expected to navigate the under developedregulatory and security markets, and flourish among the emerging markets (Khannaand Palepu 2010). Around 38.24 % of the sample firms are affiliated to businessgroups. If the business groups are perceived as the vehicle to appropriate funds(Bertrand et al. 2002), their affiliates face higher adverse selection costs. In such acase, we expect higher βPO for business group affiliates. To examine the same, weestimate separately Eq. 1 for stand-alone and business group firms. It is evident fromTable 4 that business group affiliates report higher βPO than (0.4364>0.3524) theirstand-alone counterparts. This implies that business group affiliates issue relativelymore debt. Such evidence may not be perceived as complementing the argument ofbusiness group funds appropriation. It could as well be that business group affiliatescan issue more debt than their stand-alone counterparts because of their sheer reputationor operational diversification (Gopalan et al. 2007).

Thus, we find that pecking order argument fares worse where it is expected toperform better (e.g. among small firms). Though we offer possible inferences and draw

6 The small firms, here refer to the small firms in the context of the current sample firms, they may not be thesmall among the population of Indian firms.7 Frank and Goyal (2003) report βPO as 0.164 for small firms and 0.753 for large firms during the study period1971 to 1989.

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comparison from the estimates in the case of high growth, and business group affiliatedfirms, their pecking order coefficients are significantly lower than the theoreticaloptimum. The reported evidence also suggests that asymmetric information costsinfluence the sample firms’ financing choices. In addition to asymmetric informationcosts, there seems to be other important factors influencing firms’ financing choices.Our sample firms finance a substantial portion of their external financing requirementsthrough equity issuance. Firms with higher asset value issue relatively higher amount ofdebt securities. It may be natural to expect that firms with larger size have higheramount of tangible assets, and are in a better position to prefer debt over external equity.

5.2 Pecking order tests and debt capacity

With the primary understanding that pecking order theory of capital structure fails toexplain the financing choices of sample firms. We further investigate the reasons forsuch an outcome. It can be argued that there are other important factors driving firms’financing choices. Conventional wisdom suggests that firms’ actual borrowing dependson their ability and their willingness to borrow. Firms’ borrowing then would be thelower of these two. Pecking order theory focuses on firms’ willingness to borrow, butdoes not consider firms’ ability to borrow (Agca and Mozumdar 2004). Once firmsexhaust their debt capacity, they will eventually resort to equity. Large firms with theirhigher amount of tangible assets and longer credit history have higher debt capacity.They eventually prefer to issue more debt as per the pecking order theory. Small firmson the other hand with lower profitability, and no or smaller credit history areconstrained to issue debt securities. Eventually, pecking order theory fails to explainthe financing behavior of smaller firms. This argument reconciles the findings reported

Table 4 Pecking order theory: Firm’s size, growth opportunities and business group affiliation

Overallsample

Smallfirms

Largefirms

Highgrowthfirms

Low growthfirms

Stand alonefirms

BG affiliatedfirms

Constant 0.0096** −0.0028 −0.0025 −0.0078 −0.0180 0.0152* −0.0026(0.0500) (0.7890) (0.7520) (0.4100) (0.2870) (0.0510) (0.6860)

DEF 0.3828*** 0.2605*** 0.4547*** 0.4011*** 0.3739*** 0.3524*** 0.4364***

(0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000)

Time Yes Yes Yes Yes Yes Yes Yes

R-square 0.4965 0.3249 0.5734 0.4916 0.4802 0.4621 0.5588

No. ofobservations

32,175 6,606 8,281 4,313 3,565 19,284 12,891

Table presents the findings from estimating Eq. 1. Variables included in the estimation are winsorized at 1 and99 percentile and their description is provided in Table 2. Overall sample refers to all firm year observations.Small firms refer to those that fall in the 1st quartile classified based on their asset value. Large firms are thosethat fall in 4th quartile. Low growth firms refer to those that fall in the 1st quartile classified based on theirmarket to book ratio. High growth firms are those that fall in 4th quartile. A business group firm is one that isaffiliated to one of the Indian business groups. We consider CMIE’s definition of business group affiliation.***, **, and * refer to 1, 5, and 10% significance level. Numbers in the parenthesis are corresponding p-values

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in Table 4 that pecking order theory fares poorly among small sample firms whencompared to their larger counterparts.

In the presence of debt capacity constraints, Chirinko and Singha (2000) argue thatunder the pecking order the relationship between the net debt and financing deficitwould be concave. Based on Lemmon and Zender (2010), the importance of debtcapacity is identified in two ways. First, we separately estimate the basic pecking orderEq. 1 for firms that are constrained by low debt capacity and those that are not. Second,Eq. 1 is augmented with the inclusion of a quadratic term, the square of financial deficit(DEF) as an independent variable. It is expected to capture the concave nature of therelationship between debt issuances and financial deficit. The modified pecking orderequation is as follows.

ΔDit ¼ αt þ βPODEFit þ βPO;QDEF2it þ μit ð2Þ

Debt capacity is defined as ‘sufficiently high debt ratio’ so that costs of financialdistress curtail further debt issues (Shyam-Sunder and Myers 1999; Chirinko andSingha 2000). Firms with high debt capacity are expected to have easy access todebt market. On similar lines, firms with low debt capacity can be viewed asconstrained to access debt market. Consistent with this argument, firms with bondratings can easily raise sufficient amount of debt from the market. We consider firmswhose securities are being rated by one or more of recognized credit rating agenciessuch as BRICKWORK, CARE, CRISIL, FITCH, and ICRA as having higher debtcapacity. Sample firms whose securities have never been rated by any of these agenciesare considered as firms with lower debt capacity. The criterion based on the creditrating can be justified since it facilitates firms to issue securities easily in the publicmarket. Pecking order theory is expected to perform better in explaining the financingbehavior of firms with higher debt capacity. Lemmon and Zender (2010) based on theexperience from US market report that, for firms with rated debt, the pecking ordertheory satisfactorily explains their financing behavior. We examine the same among thesample firms, by separately estimating Eq. 1 for firms with higher as well as lower debtcapacity. The findings are presented in Table 5.

Table 5 reports that the magnitude of pecking order coefficient (βPO) is significantlyhigher for firms with higher debt capacity (0.4406>0.2970). The same is also true inthe case of firms classified as small as well as large. It implies that irrespective of firms’asset size, pecking order theory explains corporate financing behavior relatively betterin the case of firms with higher debt capacity. These findings are similar to thosereported from US market by Lemmon and Zender (2010). However, our reportedfindings suggest that pecking order theory does not conclusively explain the samplefirms’ financing choices.

We report the findings of Eq. 2 in Table 6. It is expected to capture the potentialconcave nature of the relationship between changes in debt and financial deficit in thepresence of debt capacity constraints. Firms that face debt constraints resort to issuingequity once they exhaust their limited debt capacity despite its high adverse selectioncosts. It is evident from the Table 6 that the coefficient of the quadratic term is negativeand significant across all specifications. For the firms with higher debt capacity, thebasic pecking order test (Eq. 1) should perform well and the introduction of quadraticterm should not change the results. However, we report substantial change in the

J Econ Finan

magnitude of βPO (from 0.4406 to 0.5685) and marginal increase in explanatory power.As expected, pecking order coefficient improves in the case of firms with low debtcapacity. Further, we re-estimate the annual pecking order coefficients with the qua-dratic term as an additional variable. We report a clear improvement in annual pecking

Table 5 Pecking order theory and firms’ debt capacity

Firms with credit ratings Firms with no credit ratings

All firms Small firms large firms All firms Small firms large firms

Constant 0.0036 −0.0220 −0.0059 0.0111 0.0003 0.0178

(0.5510) (0.4620) (0.4920) (0.1590) (0.9790) (0.3920)

DEF 0.4406*** 0.3400*** 0.4712*** 0.2970*** 0.2380*** 0.3619***

(0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000)

Time Yes Yes Yes Yes Yes Yes

R-square 0.5734 0.4467 0.5952 0.3909 0.2946 0.4703

No. of observations 20,010 1,375 7,376 12,165 5,231 905

Table presents the findings from estimating Eq. 1. Variable included in the estimation are winsorized at 1 and99 percentile and their description is provided in Table 2. Firms with high debt capacity are those whosesecurities are being rated by one or more agencies such as BRICKWORK, CARE, CRISIL, FITCH, andICRA. Firms with low debt capacity are those whose securities are never been rated by credit rating agencies.All firms refer to all firm year observations. Small firms refers to firm year observation that fall in 1st quartileclassified based on their asset size. Large firms are those that fall in 4th quartile. ***, **, and * refer to 1, 5,and 10 % significance level. Numbers in the parenthesis are corresponding p-values

Table 6 Pecking order theory, firms’ debt capacity, and relationship between change in debt and financialdeficit

All Firms High debt capacity Low debt capacity

1 2 3 4 5 6

Constant 0.0096** −0.0012 0.0036 −0.0063 0.0111 0.0008

(0.0500) (0.8050) (0.5510) (0.2970) (0.1590) (0.9210)

DEF 0.3828*** 0.5229*** 0.4406*** 0.5685*** 0.2970*** 0.4348***

(0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000)

DEF^2 −0.1406*** −0.1316*** −0.1325***(0.0000) (0.0000) (0.0000)

Time Yes Yes Yes Yes Yes Yes

R-square 0.4965 0.5218 0.5734 0.5914 0.3909 0.4208

No. of observations 32,175 32,175 20,010 20,010 12,165 12,165

Table presents the findings from estimating Eq. 2. Variable included in the estimation are winsorized at 1 and 99percentile and their description is provided in Table 2. Firms with high debt capacity are those whose securitiesare being rated by one or more agencies such as BRICKWORK, CARE, CRISIL, FITCH, and ICRA. Firmswith low debt capacity are those whose securities are never been rated by credit rating agencies. ***, **, and *refer to 1, 5, and 10 % significance level. Numbers in the parenthesis are corresponding p-values

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order coefficients across the study period. Figure A-1 (Appendix) illustrates the same.However, the improvement in βPO does not satisfactorily support the pecking orderhypothesis among sample firms.

5.3 Pecking order vs trade off theory of capital structure

Empirical tests of pecking order use different set of information than the researchin leverage and leverage adjustment. Leverage regression aims at explaining thelevel of leverage, where as pecking order would explain the change in leverage.Following Frank and Goyal (2003), we estimate the leverage regression in firstdifferences. Such a specification allows us to appropriately nest financial deficitvariable, in the leverage regression. We consider the four conventional factors ofleverage such as tangibility, market to book ratio, size and profitability (Rajanand Zingales 1995; Bhaduri 2002; Booth et al. 2001). The specification of themodel is as follows:

ΔLevit ¼ αþ βTΔTangit þ βMBΔMBit þ βSΔSizeit þ βPΔProfitit þ βDEFΔDEFit þ ∈ it ð3Þ

Here Δ refers to the first differences over time. The four conventional factors ofleverage represent sample firms’ costs and benefits of operating at different leverageratios. Their influence can be outlined as follows:

Tangibility Ratio of firms’ fixed assets to total assets proxy their tangibility. As per theoptimal leverage argument, firms with higher tangible assets expected to have higherleverage, as tangible assets naturally serve as collateral. On the contrary, pecking ordertheory predicts that firms with fewer tangible assets issue higher amount of debtsecurities. This is because firms with fewer tangible assets face higher asymmetricinformation costs (Harris and Raviv 1991).

Growth opportunities Market to book ratio (MB) proxies firm’s growth opportunities.Conventionally, firms with higher growth opportunities are expected to maintainfinancial slack to meet their future growth opportunities and issue lower debt.Pecking order argument on the other hand expects firms with higher growth opportu-nities to face greater asymmetric information problems, and thereby issue higheramount of debt.

Size Larger firms tend to be diversified and have lower volatile cash flows. They alsohave better access to public debt markets. Hence, they are expected to have higherleverage.

Profitability Optimal leverage argument predicts that firms with higher profitabilityhave higher leverage, given their higher debt servicing capacity. On the contrary,pecking order theory foresees negative relation between firm’s profitability and itsleverage. However such a negative relation cannot be attributed only to pecking order.It could be due to firms’ potentially higher fixed leverage adjustment costs (Fischeret al. 1989). On the other hand, current profits could be the indication of futureinvestment opportunities (Kaplan and Zingales 1997).

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Nesting financial deficit (DEF) in leverage regression (in first differences) allows usto examine the strength of pecking order in explaining sample firms’ financingbehavior vis-a-vis other competing theories. If firms follow pecking order, the intro-duction of financial deficit in leverage regression should wipe out the effects ofconventional variables. The findings from estimating Eq. 3 are presented in Table 7.The estimated coefficients of conventional leverage factors have the expected signs.Tangibility and size have positive influence; market to book ratio and profitability havenegative influence on leverage ratio. The introduction of financial deficit inspecification-2 (specifications 4 and 6 as well) has not influenced the signs, magni-tudes, and significance of the coefficients of conventional factors. Tangibility (marketto book ratio) continues to have positive (negative) influence on leverage ratio.Moreover, the introduction of financial deficit could not add any extra explanatorypower as R2 more or less remains intact. Thus the evidence suggests that pecking ordertheory does not explain sample firms’ financing behavior.

6 Concluding remarks

We empirically investigate the validity of pecking order theory of capital structureamong Indian firms in the light of their debt capacity concerns. We observe that the

Table 7 Pecking order vs trade off theory of capital structure

All firms Small firms Large firms

1 2 3 4 5 6

Constant −0.0851*** −0.0813*** −0.0279*** −0.0259*** −0.1084*** −0.1051***(0.0000) (0.0000) (0.1300) (0.1530) (0.0000) (0.0000)

Tangibility 0.0656*** 0.0772*** 0.0331 0.0550* 0.0756*** 0.0824***

(0.0000) (0.0000) (0.2790) (0.0740) (0.0000) (0.0000)

MB −0.0187*** −0.0190*** −0.0242*** −0.0249*** −0.0126*** −0.0127***(0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000)

Size 0.0832*** 0.0661*** 0.1011*** 0.0587*** 0.0830*** 0.0712***

(0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000)

Profit −0.5270*** −0.5103*** −0.3310*** −0.3007*** −0.7209*** −0.7073***(0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000)

DEF 0.0235*** 0.0534*** 0.0162***

(0.0000) (0.0000) (0.0120)

Time Yes Yes Yes Yes Yes Yes

R-square 0.3543 0.3558 0.2507 0.2574 0.4525 0.4532

No. of observations 27,480 27,480 5,148 5,148 7,470 7,470

Table presents findings from estimating Eq. 3. It has been estimated with first differences. Variables includedin the estimation are winsorized at 1 and 99 percentile; and their description is provided in Table 2. Column 1and 2 represents all sample firm year observations. Small firms refers to firm year observation that fall in 1stquartile classified based on their asset size. Large firms are those that fall in 4th quartile. ***, **, and * refer to1, 5, and 10 % significance level. Numbers in the parenthesis are corresponding p-values

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equity issues are very common and debt financing does not dominate equity financingin magnitude among sample firms. We find annual pecking order coefficients rangingfrom 0.2376 to 0.5650 averaging at 0.3828, much lower than the theoretical optimum.This implies that an average Indian firm covers only 38.28 % of its financial deficit byissuing debt securities. Evidence suggests that pecking order theory fares badly amongsmaller firms where it is expected to perform better as they face higher asymmetricinformation costs. It is also observed that pecking order coefficient is higher amongbusiness group affiliated firms than their stand-alone counterparts, complementing theargument of business group funds appropriation. Additionally, we find an improvementin pecking order coefficient after firms’ debt capacity concerns are considered. Therelationship between debt issuances and financial deficit is found to be concave innature. However, the reported estimates do not satisfactorily support the pecking orderhypothesis. Further, when pecking order argument is nested with the conventionalleverage regression model, financial deficit adds an abysmally small amount of ex-planatory power and does not influence the effect of conventional factors. Thus, theoverall evidence suggests that sample firms do not follow pecking order while makingtheir financing choices, even after controlling their debt capacity concerns.

Appendix

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.2.3

.4.5

.6

1993 1995 1997 1999 2001 2003 2005 2007 2009 2011Year

Pecking order coefficient PO coefficient_Concave

Fig. 3 Pecking order slope coefficients. Figure plots pecking order slope coefficients estimated annually usingboth Eqs. 1 and 2. Equation 1 is the basic pecking order test and the resulting estimates are plotted using dottedline. Equation 2 is the pecking order test augmented with quadratic term, square of financial deficit variable;and its findings are plotted using solid line. All the annual slope coefficients are found to be significant at 1 %level. Variables included in the analysis are winsorized at 1 and 99th percentile

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