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Capital Structure in Brazilian Small and Medium Enterprises: A panel study Autoria: Wilson Toshiro Nakamura, Denis Forte, Lucas Ayres Barreira de Campos Barros, Ruy Lopes Cardoso Abstract

For the most part, the empirical capital structure literature investigates the behavior of stock market listed companies to test the extent to which capital structure theories explain the cross-section of financing decisions. Therefore, these studies ignore the peculiarities of private small and medium enterprises (SMEs), which represent the majority of firms in number and account for the largest part of the GDP and employment of most countries. This is particularly true in emerging economies, where access to credit and capital markets is arguably more limited for the vast majority of firms. This research investigates the determinants of capital structure decisions of SMEs and offers two contributions. First, we review the still incipient literature in this field and find two main distinct empirical approaches: (i) one based in primary data collected through surveys, questionnaires and interviews; and (ii) other based in secondary data collected in databases. The former approach is generally associated with more qualitative analyses, using smaller samples and seeking to describe patterns or identify behaviors, rather than propose or test theories. The latter approach, on the other hand, is associated with quantitative research and uses bigger samples and statistical methods mainly to test the adequacy of capital structure theories to SMEs. Second, we conduct our empirical analysis using an unprecedented database with over 20,000 Brazilian firms based in the state of Sao Paulo, spanning 13 years from 1994 through 2006. Our regressions are estimated using traditional (e.g., Ordinary Least Squares) and advanced (e.g., dynamic panel system GMM) procedures. Two very robust results come out from the econometric analysis: (i) profitability is negatively related to leverage, as predicted by the pecking order theory and reported in most of the extant empirical literature; and (ii) sales (or assets) growth relates positively with leverage, suggesting that SMEs tended to finance their expansion with debt after exhausting internal resources. Additionally, we find weaker evidence that: (i) size is positively related to leverage, which can be interpreted as evidence that larger firms have more access to outside financing in general and credit markets in particular; (ii) riskier SMEs tend to be less financially levered, consistent with bankruptcy costs arguments from trade-off based theories; and (iii) the age of the firm is negatively related to financial leverage, suggesting that older SMEs may be slightly more conservative in their financing choices. Finally, the magnitude of the coefficient of lagged leverage shows the high persistence of this variable and is compatible with the hypothesis that SMEs adjust their debt/equity ratio towards a target value, though at a low speed.

 

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1- Introduction

For the most part, the empirical capital structure literature investigates the behavior of stock market listed companies to test the extent to which capital structure theories explain the cross-section of financing decisions. Therefore, these studies ignore the peculiarities of private small and medium enterprises (SMEs), which represent the majority of firms in number and account for the largest part of the GDP and employment of most countries. This is particularly true in emerging economies, where access to credit and capital markets is arguably more limited for the vast majority of firms. In this research we address this gap and offer two contributions. First, we review the still incipient and fragmented literature that focuses the capital structure decisions of SMEs in several countries and find two main research approaches: one based on questionnaires, surveys and interviews and other that use secondary databases. Two tables show in chronological order the variables, methods and results of those efforts. The main contribution of this paper, however, is the direct investigation of how SMEs fit in the traditional financing decision theories using a large database of an important emerging economy (Brazil) and employing robust statistical procedures (dynamic panel data GMM estimators). GEM (2009) places Brazil as one of the world’s top entrepreneurial countries, considering altogether the number of firms created and the entrepreneurial stamina. Since 1994, after a successful economic plan, inflation has been stabilized, allowing Brazil to stand up as one of the so called Bric (Brazil, India, Russia and China) in the new economic order, with more political and economic importance in the global environment than ever before. We use an unprecedented database provided by Serasa/Experian with more than 20,000 SMEs, including a variety of business segments, based in the state of Sao Paulo, which represents approximately 40% of the country’s GDP. We collected for those firms several financial indicators referring to the period from 1994 through 2006. Even accounting for the fact that many firms had incomplete data and after removing potential outliers, we were able to use more firm-year observations than most previous empirical corporate finance studies – either using SMEs or listed companies. The econometric analysis strongly suggests that more profitable firms tend to be less financially levered. The estimated coefficients for our profitability proxies are both statistically and economically significant, which is compatible with pecking order arguments, i.e., when deciding how to finance their activities, firms favor internally generated resources over outside capital, either debt or equity. Also, we find that, after controlling for profitability and other firm characteristics, sales growth (or assets growth) is positively related to debt financing, implying that SMEs tend to finance their expansion with debt when internally generated funds are insufficient, which, again is compatible with pecking order stories. Additionally, we find weaker evidence that larger firms have more debt capacity, as predicted by traditional capital structure theories, and that older SMEs are more conservative and tend to use less debt, although the magnitude of the coefficient is small in both cases. Finally, in some regressions the coefficient for our risk proxy (rolling window five-year standard deviation of the profitability ratio) is negative and statistically significant, as predicted by traditional trade-off theories. The remaining of the paper is organized as follows: section 2 reviews the literature about capital structure theories and its application in small and medium enterprises (SMEs); section 3 describes the data and the empirical strategy; section 4 presents the results and section 5 concludes.

2- Literature Review- Capital Structure foundations and SMEs

 

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Companies are facing credit shortage and difficult economic times. Even though this sentence could refer to the aftermath of the 2008 economic turmoil, this situation is described by Durand (1952) in his paper already 58 years old. The same way as pandemics and their remedies, economic problems and financial theories seem to be cyclical too. In the 1950’s, the main problem was a shortage of equity capital for companies, which basically financed their activities with past profits and retentions. In the following years, Modigliani and Miller (1958, 1963) set the foundations of modern Finance, by looking at companies as profit seekers using a cost-benefit rationale, later known as trade-off approach. Subsequent theories argued that firm value could be maximized, taking into account tax planning issues and the cost of capital, which would leverage profits to an optimum point, before excessively enhancing bankruptcy risks. Jensen and Meckling (1976) reinterpreted the capital structure decision puzzle by considering that employees are contracted to represent the objectives of the capital owner, but are actually biased towards the preservation of their own interests. Since then, a behavioral component known as agency cost was introduced in the capital structure equation. This aspect was reinforced by Leland and Pyle (1977), who unveiled the information asymmetries of managers and investors in the process of valuing companies. Myers and Majluf (1984) advanced then an alternative theory, known as the pecking order, stating that firms would always prefer to finance their activities with internally generated funds, followed by debt issuance, and would choose to issue new equity only as a last resort. Another behavioral characteristic of the agents, based on the opportunistic timing of short term capital market movements was later observed. Baker and Wurgler (2002) proposed a theory founded in the exploitation of capital markets inefficiencies, in which companies tended to issue new shares whenever the difference between its accounting value and its market value became sufficiently favorable. This third major line of capital structure research was called Equity Market Timing. Given the diversity of theoretical approaches, the first challenge of empirical capital structure researchers is to define the operational variables to study, based on the limited information available. Understanding what is “leverage”, in one hand, and what is to be tested in the other hand always constituted a problem. In this matter, Harris and Raviv (1991) not only consolidated the myriad of proxies that were used in empirical studies, but also described the expected relationship between the dependent and independent variables. Among other relevant empirical papers, Titman and Wessels (1988) found that asset structure, non-debt tax shields, growth, singularity, industrial classification, size, profit volatility and profitability were potential determinants of the financing decisions of American companies. With the advance of computational progress in terms of hardware and software, many improved econometric techniques emerged. For example, sophisticated panel data estimation strategies were used by Ozkan (2001) and Gaud et al. (2005). Their investigation suggested that listed companies partially adjust their leverage ratios towards a target (supposedly optimal or value-maximizing) ratio, as predicted by the trade-off approaches. Notwithstanding the diversity of the capital structure literature, relatively few authors focused on studying the financial leverage of small and medium enterprises. One possible reason for this limitation is that SME data is often scarce and sometimes not reliable, since these firms are not officially required to disclose detailed information or to have their reports audited. Small and medium companies are responsible for a significant portion of economic growth and employment in most countries. Because of their specificities and their life cycle, they tend to be privately held and to employ less sophisticated accounting and financial practices than their larger counterparts. In fact, Katz and Cabezuelo apud Gartner et al. (2004) report that many start-up companies have financial problems such as inadequate capitalization, excessive

 

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debt, and poor record keeping, and relate these usual problems to the poor financial education of entrepreneurs and consequently of the company. In order to overcome the problem of obtaining reliable data, some strategies were often applied. In some cases, researchers use qualitative instruments in order to gather data and process them for academic purposes. In other cases, researchers based on banking institutions or statistical data companies, either private or public, used internal databases of small and medium companies, dealing with problems such as information standardization and data validation. One example of the latter approach is the work by Kremp, Sauvé and Paranque (1999), who presented the results of a joint project between Deutsche Bundesbank and the Banque de France describing the asset and liability structure of manufacturing firms incorporated in Germany and France. This study combined two major players in Europe and the outcome was a preliminary basis for the assessment of monetary transmission mechanisms in the European Monetary Union. The authors constructed balanced panels with 15,000 French firms and over 9,000 German firms observed in the periods 1987-1996 and 1987-1995, respectively. Although there seems to be a correlation between the degree of development of the country and its entrepreneurial activity, as GEM (2009) report points out, we choose to present the literature results chronologically. Studies that used secondary data are described in Table 1. Table 1 –SMEs capital structure studies using secondary panel data

Authors, year Database, period Main Results

Kremp, Sauvé and Paranque (1999)

15,000 SMEs in France (1987-95) and 9,000 in Germany (1987-96)

Firm growth has a positive impact on borrowing (signaling) whereas the negative correlation of profit and debt supports Pecking Order theory. Cost of finance and debt are negatively correlated, with greater impact in larger companies than in SMEs.

Michaelas, Chittenden and Poutziouris (1999)

3,500 SMEs in UK (1986 to 1995) Size, age, profitability, growth and future growth opportunities, operating risk, asset structure, stock turnover and net debtors have an effect on the level of both the short and long term debt in small firms. The paper provides evidence that the capital structure of small firms is time and industry dependent.

Benito (2003) 6,417 SMEs in Spain (1985 to 2000) and 1,784 in UK (1973 to 2000)

The results were consistent with pecking order theory in both countries, despite the local characteristic of bank-based system in Spain compared to a market system in UK.

Cassar and Holmes (2003)

1,555 SMEs in Australia (1995-1998)

The conclusion was that asset structure, profitability and growth are important determinants of financing and of capital structure, and the results were in line with static trade-off and pecking order.

Sanchez-Vidal and Martin-Ugedo (2005)

1,566 SMEs in Spain (1994 to 2000)

Pecking order theory holds for the small and medium-sized enterprises and for the high growth and highly leveraged companies.

Trovato and Alfo (2005)

1,900 SMEs in Italy (1989-1994) Subsidized firms have more capital intensive investments. Risk is significantly negative against leverage but profitability is not significant.

Abor (2007) 160 SMEs in Ghana and 200 in South Africa (1998-2003)

Conclusions showed a negative relationship between long term debt and performance.

Sogorb-Mira and Lopez-Garcia

3,569 SMEs in Spain (1995 to 2004)

Although results show consistency with pecking order, they additionally showed that trust is greater in companies that seek optimum leverage (as in a trade-

 

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(2008) off behavior). The process of funding was however somewhat different between SMEs and larger companies.

Daskalakis and Psillaki (2008)

1,252 SME´s from Greece and 2,006 from France (1997-2002)

Asset structure and profitability have a negative relationship with leverage, whereas firm size is positively related to their debt to assets ratio. Growth is statistically significant only for France and is positively related to debt. Differences in the intensity of the capital structure relationship between the two countries, due to firm-specific rather than country factors.

Liu and Tian (2009)

269 SMEs in China and 651 LSE (2005 to 2008)

“Size discrimination”, pecking order theory, tax shield effect and the negative relationship between state subsidy and capital structure were found in the sample of Small-sized firms. Medium and large-sized non-State Owned firms tend to have more bank loans than their State Owned counterparts while no such relation exists in small sized firms.

Source – the authors

Table 2 present the studies that gathered primary data (via questionnaires, surveys and interviews). Table 2 –SMEs capital structure studies based on questionnaires Authors, year Database Main Results

Hall, Hutchinson and Michaelas (2004)

500 SMEs in Belgium, Germany, Spain, Ireland, Italy, Netherlands, Portugal and UK (1995).

The paper showed that there are variations in both SME capital structure and the determinants of capital structure between the countries surveyed. The hypotheses appear to hold up reasonably well as overall explanations with that for collateral being the strongest and that for growth being the weakest.

Voulgaris, Asteriou and Agiomigianakis (2004)

143 SMEs and 75 large companies in Greece (using balance sheets) (2004)

Conclusions showed that profitability is a major determinant of large and SMEs companies. Efficient asset management and asset growth are important for large companies in contrast with efficiency of current assets, size sales, growth and fixed assets which affect the SMEs credibility.

Machado, Temoche and Machado (2004)

20 SMEs in Paraiba, Brazil (questionnaire)

Size, risk, asset structure and liquidity presented a high prediction power of the capital structure.

Nakamura and Jucá (2005) 80 (questionnaires) in Brazil

Profitability is negatively related to leverage, corroborating pecking-order theory.

Nguyen and Ramachandran (2006)

558 SMEs in Vietnam (interviews)

The capital structure of SMEs in Vietnam is positively related to growth, business risk, firm size, networking, and relationships with banks but negatively related to tangibility.

Abor (2008) 120 SMEs in Ghana (questionnaire)

Agency problems (number of shareholders, their relations) and the choice of capital structure, showed significant relation.

Wu, Song and Zeng (2008) 60 SMEs in 4 cities of China (questionnaire)

Owners have idiosyncratic choices (cultural and political).

Dogra and Gubta (2009) 120 SMEs in Punjab Conclusion is that there is a conservative attitude

 

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(questionnaire) from the entrepreneur in terms of capital structure choices, relying more in internal funds than in external financing, independently of the degree of education of the owner.

Briozzo and Vigier (2009) 129 SMEs in Argentina (questionnaire)

Conclusion is that there is a positive relation between entrepreneurial background and the capital structure choice.

Source – the authors

1.1. Some potential determinants of Small and Medium Enterprises’ capital structure

Size

Firm size is usually used as a potential determinant of capital structure decisions (e.g., Titman & Wessels, 1988; Rajan & Zingales, 1995; Kremp, Stross & Gerdesmeier, 1999; Ozkan, 2001). Studies in Brazil corroborates its importance (Nakamura, 1992; Famá & Perobelli, 2001; Martin et al. 2005; Forte, 2007). Many reasons could be listed such as credit access, investment opportunities and risk related with scale. Theoretical aspects such as the asymmetry between managers and credit evaluators (Stiglitz & Weiss, 1981) are enhanced when it comes to SMEs, due to the fact that these firms are not submitted to the same regulatory auditing and accounting procedures when compared to their larger counterparts. Instead of raising interest rates, credit suppliers may decide to limit the access, impacting the liquidity of the companies (Petersen & Rajan, 1994). The transaction costs associated with financing are usually related with size too, implying in higher proportional transaction costs for smaller firms, reinforced in case of bankruptcy (Titman & Wessels, 1988; Petersen & Rajan, 1994; Wald, 1999). Although the expected relation between size and leverage is positive, many studies found a negative relationship when considering only short-term liabilities (Michaelas et al., 1999; Fluck et al., 2000). Asset structure and Growth Asset structure should matter to financing decisions. Firms may use tangible assets as collaterals, either providing more access to credit or reducing its cost, to the extent that such assets function as a guarantee in case of default (e.g.,Titman & Wessels, 1988; Harris & Raviv,1991; Gaud et al. 2005). Similarly, it has been argued that such collaterals reduce adverse selection and moral hazard costs (Colombo, 2001; Dewatripont & Legros, 2003). Therefore, a positive relation is expected between asset structure and leverage, even in SMEs (e.g., Van der Wijst & Thurik, 1993; Chittenden et al., 1996; Jordan et al., 1998; Michaelas et al., 1999). Companies that experience high growth rates often need more aggressive financing. From a pecking order perspective, once these firms exhausted their internally generated funds, they would resort to debt financing. Therefore, for two firms with the same profitability, we should expect that the one with higher growth rate would be more levered. On the other hand, agency based arguments suggest that debt may have an important disciplining effect over managers, reducing managerial discretion and thus preventing them from wasting corporate resources in value-destroying projects. In this context, high profitability but low growth firms would benefit more from debt financing, which predicts a negative relation between leverage and growth rate, ceteris paribus. However, this prediction is more likely to hold for firms with clear separation of ownership and control, which is not the case in most SMEs. In any case, both positive and negative relations could be theoretically

 

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supported, which is confirmed by empirical studies, such as Michaelas et al. (1999), who find that future growth is positively related to leverage and long-term debt, while Chittenden et al. (1996) and Jordan et al. (1998) find mixed evidence. Financial Performance and tax opportunities The relation between firm performance (measured by its profitability ratio) and capital structure may be justified by the pecking order theory proposed by Myers (1984). In this asymmetric information scenario, firms always prefer to finance investments with internally generated funds, which are not subjected to undervaluation by providers of outside capital or to the restrictions and controls they are willing to impose (Bond & Meghir, 1994; Sunder & Myers, 1994; Babu & Jain, 1998). Consistent with this argument, most of the few studies with SMEs show a negative relation between leverage and profitability (e.g., Van der Wijst & Thurik, 1993; Chittenden et al., 1996; Jordan et al., 1998; Coleman & Cohn, 1999; Mishra & McConaughy, 1999; Michaelas et al., 1999). On the other hand, according to the M&M’s tax assumption (Modigliani & Miller, 1963), the tax shield benefit of debt encourages firms to favor debt financing over other sources of outside capital (Fosberg, 2004). In this context, firms with better past performance have more debt capacity and could be more levered in order to exploit this tax-shield benefit (Homaifar, Zietz & Benkato, 1994; Omer & Terando, 1999). Risk Intuitively, risk should be negatively related with leverage, considering the punishment for defaults and bankruptcy, represented by substantially higher interest rates when the firm approaches dangerous leverage levels. In fact, some studies using listed companies corroborate this logic (e.g., Barton & Gordon, 1988).

3- Method and data description

The extant capital structure research using SMEs lacks homogeneity in methodological procedures, partly as a consequence of the difficulty in obtaining detailed financial data for these firms. In addition, even when a database is available, it is often not standardized or audited. Comparison and generalization becomes therefore more difficult and limited. This empirical literature has two main lines of research: the questionnaire/survey based and the secondary data based. The former research approach tends to use smaller samples and to search for behavioral patterns rather than propose or test theories, while the latter employs statistical procedures more familiar to the corporate finance literature, with limited reach, however. This paper is based on an unprecedented 20,131 SMEs panel spanning 13 years (1994-2006) of data, comprising a variety of firms based in the state of São Paulo, which has one of the largest GDPs in Latin America. The database was made available by Serasa-Experian, a trustable private provider of statistical and information services. Our panel is unbalanced, meaning that we do not restrict firms to be in the sample from 1994 through 2006, thus avoiding any survivorship bias. Also, some financial indicators are missing for a substantial part of the sample firms in some or in all years, probably because those firms did not send the requested information to Serasa-Experian. However: (i) our preliminary analysis did not find any pattern in the distribution of the missing data, which suggests that it does not bias our inferences; and (ii) though in some regressions the combined missing data drastically reduces the usable sample, it still leaves us with a minimum of approximately 2,800 firms with complete data. Also, our main inferences are robust to

 

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variations of the number of usable firms caused by the exclusion or inclusion of variables with many missing values. Following most of the empirical capital structure literature, we Winsorize the majority of our proxies for the potential determinants of capital structure, as well as our leverage indicators, in order to reduce the influence of outliers. Indeed, our preliminary analysis revealed a few very unrealistic figures in many financial indicators, such as liabilities, assets and operating income, possibly because of input error. Therefore, all analyses described below use variables Winsorized at 10% (i.e., replacing the 10% highest and 10% lowest values for the next value counting inwards from the extremes). Additionally, we exclude from the regressions all firm-years with negative book equity. Table 3 summarizes the variables used in the research. Table 3 – Proxies for leverage and potential determinants of capital structure

Code Variable Operating Definition

Leverage Total Leverage

Leverage Loans and Debentures

Growth Sales Growth

Growth Asset Growth

Profitability Profitability (EBITDA)

Profitability Profitability (Operating

Income)

Profitability Profitability (ROE)

Size Sales

Size Assets

Age Firm Age in 2006 Number of years (in 2006) since the foundation of the firm

Asset Structure

Depreciation Expenses

Asset Structure

Non-debt Tax Shield

Risk Volatility of Profitability

Five-year rolling window standard deviation of the profitability index (based in the Operating Income)

 

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Industry Industry dummies Dummy indicating if the firm belongs to Industry where

Year Year dummies Dummy that takes the value in year and otherwise, where

Source: authors

Table 4 shows some descriptive statistics for the variables defined above. Except for , all variables are Winsorized at 10%.

Table 4 – Descriptive statistics

Variable Obs Mean SD Min Max

Total Leverage

116,624 0.595  0.298  0.171  1.101 

Loans and Debentures

119  0.402  0.172  0.146  0.720 

Sales Growth

187,066 0.196  0.319  ‐0.220  0.840 

Asset Growth

192,134 0.186  0.267  ‐0.147  0.723 

Profitability (EBITDA)

17,454  0.098  0.100  0.005  0.310 

Profitability (Operating Income)

216,639 0.086  0.168  ‐0.168  0.397 

Profitability (ROE)

214,965 0.114  0.260  ‐0.320  0.588 

 

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Sales

212,497 7,611,119  9,873,577  350,150  31,119,906

Assets

217,143 6,173,447  9,006,650  166,625  28,206,346

Age (in 2006)

261,703 25  11  6  99 

Depreciation Expenses/Asset

18,717  0.019  0.023  ‐0.007  0.067 

Non-debt Tax Shield

18,712  0.059  0.348  ‐0.571  0.726 

Volatility of Profitability

173,653 0.094  0.069  0  0.399 

Note: and are in original values (in Brazilian Real – BRL). Obs is the number of nonmissing observations, SD is the standard deviation, Min is the minimum and Max is the maximum value of the variable.

Table 4 shows that some variables have relatively few nonmissing observations. In particular, only a very small number of firms report usage (or not) of debentures (short or long term), which leaves our Loans and Debentures leverage proxy with only 119 usable firm-year observations (out of over 260,000 possible!). In face of this extreme data problem, we base all our inferences in the Total Leverage proxy. For the same reason, we do not use in our regressions the profitability measure based in the EBITDA and the non-debt tax shield proxy. We also note that our sample is quite heterogeneous in terms of firm age – the average firm was (in 2006) 25 years-old, with a maximum of 99 and a minimum of 6 years of existence.

The regressions are based in the dynamic linear model depicted by equation (1):

(1)

Where is the error term, represents the -th firm’s time invariant unobserved features that might influence its debt/equity decision, and stands for time fixed effects (i.e., the common effect of any shock to in time . Implemented in the form of a set of year dummies included in all regressions, controls for macroeconomic shocks that may affect the capital structure of firms, such as changes in the interest rate and inflation. The

 

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lagged dependent variable accounts for possible inertial or mean reversion effects in the dynamics of leverage. The remaining regressors are described above, except for and

. The age of the firm (in log form) serves as a control for a possible differential access of older firms to the credit market (e.g., they may be considered safer by creditors, which enhances their debt capacity) or a potential heterogeneity in their appetite for risk (i.e., managers of older firms may be more conservative, therefore favoring equity financing). Finally, is a dummy variable that indicates if firm belongs to industry in year

. The inclusion of these dummies in justified by the fact that some business segments may have access to specific credit lines or needs. The adequacy of a dynamic specification to model the capital structure decision of firms is attested by several panel data studies (e.g., Gaud et al., 2005; Martin et al., 2005), including a few with SMEs (e.g., Sogorb-Mira & Lopez-Garcia, 2008). It addresses the possibility that firms set a target (or optimal) debt/equity ratio and adjust their actual leverage towards that target, although imperfectly (i.e., it is a partial adjustment process). Even if firms do not work with a target leverage, however, the lagged dependent variable is important to control for the time persistence of this variable. In fact, the diagnostic tests that we run after all estimations confirm the need to include one or more lags of the dependent variable among the regressors. The results reported in Table 5 below were obtained using the traditional Pooled Ordinary Least Squares (POLS) estimator and the more advanced system Generalized Method of Moments (GMM-Sys) procedure described in Blundell and Bond (1998). The system GMM allows us to explicitly model the firm unobserved fixed effect represented by and consistently include the lagged dependent variable among the regressors, unlike other panel data estimators, such as the well known Fixed Effects and Random Effects. In addition, GMM-Sys enables us to deal with the likely endogenous relation between our proxies for the determinants of capital structure and the leverage ratio by using lagged values of some regressors as instrumental variables. In our case, maybe the most important problem is what has been termed dynamic endogeneity (e.g., see Wintoki, Linck & Netter, 2010) or feedback effect caused by the potential influence of over the regressors in future periods (e.g., governance, technological, or management changes that affect the capital structure decision contemporaneously and firm growth, sales or profitability in subsequent periods). Ignoring this issue may cause our estimator to be inconsistent and lead us to wrong inferences. Indeed, the diagnostic tests we run (e.g., Breusch-Pagan, Hausman and Hansen/Sargan tests) strongly suggest that the GMM-Sys estimator should be preferred.

4- Results

Table 5 shows the main results from the regressions we estimate based in equation (1). In three of the four models, we include as a regressor. However, in the specification reported in the last column, the diagnostic checks suggested the inclusion of the first five lags of the dependent variable to appropriately account for the dynamic behavior of the leverage proxy. An important difference between the first two regressions and the last ones is the omission in the latter of the variable, since this procedure drastically increases the number of usable observations. In both GMM-Sys models, we use suitable lagged values of the regressors as instruments, allowing them to be correlated with the error term contemporaneously and in future periods. By doing this, we address the endogeneity issues discussed above. The exception is and the industry and year dummies, which are assumed to be strictly exogenous (i.e., non-correlated with the error in

 

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any time period). The diagnostic tests reported in the table indicate that our identification strategy is statistically plausible.

Table 5 – Regression results

Total Leverage Total Leverage POLS GMM-Sys POLS GMM-Sys

Leverage t‐1    0.787 ***  0.735***  0.791***  0.745*** 

    106.82   28.37   286.95   36.18  

Other Lags    NO  NO  NO  t‐1 to t‐5 

Size    0.005***  0.014**  0.003***  0.002 

    3.81   2.21   7.81   0.24  

Asset Structure    0.220***  ‐0.166  ‐  ‐ 

    3.05   ‐0.66   ‐  ‐ 

Profitability    ‐0.389***  ‐0.531***  ‐0.379***  ‐0.397*** 

    28.17   11.30   68.89   5.55  

Risk    ‐0.013  ‐0.086  ‐0.02  ‐0.123*** 

 

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    ‐0.44   ‐1.2   ‐0.22   ‐1.94  

Growth    0.079***  0.126***  0.078***  0.238*** 

    9.27   4.16   23.2   3.52  

Age    ‐0.001  ‐0.009*  ‐0.009***  ‐0.002 

    ‐0.36   ‐1.92   ‐7.01   ‐0.43  

Industry Dummies  YES  YES  YES  YES 

Year Dummies    YES  YES  YES  YES 

Observations    8,249  8,249  63,634  31,259 

R‐squared    0.780  ‐  0.784  ‐ 

F‐Test    1022.95  0.00   139.25  0.00   8119.15  0.00   489.52  0.00  

Hansen J    ‐  313.75  318; 0.56 ‐  44.07  42; 0.39  

m1    ‐  ‐ 10.64  0.00   ‐  ‐ 17.16  0.00  

 

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m2    ‐  ‐ 0.62  0.54   ‐  ‐0.77  0.44  

Note: the reported regressions use Total Leverage as the dependent variable and are estimated with Pooled Ordinary Least Squares (POLS) and the system Generalized Method of Moments (GMM-Sys) proposed by Blundell and Bond (1998).

Regressors are , , ,

, (volatility of the profitability index), (Sales Growth), (Firm Age in 2006) and a set of industry and year dummy variables. All models also include the first lag of the dependent variable as a regressor, except for the last GMM-Sys model, which includes the first five lags of . All variables are Winsorized at 10% and firm-years with negative equity are excluded. POLS regressions use firm-clustered standard errors, which are robust to arbitrary forms of heteroskedasticity and autocorrelation of the error term and GMM-Sys regressions employ two-step corrected robust standard errors. Estimated coefficients are shown in bold, with t-statistics below (in parenthesis). Statistical significance at 10%, 5% and 1% are indicated by *, ** and ***, respectively. F-test statistics are reported with p-values in parenthesis. m1 and m2 are Arellano-Bond first and second-order autocorrelation tests, respectively. The test statistics are shown with p-values in parenthesis. Hansen J is the Hansen/Sargan (heteroskedasticity robust) test of overidentifying restrictions. The test statistics are shown with degrees of freedom and p-values in parenthesis, respectively.

The first inference from Table 5 is that is highly persistent. In fact, is the most important regressor in our model, in addition to being correlated with some of the potential determinants of capital structure, thus leaving little doubt regarding the importance of modeling financing decisions as a dynamic process – similarly to the findings of many previous empirical studies that used panel data sets (e.g., Martin et al. 2005 and Forte, 2007, both using Brazilian listed firms). Also, our proxy for profitability clearly stands out as an important determinant of the leverage ratio. The sign of the coefficient is negative, which is compatible with pecking order arguments based on asymmetric information or managerial overconfidence. The estimates are both statistically and economically significant, ranging from to . They are also remarkably robust to changes in the specification of the model and in the proxy for profitability. The other very robust inference is the positive influence of sales growth (also valid with asset growth) on leverage, although with a smaller magnitude compared with profitability. This result suggests that high growth SMEs often resort to debt in order to finance their expansion. We find weaker evidence that larger SMEs have increased debt capacity. However, the estimates show that this influence, when statistically significant, is not economically relevant. In models using the POLS estimator we find a positive relation

between asset structure and leverage, but this result is probably spurious – it completely disappears when the model is estimated with GMM-Sys. Finally, there is weak evidence that both and are negatively related to . Although we should be cautious with these inferences, they seem to imply that older SMEs tend to be (a little) more conservative than younger ones – favoring equity over debt financing, while, as expected, riskier firms tend to be less levered, either because they have less debt capacity or are less inclined toward this type of financing.

5- Conclusion

This research investigates the determinants of the debt/equity decisions of small and medium enterprises. In the first part of the paper, we review the theoretical capital structure literature and, more importantly, the still incipient empirical research dedicated to investigating the financing structure of SMEs in several countries. We find two main distinct research approaches in this field: (i) one based in primary data collected through surveys,

 

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questionnaires and interviews; and (ii) other based in secondary data collected in databases. The former approach is generally associated with more qualitative analyses, using smaller samples and seeking to describe patterns or identify behaviors, rather than propose or test theories. The latter approach, on the other hand, is associated with quantitative research and uses bigger samples and statistical methods mainly to test the adequacy of capital structure theories to SMEs. In the second part of the paper we conduct our empirical analysis using an unprecedented database provided by Serasa-Experian with over 20,000 Brazilian firms based in the state of Sao Paulo, spanning 13 years from 1994 through 2006. This large panel is unbalanced, meaning that we let firms enter or leave the sample during the referred period, thus avoiding any survivorship bias. Also, even after removing potential outliers and despite the large number of missing values in some variables, we were able to use a minimum of 2,800 firms with complete data in our reported regressions (and more than 15,000 firms if we omit variables with too many missing values), estimated with traditional (e.g., Ordinary Least Squares) and advanced (e.g., dynamic panel system GMM) procedures. We find two very robust results. First, similarly to what has been reported in most of the empirical capital structure literature, we find a strong negative relation between profitability and leverage ratio. In all regressions, the estimated coefficient for our profitability proxies is both statistically and economically significant, showing a clear tendency of more profitable SMEs to be less levered. This is predicted by pecking order arguments and may also be interpreted as evidence of the limited access of Brazilian SMEs to outside financing. The second robust result is the positive relation between leverage and growth rate (either sales or asset growth). This result is, again, compatible with pecking order (i.e., the prediction that firms will resort to debt financing after exhausting internally generated funds), since we control for profitability and size, as well as other firm characteristics (either observed or unobserved). Additionally, we find weaker evidence that: (i) size is positively related to leverage, which can be interpreted as evidence that larger firms have more access to outside financing in general and credit markets in particular; (ii) riskier SMEs tend to be less financially levered, consistent with bankruptcy costs arguments from trade-off based theories; and (iii) the age of the firm is negatively related to financial leverage, suggesting that older SMEs may be slightly more conservative in their financing choices. Finally, the magnitude of the coefficient of lagged leverage shows the high persistence of this variable and is compatible with the hypothesis that SMEs adjust their debt/equity ratio towards a target value, though at a low speed. References

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