Performance Implications of Capital Structure and the Maturity Choice of Debt during a Recession

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Performance Implications of Capital Structure and the

Maturity Choice of Debt during a Recession

An Analysis of the Dutch Construction Industry in 2001-2005

Author:

Q.M. van Eick

Supervisor:

Dr. L. Dam

July 2009

University of Groningen

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ABSTRACT

This paper examines the influence of a firm’s capital structure and the maturity structure of debt on firm performance. The paper uses panel data with a sample of 224 Dutch construction firms, which are investigated for the years 2001-2005. In this period a sharp economic decline occurred during 2001 and 2002. The results show that higher leverage negatively affects performance, whereas using relatively more short maturity debt has a positive impact on performance. These results are discussed in an agency theoretical framework, which indicates that higher leverage induces higher agency costs of debt for the sample firms. In contrast, using relatively more short maturity debt alleviates some of these agency costs. Furthermore, the evidence shows that larger firms with more financial slack attain higher performance, and that the two-year lagged economic growth figure positively impacts performance. The negative capital structure-performance relationship shows to be more robust than the positive maturity structure-performance relationship.

Quinty van Eick quintyv@hotmail.com Student number: 1386123

JEL Codes: C23, G32, G34

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PREFACE

The last six months I have been writing my thesis as a final project in order to complete my Master Business Administration Finance. This thesis does not only complete my Master, it also reflects the end of the fantastic time I had while studying in Groningen. Writing my thesis was a very useful experience for me and my future career.

There are a number of people that I would like to thank for their support in writing this thesis. First of all, I want to thank my supervisor Lammertjan Dam for his useful comments and advice. Through the tight deadlines we made together I was able to make such major progress the last couple of months. Additionally, I would like to show my gratitude to Erik Steinmaier and David Kemps, my supervisors at ABN AMRO during my internship. They helped me to gain a practical perspective on the subject during the writing process. This perspective made it possible for me to write a thesis, which combines academic with practical relevance. Last but not least, I would like to thank my family, friends, and boyfriend for their never-ending support.

Q.M. van Eick

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1 _INTRODUCTION

The objective of this paper is to analyze the impact of a firm’s capital structure and the maturity structure of debt on performance with an agency theoretical perspective. More specifically, this relationship is investigated using a panel of 224 Dutch construction firms sampled over the years 2001-2005. In the first two years of the sample period, the Dutch economy faced a sharp decline. Since an increasing number of firms face financial distress in such a period, determining how performance can be improved is even more important than under normal economic conditions. The relationship between the choice of debt and performance is investigated in an agency theoretical framework. This theory suggests that market frictions such as moral hazard and adverse selection causes agency costs, and thereby affect a firm’s performance. Capital structure and maturity structure may play a significant role in alleviating such market frictions and may be able to reduce agency costs.

The capital structure choice has been a subject of debate for many years. Starting from the capital structure irrelevance theory (Modigliani and Miller, 1958), to capital structure choice under tax and bankruptcy considerations (DeAngelo and Masulis, 1980), information asymmetry (Myers, 1984), and agency costs (Jensen and Meckling, 1976). These theories show that market imperfections induce the capital structure choice to be relevant for firms.

In their seminal paper, Jensen and Meckling (1976) introduce the concept of agency costs, caused by conflicts of interest and asymmetric information. From this concept the theoretical framework of this study is derived. The framework underlines the role of financing in the reduction of market imperfections induced by agency costs, through which firm performance may be enhanced. Two hypotheses test the main question of the paper; both hypotheses are empirically tested by OLS regression. The hypotheses try to find an answer in the ways capital structure and maturity structure serve as a mechanism to alter agency problems and, consequently, performance. The first hypothesis relates to capital structure and tries to answer the question whether debt levels (short-term and long-term) have an influence on performance. The second hypothesis investigates maturity structure and tries to answer the question whether the use of more short-term debt as a percentage of total debt affects firm performance. The dependent variable is firm performance, which is measured by the return on assets. The hypotheses are empirically tested using panel data techniques, where the pooled regression model and the fixed effects model are adopted. I use varying measures of performance and varying samples in order to enhance robustness of the results.

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implications of capital structure and debt maturity structure for the Netherlands, which is, to my knowledge, never done before. Furthermore, it controls for heterogeneity among individual firms by adopting a fixed effects model; an issue most related studies do no address. Additionally, the analysis is set in a sample period that includes a recession, a time in which performance-enhancement is even more valuable. Finally, by combining an agency theoretical framework for leverage as well as the maturity of leverage, the paper opens up a whole new area of research.

This study proposes a new framework of capital structure, which incorporates a trade-off between the benefits and the costs of debt from an agency theoretical perspective. On one hand, debt reduces moral hazard through the threat of liquidation (Grossman and Hart, 1982) and the reduction of the free cash flow problem (Jensen, 1986). On the other hand, it creates the asset substitution problem (Jensen, 1986) and the debt overhang problem (Myers, 1977). The model proposes that the costs and benefits of debt should be traded-off in order to optimize capital structure; hence an unambiguous sign of the capital structure-performance relationship is not determined at forehand. Furthermore, this paper shows how agency problems can be addressed by altering the maturity of debt, which alleviates the debt overhang problem and the adverse selection problem proposed by Diamond (1991). By reducing agency costs the firm is able to make more efficient use of profitable investment opportunities through reduced moral hazard and increased flexibility to exploit these profitable projects. This, in turn leads to the expectation of a positive relationship between shorter debt maturity and performance.

The empirical results of this paper show that higher leverage negatively influences firm performance, which suggests that firms have higher leverage than is optimal from an agency cost point of view. The agency costs of debt are more important than initially assumed, which induces suboptimal investments through foregone profitable investment opportunities or value-decreasing investments. These inefficiencies in turn negatively affect performance. The results are in line with empirical evidence found by Zeitun and Tian (2007) and Gleason et al. (2000), although they are in contrast with the results of Berger and di Patti (2006). Using a relative larger fraction of short maturity debt has a positive effect on performance. Shortening debt maturity alleviates agency costs related to the underinvestment problem and the adverse selection problem, as the results of Baum et al. (2006) indicate as well. More detailed evidence about different sub samples is given as well in the results section.

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its survival prospects during a recession. From a creditor’s point of view, its risk towards borrowers has to be assessed and managed. Gaining more insight into the processes that come along with the financing decision, creditors are more informed about the firm’s decision process regarding capital and maturity structure and the agency problems that may arise during this process.

Two important characteristics of this paper are the economic decline and the fact that it investigates construction firms. The construction industry is known to be tremendously cyclical1_{. Understanding}

the way financing influences construction firms’ performance during a recession is important, as many construction firms might face financial distress when their economic environment deteriorates. Moreover, focusing on a single industry makes a comparison of the results more straightforward, because firms within an industry are more similar regarding capital structure (Harris and Raviv, 1991; Michaelas et al., 1999). The sample period includes a recession, namely in 2001 and 2002. In such times it is important for firms to understand how certain decisions they make affect their performance, how they can act in order to improve their performance and thereby increase their chance on surviving economic turmoil.

This paper is organized as follows. Section 2 reviews previous theoretical and empirical literature and outlines a theoretical framework for the capital performance and maturity structure-performance relationship. Section 3 describes the data and section 4 presents the methodology used for the regressions. The results of these regressions are shown in section 5. Section 6 summarizes and concludes the paper.

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2 _{LITERATURE REVIEW}

In order to develop a better understanding of the formulation of the hypotheses, a theoretical background is presented in this section. The purpose of this section is to show how capital structure and maturity structure may affect firm performance through the presence of agency costs. This section gives the input for the formulation of the hypotheses and the empirical examination in the following sections. First, some theoretical and empirical literature on capital structure and its relation to firm performance is discussed. Next, a same set up is followed for maturity structure.

2.1 Capital Structure

In this subsection an agency theoretical framework of capital structure theory is used to explain the relationship between leverage and firm performance. This framework proposes that agency costs can be minimized through the use of moderate debt levels in order to obtain higher performance. This subsection starts with a brief development of capital structure theories, subsequently it sets up an agency theoretical framework of capital structure, followed by evidence from previous empirical literature on the capital structure-performance relationship.

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Agency Theoretical Framework

The capital structure-performance relationship is explained through the concept of agency theory. This framework suggests that market frictions such as moral hazard, adverse selection, and asymmetric information affect a firm’s performance and that capital structure and maturity structure may play a significant role in alleviating such market frictions. In their seminal paper, Jensen and Meckling (1976) state that the firm is a nexus of relationships that can be described as the principal-agent relationship. The security holders of the firm (debt holders as well as equity holders) are the principals and the management acts in behalf of the principals by managing the principals’ assets. Thus, ownership and control of the firm is separated. Agency problems stem from the combination of conflicts of interest and asymmetric information. Conflicts of interest may arise when the interests of the firm’s management and the owners are not always aligned and managers hold less than 100% of the residual claim on the firm. Managers do not capture the entire gain from profitable activities, but they do bear the entire cost. Consequently, it is appealing for them to engage in activities that will increase their private benefits, which is called moral hazard. This way, corporate managers act in their own interests and seek to maximize their salaries and perquisites, strive for job security, or exert insufficient work effort. All these actions create organizational inefficiencies and thereby costs that represent the loss of value compared to an efficiently managed firm. Agency problems lead to suboptimal investment decisions, which negatively affect firm performance. Therefore, reducing these agency costs is then useful from a performance perspective. These inefficiencies are one main source of agency costs; the costs that relate to using an agent. Another source of agency costs are the costs relating to mechanisms to mitigate the problems of using an agent. The owners of the firm can mitigate agency problems through the use of monitoring and control. Examples of such devices are the threat of a takeover, monitoring by an independent board of directors, incentivizing compensation packages, or protective covenants. However, implementing such devices is costly. For example, writing and enforcing covenants is costly and, moreover, it reduces the profitability of the firm (Jensen and Meckling, 1976). Reduced profitability stems from the fact that covenants may limit optimal investment decisions by managers because of imposed restrictions, which may lead to foregone profitable investment opportunities and hence reduces the firm’s performance.

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0 10 20 30 40 50 60 1 2 3 4 5 6 7 8 9 10 11 12 13 Agency Costs Debt Level Optimal Trade-Off Agency costs of equity

Equity holders vs. Ma nagement: •Threat of liquida tion • Reduction of “free” cash

Agency costs of debt Equity holders vs. Debt holders:

•Asset substitution • Debt overhang problem Figure 1

The Benefits and Agency Costs of Debt

Debt can be used to reduce the conflict of interest between the firm’s owners and management. On the other hand, agency costs of debt exist as well. An optimal capital structure can be obtained by trading off these costs and benefits.

debt exactly offset the marginal benefits. Gleason et al. (2000) suggest that firms might deviate from the optimal trade-off point because managers underestimate the costs of bankruptcy reorganization or liquidation. Managers may underestimate the costs of debt and choose a higher than appropriate amount of debt in the capital structure.

Debt as a Disciplining Device: The Benefits of Debt

The benefits from higher debt levels in a firm’s capital structure originate from the reduction in agency costs of outside equity. The mitigation of the conflicts between equity holders and managers constitutes the benefit of debt financing.

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A second benefit is that higher leverage mitigates the free cash flow problem of Jensen (1986), which states that free cash flow above the level required for all investments in positive net present value (NPV) projects creates an incentive for managers to invest the free cash flow inefficiently. The problem is expressed in a brief but widely cited quotation: “The problem is how to motivate managers

to disgorge the cash rather than investing it below the cost of capital or wasting it on organizational inefficiencies.” A possible solution to the free cash problem is debt. Debt forces firms to pay out cash

in the form of interest or debt repayment. Clearly, extremely high debt levels are not desirable either but moderate debt levels function as a disciplining device for managers. Especially for firms that are prone to overinvestment, the use of debt may significantly decrease agency costs. As Myers (2001) states, putting the firm on a diet by reducing free cash flow can add firm value.

The Agency Costs of Debt

Although agency costs of equity are partially mitigated by debt, leverage has some drawbacks; debt creates agency problems between equity holders and debt holders. First, Harris and Raviv (1991) describe the asset substitution effect suggested by Jensen (1986), also called risk shifting. Equity holders are prone to undertake risky investments, because equity holders face the upside potential, whereas debt holders bear the downside potential as they only receive a fixed return. Thus, because of myopic maximization of equity value, equity holders have an incentive to invest in very risky projects, even when the projects are value-decreasing overall. This incentive, created through the use of debt, imposes a cost on the firm since risky value-decreasing projects are more likely to be taken on. The increased risk affects not only the probability of bankruptcy and its associated costs. In turn, it affects revenues and operating costs as well (Jensen and Meckling, 1976). For example, a firm with high bankruptcy risk is most likely forced to pay executives higher salaries for them to accept higher risk of unemployment. These higher costs can be marked as agency costs. Reducing risk shifting lowers agency costs through enhanced revenues and reduced operating costs, which ultimately reflects in higher performance levels. Hence, moderating a firm’s debt level reduces these agency costs and enhances performance.

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al., 2001). As Stulz (1990) argues, the optimal capital structure is determined by the trade-off of the benefit of debt in preventing investment in value-decreasing investments (Jensen’s free cash flow problem) and the cost of debt in preventing investment in value-increasing projects (the debt overhang problem).

The Capital Structure-Performance Relationship

Although there is no vast array on empirical findings of the capital structure-performance relationship yet, some evidence has been established. Gleason et al. (2000) find a negative relationship between leverage and return on assets (ROA) for European retailers, because those firms are overleveraging themselves because of agency conflicts. Furthermore, they mark that it might be valuable to managers to investigate how agency conflicts and costs might be reduced so that firms are not overleveraged. Zeitun and Tian (2007) find similar results when analyzing the performance of Jordan firms using book values as well as market values to measure performance. They find a negative relationship between leverage and performance, which they ascribe to agency conflicts.

In contrast, Berger and di Patti (2006) find that reducing conflicts of interest between equity holders and managers is more valuable than the incurred agency costs of debt. The authors thereby imply that increasing a firm’s debt level would result in enhanced performance. They test this trade-off for the banking industry using profit efficiency as the performance measure and find that leverage is associated with higher profit efficiency ranges. Abor (2005) finds mixed results for Ghanaian companies; short-term debt to total assets positively impacts return on equity (ROE), whereas the opposite occurs for the long-term debt level and ROE. Although the author finds a significant relationship between leverage and performance, he does not link the evidence to support a theory in specific. Phillips and Sipahioglu (2004) find no significant relationship between capital structure and performance for the UK hotel sector, whereas Nucci et al. (2004) find a significant negative impact of leverage on a firm’s productivity. Thus, although most studies find a significant relationship, the sign of this relationship is mixed.

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thereby affects performance. Following the mixed signs of previous empirical evidence, a relationship between capital structure and performance is expected, but the sign of the relationship remains unclear.

2.2 Maturity Structure

This subsection presents some explanatory theories of the maturity structure-performance relationship. Most theories on the maturity structure of debt focus on the ability of the maturity structure to reduce agency problems and thereby increase organizational efficiency, resulting in higher performance. Two theories that explain how agency costs can be reduced by the use of shorter maturities of debt are the contracting-cost hypothesis and the signaling hypothesis (Barclay and Smith Jr., 1995; Baum et al., 2006; Lamieri, 2009). This section follows with a presentation of the two theories, followed by empirical evidence of the maturity structure-performance relationship.

Contracting-Cost Hypothesis

The underinvestment problem – caused by debt overhang due to a high initial debt level – also affects debt maturity. A solution to this problem would be to lower the amount of debt in a firm’s capital structure. However, this would give rise again to agency problems between equity holders and management. As Jensen (1986) and Myers (1977) suggest, shortening debt maturity is another way to overcome the problem of underinvestment; this is called the contracting-cost hypothesis. Myers (1977) argues that a firm’s growth options in the form of profitable investment opportunities are like options. These options have to be exercised within a specific timeframe. Debt that matures before the option to invest has to be exercised implies that refinancing is necessary before the investment option expires. Lenders and borrowers need to recontract, which allows debt to be repriced so that gains from new projects do not accrue to debt holders. This way, equity holders are able to capture more of the benefits from new investments, which reduces their disincentive to invest. Hence, equity holders pass up a smaller number of profitable investments. Consequently, the organizational inefficiencies of foregone growth opportunities are reduced, which ultimately reflects in higher performance. Barnea et al. (1980) conclude that the agency problem disappears if debt matures before the investment option has to be exercised, in line with (Myers, 1977). The strategy is then to roll over short maturity debt claims instead of using a single long-term debt claim in order to mitigate underinvestment and increase performance.

Signaling Hypothesis

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prospects and therefore face uncertainty about their own future payoff. Therefore, in order for these investors to actually provide finance they will require a higher rate of return on their investments and will pose more restrictive loan agreements on the firm. More favorable loan agreements do not limit future debt issues or it might enable the firm to be granted a larger loan amount. Hence, if a mechanism exists that is able to alleviate information asymmetries; future investors are willing to write less restrictive provisions. This enables the managers to act upon future value-increasing investment opportunities, without having to deal with possible inefficient limitations to future investments. Agency costs are then reduced by the value of the projects that are taken on now, but which would have been foregone due to restrictive debt covenants.

The mechanism to alleviate information asymmetries between insiders and outsiders constitutes the signaling hypothesis. It reviews agency conflicts in an adverse selection context. The model assumes asymmetric information about the borrower’s type: a high or a low quality borrower (Diamond, 1991). It predicts that firms with positive private information about future profitability (high quality firms) will prefer to incur short-term debt. Lenders are reluctant to refinance if bad news on profitability and hence repayment arrives. Such firms face a refinancing risk when incurring short-term debt. Firms with a very positive outlook will issue short-term debt, since the probability of bad news is smaller than for firms with lower future EBIT expectations. On the contrary, bad type borrowers are more inclined to use long-term debt. The probability of nonrepayment is larger for such borrowers, and only the borrowers themselves know this. They are not willing to face the risk of the short-term debt not being rolled over, so long-term debt is dominant in their capital structure. Therefore, the good type is typically short-term financed, whereas the bad type prefers long-term financing. When firms act in this manner, they are able to signal their creditworthiness by securing less future liquidity than would be efficient under symmetric information. This is the case if the firm chooses for short-term financing. The firm faces more risk of future illiquidity, but she conveys a signal of confidence about future prospects and that she is not afraid of going back to the creditor at an intermediate stage. From the lender’s perspective, shorter maturity is a way to cope with unknown and riskier borrowers by introducing a monitoring device. During a recession - when many firms may face financial distress - more information asymmetries exist and signaling creditworthiness may be even more valuable for those firms who do manage to perform well during these times.

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The Maturity Structure-Performance Relationship

Just as the performance implications of capital structure is a relatively new field of research; the same holds for the performance implications of maturity structure. Nevertheless, some papers find empirical evidence for this relationship. Baum et al. (2006) conclude that short-term liabilities affect profitability positively for their sample of German companies. The authors assign this positive relationship to enhanced monitoring and control. Moreover, they remark that short-term debt offers more flexibility to exploit profitable investment opportunities. They arrive at this conclusion by analyzing ROA in a system GMM approach with short-term liabilities divided by total liabilities as the main explanatory variable. Schiantarelli and Sembenelli (1997) find contrasting evidence. They find a negative relationship between shorter debt maturity and performance for Italian and UK firms. Initially, a positive relationship is expected as they suggest that short-term debt reduces the probability that a firm will forgo profitable investment opportunities. Hence, greater reliance on short-term debt and the resulting flexibility in the firm’s capital structure may be associated with higher levels of profitability. They focus on the fact that debt with shorter maturity may help to solve the underinvestment problem created by high leverage. Their results show that more profitable firms have longer maturity of debt and that longer debt maturity has a positive influence on performance, however using a different performance variable than in this paper. These results are in contrast with the theory that shorter maturity leads to financial pressures in inducing managers to make performance-enhancing choices. The authors do not give a theoretical explanation of why a negative relationship is justified. This paper’s regression analysis should give additional empirical evidence of the impact maturity structure has on firm performance.

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3 _DATA

This section explains how the data is collected, how outlying observations are handled, and how the included variables are constructed. Furthermore, some basic information about the variables is presented in the descriptive statistics. Finally, a preliminary indication of the relationships across the variables is found in the correlation matrix at the end of the section. This section enables the reader to develop a thorough understanding of the dataset.

3.1 Data Collection

In order to analyze the effect of capital structure and maturity structure on firm performance during a recession, data is collected for 224 Dutch companies in the construction sector for the period 2001-2005, a period in which the Dutch economy faced a sharp decline. All information is obtained from REACH, a Dutch company accounts database compiled by Bureau van Dijk. REACH provides unconsolidated accounts data extending back over a number of years. The inclusion of the sample firms is restricted through several required firm-specific aspects.

First, an initial sample is drawn using the main activities’ industry classification (through the use of BIK codes). Second, all the financial reports in the years of interest must be available. Finally, to make sure that the sample firms are of substantial size, I extracted a list with the 400 largest firms, which enhances the probability of full data availability. As a final step I reviewed these firms on correct activity specification in REACH and eliminated those firms that actually operate in another industry than the construction industry. Eventually, a list of 279 firms remained. After extracting the relevant variables from these firms’ financial accounts, a sample of 224 firms with complete data for 2001-2005 remained, yielding 1120 observations. The full list of the firms included in the analysis is reported in Appendix 1. The focus lies on one specific sector, the construction industry. The construction industry is a very cyclical industry, which makes investigating this industry interesting during a recession. Moreover, focusing on a single industry makes a comparison of the results more straightforward, because firms within an industry are more similar regarding capital structure (Harris and Raviv, 1991; Michaelas et al., 1999).

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Wholesale 18% Supplier 34% Contractor 48%

choice during a recession, Michaelas et al. (1999) use book values of variables similar to those used in this study. Following these empirical studies, this research uses accounting-based data in estimating the impact of capital structure and the maturity choice of debt on the financial performance of companies.

In rare cases REACH classifies entries on the profit and loss account and the balance sheet erroneously. This error in measurement may affect the outcomes heavily. Therefore, outliers are removed from the sample when the observation is more than three standard deviations away from the mean of that particular variable, which yields 111 observations marked as outliers for all the variables. Firms within the dataset are divided into three branches of industry: supplier, contractor, or wholesale. The suppliers produce construction materials and supply them to the contractor or to wholesalers. Subsequently, the contractors actually execute the constructional work. Finally, the wholesalers sell construction materials to other companies within the construction industry. Of the 225 sample firms, 76 are suppliers, 108 firms are contractors, and 40 firms are wholesalers. This division is visualized below in Figure 2.

The process of collecting the relevant data for this research yields a comprehensive dataset, including information on firm performance, capital structure, maturity structure, and several control variables. Which variables are included and how they are constructed is explained next.

Figure 2

Division of Sample Firms across Sectors of Industry

3.2 Construction of the Variables

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Table 1

Construction of the Variables

Variable Calculation

Return on assets (ROA) Earnings Before Interest and Taxes (EBIT) to Total assets Short-term debt Short-term debt to Total assets

Long-term debt Long-term debt to Total assets Short maturity Short-term debt ratio to Total debt ratio

Total debt (Short-term debt + Long-term debt) to Total assets

Size Total sales (x € 1.000)

Financial slack Current assets to Current liabilities

GDP Growth in Dutch Gross Domestic Product (2001-2005)

GDP (-2) Growth in Dutch Gross Domestic Product, lagged 2 years (1999-2003)

debt. In both regressions firm size, financial slack, Gross Domestic Product (GDP) and lagged GDP are included as control variables. In Table 1 the construction of all the variables is summarized.

The performance of a firm is measured by its return on assets (ROA). As can be seen in Table 1, ROA is defined as the earnings before interest and taxes (EBIT) divided by the total assets of the company. EBIT is used because it represents the firm’s operational profit; it excludes effects of different capital structures and taxes across companies in order to remain focused on the operational performance. ROA is a widely used measure in the empirical literature when analyzing the performance of companies such as in Gleason et al. (2000), Latham and Braun (2009), Zeitun and Tian (2007), and Baum et al. (2006).

The short-term debt measures the amount of short-term debt relative to total assets. The variable long-term debt is calculated in an equal manner. It is the broadest definition of debt. The calculation is in line with the analysis of Abor (2005) and Feidakis and Rovalis (2007). Bernstein (1993) states that these ratios are the most comprehensive ratios for capital structure measurement. These two variables are the key variables in the first regression, analyzing the effect of capital structure on firm performance.

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Next to the variables representing performance and debt, several control variables are included: size, financial slack, and GDP.

The natural logarithm of total sales is used as a proxy for size (Rajan and Zingales, 1995; Feidakis and Rovolis, 2007). Size is a variable that has proven to be of influence on the performance of a firm. Large firms tend to have certain advantages over small firms, such as economies of scales and market power (Hall and Weiss, 1967). Moreover, Baumol (1967) argues that large firms have easier access to capital markets since they are able to invest in large scales. Additionally, large firms are associated as being diversified firms, which may reduce risk. These arguments lead to the expectation of a positive relationship between performance and firm size.

Because this research is focused on a period of economic downturn, financial slack is included as a control variable as well. Financial slack is defined as the ratio of current assets to current liabilities. In the regressions, the natural logarithm of the ratio is included. Because of agency theoretical considerations, financial slack may create inefficiencies that desensitize managers to change the internal structure during environmental discontinuities (Jensen, 1986; Geroski and Gregg, 1997). On the other hand, it allows firms to shift resources from financial slack to do strategic investments during a recession. Latham and Braun (2009) show that firms with high levels of financial slack tend to exhibit a steeper rate of decline during a recession, but are more resilient and show a more rapid rate of recovery. To control for Dutch macroeconomic changes GDP and two-year lagged GDP are included, following Booth et al. (2001) and Michaelas et al. (1999). The two-year lagged GDP is included because recovery in the construction is delayed in relation to the recovery in GDP. This delay can be seen in Figure 3 where the Dutch GDP growth and the growth in total output of the construction sector are visually depicted. Because a lagged relationship is expected between performance and GDP, the coefficient of the two-year lagged GDP variable is expected to be positive.

Figure 3

Gross Domestic Product (GDP) and Growth of Total Output in the Construction Sector in the Netherlands for 2000-2010

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3.3 Descriptive Statistics

Table 2 contains the descriptive statistics of the variables: the mean, the median, the highest and lowest observed value, the standard deviation, and the total number of observations. To get a better understanding of the variables, the statistics are also given for each separate year within the sample. The statistics per year are shown in Appendix 2 and per sector of industry in Appendix 3.

The mean performance (ROA) of the sample firms is 7.3%. Large differences among the cross-sectional units are observable. Even after removing the outliers, the highest observed performance is 39.1%, whereas the minimum is a loss of 29.6%. These numbers suggest high variability in the performance of firms in the construction industry. ROA decreases somewhat during 2002 and 2003. In 2003 ROA is 16.3% lower than in 2001. Striking is the fact that the level of long-term debt actually increases, with the highest level being in 2003 when ROA is at its low. The high level of long-term debt in combination with low ROA in 2003 is a first clue of the negative relationship between long-term debt and firm performance, which is investigated more profoundly using regression analysis. Firms in this sample are financed through long-term debt by an average of 18.2% of their total assets. For the short-term debt level this number lies much higher, at 47.0%. These figures reflect that, on average, the maturity structure is quite short; 72.2% of total debt is classified as short-term debt. In contrast to long-term debt, total sales move in the same direction as ROA, which is an indication of a positive relationship between performance and firm size. Mean sales are € 231,512.20 whereas the median is € 81,541.00. There are some very large firms included in this sample, resulting in a substantial difference between the mean and the median of the size proxy. The highest amount of sales is almost € 4 billion and the lowest is ‘just’ € 7.7 million. The average financial slack of firms is 1.506, implying that the value of companies’ current assets is one and a half times the value of their current liabilities.

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Table 2

Descriptive Statistics of the Complete Sample

This table shows the mean, the median, the maximum, and the minimum observation for each variable in the sample. Furthermore, the standard deviation and the total number of observations are given. Return on assets (ROA) is measured by EBIT divided by total assets. Short-term debt is total short-term debt as a ratio of total assets. Long-term debt is total long-term debt as a ratio of total assets. Short maturity measures the ratio of short-term debt to long-term debt and reflects the percentage of total debt that is marked as short-term. Total debt is the sum of total short-term debt and long-term debt as a ratio of total assets. Sales are the total revenues of the firm. Financial slack is calculated by dividing current assets by current liabilities. GDP is the growth in the Dutch Gross Domestic Product for the years 1999-2005. All other variables are measured from 2001-2005.

ROA Short-Term Debt Long-Term Debt Short Maturity Total Debt Sales (x 1.000) Financial Slack GDP

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Just as the development of long-term debt and performance over time indicates a possible negative relationship between these variables, other statistics support this view as well. On average, suppliers and contractors, both with lower long-term debt levels, both have a higher ROA than wholesalers. Suppliers earned a mean ROA of 7.8% (20% higher), contractors 7.3% (12.3% higher), and wholesalers a mere 6.5%.

The descriptive statistics give more insight into the characteristics of the variables. Next, it is discussed how they are related to one another in the form of a correlation matrix. The correlations across the variables give an even more comprehensive understanding of the data, which is useful when the equations are estimated through regression analysis.

3.4 Correlations

To conclude this section, the correlations across the performance, leverage, maturity, and control variables are examined in Table 3. The negative relationships between ROA and short-term debt as well as long-term debt are a preliminary indication of the regression results, namely that these ratios negatively impact performance. Of course, from these correlations no direction of causality can be estimated. Moreover, a strong correlation is present among the variables short-term debt, long-term debt, and total debt. It makes sense that these factors are correlated. If a firm takes on extra short-term or long-term debt this, ceteris paribus, leads to higher total debt. Thus, if this firm does so without paying off old debt, total leverage increases. Short-term debt and long-term debt are negatively correlated (-0.458), which implies that having more long-term debt compensates for having less short-term debt, and vice versa.

Short maturity is negatively correlated with financial slack (-0.551), which makes sense as both variables include ‘current liabilities’. For the short-term debt measure, current liabilities are the denominator. For financial slack, current liabilities are the nominator. So, when current liabilities increase it will have opposite effects on these variables, which reflects in the negative correlation figure.

The strong negative correlation of GDP and the two-year lagged GDP may be the result of the fact that the period of analysis, the years 2001 to 2005, is a period in which the growth of the Dutch GDP made a reversal from decline to growth.

Correlations across explanatory variables are at moderate levels; there is no assumption of multicollinearity. To check this assumption, a regression without the control variables is done as well as a robustness check. This is explained in the methodology section.

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Table 3 Correlation Matrix

This table shows the correlations across the variables. Return on assets (ROA) is measured by EBIT divided by total assets. Short-term debt is total short-term debt as a ratio of total assets. Long-term debt is total long-Long-term debt as a ratio of total assets. Short maturity measures the ratio of Long-term debt to long-Long-term debt and reflects the percentage of total debt that is marked as short-term. Total debt is the sum of total short-term debt and long-term debt as a ratio of total assets. Sales are the total revenues of the firm. Financial slack is calculated by dividing current assets by current liabilities. GDP is the growth in the Dutch Gross Domestic Product; GDP (-2) reflects the two-year lagged GDP figure and, therefore, is measured from 1999-2003. All other variables are measured from 2001-2005. *** Significant at 1% level, ** Significant at 5% level, * Significant at 10% level.

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4 _METHODOLOGY

In this section detailed information about the methodology is given. From previous empirical findings hypotheses are derived, which are presented here. Furthermore, the type of analysis is explained and justified. As this analysis makes use of panel data, this section briefly discusses issues encountered using this type of data. It also describes the equation used for the regression analysis and presents how econometric issues are addressed.

4.1 Hypotheses

Following from the review of literature on capital structure, there is no universal theory of the debt-equity choice and how this choice influences the performance of firms, which is measured by ROA in this study. Debt can be used to mitigate agency costs of equity through the threat of liquidation and the reduction of free cash flow to managers (Grossman and Hart, 1982; Jensen, 1986). On the other hand, debt brings along its own agency costs as well, namely the underinvestment problem and the asset substitution effect (Myers, 1977; Jensen, 1986). Following from the theoretical background a hypothesis follows, which is tested against its two-sided alternative:

H0: The levels of short-term debt and long-term debt do not have an influence on the return on assets.

H1a: The levels of short-term debt and long-term debt have a positive influence on the return on assets.

H1b: The levels of short-term debt and long-term debt have a negative influence on the return on

assets.

From a theoretical perspective, a positive relationship between the percentage of short-term debt relative to total debt and the return on assets is expected. The use of shorter maturity of debt reduces agency costs arising from the debt overhang problem and the adverse selection problem (Myers, 1977; Diamond, 1991). Although empirical evidence is mixed (Schiantarelli and Sembenelli, 1997; Baum et al., 2006), I still expect the relationship to be positive because theory points in such a direction. The expectation is that the reduction in agency costs results in more efficiently managed firms, which increases firm performance. The hypothesis is formalized as follows:

H0b: The use of more short-term debt as a percentage of total debt does not have an influence on the

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H1b: The use of more short-term debt as a percentage of total debt has a positive influence on the

return on assets.

Several analyses are executed in a panel data context to test these two hypotheses. The basics of the use of panel data methodology are explained next, as well as the regression models specific to this research.

4.2 Econometric Specification

In the data section the correlation analysis was presented in order to give a first indication of the possible capital structure-performance and maturity structure-performance relationship. The next step is to analyze the data using regression analysis. The influence of capital structure and debt maturity on a company’s financial performance is identified by using panel data, in which there is a cross-sectional dimension as well as a time-series dimension (Brooks, 2008). Such a setup is used when several objects are measured over a period of time, as is done in this study where 224 construction firms are analyzed over a period of 5 years, resulting in a dataset containing 1120 observations.

The advantage of using panel data is the fact that it gives more insight into the development of variables and their relationships over time. Brooks (2008), Hsiao (1985), and Baltagi (1995) list some benefits of using panel data. First, panel data include a large number of data points; it increases the degrees of freedom and decreases problems with multicollinearity among variables, hence improving the efficiency of econometric estimates (Hsiao, 1985). Additionally, panel data enable to measure and identify effects that are simply not detectable in pure cross-sections or pure time-series data by studying the dynamic behavior of the variables over time (Baltagi, 1995). Finally, it can remove the impact of the omitted variable bias by structuring the model in an appropriate way (Brooks, 2008).

There are several models available to analyze panel data. In relation to this particular study the pooled model and the fixed effect model are explained. Their main characteristics are summarized in Table 4. The pooled regression model (or constant coefficients model) is the simplest way to handle panel data. As the name suggests all the observations are pooled together, in which case there is one fixed intercept. It is similar to running an ordinary least squares regression (OLS) where no distinction is made for the cross-sectional or time-series effects and the corresponding heterogeneity. Such a distinction is present with the fixed effects or the random effects model. The first and most simple model is the pooled regression model and looks as follows:

it it

it x u

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Where yit is the dependent variable,

α

is the intercept,

β

is a k×1 vector of parameters to be

estimated on the explanatory variables, x_it :i=1,...,N;t =1,...,T (Brooks, 2008).

To overcome the limitations of a pooled regression model there are several models that can be used. A widely used model is the fixed effects model. The fixed effects model includes an individual effect, which is different for each cross-sectional unit but is constant over time. So, instead of having one constant

α

, there are many constants

α

_i that vary across the individuals. The model assesses different starting points for each company. This results in different regression lines than the pooled regression model, because with the fixed effects model it is possible to observe fixed individual effects, which are unobservable in the relatively simple pooled model. Because the fixed effects model assigns a dummy for each company in the cross-section to the OLS regression, the model is sometimes called the Least Squares Dummy Variable (LSDV) model. This model is appropriate to identify and control for omitted variables, differing for each company but which are constant over time. The equation of the fixed effects regression looks the same as the pooled regression equation (4.1) but the disturbance term can be decomposed:

it i

it v

u =

µ

+ (4.2)

The disturbance term consists of two parts, where

µ

_i is the firm-specific effect and v_it is the remainder disturbance.

µ

_i is constant over time but differs for each firm. The cross-sectional specific constant,

α

_i, can also be thought of as

α

+

µ

_i; the common intercept plus the individual fixed effect. Next to the inclusion of company specific effects in the fixed effects model, there is also the possibility to include time dummies. For each year a time dummy can be included in the regression to control for time effects. Time dummies are not used in this study as time dummies rule out the possibility of analyzing the effect of the time effect of GDP. The GDP is of particular interest in this study as it focuses on recession, which can be measured by economic growth.

Table 4

Summarized characteristics of the panel data models used for the regressions

Pooled Regression Model Fixed Effects Model

Single equation on all data together Separate regression for each cross-sectional unit

it it

it x u

y =

α

+

β

+ yit =

α

+

β

xit +uit, where uit =

µ

i +vit

Simplest way to handle data Possibility to observe fixed individual effects, controls for omitted variables

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Since the fixed effects model assigns a dummy for each explanatory variable to the model, the fixed effects model suffers from a loss of degrees of freedom as the number of variables increases. Less degrees of freedom aggravates the problem of multicollinearity among the regressors, which increases the standard errors and reduces the statistical power of the model (Baltagi, 1995).

The problem of too many parameters in the fixed effects model can be avoided through the use of the

random effects model. Brooks (2008) explains the specification of the model. This model adopts some

restrictive assumptions on the error term. It requires that the error term has zero mean, is independent

of vit, has constant variance

2

t

σ

, and is independent of the explanatory variables. To test the requirement of no correlation between the error terms and the explanatory variables, the Hausman test is used (Hausman, 1978). For both the capital structure as well as the maturity structure equation the test gives a statistically significant outcome, indicating that the random effects specification is not appropriate and the fixed effects model or the pooled model should be adopted. Results of this test are found Appendix 4. Therefore, the random effects model is not further discussed in this paper.

4.3 Regression Analysis

Two separate regressions are done, one to analyze the effect of capital structure and the other in order to assess the same for the maturity structure of debt. The first equation is:

it t t it it it it it

u

GDP

Slack

Size

bt

Longtermde

ebt

Shorttermd

ROA

+

−

+

=

)

2 (

(

)

(

)

ln(

)

ln(

)

(

)

(

6 5 4 3 2 1

β

α

(4.4)

Here, idenotes the individual cross-sectional units and t denotes time. Short-term debt refers to the level of short-term debt divided by total assets. The long-term debt variable is constructed in the same manner. Size refers to total sales. Slack represents the financial slack of a firm, calculated by the current ratio. For both size as well as slack the natural logarithm is used, as this reduces possible multicollinearity. GDP and GDP(-2) are the growth figures for the Dutch economy, where GDP(-2) is lagged two years. The estimates of the coefficients tell us more about whether capital structure has effect on the performance of firms. Maturity structure is analyzed in the second model where the variables ‘short-term debt’ and ‘long-term debt’ are replaced by ‘ short maturity’ and ‘total debt’:

it t t it it it it it

u

GDP

Slack

Size

Totaldebt

ity

Shortmatur

ROA

+

−

+

=

)

2 (

(

)

(

)

ln(

)

ln(

)

(

)

(

6 5 4 3 2 1

β

α

(4.5)

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debt ratio. This equation tries to explain the effect of the maturity of debt, given that the firm has a certain level of debt. To control for the fact that firms do have different debt levels, ‘total debt’ is included in the equation.

A Wald test is performed on the joint significance of the cross-sectional dummies, which indicates that the individual effects are indeed jointly significant. This result is in favor of the use of the fixed effects model. Hence, the unobservable effects in the pooled model that can be observed in the fixed effects model are important for this sample. However, Hsiao (1986) points out that in the presence of measurement error the fixed effects model is more likely to produce biased estimates than with simple pooling. Therefore, next to the results of the fixed model, the pooled regression results are reported as well. For both the pooled model as well as the fixed effects model, the specification looks similar. The difference lies in the error term. In the fixed effects model this disturbance term includes a term for individual effects, which is constant over time. Such a term is not present in the pooled model.

Next to the results of these regressions, the results of several alternative specifications are presented in the results section to check whether the results are robust to different measures of performance, different samples, and different econometrical methodologies.

4.4 Diagnostics

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5 _RESULTS

In this section the results of the OLS regressions are presented, which are used to answer the main question of this paper: How do capital structure and the maturity structure of debt influence firm performance? First, the results of the capital structure equation are shown, followed by those for the maturity structure equation. Moreover, several robustness checks are done to assess whether the results are robust with respect to alternative models and to a different measurement of variables. Finally, the results are discussed in context with the theory proposed in the literature review.

5.1 Capital Structure Regression Results

Table 5 contains the results based on the OLS regression analysis with ROA as the dependent variable. The OLS regressions show evidence for the theorized capital structure-performance relationship. The table reports the results using the pooled regression model in the second column whereas the fixed effects model is adopted in the third column. Furthermore, separate fixed effects regressions are done for each sector of industry: supplier, construction, and wholesale.

The two capital structure variables are short-term debt and long-term debt, both as a percentage of total assets. The regression coefficients of these variables clearly show a negative relationship with firm performance for both the pooled regression as well as the fixed effects model. The negative relationship is statistically significant at the 1%-level. This suggests that firms that have higher levels of debt - whether that is short-term or long-term - have lower firm performance. Hence, the null-hypothesis that capital structure does not influence firm performance is rejected. The results are consistent with the theory that debt comes along with agency costs as well, such as the asset substitution effect and the debt overhang problem. The negative sign of the coefficients of short-term debt and long-term debt implies that the agency costs that come along with leverage cause managerial inefficiencies, which negatively impact performance. Following the trade-off proposed earlier, the results indicate that sample firms are leveraged beyond the optimal capital structure. They are positioned to the right of the optimal trade-off point. The coefficients estimated from the pooling regression are smaller than those estimated from the fixed effects regression suggesting that ignoring individual firm effects leads to underestimation of the impact on performance.

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Table 5

Regressions Estimating the Capital Structure-Performance Relationship

The dependent variable is return on assets, measured by EBIT divided by total assets. The capital structure variables are short-term debt and long-term debt as a ratio of total assets. The natural logarithm of sales and financial slack are included as control variables, next to the Dutch growth in GDP and two-year lagged GDP figure. The regression is done using the pooled regression model and the fixed effects model. The results of the sub sample regressions are done using the fixed effects model. Standard errors are robust to White time series and cross-section heteroscedasticity. The sample period is 2001-2005. *** Significant at the 1% level, ** Significant at the 5% level, * Significant at the 10% level.

Pooled Model

Fixed Effects Model

Supplier Contractor Wholesale

Intercept 0.137 (0.000)*** -0.745 (0.000)*** -0.931 (0.000)*** -0.474 (0.045)** -1.107 (0.012)** Short-term debt -0.072 (0.001)*** -0.195 (0.000)*** -0.209 (0.035)** -0.157 (0.026)** -0.272 (0.004)*** Long-term debt -0.110 (0.000)*** -0.262 (0.000)*** -0.284 (0.000)*** -0.227 (0.000)*** -0.309 (0.002)*** Sales -0.002 (0.231) 0.080 (0.000)*** 0.098 (0.000)*** 0.054 (0.007)*** 0.113 (0.003)*** Financial slack 0.027 (0.000)*** 0.050 (0.001)*** 0.047 (0.037)** 0.051 (0.036)** 0.039 (0.111) GDP 0.307 (0.261) 0.161 (0.399) -0.264 (0.426) 0.068 (0.813) 0.978 (0.003)*** GDP (-2) 0.255 (0.045)** 0.595 (0.000)*** 0.241 (0.235) 0.750 (0.000)*** 0.765 (0.000)*** R2 0.068 0.649 0.725 0.595 0.635 Observations 1037 1037 335 519 183

(1967) and Hall and Weiss (1967) firm size positively influences performance. This may be due to two factors. First, larger firms enjoy economies of scale, with large firms being more diversified. Second, this result may relate to increased market power of larger firms. The positive size effect on performance is significant for the fixed effects model, however the pooled regression shows no significant relationship. This implies that large firms are not necessarily more profitable compared to small firms, but firms that become larger over time become more profitable.

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The macro-economic control variables that control for the Dutch economic growth show that performance of construction firms is indeed delayed with respect to the economy as the significant positive coefficient of the two-year lagged GDP shows. When the economy starts to recover from economic decline, it takes a while before the construction firms’ performance climbs up again as well. This may be explained by the fact that it takes these firms some time to build up their strengths and fill their order books up until pre-recession levels.

The R2 for the pooled regression is clearly lower (6.8%) than the R2 of the fixed effects model (64.9%). The R2 shows how much of the variation in firm performance is explained by the independent variables of the regression. Although a higher R2 for the fixed effects model may be partially explained by actual higher explanatory power of the specification, the enhancement is explained as well by the fact that the fixed effects regression has a better fit with the data as the fixed effects model estimates a separate regression line for each firm.

Looking at the results of the separate regressions for the wholesalers, contractors, and suppliers, three conclusions can be drawn. First, the negative effect of leverage is similar across all sectors of industry of construction firms, which enhances the robustness of the results although the effect is somewhat stronger for the wholesale group. Moreover, all the significant coefficients of the control variables show the same sign as for the overall sample. Finally, whereas GDP does not significantly influences firm performance for the whole sample, this is the case for wholesalers. The coefficient of GDP is quite high for wholesalers, indicating that this group of firms is prone to be more heavily influenced by macro-economic conditions.

This subsection can be summarized by the fact that the OLS regression results show a significant negative relationship between leverage and firm performance. The results indicate that firms have too much leverage in their capital structure in relation to the agency theoretical trade-off. The result holds whether the pooled model or the fixed effects model is adopted, as well as for different sub samples. The negative relationship is more pronounced for the wholesale group though. Size, financial slack, and two-year lagged GDP all positively influence firm performance.

5.2 Maturity Structure Regression Results

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Table 6

Regression Results Estimating the Maturity Structure-Performance Relationship

The dependent variable is return on assets, measured by EBIT divided by total assets. The maturity structure variable is short maturity, which is the ratio of short-term debt to total debt, and total debt. The total debt ratio is included to control the effect of short-term debt for a given debt level. The natural logarithm of sales and financial slack are included as control variables, next to the Dutch growth in GDP and two-year lagged GDP figure. The regression is done using the pooled regression model and the fixed effects model. The results of the sub sample regressions are done using the fixed effects model. Standard errors are robust to White time series and cross-section heteroscedasticity. The sample period is 2001-2005. *** Significant at the 1% level, ** Significant at the 5% level, * Significant at the 10% level.

Pooled Model

Fixed Effects Model

Supplier Contractor Wholesale

Intercept 0.117 (0.000)*** -0.762 (0.000)*** -0.845 (0.000)*** -0.544 (0.023)** -1.154 (0.012)** Short maturity 0.036 (0.012)** 0.080 (0.012)** 0.078 (0.194) 0.095 (0.068)* 0.065 (0.132) Total debt -0.097 (0.000)*** -0.195 (0.000)*** -0.201 (0.001)*** -0.163 (0.010)*** -0.262 (0.001)*** Sales -0.002 (0.257) 0.075 (0.000)*** 0.084 (0.000)*** 0.053 (0.010)*** 0.112 (0.004)*** Financial slack 0.031 (0.000)*** 0.059 (0.000)*** 0.050 (0.020)** 0.070 (0.009)*** 0.052 (0.008)*** GDP 0.357 (0.185) 0.202 (0.283) -0.088 (0.788) 0.085 (0.766) 0.918 (0.006)*** GDP (-2) 0.292 (0.020)** 0.612 (0.000)*** 0.284 (0.152) 0.780 (0.000)*** 0.735 (0.000)*** R2 0.090 0.653 0.724 0.599 0.643 Observations 1042 1042 341 519 182

maturity in alleviating agency problems, thereby increasing the efficiency of investment decisions. The coefficient shows a positive effect of short-term debt on firm performance. Thus, an increase in the relative amount of short-term debt positively affects firm performance. The relationship is significant at the 5%-level for both models. Hence, the null hypothesis that the use of more short-term debt as a percentage of total debt does not have an influence on the return on assets is rejected. The regression results of maturity structure are in line with the theory suggesting that short-term debt enhances efficiency of managers and thereby increases performance, in line with evidence found in Baum et al. (2006).

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explaining the performance of a firm, whereas the two-year lagged GDP is. Both firm size and financial slack have a significant and positive coefficient in the fixed effects model.

Whereas the regression of the various subsectors showed similar results for capital structure, backing up the evidence from the main regression, for maturity structure there are some deviations. When dividing the firms over the three sectors, a significant coefficient is found only for the contractors. Although the maturity structure-performance relationship remains positive for the other two groups, the impact is not statistically significant. This might be because the sample size of these two groups is quite small; contractors represent the largest fraction with 519 out of 1042 observations. There are 341 supplier observations included and 182 of wholesalers, which is obviously less than the group of contracting firms. The smaller amount of significant results might also be an indication that the results are not robust, which needs further investigation. The investigation is done in the next subsection where the robustness of the results is checked using different samples and different measures of performance. The total debt level, firm size, and financial slack remain to influence performance significantly and in the same manner as for the complete sample. Again, wholesalers are the only group to show a significant positive GDP coefficient whereas the two-year lagged GDP shows to be positive and significant for all three groups. Roughly speaking, the variation in performance explained by the model is equal to the capital structure regression; for the pooled model R2 is 9.0% and for the fixed effects model it is 65.3%.

To conclude, the results indicate that using relatively more short-term debt in the capital structure significantly and positively affects firm performance. The results support that short debt maturity in alleviating agency costs and thereby increases performance. When the sample is split, a significant relation is found only for the contractors group. Similar to the capital structure equation, firm size, financial slack, and two-year lagged GDP positively and significantly influence the performance.

5.3 Robustness Checks

Performance Implications of Capital Structure and the Maturity Choice of Debt during a Recession