The relationship between capital structure and product markets: Evidence from New Zealand David J. Smith∗, J.G.Chen, and Hamish Anderson
Department of Economics and Finance, Massey University, Private Bag 11-222, Palmerston North, New Zealand
Abstract We investigate the relationship between the capital structure of New Zealand manufacturing firms and their product market strategies in the period from 1993 to 2006. Using the seemingly unrelated regression method, and controlling for whether or not firms are exporters, we find that long-term leverage both influences and is influenced by product-market competition. As firms’ relative-to-industry sales increase in New Zealand manufacturing industries, their use of long-term debt also increases. Looking at the relationship in the other direction, we find that greater use of long-term debt results in an increase in firms’ relative-to-industry sales. However with respect to the impact of product-market competition on the use of leverage, the competition variable may be acting as a proxy for size. Moreover when industry dummy variables are substituted for the exporter dummy variable, the competition and long-term debt variables are no longer significant. Key words: Capital structure; product markets; imperfect markets; manufacturing JEL classification: G31; G32; L11; L13; L60
∗
Corresponding author. Tel.: +64-6-350-5799 ext 7386; fax: +64-6-350-5651. E-mail address:
[email protected] (D.J.Smith).
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1. Introduction A growing area of finance research examines the relationship between capital structure and product markets. More specifically it investigates whether a firm’s leverage has an impact on or is influenced by factors such as the firms’ pricing and production strategies, their decisions on entering and exiting markets, and their interactions with competitors, customers, suppliers and employees. To date little literature has been published on the capital structure of New Zealand firms, and their input and output markets, and we believe there is scope for further research on these topics. Moreover precisely because of its size and nature, the New Zealand market is an interesting one to use for studies that focus on capital structure. For example, there are relatively few producers in New Zealand and it is more likely that monopolies and oligopolies exist. Research in this area may therefore provide insights into whether New Zealand monopolies and oligopolies are using financial structures which help them to maintain or strengthen their position in their markets. Our paper investigates the relationship between the capital structure of New Zealand manufacturing firms and their product market strategies in the period from 1993 to 2006. In particular we examine whether the use of long-debt influences this relationship. Using the seemingly unrelated regression method, and controlling for whether or not firms are exporters, our results indicate that long-term leverage both influences and is influenced by product-market competition. As firms’ relative-toindustry sales increase in New Zealand manufacturing industries, their use of longterm debt also increases. One possible interpretation of this result is that firms with growing sales are adopting more aggressive product-market strategies (for example, selling their products at lower prices) and using more leverage as a signal of their intention to continue competing aggressively. Another interpretation is that New Zealand manufacturing firms with growing relative-to-industry sales may believe they can drive rival firms out of their market. They therefore anticipate lower levels of competition and believe they can safely take on larger amounts of debt. Looking at the relationship in the other direction, we find that greater use of long-term debt results in an increase in firms’ relative-to-industry sales. A possible interpretation of
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this finding is that leveraged firms are competing more aggressively in order to increase returns to shareholders and to eliminate rival firms. However we qualify our result with respect to the impact of product-market competition on the use of leverage, with the observation that the competition variable may in fact be acting as a proxy for size. Moreover when industry dummy variables are substituted for the exporter dummy variable, the competition and long-term debt variables are no longer significant. The remainder of the paper is structured as follows. Section 2 reviews relevant literature. Sections 3 and 4 describe the variables used in the analysis and the methodology used. Section 5 outlines how the data was collected for the study. Section 6 presents the results from the analysis of the data. Section 7 summarises our findings. 2. Literature Brander and Lewis (1986) is often cited as the seminal paper on the relationship between financial decisions and product market decisions. Previous literature had analysed these decisions separately. Assuming an oligopolistic market, Brander and Lewis show that a limited liability firm that uses debt may choose to trade more aggressively by increasing its output. Such a strategy increases returns for shareholders when the firm is doing well. When the firm is doing poorly, shareholders are indifferent because debt holders have the prior claim on the firm’s assets in the event that the firm becomes bankrupt. Subsequent theoretical papers extend and in some cases contradict the findings of Brander and Lewis. For example, Maksimovic (1988) shows that in an oligopolistic industry, optimal capital structure is also affected by the number of firms in the industry, the industry discount rate, and the industry’s elasticity of demand. Bolton and Scharfstein (1990) argue that a firm that relies too much on external financing will be more vulnerable to predation in its product markets. The firm may therefore choose to employ internal sources of financing. Showalter (1995) finds that the nature of uncertainty in the output market will determine whether or not a firm decides to use 3
strategic debt. In particular, if costs are uncertain, a firm will not use debt since it has no strategic advantage. If on the other hand demand is uncertain, a firm will increase its leverage in order to raise prices in the industry, and this will result in higher expected profits for the firm. Characteristics of debt may also be related to product market behaviour. For example, Glazer (1994) argues that the way firms compete in product markets depends on whether they are using long-term debt, short-term debt, or no debt. The author shows that if firms are competing on the basis of output, the use of long-term debt will tend to encourage collusion between the firms. If on the other hand firms are competing on the basis of price, employing long-term debt will result in the firms competing more aggressively. Kanatas and Qi (2001) also examine debt maturity in the context of product market competition. They argue that when an industry is more concentrated, the use of short-term debt makes a firm an easier target for rivals; and that developments such as the introduction of a new product in an industry that competes with an existing product, may lead to an increase in the industry’s price elasticity of demand, and therefore more use of long-term debt. Empirical studies have tended to contradict the argument of Brander and Lewis (1986) that firms use debt to aggressively expand output. Papers by Chevalier (1995a; 1995b) examine evidence from the American supermarket industry in the 1980s. Chevalier finds that announcements of leveraged buyouts (LBOs) by supermarkets increase the expected returns of rival firms in the same locality and encourage entry and expansion by rivals. There is also a higher probability that a firm will exit its local market following its LBO if prices fall. Phillips (1995) examines four United States industries and finds in three of them that higher leverage encourages firms to undertake fewer investment opportunities and to behave less aggressively. Kovenock and Phillips (1995; 1997) find that firms are more likely to recapitalise and use more leverage when they have relatively unproductive plants and operate in highly concentrated industries, and when industry capacity is relatively under-utilised. Moreover, firms in highly concentrated industries that increase their debt are more likely to close down plants and reduce plant investment. If the market share of these leveraged firms is high, rival firms are less likely to close plants. When firms are highly leveraged, rival firms are also more likely to increase their investment. 4
Zingales (1998) is one of the first papers to address an important methodological issue. Zingales finds that more efficient trucking firms are more likely to survive deregulation of their industry. Firms that under-invest because of higher leverage are less likely to survive. As in previous studies, Zingales employs regression analyses to test the relationship between debt and product-market competition. However he claims that his results are more robust than those in Chevalier (1995a; 1995b), Phillips (1995), and Kovenock and Phillips (1997). In these studies it is possible that a firm’s financing choices are made in anticipation of their effect on its competitive position. Therefore it is difficult to determine whether financing choices influence a firm’s competitive position or vice versa. The causal relationship is clearer in Zingales, because deregulation was an external event that unexpectedly affected competition and capital structure choices in the trucking industry. Khanna and Tice (2000) make a similar claim to Zingales (1998) for the robustness of their results, because they also examine the way firms respond to an external event, namely the entry of a powerful rival. Khanna and Tice examine the expansion by Wal-Mart into the American discount department store industry. Their study finds that firms with high inside ownership, greater leverage or a greater presence in their markets are not as aggressive in their response to the entry of Wal-Mart. However, firms that have undergone LBOs tend to be more aggressive in their response, which contradicts the results in Chevalier (1995a). Other recent papers also attempt to deal with the issue of cause and effect. Istaitieh and Rodriguez (2002; 2003) use a simultaneous regression equations model. In one equation debt is the dependent variable, while product market factors are included as independent variables. In the other equation a key product market factor is specified as the dependant variable and debt is included as one of the independent variables. Using data from Spanish manufacturing companies, the authors find that industry concentration and product market competition both influence and are influenced by leverage. Grullon, Kanatas, and Kumar (2002) use advertising expenditure as a proxy for non-price competition, and find that firms that use less debt compete more aggressively. They also test the relationship in the opposite direction but find that
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significant increases in advertising expenditure do not lead to changes in the amount of leverage used. A series of papers by Campello (2003; 2006; 2007) adopt a similar approach to Zingales (1998) and Khanna and Tice (2000) by using variables in their analysis that are outside the control of the firm, namely shocks to aggregate demand, creditors’ valuation of assets and asset tangibility. Campello (2003) finds that during economic downturns, highly indebted firms experience a significant decline in their sales growth in industries in which their competitors are less indebted. This outcome is not observed if all firms in an industry are highly leveraged. Campello believes his study is the first to find evidence of a relationship between capital structure, product markets and business cycles, and that the evidence indicates that capital structure systematically affects firms’ performance in the marketplace. Campello (2006) finds that firms with significantly higher long-term debt than the industry average may experience sales growth as they take on debt at the margin. However firms with very high levels of debt in comparison to the industry standard may experience no gains in market share, or even losses. The study also finds that market leader firms in concentrated industries do not do as well as their competitors when their debt levels exceed the industry average. In the same industries less indebted leader firms increase their market share as they take on more debt. Campello (2007) finds that when a firm’s investments are funded with debt and the firm’s assets are observed to be more tangible after the debt has been raised, the firm’s product-market performance is better than that of its rivals. Our paper contributes to the existing literature in terms of both content and methodology. We examine the relationship between firms’ capital structure and their product markets but devote particular attention to how long-term debt influences the relationship. We also attempt to separate cause and effect by analysing the relationship in both directions, using the ordinary least squares and seemingly unrelated regression methods.
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3. Variables We first examine the determinants of long-term debt. The product-market attribute we isolate is competition. Control variables are profitability, fixed assets, size, growth, risk, an exporter dummy variable, and industry sector dummy variables. We then examine the determinants of product-market competition. The principal independent variable of interest is long-term debt. Control variables are profitability, fixed assets, size, an exporter dummy variable, and industry sector dummy variables. 3.1 Determinants of debt Product-market competition Istaitieh and Rodriguez (2002; 2003) argue that firms experiencing tough productmarket competition may use more leverage as a signal of their intention to compete aggressively. If on the other hand product-market competition is softer, firms may use more leverage as a signal that they wish to cooperate with rival firms. However Kanatas and Qi (2001) suggest that developments such as the introduction of a new product in an industry that competes with an existing product, may lead to an increase in the industry’s price elasticity of demand, and therefore more use of long-term debt. Because of these conflicting arguments, we propose the following null and alternative hypotheses: Hypothesis 1a: Product-market competition has no impact on the amount of long-term debt used by firms. Hypothesis 1b: Product-market competition has an impact on the amount of long-term debt used by firms. Our proxy for competition is the firm’s sales divided by the income from total sales for the firm’s industry. This is similar to the relative-to-industry sales growth proxy for competitive performance used by Campello (2006). Campello argues that the proxy is a measure of the total impact of pricing and other product-market strategies. If a firm’s sales increase in relation to the total sales within its industry, then the firm 7
may be adopting more aggressive product-market strategies. For example, it may be selling its products at lower prices. On the other hand, as the firm’s sales increase, its market power within the industry becomes greater. Consequently rival firms may be forced out of business and the level of competition within the industry may actually become less intense. Profitability Myers (1984) argues that as firms’ profits increase, there are more retained earnings available to finance investments. Retained earnings are a cheaper source of financing than debt, and therefore increased profitability leads to a decline in the use of leverage. Our proxy for profitability is earnings before interest and tax (EBIT) divided by the book value of the firm’s assets. Book value of assets is represented as the book value of the firm’s interest-bearing short-term and long-term debt and shareholders’ equity. Fixed assets Work by Jensen and Meckling (1976) and Myers (1977) suggests that as firms use more fixed assets, the cost of financing with debt declines and firms therefore use more leverage. Stohs and Mauer (1996) note the widely held belief that the maturity of a firm’s debt should be matched to the maturity of its assets. This implies that as firms use more fixed assets they will tend to use more long-term debt. Following Titman and Wessels (1988), we use two proxies as indicators for the fixed assets attribute: the book value of the firm’s plant and vehicles divided by the book value of the firm’s assets; and the book value of the firm’s intangible assets divided by the book value of the firm’s assets. The first proxy is positively related to fixed assets and the second proxy is negatively related to fixed assets. Size Warner (1977) and Ang, Chua, and McConnell (1982) argue that the bankruptcy costs associated with carrying debt tend to decline as firms become larger, and therefore large firms will carry more debt. However Titman and Wessels (1988) and Barclay 8
and Smith Jr. (1995) note that small firms may use more short-term debt because it is a less expensive form of financing than issuing long-term debt. Our proxy for firm size is the natural log of the book value of the firm’s assets. Growth Jensen and Meckling (1976) and Myers (1977) argue that firms may invest in unnecessarily risky projects in order to reduce the returns to the firm’s creditors. This strategy may be particularly costly for firms in growth industries that have a wider range of future investments to choose from and therefore growth firms may use less long-term debt. Our proxy for growth is the market value of the firm’s equity divided by the book value of the firm’s equity. Risk Showalter (1999) notes that as earnings become more volatile, the possibility of financial distress increases. Consequently debt becomes more expensive and is less likely to be used. Following a similar measure in Boyle and Eckhold (1997), our proxy for risk is the standard deviation of the firm’s EBIT divided by the absolute value of average EBIT, calculated over a five-year period ending with year t, when the risk is observed, and beginning with year t-4. Exporter classification It is possible that unobservable characteristics specific to exporting firms may influence the extent to which those firms use debt in their capital structures. We therefore include an exporter dummy in our analysis. We assign 1 to firms whose activities include a significant amount of exporting, and 0 to firms which do no (or very little) exporting. Industry classification Showalter (1999) notes that unobservable characteristics specific to a particular industry may influence the levels of debt within that industry. We therefore include 9
dummy variables for each of the sectors in our sample. However, because most industries in our final sample consist wholly or primarily of exporting companies, industry dummies are included in the regression analysis as an alternative to the exporter dummy. 3.2 Determinants of product-market competition Debt Brander and Lewis (1986) predict that firms that use more debt will trade more aggressively in their product markets. However most empirical studies have found that debt tends to put firms at a competitive disadvantage and encourages predation by rival firms. Glazer (1994) argues that the use of long-term debt reduces output-based competition but increases priced-based competition. Campello (2006) finds that firms with significantly higher long-term debt than the industry average may experience sales growth as they take on debt at the margin. This result may indicate that higher debt commits firms to compete more aggressively in their product markets, assuming that sales growth is a measure of the total impact of pricing and other product-market strategies. Because of the conflicting arguments and evidence, we propose the following null and alternative hypotheses: Hypothesis 2a: The use of long-term debt by firms has no impact on productmarket competition. Hypothesis 2b: The use of long-term debt by firms has an impact on productmarket competition. Long-term debt is represented as the book value of the firm’s capital notes and mortgages divided by the book value of the firm’s assets. Profitability, fixed assets and size Campello (2006) examines the impact of long-term debt on competitive performance and includes profitability, investment in fixed capital and size as control variables in 10
his regression analysis. Larger firms that are more profitable and make greater investments in fixed assets might be expected to perform better than rival firms and to compete more aggressively. For these control variables we use the proxies described in the previous section of the paper. Exporter classification Ratnayake (1999) argues that when exports are a greater proportion of a firm’s output, there are more export opportunities for firms and therefore the firm’s market is likely to expand. Consequently competition will become more intense, as reflected in a lower industry concentration. We therefore include an exporter dummy in our analysis, as defined in the previous section of the paper. Industry classification It is possible that unobservable characteristics specific to a particular industry may also influence competition within that industry. We therefore include dummy variables for each of the sectors in our sample. As explained in the previous section of the paper, these are included as an alternative to the exporter dummy. Table 1 summarises the predicted coefficient signs for the determinants of long-term debt and competition (excluding industry dummies).
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Table 1. Predicted signs for the determinants of long-term debt and competition
Variable
Long-Term Debt
Long-Term Debt
Competition +/-
Competition
+/-
Profitability
-
+
Fixed Assets (Plant)
+
+
Fixed Assets (Intangibles)
-
-
Size
+
+
Growth
-
Risk
-
Exporter
+/-
+
4. Model specification The regression equations we test are as follows: LTDebt = α 0 + α1 Comp + α 2 Prof + α 3 Fixed1 + α 4 Fixed2 + α 5 Size + α 6 Growth +
α 7 Risk + α 8 Exporter + u1
(1)
Comp = β 0 + β1 LTDebt + β 2 Prof + β 3 Fixed1 + β 4 Fixed2 + β 5 Size + β 6 Exporter + u2
(2)
where: •
LTDebt = long-term debt: the book value of the firm’s capital notes and mortgages divided by the book value of the firm’s assets;
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•
Comp = competition: a proxy for product-market competition equal to the firm’s sales divided by the income from total sales for the firm’s industry;
•
Prof = firm profitability: a proxy for the profitability of the firm equal to EBIT divided by the book value of the firm’s assets;
•
Fixed1 = first fixed assets proxy: the book value of the firm’s plant and vehicles divided by the book value of the firm’s assets;
•
Fixed2 = second fixed assets proxy: the book value of the firm’s intangible assets divided by the book value of the firm’s assets;
•
Size = firm size: a proxy for the size of the firm equal to the natural log of the book value of the firm’s assets;
•
Growth = firm growth: a proxy for growth of the firm equal to the market value of the firm’s equity divided by the book value of the firm’s equity;
•
Risk = firm risk: a proxy for the risk of the firm equal to the standard deviation of the firm’s EBIT divided by the absolute value of average EBIT, calculated over a five-year period ending with year t, when the risk is observed, and beginning with year t-4;
•
Exporter = exporter dummy: 1 is assigned to firms whose activities include a significant amount of exporting, and 0 to firms which do no (or very little) exporting.
We initially employ ordinary least squares regressions. However, in order to allow for possible correlations between the error terms of the equations, we also use the seemingly unrelated regression method. 5. Data We collected accounting data for New Zealand companies from the Investment Research Group Ltd (Datex) archives. New Zealand manufacturing industry data was sourced from New Zealand Statistics. Our original data set contains 191 New Zealand companies, including both listed and delisted firms. It has 2003 company-years of data for the years from 1984 to 2007. From this preliminary data set, 1,530 company-years were eliminated, either because
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firms were not manufacturers or because manufacturing industry data was incomplete for some years. As a consequence our sample is restricted to the years from 1993 to 2006. We excluded 150 company years for which the risk proxy could not be calculated, and one company year for which the book value of assets is calculated as zero. After these exclusions, the final sample has 322 company-years of data. One company year of data has missing values, and therefore 321 company years are used in the regression calculations. Of the 45 firms in our final sample, we classify 38 as exporters. Table 2 summarises the final sample of firms broken down by industry and the number of company years of data available for each industry. We use Datex’s industry classification. The industries in Table 2 are those for which manufacturing data was available from Statistics New Zealand. Analysis of the data was performed using SAS. Table 2. Number of Firms and Company Years by Industry
Industry
Firms
Years
Agriculture and Fish
5
36
Forestry
8
61
Building
3
36
Food
7
41
Textile and Apparel
6
44
Intermediate
13
75
Consumer
1
14
Media and Communications
2
15
Total
45
322
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6. Results 6.1 Summary statistics Table 3 presents summary statistics for each variable of interest. Long-term debt comprises on average 19 percent of book assets. The first fixed assets proxy indicates that plant and vehicles comprise on average 22 percent of book assets, and the second fixed assets proxy indicates that intangibles comprise on average 8.5 percent of book assets. The mean value for the ratio of EBIT to book assets is 11 percent. Table 3. Summary Statistics
Variable
N
Mean
Std Dev
Min
Max
LT Debt
322
0.191
0.179
0
0.698
Comp
322
0.073
0.126
0
0.639
Prof
322
0.112
0.247
-1.523
1.452
Fixed (1)
322
0.221
0.297
0
3.945
Fixed (2)
322
0.085
0.304
0
4.163
Size
322
11.454
1.833
6.589
15.807
Growth
321
156.454
165.835
0.088
1,668
Risk
322
1.875
5.963
0.036
77.688
Exporter
322
0.795
0.404
0
1
6.2 Correlations between variables Table 4 presents correlation coefficients for the explanatory variables. The highest correlation between independent variables in the same regression equation is between the competition and size proxies, with a coefficient of 0.731. As might be expected, larger firms tend to have a greater proportion of industry sales. However because the competition variable is a measure of firm sales, the competition and size variables may both be serving as proxies for size. The correlation between the first and second fixed assets proxies is also moderately high, with a coefficient of 0.471. This indicates
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that firms with a greater investment in plant and vehicles also tend to have a greater investment in intangible assets. The correlations between five of the eight industry dummy variables and the exporter dummy have an absolute coefficient value greater than or equal to 0.2. The correlation between the exporter dummy and sector 8 (media and telecommunications) has a coefficient of -0.435. Moreover, as noted in Section 3 of the paper above, most industries in our final sample consist wholly or primarily of exporting companies. Therefore industry dummies are included in the regression analysis as an alternative to the exporter dummy.
Table 4. Correlation Matrix LT Debt
Comp
Prof
Fixed(1)
Fixed(2)
Size
Growth
Risk
Exporter
LT Debt
1
Comp
0.228
1
Prof
-0.027
0.044
1
Fixed(1)
0.030
0.093
0.212
1
Fixed(2)
-0.125
0.037
0.100
0.471
1
Size
0.300
0.731
0.131
-0.021
-0.028
1
Growth
-0.062
-0.064
-0.086
0.080
0.036
-0.145
1
Risk
-0.020
-0.090
-0.163
-0.011
-0.042
-0.090
-0.080
1
Exporter
0.276
-0.107
0.128
0.180
-0.194
0.121
0.247
0.006
1
Exporter
Sector1
Sector2
Sector3
Sector4
Sector5
Sector6
Sector7
Sector8
1
0.180
-0.265
-0.015
0.194
0.202
-0.030
0.108
-0.435
Exporter
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6.3 Regression results with exporter dummy variable included For the purposes of our study, the key independent variables are competition in equation (1), which has long-term debt as the dependent variable, and long-term debt in equation (2), which has competition as the dependent variable. First, we discuss the equations in which the exporter dummy variable is included and the industry dummy variables are excluded. The results are reported in Table 5. The ordinary least squares (OLS) regression results indicate that the competition variable in equation (1) is significant at the ten percent level and has a positive sign. The long-term debt variable in equation (2) is not significant. In order to allow for possible correlations between the error terms of the equations, we also use the seemingly unrelated regression (SUR) method. In equation (1), the competition proxy now becomes significant at the one percent level and has a positive sign. The longterm debt variable in equation (2) becomes significant at the one percent level and has a positive sign. Thus, when the SUR method is used, the competition variable in equation (1) is highly positively significant. This result indicates that as firm sales as a proportion of industry sales increase in New Zealand manufacturing industries, the use of long-term debt by firms in those industries increases. Campello (2006) suggests that relative-toindustry sales growth may reflect more product-market aggression by firms. Therefore one possible interpretation of our result is that firms with growing sales are adopting more aggressive product-market strategies (for example, selling their products at lower prices) and using more leverage as a signal of their intention to continue competing aggressively. Such a signalling strategy may be particularly effective in a country like New Zealand, where industries have relatively few firms. On the other hand, as firms’ sales increase, their market power within the industry becomes greater. Consequently rival firms may be forced out of business and the level of competition within the industry may actually become less intense. Our results may therefore indicate that New Zealand manufacturing firms with growing relative-toindustry sales anticipate lower levels of competition and believe they can safely take on larger amounts of debt.
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Table 5. Regression Results The table reports ordinary least squares (OLS) and seemingly unrelated regression (SUR) results for the following equations: (1) LTDebt = +
α0
+
α1 Comp + α 2 Prof + α 3 Fixed1 + α 4 Fixed2 + α 5 Size + α 6 Growth + α 7 Risk + α 8 Exporter
u1
(2) Comp =
β0
+
β1 LTDebt + β 2 Prof + β 3 Fixed1 + β 4 Fixed2 + β 5 Size + β 6 Exporter + u2 OLS
Equation (1) LT Debt SUR OLS
OLS
Equation (2) Comp SUR OLS
SUR
SUR
Industry dummy variables
No
No
Yes
Yes
No
No
Yes
Yes
Intercept
-0.065 (-0.76)
0.016 (0.19)
-0.249 (-2.53)***
-0.275 (-2.81) ***
-0.482 (-16.2)***
-0.474 (-15.9)***
-0.426 (-9.48)***
-0.430 (-9.57) ***
0.041 (1.48)
0.080 (2.93) ***
-0.017 (-0.58)
-0.033 (-1.15)
LT Debt Comp
0.205 (1.76)*
0.372 (3.21) ***
-0.048 (-0.44)
-0.111 (-1.01)
Prof
-0.070 (-1.76)**
-0.064 (-1.61)**
-0.140 (-3.63)***
-0.142 (-3.69) ***
-0.031 (-1.63)**
-0.029 (-1.50)*
-0.037 (-1.89)**
-0.039 (-1.99)**
Fixed (1)
0.020 (0.51)
0.005 (0.14)
0.075 (2.07)**
0.080 (2.19) ***
0.084 (4.60)***
0.083 (4.52)***
0.072 (3.89)***
0.073 (3.95)***
Fixed (2)
-0.042 (-1.16)*
-0.037 (-1.02)
-0.088 (-2.50)***
-0.089 (-2.54) ***
-0.030 (-1.67)**
-0.027 (-1.55)*
-0.023 (-1.23)*
-0.024 (-1.31)*
Size
0.014 (1.76)**
0.006 (0.68)
0.039 (5.14) ***
0.042 (5.55) ***
0.052 (20.04)***
0.051 (19.63)***
0.049 (16.49)***
0.050 (16.69)***
Growth
-0.000 (-2.06)**
-0.000 (-2.04)**
-0.000 (-2.76)***
-0.000 (-2.75)***
Risk
-0.001 (-0.44)
-0.001 (-0.44)
0.001 (0.62)
0.001 (0.62)
Exporter
0.131 (4.75)***
0.143 (5.21)***
-0.080 (-6.52)***
-0.084 (-6.84)***
R square
0.179
0.305
0.606
0.625
Adj. R square
0.157
0.273
0.598
0.610
F statistic
8.47***
9.60***
80.44***
42.69***
System weighted 0.4842 0.5106 0.4842 0.5106 R square For each variable, the top number is the coefficient estimate, and the bottom number is the t-value. ***, **, * indicate significance at the 0.01, 0.05 and 0.10 levels respectively.
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However, we must be somewhat cautious about the equation (1) results. As was noted above, there is a high correlation between the competition and size variables. Moreover the size variable is not significant in equation (1) when the SUR method is used. It is therefore possible that the competition variable is acting as a proxy for size rather than being a true proxy for competitive effects. When the SUR method is used, the long-term debt variable in equation (2) is highly significant and has a positive sign. This result indicates that as firms in New Zealand manufacturing industries use more long-term debt, firm sales as a proportion of industry sales increase. Our result is consistent with the finding in Campello (2006) that debt-taking is initially associated with sales gains. If we again interpret relativeto-industry sales growth as a reflection of more product-market aggression by firms, then our findings may indicate that New Zealand manufacturing firms use debt to compete more aggressively. Consistent with the argument of Brander and Lewis (1986), it may be that the purpose of competing more aggressively is to increase returns to shareholders. Moreover, in New Zealand’s relatively small industries, a particularly effective strategy for stronger firms may be to use long-term debt to increase production and boost sales, with the intention of driving out weaker rival firms, and reducing competition. Turning to the control variables, we find that in equation (1) the first fixed assets variable, which is the ratio of plant and vehicles to book assets, and the risk variable are not significant. The profitability variable is significant at the five percent level and has the expected negative sign. This result indicates that profitable companies use lower levels of long-term debt. The second fixed assets variable, which is the ratio of intangible assets to book assets, is significant at the ten percent level and has the expected negative sign, but only when the OLS method is used. The size variable is significant at the five percent level and has the expected positive sign, but only when the OLS method is used. The growth variable is significant at the five percent level and has the expected negative sign. This result indicates that growth companies use lower levels of long-term debt. The exporter variable is significant at the one percent level and has a positive sign. In other words firms that export at least some of their products tend to use more long-term debt. A possible interpretation of this result is
19
that firms choosing to export require additional external financing to fund their exporting activities. In equation (2), the profitability variable is significant at the five percent level when the OLS method is used but only at the ten percent level when the SUR method is used, and does not have the expected positive sign. The first fixed assets variable and the size variable are both significant at the one percent level and both have the expected positive sign. In other words larger firms which make greater investments in fixed assets have stronger relative-to-industry sales. The second fixed assets variable is significant at the five percent level when the OLS method is used but only at the ten percent level when the SUR method is used, and has the expected negative sign. The exporter variable is significant at the one percent level but does not have the expected positive sign. In other words firms that export at least some of their products tend to have weaker relative-to-industry sales, which may indicate that such firms are competing less aggressively. 6.4 Regression results with industry dummy variables included We now examine the effect of excluding the dummy exporter variable and including industry dummy variables instead. The results are also reported in Table 5. Including the industry dummies makes the OLS models more powerful, as indicated by the larger adjusted R squares. However the key competition variable in equation (1) and the key long-term debt variable in equation (2) now have negative signs and are no longer significant, when both the OLS and SUR methods are used. Thus when industry dummies are substituted for the exporter dummy there is no evidence to support our original conclusions. However the absolute size of the t-values for the competition and long-term debt variables do increase significantly when the SUR method rather then the OLS method is used (from -0.44 to -1.01 and from -0.58 to 1.15 respectively). This suggests that the two variables may have some explanatory power. Many of the control variables are highly significant. Thus in equation (1), the profitability, second fixed assets, size and growth variables are all significant at the one percent level with the expected signs (negative, negative, positive and negative 20
respectively). The first fixed assets variable is significant at the five percent level when the OLS method is used and at the one percent level when the SUR method is used, and has the expected positive sign. One of the industry dummy variables is significant at the one percent level (not reported). The risk variable is not significant. In equation (2), the profitability variable is significant at the five percent level, but does not have the expected positive sign. The first fixed assets and size variables are significant at the one percent level and have the expected positive signs. The second fixed assets variable is significant at the ten percent level and has the expected negative sign. Five of the industry dummy variables are significant at the one percent level (not reported). 6.5 Robustness tests To test the robustness of our results, we split our sample into two periods, from 1993 to 1999 and from 2000 to 2006, and repeated the analysis using the SUR method. The results are reported in Table 6. For the period from 1993 to 1999, the competition and long-term debt variables in equation (1) and equation (2) respectively have the positive sign and are significant at the one percent level, when either the exporter dummy variable or the industry dummy variables are included in the equations. In contrast, the results for the key variables using the complete sample were only significant when the exporter dummy was used. For the period from 2000 to 2006, when the exporter dummy is included, the competition and long-term debt variables continue to have the positive sign but are not significant. In contrast, the results for the key variables using the complete sample were highly significant when the exporter dummy was used. When the industry dummies are included, the competition and long-term debt variables are significant at the one percent level but now have a negative sign. In contrast, the results for the key variables using the complete sample were not significant when the industry dummies were used.
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Table 6. Regression Results for the Sub-Periods 1993-1999 and 2000-2006 The table reports seemingly unrelated regression (SUR) results for the following equations: (1) LTDebt = +
α0
+
α1 Comp + α 2 Prof + α 3 Fixed1 + α 4 Fixed2 + α 5 Size + α 6 Growth + α 7 Risk + α 8 Exporter
u1
(2) Comp =
β0
+
β1 LTDebt + β 2 Prof + β 3 Fixed1 + β 4 Fixed2 + β 5 Size + β 6 Exporter + u2 1993-1999
Industry dummy variables Intercept
2000-2006
Equation (1) LT Debt No Yes
Equation (2) Comp No Yes
Equation (1) LT Debt No Yes
Equation (2) Comp No Yes
0.253 (1.94)**
-0.540 (-14.1)***
-0.384 (-8.09)***
-0.094 (-0.78)
-0.437 (-9.50)***
-0.509 (-6.67)***
0.159 (4.49) ***
0.118 (3.51)***
0.005 (0.12)
-0.166 (-3.60)***
-0.016 (-0.11)
LT Debt Comp
0.786 (4.54)***
0.666 (3.40)***
Prof
-0.143 (-1.93)**
-0.188 (-2.57)***
0.016 (0.51)
Fixed (1)
-0.028 (-0.30)
0.060 (0.62)
Fixed (2)
-0.003 (-0.02)
Size
-0.332 (-2.27)**
0.064 (0.39)
-0.473 (-3.52)***
0.024 (0.85)
-0.050 (-0.97)
-0.151 (-3.32)***
-0.036 (-1.45)*
-0.059 (-2.22)***
0.185 (4.86)***
0.171 (4.71)***
0.025 (0.55)
0.080 (1.99)**
0.067 (2.98)***
0.059 (2.47)***
-0.472 (-3.21)***
0.043 (0.70)
0.278 (4.76) ***
-0.054 (-1.29)*
-0.072 (-1.85)**
-0.025 (-1.21)*
-0.015 (-0.66)
-0.021 (-1.69)**
0.009 (0.72)
0.056 (16.43)***
0.045 (13.75)***
0.020 (1.77)**
0.050 (4.80)***
0.049 (12.55)***
0.053 (10.76)***
Growth
-0.000 (-0.93)
-0.000 (-1.18)*
-0.000 (-1.32)*
-0.000 (-2.22)***
Risk
-0.007 (-2.54)***
-0.006 (-2.15)**
0.001 (0.72)
0.003 (1.79)**
Exporter
0.219 (5.19) ***
System weighted R square
0.6371
-0.122 (-6.97)*** 0.7125
0.6371
0.095 (2.43)*** 0.7125
0.4056
-0.070 (-3.87)*** 0.5111
0.4056
0.5111
For each variable, the top number is the coefficient estimate, and the bottom number is the t-value. ***, **, * indicate significance at the 0.01, 0.05 and 0.10 levels respectively.
22
Following Boyle and Eckhold (1997), we also omitted observations with negative EBIT (results not reported). Using the SUR method with the exporter dummy included, we find that the competition variable in equation (1) and the long-term debt variable in equation (2) have positive signs and are significant at the one percent level. When the exporter dummy is excluded and the industry dummies are included, the two key variables are not significant. These results are consistent with the main findings of our paper. For the period from 1993 to 1999, the results obtained when negative EBIT values are excluded are the same as those for the complete sample. For the period from 2000 to 2006, the results are also similar to those for the complete sample, except that the key variables are only significant at the five percent level when the exporter dummy is included. Finally, Showalter (1999) notes that in models such as equation (1) above, risk may be an endogenous variable. This is because risk may be at least partly determined by levels of debt, as well as itself being a determinant of debt. Following Showalter, we excluded the risk variable from our final sample and found that the impact on our results was negligible (results not reported). 7. Conclusion We present evidence that long-term leverage both influences and is influenced by product-market competition, when we control for whether or not firms are exporters. As firms’ relative-to-industry sales increase in New Zealand manufacturing industries, their use of long-term debt also increases. One possible interpretation of this result is that firms with growing sales are adopting more aggressive product-market strategies (for example, selling their products at lower prices) and using more leverage as a signal of their intention to continue competing aggressively. This strategy may be particularly effective in New Zealand, where industries have relatively few firms. Another interpretation is that New Zealand manufacturing firms with growing relative-to-industry sales may believe they can drive rival firms out of their market. They therefore anticipate lower levels of competition and believe they can safely take on larger amounts of debt. Looking at the relationship in the other direction, we find that greater use of long-term debt results in an increase in firms’ relative-to-industry sales. It is possible that leveraged firms are competing more aggressively in order to 23
increase returns to shareholders. Moreover, in New Zealand’s smaller markets, stronger firms may use long-term debt to aggressively increase output, and thereby put pressure on weaker rival firms. However an important caveat with respect to the impact of product-market competition on the use of leverage is that the competition variable used in the analysis may in fact be acting as a proxy for size. Moreover when industry dummy variables are substituted for the exporter dummy variable, the competition and long-term debt variables are no longer significant. Some of the robustness tests we conducted also indicate that our results may not be entirely conclusive. In particular the evidence supporting our main conclusions appears to be stronger and more consistent for the earlier of the two sub-periods into which we divided our complete sample.
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