Abstract:
A large literature is dedicated to estimating the benefits and costs of corporate debt, including tax benefits, bankruptcy costs, agency benefits and costs, and other costs of financial distress. More recently, this research has focused on estimating the costs of debt, or leverage, arising from
changes in rank-and-file employee behavior in response to changes in firms' capital structures. Chang (1992) and Berk, Stanton and Zechner (2010) show theoretically that, in the presence of costs to employees when firms enter financial distress, employees may demand ex ante higher wages to
compensate for such risk as firms increase their leverage. This, in turn, may limit the amount of debt that firms issue.
Estimating the effect of leverage on wages is challenging due to endogeneity concerns. First, selection bias is likely important. In particular, if firms need to compensate individuals for unemployment risk, optimal leverage ratios will be lower than if workers do not demand compensation. Second,
omitted variables such as the marginal product of labor will also lead to biased estimates. For instance, firms may issue equity to finance new investment in labor-augmenting technology. As a result, leverage ratios decrease and, because the marginal product of labor increases, wages will likely
increase. Therefore, the observed relationship between leverage and employee compensation does not represent a causal effect but rather may arise due to an important omitted variable.
In our paper, we provide estimates of the wage costs of firm debt in a broad sample of publicly traded U.S. firms over the period 1991 to 2008. Our approach addresses the above concerns by using matched firm-worker-level data from the Census Bureau's Longitudinal Employer-Household Dynamics (LEHD)
program to exploit within-firm variation in employees' expected costs of unemployment. Expected unemployment costs may vary across workers due to geographical differences in labor search and matching frictions. Therefore, when a firm increases its leverage, workers at the firm with higher expected
unemployment costs should demand a higher wage premium than other workers. By exploiting within-firm variation in expected unemployment costs, we are able to account for firm-level shocks that determine firm leverage. Our analysis of a broad sample of U.S. firms also allows us to obtain estimates
of the wage costs of debt that are more generalizable than those that are focused on particular subsamples of firms, such as those in financial distress or those engaged in bargaining with unions.
Using this analytical framework, we provide evidence that higher leverage does lead to higher wages in a broad set of firms. For each worker, we proxy for expected unemployment costs using the relative size of the individual's labor market, which we calculate as the industry share of MSA employment
relative to the industry share of national employment. The choice of proxy is informed by the literature on job search, such as Petrongolo and Pissarides (2006), who finds evidence of economies of scale in labor markets. In particular, Petrongolo and Pissarides (2006) find that, individuals in
larger labor markets have significantly higher reservation wages during unemployment and earn significantly higher wages following unemployment than those in smaller labor markets.1 The use of the relative industry share of MSA employment as our measure of labor market size is driven by two considerations. First, it permits the inclusion of MSA-year fixed effects to control for local economic shocks. And second, it introduces a degree of
non-linearity into the effect of labor market size. Moreover, as we show, the results are robust to alternative measures of labor market size.
We find that, within a firm, wages for employees in smaller labor markets grow faster than other employees at the firm in response to an increase in firm leverage. The estimates imply that, in response to a 10 percentage point increase in leverage, employees in small labor markets, defined as equal
to the 25th percentile in size, earn a wage premium of 0.2% relative to employees who work at the same firm but in large labor markets, defined as equal to the 75th percentile in size.
We then use this cross-sectional result to estimate the effect of firm leverage on employee pay. To do so, we assume that workers in the largest labor markets require no wage premium in return for higher firm leverage. We then use our estimate to calculate how a 10 percentage point increase in
leverage affects the pay for each employee at the firm on the basis of the size of the labor market in which they work. Finally, we aggregate these employee-specific estimates to the firm level. This calculation suggests that a 10 percentage point increase in leverage increases compensation for the
median worker by about 1.9% and total employee compensation at the firm by approximately 17 basis points of firm value, implying that labor costs are an important consideration for firms when choosing their capital structure.
For employees to negotiate to be compensated for greater unemployment risk due to higher leverage, these employees need to understand the effect of firm leverage on unemployment risk and they need sufficient bargaining power. We find evidence that both factors are important. First, the effect is
stronger for workers likely to understand the relationship between leverage and unemployment; the effect is stronger for employees with higher wages and for employees with exposure to previous bankruptcies. Second, we find that the bargaining power of employees is important; the effect is stronger
in more competitive labor markets and markets with low unemployment.2,3
We also find that the effect is stronger amongst new employees, who are also more likely to negotiate wage increases vis-à-vis continuing employees.4 We find
that, among new employees at a given firm, a 10 percentage point increase in leverage leads new employees in small labor markets to earn approximately 0.5% more than new employees in larger labor markets, double the effect estimated across all workers at firms.
We explore alternative explanations for our empirical results that may bias our main estimates of the wage costs of leverage. We first examine the possibility of reverse causality, where increasing employee wages lead firms to increase leverage. If this were the case, we would expect to find a
significant relationship between employee wage growth in the years preceding a change in leverage and the interaction of changes in firm leverage and labor market size in the years. Contrary to this expectation, we only find a significant relation for wage growth in the year following the change in
leverage.
We also find no evidence that the results are due to an unobserved productivity shock. To rule out this explanation, we study within-firm variation in growth rates across MSAs. If our results are due to a localized productivity shock, we would expect the establishments benefitting from positive
shocks to grow faster than the firm's other establishments.5 We find no evidence of differential effect on growth rates in employment, establishment counts,
sales, valued added, or capital expenditures.
Our paper contributes to the growing literature on the relationship between firms' financial and the labor market decisions and provides new estimates of the wage costs associated with financial leverage. The most closely related papers are Chemmanur, Cheng, and Zhang (2013), Graham, Kim, Li, and
Qiu (2016), Agrawal and Matsa (2013), and Kim (2015).6 Chemmanur, Cheng, and Zhang (2013) study the relationship between leverage and employee compensation and
find that workers are paid higher wages when leverage ratios are higher. However, their measure of employee compensation is based on Compustat data on labor and related expenses. This variable is missing for approximately 90% of firms and cannot account for the changing composition of workers over
time. Graham, Kim, Li, and Qiu (2016) study the long term effects on employee earnings following bankruptcy and uses the ex post wage loss to calculate an ex ante premium required to offset the realized losses. Agrawal and Matsa (2013) study the effects of changes in state unemployment benefits on
firm leverage and use the observed relationship to calculate the labor costs of financial distress.7 Kim (2015) finds that the opening of new manufacturing
plants leads to an increase in leverage for other manufacturing firms in the same county and interprets these findings as when employees are more costly, firms take on less debt. In contrast, our approach calculates the ex ante wage premium that employees do receive as compensation for the
increased unemployment risk using a broad sample of public firms.8 Recent papers such as Agrawal and Matsa (2013) and Graham, Kim, Li, and Qiu (2016) have
incorporated labor costs into these calculations. Unlike these papers, however, we estimate the labor costs of financial distress using actual ex ante employee compensation.
Our paper also relates to the labor economics literature on compensating differentials. For example, Topel (1984) uses variation in unemployment insurance coverage to estimate a compensating differential of 2.5% for a one point increase in the probability of unemployment.9 While papers in this literature typically exploit variation in aggregate risk, we incorporate firm-specific variation in unemployment risk into the analysis, which
likely better captures the risk of employment of individual workers.
The paper is organized as follows. Section I describes our theoretical motivation and empirical framework. Section II describes the data and variable construction. Section III describes the main results on the relation between firm leverage and wages, and Section IV discusses alternative measures
of expected unemployment costs. Section V examines alternative explanations of our results while Section VI discusses our estimates in relation to other estimates of the wage costs of firm debt. Section VII concludes.
Financial distress imposes significant costs on employees. Following periods of financial distress, firms significantly reduce employment (Hotchkiss (1995), Agrawal and Matsa (2013), Falato and Liang (2016)). This imposes costs on employees through two channels. First, search and matching
frictions give rise to periods of unemployment (Mortensen and Pissarides (1994)), leading to lost wages and a deterioration in skills. Second, an unemployment spell can lead to lower wages in the long run due to the elimination of firm-specific capital (Becker (1962)) or due to a lower quality
match between employee and employer (Jovanovic (1979)). Consistent with this theoretical evidence, Graham, Kim, Li, and Qiu (2016) find empirically that workers experience significantly lower wages for at least five years following a bankruptcy of their employer.
The ex post reduction in lifetime earnings suggests that employees of highly levered firms should be compensated for the increased distress risk. In other words, higher firm leverage should lead to higher employee compensation (Berk, Stanton, and Zechner (2010)).
Firm financial distress leads to a significant decline in employment, imposing large costs on its employees. These costs arise due to the fact that unemployment leads to lower lifetime earnings. The reduction in earnings is due both to long unemployment spells (Katz and Meyer (1990), Meyer (1990),
and Krueger and Mueller (2010)) and lower wages in subsequent employment (Gibbons and Katz (1991), Farber (2005), Couch and Placzek (2010)).
Theoretical and empirical evidence suggests that firms compensate individuals for bearing unemployment risk. For instance, in Abowd and Ashenfelter (1981), workers require a wage premium, also known as a compensating differential, to work for a sector with unemployment risk. Exploiting variation in
unemployment risk across industries, they estimate individuals earn compensating differentials of up to 14%. Berk, Stanton, and Zechner (2010) provide theoretical support for a positive relationship between leverage and employee compensation which Chemmanur, Cheng, and Zhang (2013) find to be true
empirically.
While the evidence suggests that increased firm leverage will lead to higher compensation for workers, unemployment risks are not constant across workers at a firm. For example, the individual's labor market plays an important role in the magnitude of lost earnings. In particular, unemployment is
less harmful for individuals in larger labor markets as they earn higher wages upon returning to employment (Helsley and Strange (1990) and Petrongolo and Pissarides (2006)). Most related to our analysis, Graham, Kim, Li, and Qiu (2016) show that workers in larger labor market experience smaller
wage losses following their employer's bankruptcy than workers in smaller labor markets.
Therefore, the compensation that workers receive in return for bearing unemployment risk should vary, even within a single firm. Workers in relatively larger labor markets should receive a lower wage premium for unemployment risk than workers in relatively small labor markets. In other words, when
a firm increases its leverage, workers in large labor markets should experience lower pay growth than workers with small labor markets.
To test this implication, we run panel regressions using worker-firm level data relating changes in worker pay to labor market size and its interaction with changes in firm leverage. Specifically, we estimate:
| $$\displaystyle \Delta_{}Pay_{ijkl,t\rightarrow{}t+1}$$ | $$\displaystyle =$$ | $$\displaystyle \alpha + \beta_{1}\Delta_{}Leverage_{j,t-1\rightarrow{}t}Size_{klt}$$ | |
| $$\displaystyle +$$ | $$\displaystyle \beta_{2}\Delta_{}X_{j,t-1\rightarrow{}t}Size_{klt}$$ | ||
| $$\displaystyle +$$ | $$\displaystyle \beta_{3}Size_{klt} + \beta_{4}Y_{it}$$ | ||
| $$\displaystyle +$$ | $$\displaystyle \gamma_{jt} + \eta_{kt} + \sigma_{km} + \nu_{ijkl,t\rightarrow{}t+1}$$ | (1) |
We construct a unique worker-firm-level dataset that combines data on individual workers with data on the firms for which they work. Worker-level data are from the U.S. Census Bureau's Longitudinal Employer-Household Dynamics (LEHD) program. The LEHD data cover 25 states11 and provides detailed data on worker earnings and other characteristics. The Employment History File (EHF) provides data on quarterly earnings for each
worker-firm pair. The Individual Characteristics File (ICF) provides data on worker age, place of birth, gender, education, and race.
We match the worker data to firm data from other Census datasets as well as Compustat and CRSP. We use the Census Bureau's Longitudinal Business Database (LBD) to construct measures of employment and the number of establishments at the level of the firm and the firm-MSA. We also use the Census
Bureau's Census of Manufactures (CMF) and Annual Survey of Manufactures (ASM) to calculate measures of the value of shipments and value added at the level of the firm and the firm-MSA. The Census data are matched with Compustat and CRSP using the Compustat-SSEL bridge.12
Firms are classified to three digit SIC industries using industry codes from Compustat. In cases where the industry code is missing in Compustat, we use the industry code from CRSP. We exclude financial firms (SIC codes 6000-6999), utilities (SIC codes 4900-4999), and public administration firms
(SIC codes 9000-9999) and restrict the sample to workers between the ages of 25 and 64. This yields a sample of 51,293,300 worker-level observations and 27,500 firm-year observations, covering approximately 14,000,000 workers at 4,200 firms between the years 1991 and 2008.13,14
Our main dependent variable is the change in log average quarterly earnings at the firm. We calculate book leverage as debt in current liabilities plus long-term debt relative to assets.15 We follow Leary and Roberts (2014) to construct firm-level controls for profitability, size, market-to-book ratio, and asset tangibility. Marginal tax rates are from John Graham's website.16 Finally, our measure of labor market size is based on data from the LBD. This measure is calculated as the industry share of employment in an MSA relative to the industry share of employment
for the nation, where industry is defined using three-digit SIC codes. For more detail on variable construction, see Appendix Table A1.
Table 1 provides summary statistics of key variables for our sample.17 Panel A presents statistics for worker data. Average pay growth is high at 8.7% per
year.18 Median pay growth, in contrast, is 1.4% per year. Average quarterly earnings are approximately $11,000 with a median of approximately $9,000.
Finally, the average labor market size is approximately 3.8, suggesting a high degree of industry agglomeration; the local industry share of employment is almost four times greater on average than the national industry share of employment. However, given that the median is only 1.08 and the
standard deviation is approximately 10, there is significant variation across workers in the size of their labor market.
[Insert Table 1 here]
Panel B presents statistics for data consolidated to firm-year observations. Average (median) firm book leverage is approximately 23% (21%). Consistent with the literature on the stability of firm leverage,19 the average (median) change in leverage is only 0.3% (-0.1%). However, the standard deviation is approximately 8%, suggesting that a substantial set of firms do exhibit large changes in leverage.20
As discussed above, due to the geographical coverage of LEHD, we match approximately 40% of firm-year observations from the intersection of CRSP and Compustat data. Comparing the summary statistics of our final sample with those for the CRSP-Compustat data, we find that, while firms in our sample
are larger and more profitable than in the broader sample, leverage ratios are similar across the two samples. In particular, the mean firm sales in our sample is $2.27 billion and the mean profitability is 12.6%, the mean firm sales in the full sample is $1.22 billion and mean profitability is
4.4%. However, mean book leverage is very similar, at 23.1 for our sample and 22.2 for the full sample. Similarly, differences in mean market-to-book ratio (1.49 versus 1.75) and asset tangibility (0.291 versus 0.269) are small across the two samples.
In this section, we examine the effect that firm leverage has on employee wages. Before we present the estimates of equation 1, we first analyze the correlation between leverage and wages. To do so, we estimate the regression
| $$\displaystyle Pay_{ijt}$$ | $$\displaystyle =$$ | $$\displaystyle \alpha + \beta_{1}Leverage_{j,t-1} + \beta_{2}X_{i,t-1} + \beta_{3}Y_{j,t-1} + \eta_{it}$$ | (2) |
[Insert Table 2 here]
We find no evidence that higher leverage is associated with higher employee pay. The estimated effect of leverage is negative in four of the six specifications and even negative and marginally significant in one specification.21
However, as discussed above, this analysis fails to account for potential selection bias, where we may not observe firms increasing leverage if doing so would lead to large increases in employee wages, and omitted variable bias, where unobservable factors such as productivity shocks affect both
firm leverage and employee compensation. In other words, the estimates in Table 2 may be downwardly biased. For instance, if firms tend to raise equity to invest in labor-augmenting technology, which increases labor productivity and therefore wages, we would expect to find a negative correlation
between leverage and wages. To account for these sources of bias, we then estimate equation 1, which estimates changes in wages within firms on the interaction of changes in firm leverage and labor market size, and present the results in Table 3.
[Insert Table 3 here]
In contrast to the previous results, we find that leverage has an important effect on employee wages. In column 1, we find that the estimate of the interaction between the change in firm leverage and labor market size is negative and highly significant. In other words, the pay of employees in
relatively small labor markets increases in response to increased firm leverage, relative to employees at the same firm in larger labor markets.
In column 2, we include interactions between labor market size and several firm level controls - the change in EBITDA relative to assets, the change in market to book ratio, the change in log sales, the change in asset tangibility, and the change in the firm's marginal tax rate. The coefficient on
the interaction term remains negative and significant and is largely unchanged in magnitude. Next, in column 3, we include worker fixed effects to control for worker-level unobservable characteristics. While slightly smaller in magnitude, the coefficient on the interaction term remains negative and
significant.
In column 4, rather than use a continuous measure for the firm controls, we use indicator variables for each quartile across the distribution of changes. We find that wages only respond to large increases in firm leverage; only the interaction between labor market size and the indicator variable
for the top quartile of the change in firm leverage is negative and significant. This result is reasonable for several reasons. In particular, large increases in firm leverage likely have the largest effect on unemployment risk. Moreover, employees are more likely to be aware of large increases in
firm leverage than small changes in leverage as these changes are more likely to result in tangible changes at the firm and are more likely to attract media attention. As a result, it is these changes in firm leverage for which employees would plausibly demand compensation for increased risk.
In column 5, we interact labor market size with the log change in total firm debt, rather than the change in leverage. One potential concern is whether firm leverage is changing due to changes in the denominator of the leverage ratio, firm assets, rather than the numerator, firm debt. However, the
estimates in column 5 show that the effect is due to changes in debt levels. The interaction of labor market size and the change in firm debt also enters negatively and significantly.
To understand the economic magnitudes of the estimate, consider two employees at a firm. The labor market of Employee A is in the 25th percentile of size while the labor market of Employee B is at the 75th percentile of size. If the firm increases its leverage by 8 basis points, the estimates in
column 2 imply that Employee A earn approximately 0.2% more than Employee B due to the change in leverage.
Furthermore, we can use these cross-sectional estimates to calculate the effect of firm leverage on compensation. To do so, we split the samples into deciles based on labor market size and classify workers in the top decile as the control group. We then calculate the wage effect by multiplying the
estimated coefficient for the interaction term by each decile's average labor market size minus the top decile's average labor market size. Under the assumption that the top decile is a legitimate control group, the estimate implies that, for a worker in the middle two deciles of the labor market
size distribution, a 10 percentage point increase in firm leverages increases pay by approximately 1.9%.
We then map these estimates to yield a firm-level estimate of the effect of firm leverage on employee compensation. For each worker at each firm, we calculate the effect of an increase in leverage in the manner described above.22 We then sum the worker-level effect across all workers at the firm to calculate the total change in pay at the firm.
Assuming again that the top decile is a legitimate control group, the estimate in column 2 implies that, for a 10 percentage point increase in the leverage of the average firm, total firm payroll increases by approximately 17 basis points of firm market value. Almeida and Philippon (2007) calculate
that the difference between the tax benefits and costs of financial distress are at most 65 basis points of firm value (for BBB-rated firms). Our estimates therefore imply that the added labor costs can account for a meaningful fraction of this difference.
While we document a significant wage effect across all workers on average, certain workers may be more aware of the unemployment risks associated with an increase in firm leverage or have greater bargaining power and may therefore negotiate greater wage increases relative to other employees who
may be less informed or have lower relative bargaining power. For instance, Barattieri, Basu and Gottschalk (2014) find that workers that switch jobs are much more likely to have a change in wage than workers that remain at the same firm.23 Therefore, we next examine the effect on wage levels for new employees at the firm. Specifically, we re-estimate equation 1 where the dependent variable is the log average quarterly wage in year t+1 for all workers who joined the firm at any point in year t. The results are presented in Table 4.
[Insert Table 4 here]
As expected, the effects on new employee wages are stronger than the effects on existing employees. In columns 1 and 2, the estimate on the interaction term is positive and statistically significant. Moreover, the estimate in column 2 implies that a new employee whose labor market size is equal
to the 25th percentile will earn approximately 0.5% more than a new employee at the 75th percentile of labor market size due to a 10 percentage point increase in leverage.
Exploiting the cross-sectional results to estimate the effect of leverage on pay as described above, the estimate implies that the 10 percentage point increase in leverage increases pay for the average new worker by 8.5%. Similar to the results in Table 3, we find in column 3 that the effect on pay
arises due to large increases in leverage. Finally, in column 4, we use the change in log debt in place of the change in leverage and again find that interaction term enters negatively and significantly.
For employees to be compensated for the higher risk of unemployment associated with higher firm leverage, they need to understand that higher leverage, all else equal, does increase the probability of unemployment and they need to possess sufficient bargaining power to be compensated for the higher
risk of unemployment. Next, we show that the effects are stronger for employees who are more likely to understand the relationship between firm leverage and unemployment risk and for employees with relatively more bargaining power.
[Insert Table 5 here]
First, in Table 5, to show that employees who likely have a greater understanding of the relationship between firm leverage and the probability of unemployment, we employ two sample splits. In columns 1 through 4, we divide the sample in quartiles on the basis of income within the firm. Income
is a proxy for human capital and so more highly paid individuals are more likely to understand the downsides of higher firm leverage. In addition, more highly paid workers are likely more involved in the decision making of the firm and thus are more aware of the tradeoffs of higher leverage.
We do, in fact, find that the effect is increasing in the level of employee compensation. In columns 1 and 2, where the sample is employees in the lowest two quartiles of compensation at the firm, the coefficient on the interaction of the change in firm leverage and labor market size is
insignificant. In other words, we find no evidence that the lowest paid employees at the firm are compensated as a result of higher firm leverage. Looking at the results in columns 3 and 4, for employees in the third and fourth quartiles of compensation of the firm, we find that the coefficient is
negative and significant for both samples. Moreover, the magnitude of the coefficient is largest for the top quartile of earners, suggesting that the more highly paid a worker is, the higher the premium he receives in response to higher firm leverage.
In columns 5 and 6, we divide the sample on the basis of exposure to the effects of bankruptcy on employment. To do so, we identify bankruptcies of public firms from the UCLA-LoPucki Bankruptcy Research Database. After matching these firms to Census data, we identify MSA-industry markets where a
public firm declared bankruptcy in the prior 5 years and classify individuals in those markets as being exposed to the effects of bankruptcy. We find evidence that, in fact, the compensation of individuals with exposure to bankrupt firms is more sensitive to changes in firm leverage. In particular,
the estimate on the interaction of the change in book leverage and labor market size is negative and large in magnitude, albeit only marginally significant, for individuals with a public firm bankruptcy within their labor market in the prior 5 years. For individuals without a recent public firm
bankruptcy, the estimate on the interaction term is positive, small in magnitude, and statistically insignificant.
Next, to show that the relationship between firm leverage and employee compensation is stronger when employees have greater bargaining power, we split the sample in two ways. In columns 1 and 2 of Table 6, we separate the sample on the basis of the local unemployment rate; in column 1, the sample
is restricted to markets with an unemployment rate of 5 percent or less and, in column 2, the sample is restricted to markets with an unemployment rate of more than 5 percent. In times of low unemployment, employees have more attractive outside options and therefore have relatively more bargaining
power than in times of high unemployment (Christofides and Oswald (1992)). As a result, workers should be more likely to receive compensation for higher firm leverage.
[Insert Table 6 here]
This is precisely what we find in columns 1 and 2. In labor markets with more slack, however, employees do not receive additional compensation for higher firm leverage, as the interaction term in column 1 is negative but insignificant. In column 2, the interaction term is negative and
significant; in other words, in tighter labor markets, employees are compensated for increases in firm leverage
Then, in columns 3 and 4, we split the sample on the basis of the competitiveness of the local labor market. For each MSA-industry market, we calculate the Herfindahl-Hirschman index (HHI) across firms using employment shares. The sample in column 3 is restricted to less competitive labor markets,
defined as markets with an HHI of 1500 or greater, and the sample in column 4 is restricted to more competitive markets, defined as markets with an HHI of less than 1500. As with the splits based on local unemployment rates, we find that the relationship is stronger for markets where workers likely
have relatively more bargaining power. While the estimate in column 3 is positive and insignificant, the estimate of the interaction of the change in firm leverage and labor market size in column 4 is larger in magnitude than the full sample results. In other words, workers in more competitive
labor markets are compensated for increased firm leverage while workers in less competitive markets are not.
Thus far, we have documented an important relationship between employee wages, firm leverage, and labor market size. If this relationship arises as compensation for increased unemployment risk, we would expect that the effects are strongest for workers with an elevated probability of
unemployment. Therefore, in Table 7 we re-estimate equation 1 for workers at firms that are expanding employment and firms that are not separately. Specifically, in column 1, the sample is restricted to employees at firms whose total employment in year t+1 was greater
than year t employment and, in column 2, the sample is employees at firms whose year t+1 employment was no greater than year t. Simiarly, in column 3, the sample is restricted to employees at firms whose MSA
employment in year t+1 was greater than year t employment and, in column 4, the sample is employees at firms whose year t+1 MSA-specific employment was no greater than year t.
[Insert Table 7 here]
Regardless of how expanding firms are identified, we find that the relationship between firm leverage, labor market size, and compensation is only significant at firms that are not expanding. In columns 1 and 3, the coefficient on the interaction term is positive and insignificant. For the
samples of workers at firms that are not explanding in columns 2 and 4, the estimate is negative and significant. Thus, it does appear that our results are driven by workers with an elevated risk of unemployment. In the results discussed above, we have used a specific measure of labor market size
as a proxy for unemployment risk. We now discuss alternative measures of labor market size as well as other possible proxies for unemployment risk.
[Insert Table 8 here]
In Table 8, we replicate our main results from Tables 3 and 4 using three alternative measures of labor market size. First, in columns 1 and 4, we measure the size of an individual's labor market as the industry share of employment in an MSA. In columns 2 and 5, we use the industry share of
establishments in an MSA relative to the national average. In columns 3 and 6, we use the industry share of young firms -- defined as firms that are ten years old or younger -- in an MSA.24 With all three measures, we find results that are qualitatively similar to our main results. For continuing workers, the same sample as in Table 3, we find that the estimate on the interaction of labor market size and the
indicator for a large increase in firm leverage is negative and significant. Similarly, for new workers, the sample from Table 4, the estimate is consistently negative and significant. In other words, across these three measure of labor market size, workers in smaller labor markets earn more than
workers at the same firm in larger labor markets due to an increase in firm leverage.
The size of an individual's labor market is not the sole determinant of an individual's unemployment risk. For instance, Graham, Kim, Li, and Qiu (2016) find that wage losses due to firm bankruptcy are more severe for employees at small firms, employees in industries with high union membership
rates, and for older employees. Moreover, more generous unemployment insurance (UI) benefits also can reduce the negative consequences of unemployment spells.
Our empirical design precludes us from using firm- and industry-level variables as proxies for unemployment risk due to the use of firm-year fixed effects. Therefore, we cannot use firm size or industry union membership rates that Graham, Kim, Li, and Qiu (2016) find predict wage losses as proxies
for unemployment risk.
We have explored the use of worker age as a proxy for unemployment risk but have not found a robust relationship between leverage and compensation in these specifications. One explanation for why older workers might not be compensated ex ante for higher firm leverage even though they suffer greater
ex post wage losses in bankruptcy is due to bargaining power. In particular, the gap between a worker's wage and her marginal product of labor is increasing in age and in tenure, which is positively correlated with age (Topel (1991), Hellerstein and Neumark (2007)). This evidence suggests that
older workers have less bargaining power than younger workers since their outside option, which would not pay such a large premium over their marginal product, is relatively worse.
Finally, we have also explored the use of various measures of UI generosity to proxy for unemployment risk. These proxies include both aggregate measures such as the state-specific weekly maximum benefit and individual-specific measures of benefits that we can estimate using LEHD data. We do not
find a robust relationship between leverage and employee wages using these UI measures as proxies for unemployment risk.
However, given our results discussed above, this is not surprising. In Table 6, we find that the effects of leverage on wages are strongest for the most highly paid workers at the firm. These workers, however, are the ones who likely are helped by UI benefits the least. For instance, Chetty (2008)
analyzes the effects of UI benefits on job search behavior and finds that increases in benefits affects liquidity constrained households, namely those with fewer assets, significantly more than unconstrained households. Taken together, the results suggest that the individuals who benefit more from
greater UI generosity are not the ones who earn higher wages due to higher firm leverage. Thus, one should not expect that using measures of UI generosity as proxies for unemployment risk in our empirical framework would yield a significant relationship between firm leverage and employee wages.
While we find evidence that the associations between firm leverage, labor market size, and employee compensation arise due to firms compensating employees for bearing unemployment risk, there are alternative explanations for our results. In this section, we explore several of these explanations
and find evidence that is inconsistent with these explanations, further lending support to our interpretation that higher wages are compensation for increased unemployment risk associated with increases in leverage.
The first alternative explanation for our results is reverse causality – firms are raising new and increasing leverage in order to pay higher wages. To rule out this explanation, we explore the timing of the relationship in Table 9. In columns 1-4, the dependent variable is log pay growth from year
t-2 to t-1, log pay growth from year t-1 to t, log pay growth from year t+1 to t+2, and log pay growth from year t+2 to t+3, respectively.
[Insert Table 9 here]
For all four years, we find no evidence that the compensation of employees in smaller labor markets is not more sensitive to changes in leverage than that of employees in larger labor markets at the same firm. In all specifications, we find an insignificant relationship between wage growth and
the interaction of labor market size and changes in firm leverage. Thus, given that the relationship between firm leverage, labor market size, and employee compensation is observed in only the year immediately following the increase in firm leverage, it is unlikely that reverse causality accounts
for our findings.
An alternative explanation for the effect on wages is that it is due to higher labor productivity rather than compensation for unemployment risk. For instance, suppose that there is a positive productivity shock in a given MSA-industry. Firms respond by increasing employment, thereby increasing the
size of the labor market. At the same time, public firms raise equity to increase more heavily in their establishments in that market. These investments increase labor productivity and therefore wages rise. While this provides an explanation for our previous results, we do not find evidence of a
contemporaneous productivity shock.
First, we test directly for an effect on labor productivity. We use data on establishment-level output from the Census of Manufactures and Annual Survey of Manufactures to calculate measures of average labor productivity at the firm-MSA level. We then re-estimate equation 1 with measures of labor
productivity as the dependent variable. The results are presented in Table 10.
[Insert Table 10 here]
In column 1 and 2, we study the effect on the growth in average output per worker. We find that the estimate on the interaction of the change in firm leverage and labor market size is insignificant and small in magnitude. Similarly, in the estimates of the growth in average value added per
worker in columns 3 and 4, the interaction term enters negatively but insignificantly. Thus, there is no evidence of a differential effect on labor productivity.25
[Insert Table 11 here]
Second, we test whether firm behavior is consistent with localized productivity shocks. In particular, if a particular set of a firm's establishments become more productive, we would expect those establishments to grow faster than the firm's other establishments. To test for difference in growth rates within a firm, we calculate firm-state measures of growth in employment and number of establishments from the LBD and growth in output per worker, value added per worker, and capital expenditures for manufacturing firms from the CMF and ASM and then re-estimate equation 1. The results are presented in Table 11. In all five tests, we find an insignificant effect of the interaction of the change in leverage and labor market size. Thus, there is no evidence that firms are reallocating resources towards its operations in smaller labor markets following an increase in leverage, which is inconsistent with those markets receiving a positive productivity shock.
In the previous sections, we estimate how increases in firm leverage differentially affect the compensation of workers at a given firm depending on the relative size of their labor markets. In this section, we use these cross-sectional estimates to estimate the effect of leverage on the employee
compensation and on the firm's total wage bill.
First, to use the cross-sectional estimates to back out the effect of firm leverage on worker compensation, we need to identify a control group, a group of workers for whom increased leverage has no effect on their compensation. We choose as our control group the set of workers in the top decile of
labor market size. Because their labor market is so large, their unemployment risk is likely to be very low and, as a result, they likely receive little to no addition compensation due to higher firm leverage. To the extent to which workers in the top decile are compensated as a result of increased
leverage, the estimates below will understate the true wage costs of leverage.
We then split all workers into subsamples based on the decile of labor market size and then calculate the average labor market size for each of the deciles. Under the assumption that workers in the top decile are a valid control group and thus require no wage premium for increased firm, we can then
calculate the effect of firm leverage on compensation for workers in each decile of the labor market size distribution. To do so, we calculate the difference between the average labor market size for that decile and the average for the top decile and then multiply that by the estimate on the
interaction between labor market size and the change in firm leverage. This yields an estimate for the effect of a one-unit change of firm leverage on a given worker's compensation.
To be more concrete, consider a worker in the fifth decile of labor market size. To calculate the effect of firm leverage on that worker's compensation, we calculate the difference between the average labor market size for workers in that decile and the average size for workers in the top decile,
which is approximately 23.8. We then multiply 23.8 by the estimate on the interaction between labor market size and the change in firm leverage. Using the estimate from column 2 of Table 3 of -0.008, this yields approximately 0.2. Since the dependent variable is the natural log of the average
quarterly earnings in year t+1 minus the natural log of the average quarterly earnings in year t, we then raise e to the power of 0.2 and substract 1 to calculate the effect of a one unit change on the level of
worker's compensation. We then multiply that effect by 0.078, equal to one standard deviation of the change in leverage in our sample, to estimate that a 10 percentage point increase in firm leverages increases pay by approximately 1.9%.
With this framework, we can estimate the effect of leverage on the compensation for every worker in the firm as a function of their labor market size.26 We
then sum the worker-level effect across all workers at the firm to calculate the total change in pay at the firm.
Assuming again that the top decile is a legitimate control group, the estimate in column 2 of Table 3 implies that, for a 10 percentage point increase in the leverage of the average firm in CRSP and Compustat that is matched to the LBD, total firm payroll increases by approximately 0.5%. Calculated
relative to the market value of firm assets, as in Almeida and Philippon (2007), this estimate implies that payroll increases by approximately 17 basis points of firm market value. Almeida and Philippon (2007) calculate that the difference between the tax benefits and costs of financial distress
are at most 65 basis points of firm value for BBB-rated firms. Our estimates therefore imply that the added labor costs can account for a meaningful fraction of this difference.
We can replicate this procedure to estimate the effect for new employees as well. Exploiting the cross-sectional results to estimate the effect of leverage on pay as described above, the estimate in column 2 of Table 4 implies that the 10 percentage point increase in leverage increases pay for the
average new worker by 8.5%.
In the previous sections, we have documented significant heterogeneity in the effect of leverage across workers depending on worker, firm, and labor market effects. Thus, while a 10 percentage point increase in firm leverage increases compensation for the worker in the median labor market size by
about 1.9%, some workers will experience significantly higher compensation growth while the compensation of others will be relatively unaffected.
In particular, we find evidence that the effects of firm leverage are stronger for workers who are more highly paid, who work in labor markets previously affected by firm bankruptcies, who work in labor markets with low unemployment rates, who work in labor markets with relatively low concentration
of employment across firms, and who work for firms with declining employment. Replicating the process described above, we now discuss the magnitudes of the effect for these subsamples.
The magnitudes vary relatively little across firm pay quartiles, as shown in Table 5. In particular, for the highest quartile, the estimates imply that a 10 percentage point increase in firm leverage increases compensation for a worker in the median decile of labor market size by approximately
2.1%, only slightly higher than the effect for the total population. In contrast, the magnitude of the effect is much larger for workers in labor markets that have had a public firm go bankrupt in the preceding five years; the estimates in Table 5 imply workers in those labor markets earn
approximately 7.1% more in response to a 10 percentage point increase in firm leverage.
The magnitudes similarly are much higher for workers with relatively more bargaining power. In particular, the estimates in Table 6 imply that a 10 percentage point increase in leverage increases worker compensation by 4.2% for those in MSAs with an unemployment rate of less than 5% while it
increases by 0.6% for workers in MSAs with an unemployment rate of 5% or higher. Employment concentration across firms in the market also dramatically influences the effect; in markets with low employment concentration, with an HHI of less than 1500, compensation increases by 2.5% for workers in
the median size decile.
Finally, in Table 7, we find that the effects of leverage are limited to workers at firms with declining employment. In terms of magnitudes, the estimates imply that leverage increases compensation by approximately 4.5% for workers in the median size decile at firms with declining total employment
and by approximately 5.1% for workers at firms whose employment in the MSA is declining.
In this section, we discuss our estimates in relation to other estimates of the effects of changes of firm leverage on employee compensation. As discussed above, our estimates from Table 3 imply that a 10 percentage point in book leverage increases compensation for the median worker by
approximately 1.9%. We also find that new employees earn approximately 8.5% more as a result of the increased leverage.
This estimate is very similar to other estimates of wage premium elasticities. For instance, Chemmanur, Cheng, and Zhang (2013) use Compustat data to examine the relationship between firm leverage and average employee pay. Using marginal corporate tax rates as an instrument for leverage, they find
that an increase in market leverage of 10 percentage points, average employee pay increases by approximately 2.4% for the average firm.
Graham, Kim, Li, and Qiu (2016) take a different approach to estimating the required premium by calculating the realized wage losses workers experience as a result of a corporate bankruptcy. They then estimate the implied ex ante wage premium that would be required to offset these ex post losses
for employees at firms with a given credit rating, using the risk neutral probability of default following Almeida and Phillippon (2007). Using this procedure, they estimate that, for workers at the average firm, compensation needs to increase by 1.0% for a 10 percentage point increase in book
leverage and 2.3% for a similar increase in market leverage to compensate for expected wage loss.
Other studies have focused on the effect of leverage on aggregate labor costs at the firm. For instance, Agrawal and Matsa (2013) use variation in the generosity of state unemployment insurance (UI) benefits to back out the cost of distress due to unemployment. In particular, they first estimate
the effect that changes on UI benefits has on firm capital structures and find that increased UI benefits leads to increased leverage for firms headquartered in the state. To calculate the ex ante labor costs of leverage, they combine their main findings with estimated default probabilities by
credit rating from Altman (2007), the wage premium per unit of unemployment risk from Topel (1984), and the probability of being laid off conditional on a bond default, which they estimate using Compustat data. Combining these data, they are able to estimate the average value of the compensating
wage premium, which they calculate to be approximately 5 basis points of firm value for a AA-rated firm and 159 basis points of firm value for a B-rated firm.
Graham, Kim, Li, and Qiu (2016) also back out the effects of the required wage premium at the firm level using their worker-level estimates. They find slightly higher values of 20 basis points of firm value for a AA-rated firm and 419 basis points of firm value for a B-rated firm. In our framework,
we find increased leverage increases compensation for continuing workers by approximately 17 basis points for the average firm.
Our estimates are likely a lower bound for the wage costs of leverage, given that we assume that the workers in the top decile of labor market size are not compensated for increased leverage. To the extent that those workers are compensated, that would increase the effect for workers across the
distribution of labor market size. Despite this potential bias, our approach has several advantages relative to the approaches of these other papers. First, with regards to Chemmanur, Cheng, and Zhang (2013), because they use firm-level data from Compustat, they are unable to account for important
heterogeneity across workers within the same firm, particularly in terms of how worker and labor market characteristics affect how compensation reacts to changes in leverage.
Second, with regard to Graham, Kim, Li, and Qiu (2016), while they exploit worker-level data from the LEHD, their analysis focuses solely on workers at bankrupt firms and the ex-post wage losses they experience. They then use this ex-post wage loss to estimate the required wage premium that workers
should demand for bearing bankruptcy risk. In other words, they estimate what the workers should receive, rather than what they do receive, as compensation. Our evidence shows that bargaining power is an important determinant of the actual wage increase received, suggesting that the Graham, Kim,
Li, and Qiu (2016) estimate is an upper bound for the wage costs of leverage.
Finally, with regard to Agrawal and Matsa (2013), they do not observe employee wages; rather, their main estimation focuses on the effect that changes in state UI benefits has on firm capital structures. To convert this result into an estimate of the wage costs of leverage, they need to make
several assumptions about the probability of firm default, the probability of being laid off conditional on a bond default, and the wage premium for increase unemployment risk. Slight changes in these assumptions can have large effects on their estimated wage premium.
In contrast to these papers, we are able to exploit worker-level data on wages for a broad sample of firms. Importantly, we are able to explicitly estimate the ex-ante wages that workers receive in return for increased leverage rather than rely on an array of assumptions to back out the wage
premium from other findings. Finally, our use of worker-level data allows us to exploit important heterogeneity across workers to see how and why this wage premium varies.
We find evidence that higher firm leverage increases employee wages. Exploiting within-firm variation in labor market size as a proxy for expected unemployment risk, we find that employees in smaller labor markets experience higher wage growth than other employees in response to increased firm
leverage. This effect is stronger for new employees and is robust to alternative specifications of labor market size.
The results suggest that the increased wages are compensation for unemployment risk and not driven by an unobserved productivity shock. In particular, the results are due to employees at firms with declining employment. Moreover, there is no evidence that the increased pay reflects higher
productivity.
At the firm level, the added labor costs due to higher leverage are significant and represent a significant fraction of the difference between the tax benefits of debt and costs of financial distress. Thus, labor costs appear to be a significant factor in determining optimal debt levels.
Our findings mirror previous work on the effect of firm debt on employee wages. However, our analysis has several key advantages relative to previous studies. In particular, we exploit worker-level data for a large sample of firms, not only those have experienced bankruptcy or other types of
financial distress. Moreover, we use this worker-level data to estimate the realized wage premia employees earn for bearing the risks associated with higher firm leverage and to understand how the premia varies across workers.
Higher wage costs are just one potential effect of debt on firm employees. Higher leverage may also have an effect on employee turnover or the ability of firms to hire employees. Indeed, Brown and Matsa (2016) show that increased firm financial distress leads to fewer employment applications, and
effect that may also be present for firms increasing their leverage ratios. While we find that employees are compensated for higher leverage on average, variation in bargaining power, firm-specific capital, and risk aversion likely means that a substantial fraction of employees are not adequately
compensated for bearing this risk. As a result, employees would likely benefit from moving to a new firm in response to an increase in firm leverage which raises the likelihood of financial distress. Higher debt levels may also reduce the incentive for development of firm-specific capital as the
higher probability of distress reduces its long-term benefits. Therefore, labor considerations beyond total wage compensation likely factor into the costs of debt and may play a larger role in determining optimal debt levels than wage effects suggest. Such questions pose fruitful areas of future
research.
Appendix
This table presents definitions for the key variables used in the analysis. Workers, firms, establishments, MSA, industries, years and quarters are indexed by i, j, e, k, l, t, and q, respectively. Data sources are the Census Bureau's Longitudinal Employer-Household Database (LEHD), the Census Bureau's Longitudinal Business Database (LBD), Compustat, the Census Bureau's Census of Manufactures (CMF), and the Census Bureau's Annual Survey of Manufactures (ASM).
| Variable Name | Description | Source | Definition |
|---|---|---|---|
| Pay | Sum of quarterly earnings (in 2007 dollars) relative to number of quarters with positive earnings | LEHD | $$ \sum\limits_{q=1}^{4} Earn_{ijtq} / \sum\limits_{q=1}^{4} \mathbb{1}_{Earn_{ijtq} > 0}$$ |
| $$ \Delta$$Pay | Change in log Pay | LEHD | $$ Ln(Pay_{t+1}) - Ln(Pay_{t})$$ |
| Size | Industry share of MSA employment relative to industry share of national employment | LBD | $$ \frac{(Emp_{klt} / Emp_{kt})}{(Emp_{lt} / Emp_{t})}$$ |
| Total Debt | Book value of firm debt | Compustat | $$ dltt_{jt} + dlc_{jt}$$ |
| $$ \Delta$$Total Debt | Change in log Debt | Compustat | $$ Book\: Total\: Debt_{t} - Total\: Debt_{t-1}$$ |
| Leverage | Firm book leverage | Compustat | $$ \frac{dltt_{jt} + dlc_{jt}}{at_{jt}}$$ |
| $$ \Delta$$Leverage | Change in Leverage | Compustat | $$ Book\: Leverage_{t} - Book\: Leverage_{t-1}$$ |
| Market Value | Firm market value of assets | Compustat | $$ prcc_{f,jt}*cshpri_{jt}+dlc_{jt}+dltt_{jt}+pstkl_{jt}-txditc_{jt}$$ |
| Market / Book | Firm market-book ratio | Compustat | $$ \frac{Market\: Value}{at_{jt}}$$ |
| $$ \Delta$$Market / Book | Change in $$ Market\: /\: Book$$ | Compustat | $$ Market\: /\: Book_{t} - Market\: /\: Book_{t-1}$$ |
| Sales | Firm sales | Compustat | salejt |
| $$ \Delta$$Sales | Change in log Sales | Compustat | $$ Ln(Sales_{t}) - Ln(Sales_{t-1})$$ |
| EBITDA / Assets | Firm return on assets | Compustat | $$ \frac{oibdp_{jt}}{at_{jt}}$$ |
| $$ \Delta$$EBITDA / Assets | Change in $$ EBITDA\: /\: Assets$$ | Compustat | $$ EBITDA\: /\: Assets_{t} - EBITDA\: /\: Assets_{t-1}$$ |
| Asset Tangibility | Firm asset tangibility | Compustat | $$ \frac{ppent_{jt}}{at_{jt}}$$ |
| $$ \Delta$$Asset Tangibility | Change in $$ Asset\: Tangibility$$ | Compustat | $$ Asset\: Tangibility_{t} - $$ |
| Marginal Tax Rate | Firm marginal tax rate | John Graham | bcgmtrintjt |
| $$ \Delta$$Marginal Tax Rate | Change in $$ Marginal\: Tax\: Rate$$ | Compustat | $$ Marginal\: Tax\: Rate_{t} - Marginal\: Tax\: Rate_{t-1}$$ |
| MSA Emp | Firm-MSA employment | LBD | $$ \sum\limits_{e} emp_{jkt}$$ |
| $$ \Delta$$MSA Emp | $$ M\!S\!A\: Emp$$ growth rate | LBD | $$ \frac{M\!S\!A\: Emp_{t+1} - M\!S\!A\: Emp_{t}}{M\!S\!A\: Emp_{t}}$$ |
| MSA Estab | Firm-MSA establishment count | LBD | $$ \sum\limits_{e} \mathbb{1}_{emp_{jkt} > 0}$$ |
| $$ \Delta$$MSA Estab | $$ M\!S\!A\: Estab$$ growth rate | CMF, ASM | $$ \frac{M\!S\!A\: Estab_{t+1} -M\!S\!A\: Estab_{t}}{M\!S\!A\: Estab_{t}}$$ |
| MSA Sales | Firm-MSA sales | CMF, ASM | $$ \sum\limits_{e} tvs_{jkt}$$ |
| $$ \Delta$$MSA Sales | $$ M\!S\!A\: Sales$$ growth rate | CMF, ASM | $$ \frac{M\!S\!A\: Sales_{t+1} - M\!S\!A\: Sales_{t}}{M\!S\!A\: Sales_{t}}$$ |
| MSA Value Add | Firm-MSA value added | CMF, ASM | $$ \sum\limits_{e} va_{jkt}$$ |
| $$ \Delta$$MSA Value Add | $$ M\!S\!A\: Value Add$$ growth rate | CMF, ASM | $$ \frac{M\!S\!A\: Value Add_{t+1} - M\!S\!A\: Value Add_{t}}{M\!S\!A\: Value Add_{t}}$$ |
| MSA CapEx | Firm-MSA capital expenditures | CMF, ASM | $$ \sum\limits_{e} capex_{jkt}$$ |
| $$ \Delta$$MSA CapEx | $$ M\!S\!A\: CapEx$$ growth rate | CMF, ASM | $$ \frac{M\!S\!A\: CapEx_{t+1} -M\!S\!A\: CapEx_{t}}{M\!S\!A\: CapEx_{t}}$$ |
| Labor Prod | Firm-MSA sales per employee | CMF, ASM | $$ \frac{M\!S\!A\: Sales}{M\!S\!A\: Emp}$$ |
| $$ \Delta$$Labor Prod | Change in log $$ Labor\: Prod$$ | CMF, ASM | $$ Ln(Labor\: Prod_{t+1}) - Ln(Labor\: Prod_{t})$$ |
| Value Add per Emp | Firm-MSA value added per employee | CMF, ASM | $$ \frac{M\!S\!A\: Value\: Add}{M\!S\!A\: Emp}$$ |
| $$ \Delta$$Value Add per Emp | Change in log $$ Value\: Add\: per\: Emp$$ | CMF, ASM | $$ Ln(Value\: Add\: per\: Emp_{t+1}) -Ln(Value\: Add\: per\: Emp_{t})$$ |
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This table presents summary statistics for worker-level variables in Panel A and firm-level variables in Panel B. The sample consists of 51,293,300 worker-firm-year observations, covering approximately 14,000,000 unique workers and 4,200 unique firms, from 1991 through 2008. See Appendix Table A1 for variable definitions.
| Panel A: Worker Level Variables | N | Mean | Std. Dev. | Median |
|---|---|---|---|---|
| $$ \Delta$$Pay | 51,293,300 | 0.087 | 0.652 | 0.014 |
| Pay | 51,293,300 | 11,029.25 | 8,870.277 | 9,101.673 |
| Size | 51,293,300 | 3.809 | 10.004 | 1.077 |
| Panel B: Firm Level Variables | N | Mean | Std. Dev. | Median |
|---|---|---|---|---|
| Leverage | 27,500 | 0.231 | 0.191 | 0.210 |
| $$ \Delta$$Leverage | 27,500 | 0.003 | 0.078 | -0.001 |
| Market / Book | 27,500 | 1.485 | 1.137 | 1.114 |
| $$ \Delta$$Market / Book | 27,500 | -0.045 | 0.636 | -0.008 |
| Sales | 27,500 | 2,270.289 | 8,732.016 | 379.234 |
| $$ \Delta$$Sales | 27,500 | 0.099 | 0.214 | 0.081 |
| EBITDA / Assets | 27,500 | 0.126 | 0.091 | 0.129 |
| $$ \Delta$$EBITDA / Assets | 27,500 | -0.005 | 0.058 | 0.000 |
| Asset Tangibility | 27,500 | 0.291 | 0.207 | 0.242 |
| $$ \Delta$$Asset Tangibility | 27,500 | -0.002 | 0.043 | -0.002 |
| Marginal Tax Rate | 27,500 | 0.276 | 0.093 | 0.322 |
| $$ \Delta$$Marginal Tax Rate | 27,500 | -0.002 | 0.049 | 0.000 |
This table presents OLS regressions using one year ahead worker log average quarterly pay as the dependent variable. The key independent variable is firm leverage (Leverage). Worker controls include indicator variables for worker age = 25-34, worker age = 35-44, worker age = 45-54, race, male, high school graduate, and college graduate. See Appendix Table A1 for variable definitions. The unit of observation is worker-firm-year. In columns 1 through 3, the dependent variable and the independent variables are in levels, and in columns 4 through 6, they are first differenced. Standard errors are adjusted for clustering by firm and are reported in parentheses. *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively.
| (1) | (2) | (3) | (4) | (5) | (6) | |
|---|---|---|---|---|---|---|
| Leverage |
0.234 (0.240) |
0.120 (0.140) |
-0.028 (0.038) |
-0.018 (0.017) |
-0.001 (0.021) |
-0.027* (0.016) |
| Market / Book |
0.069*** (0.024) |
0.003 (0.007) |
0.019*** (0.005) |
0.011*** (0.002) |
||
| EBITDA / Assets |
-0.821*** (0.278) |
-0.035 (0.082) |
-0.006 (0.042) |
0.063** (0.032) |
||
| Log Sales |
0.060*** (0.017) |
0.008 (0.012) |
0.041*** (0.012) |
0.017** (0.008) |
||
| Asset Tangibility |
-0.255* (0.143) |
-0.153** (0.064) |
-0.086** (0.040) |
-0.066* (0.037) |
||
| Marginal Tax Rate |
-0.362** (0.182) |
-0.045 (0.060) |
0.038 (0.069) |
0.033 (0.075) |
||
| Obs | 51,293,300 | 51,293,300 | 51,293,300 | 51,293,300 | 51,293,300 | 51,293,300 |
| R-squared | 0.00 | 0.36 | 0.57 | 0.00 | 0.02 | 0.05 |
| Levels/Diff | Levels | Levels | Levels | Diff | Diff | Diff |
| Worker Controls | no | yes | yes | no | yes | yes |
| Year FE | no | no | yes | no | no | yes |
| MSA FE | no | no | yes | no | no | yes |
| Firm FE | no | no | yes | no | no | yes |
This table presents OLS regressions using change in worker log average quarterly pay as the dependent variable. The key independent variable is the interaction of labor market size (Size) and the change in firm leverage ($$ \Delta$$Leverage). Labor market size interacted with the change in firm profitability, market-to-book ratio, log sales, asset tangibility, and marginal tax rate are included as controls in columns 2-8. Worker controls include log average quarterly pay and indicator variables for worker age = 25-34, worker age = 35-44, worker age = 45-54, race, male, high school graduate, and college graduate and are included in all specifications. See Appendix Table A1 for variable definitions. The unit of observation is worker-firm-year. Standard errors are adjusted for clustering by firm and are reported in parentheses. *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively.
| (1) | (2) | (3) | (4) | (5) | |
|---|---|---|---|---|---|
| Size * $$ \Delta$$Leverage |
-0.008** (0.004) |
-0.008** (0.004) |
-0.006* (0.004) |
||
| Size * (2nd Quartile $$ \Delta$$Leverage) |
-0.001 (0.001) |
||||
| Size * (3rd Quartile $$ \Delta$$Leverage) |
-0.001 (0.001) |
||||
| Size * (4th Quartile $$ \Delta$$Leverage) |
-0.001** (0.001) |
||||
| Size * $$ \Delta$$TotalDebt |
-0.002** (0.001) |
||||
| Size |
-0.001* (0.001) |
-0.001* (0.001) |
0.000 (0.000) |
-0.001 (0.001) |
-0.001*** (0.000) |
| Worker Controls | yes | yes | yes | yes | yes |
| Firm-Year FE | yes | yes | yes | yes | yes |
| MSA-Year FE | yes | yes | yes | yes | yes |
| MSA-Industry FE | yes | yes | yes | yes | yes |
| Worker FE | no | no | yes | no | no |
| Obs | 51,293,300 | 51,293,300 | 51,293,300 | 51,293,300 | 51,293,300 |
| R-Squared | 0.06 | 0.06 | 0.42 | 0.06 | 0.06 |
This table presents OLS regressions using worker log average quarterly pay as the dependent variable. The key independent variable is the interaction of labor market size (Size) and the change in firm leverage ($$ \Delta$$Leverage). The change in firm profitability, market-to-book ratio, log sales, asset tangibility, and marginal tax rate are included as controls in column 1. Labor market size interacted with the change in firm profitability, market-to-book ratio, log sales, asset tangibility, and marginal tax rate are included as controls in columns 2 through 4. Worker controls include log average quarterly pay and indicator variables for worker age = 25-34, worker age = 35-44, worker age = 45-54, race, male, high school graduate, and college graduate. See Appendix Table A1 for variable definitions. The unit of observation is worker-firm-year and the sample is restricted to new employees at the firm. Standard errors are adjusted for clustering by firm and are reported in parentheses. *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively.
| (1) | (2) | (3) | (4) | |
|---|---|---|---|---|
| Size * $$ \Delta$$Leverage |
-0.093*** (0.028) |
-0.077*** (0.024) |
||
| Size * (2nd Quartile $$ \Delta$$Leverage) |
-0.009 (0.009) |
|||
| Size * (3rd Quartile $$ \Delta$$Leverage) |
-0.003 (0.007) |
|||
| Size * (4th Quartile $$ \Delta$$Leverage) |
-0.008** (0.004) |
|||
| Size * $$ \Delta$$TotalDebt |
-0.008** (0.004) |
|||
| Size |
0.003 (0.002) |
0.002 (0.003) |
0.004 (0.003) |
0.000 (0.003) |
| Worker Controls | yes | yes | yes | yes |
| Firm-Year FE | yes | yes | yes | yes |
| MSA-Year FE | yes | yes | yes | yes |
| MSA-Industry FE | yes | yes | yes | yes |
| Obs | 19,551,900 | 19,551,900 | 19,551,900 | 19,551,900 |
| R-squared | 0.79 | 0.80 | 0.85 | 0.80 |
This table presents OLS regressions using the change in worker log average quarterly pay as the dependent variable. The key independent variable is the interaction of labor market size (Size) and the change in firm leverage ($$ \Delta$$Leverage). The change in firm profitability, market-to-book ratio, log sales, asset tangibility, and marginal tax rate are included as controls in column 1. Labor market size interacted with the change in firm profitability, market-to-book ratio, log sales, asset tangibility, and marginal tax rate are included as controls in columns 2 and 3. Worker controls include log average quarterly pay and indicator variables for worker age = 25-34, worker age = 35-44, worker age = 45-54, race, male, high school graduate, and college graduate. See Appendix Table A1 for variable definitions. The unit of observation is worker-firm-year. Column 1 restricts the sample to workers in the bottom quartile of earnings at the firm, column 2 restricts the sample to workers in the second quartile of earnings at the firm, column 3 restricts the sample to workers in the third quartile of earnings at the firm, column 4 restricts the sample to workers in the top quartile of earnings at the firm, column 5 restricts the sample to workers in an MSA-SIC3 that had a public firm bankruptcy in the previous five years, and column 6 restricts the sample to workers in an MSA-SIC3 that had a public firm bankruptcy in the previous five years. Standard errors are adjusted for clustering by firm and are reported in parentheses. *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively.
| (1) | (2) | (3) | (4) | (5) | (6) | |
|---|---|---|---|---|---|---|
| Size * $$ \Delta$$Leverage |
-0.008 (0.009) |
-0.005 (0.004) |
-0.008** (0.004) |
-0.009** (0.004) |
-0.030* (0.015) |
0.003 (0.004) |
| Size |
-0.002* (0.001) |
-0.001 (0.001) |
-0.001 (0.000) |
0.000 (0.001) |
-0.001 (0.002) |
-0.001** (0.001) |
| Sample |
Bottom Quartile Earner |
2nd Quartile Earner |
3rd Quartile Earner |
Top Quartile Earner |
Bankruptcy Exp |
No Bankruptcy Exp |
| Worker Controls | yes | yes | yes | yes | yes | yes |
| Firm-Year FE | yes | yes | yes | yes | yes | yes |
| MSA-Year FE | yes | yes | yes | yes | yes | yes |
| MSA-Industry FE | yes | yes | yes | yes | yes | yes |
| Obs | 12,890,500 | 12,796,700 | 12,840,100 | 12,766,000 | 23,946,900 | 27,346,400 |
| R-squared | 0.19 | 0.06 | 0.07 | 0.10 | 0.07 | 0.07 |
This table presents OLS regressions using the change in worker log average quarterly pay as the dependent variable. The key independent variable is the interaction of labor market size (Size) and the change in firm leverage ($$ \Delta$$Leverage). The change in firm profitability, market-to-book ratio, log sales, asset tangibility, and marginal tax rate are included as controls in column 1. Labor market size interacted with the change in firm profitability, market-to-book ratio, log sales, asset tangibility, and marginal tax rate are included as controls in columns 2 and 3. Worker controls include log average quarterly pay and indicator variables for worker age = 25-34, worker age = 35-44, worker age = 45-54, race, male, high school graduate, and college graduate. See Appendix Table A1 for variable definitions. The unit of observation is worker-firm-year. Column 1 restricts the sample to workers in MSAs with an unemployment rate greater than 5 percent, column 2 restricts the sample to workers in MSAs with an unemployment rate less than or equal to 5 percent, column 3 restricts the sample to workers in MSA-industries with an employment HHI greater than 1500, and column 4 restricts the sample to workers in MSA-industries with an employment HHI less than or equal to 1500. Standard errors are adjusted for clustering by firm and are reported in parentheses. *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively.
| (1) | (2) | (3) | (4) | |
|---|---|---|---|---|
| Size * $$ \Delta$$Leverage |
-0.002 (0.007) |
-0.018** (0.007) |
0.004 (0.009) |
-0.011** (0.005) |
| Size |
-0.001* (0.001) |
0.001 (0.001) |
-0.004*** (0.001) |
-0.000 (0.001) |
| Sample | Unemp Rate $$ \ge$$ 5 | Unemp Rate < 5 | HHI $$ \ge$$ 1500 | HHI < 1500 |
| Worker Controls | yes | yes | yes | yes |
| Firm-Year FE | yes | yes | yes | yes |
| MSA-Year FE | yes | yes | yes | yes |
| MSA-Industry FE | yes | yes | yes | yes |
| Obs | 20,873,900 | 30,419,400 | 29,313,500 | 21,979,800 |
| R-squared | 0.07 | 0.07 | 0.10 | 0.06 |
This table presents OLS regressions using the change in worker log average quarterly pay as the dependent variable. The key independent variable is the interaction of labor market size (Size) and the change in firm leverage ($$ \Delta$$Leverage). Labor market size interacted with the change in firm profitability, market-to-book ratio, log sales, asset tangibility, and marginal tax rate are included as controls in all specifications. Worker controls include log average quarterly pay and indicator variables for worker age = 25-34, worker age = 35-44, worker age = 45-54, race, male, high school graduate, and college graduate. See Appendix Table A1 for variable definitions. The unit of observation is worker-firm-year. Column 1 restricts the sample to workers at firms whose year t+1 employment was greater than year t employment. Column 2 restricts the sample to workers at firms whose year t+1 employment was less than or equal than year t employment. Column 3 restricts the sample to workers at firms whose year t+1 employment in the MSA was greater than year t employment in the state. Column 4 restricts the sample to workers at firms whose year t+1 employment in the MSA was less than or equal than year t employment in the state. Standard errors are adjusted for clustering by firm and are reported in parentheses. *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively.
| (1) | (2) | (3) | (4) | |
|---|---|---|---|---|
| Size * $$ \Delta$$Leverage |
0.002 (0.006) |
-0.012*** (0.007) |
0.004 (0.007) |
-0.021*** (0.005) |
| Size |
-0.001 (0.001) |
0.000 (0.001) |
-0.003*** (0.001) |
0.001 (0.001) |
| Sample | $$ \Delta$$Firm Emp > 0 | $$ \Delta$$Firm Emp $$ \leq$$ 0 | $$ \Delta$$Firm-MSA Emp > 0 | $$ \Delta$$Firm-MSA Emp $$ \leq$$ 0 |
| Worker Controls | yes | yes | yes | yes |
| Firm-Year FE | yes | yes | yes | yes |
| MSA-Year FE | yes | yes | yes | yes |
| MSA-Industry FE | yes | yes | yes | yes |
| Obs | 25,453,200 | 25,840,100 | 23,795,600 | 27,497,700 |
| R-squared | 0.07 | 0.07 | 0.07 | 0.07 |
This table presents OLS regressions using measures of worker pay as the dependent variable. In columns 1 through 3, the dependent variable is change in worker log average quarterly pay and the sample is restricted to continuing employees. In columns 4 through 6, the dependent variable is worker log average quarterly pay and the sample is restricted to new employees at the firm. The key independent variable is the interaction of labor market size (Size) and the change in firm leverage ($$ \Delta$$Leverage). In columns 1 and 3, labor market size is measured as the share of MSA employment in the industry. In columns 2 and 4, labor market size is measured as the share of MSA establishments in the industry relative to the national share of establishments in the industry. In columns 3 and 6, labor market size is measured as the share of young firms in the industry relative to the national share of young firms in the industry. Worker controls include log average quarterly pay and indicator variables for worker age = 25-34, worker age = 35-44, worker age = 45-54, race, male, high school graduate, and college graduate. See Appendix Table A1 for variable definitions. The unit of observation is worker-firm-year. Standard errors are adjusted for clustering by firm and are reported in parentheses. *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively.
| (1) | (2) | (3) | (4) | (5) | (6) | |
|---|---|---|---|---|---|---|
| Size * (2nd Quartile $$ \Delta$$Leverage) |
0.001 (0.002) |
0.002 (0.001) |
0.006 (0.005) |
-0.008 (0.015) |
-0.003 (0.020) |
-0.010 (0.016) |
| Size * (3rd Quartile $$ \Delta$$Leverage) |
-0.001 (0.002) |
-0.001 (0.002) |
-0.002 (0.004) |
-0.008 (0.011) |
-0.001 (0.018) |
-0.007 (0.015) |
| Size * (4th Quartile $$ \Delta$$Leverage) |
-0.003** (0.002) |
-0.003* (0.001) |
-0.011*** (0.003) |
-0.019** (0.009) |
-0.025* (0.015) |
-0.021** (0.010) |
| Size |
-0.006 (0.005) |
0.003 (0.002) |
0.005 (0.011) |
0.016 (0.011) |
0.030 (0.021) |
0.017 (0.013) |
| Sample | Continuing Employees | Continuing Employees | Continuing Employees | New Employees | New Employees | New Employees |
| Size Measure | $$ \frac{Emp_{klt}}{Emp_{kt}}$$ | $$ \frac{\left(\frac{Estab_{klt}}{Estab_{kt}}\right)}{\left(\frac{Estab_{lt}}{Estab_{t}}\right)}$$ | $$ \frac{\left(\frac{Young Firms_{klt}}{Young Firms_{kt}}\right)}{\left(\frac{Young Firms_{lt}}{Young Firms_{t}}\right)}$$ | $$ \frac{Emp_{klt}}{Emp_{kt}}$$ | $$ \frac{\left(\frac{Estab_{klt}}{Estab_{kt}}\right)}{\left(\frac{Estab_{lt}}{Estab_{t}}\right)}$$ | $$ \frac{\left(\frac{Young Firms_{klt}}{Young Firms_{kt}}\right)}{\left(\frac{Young Firms_{lt}}{Young Firms_{t}}\right)}$$ |
| Worker Controls | yes | yes | yes | yes | yes | yes |
| Firm-Year FE | yes | yes | yes | yes | yes | yes |
| MSA-Year FE | yes | yes | yes | yes | yes | yes |
| MSA-Industry FE | yes | yes | yes | yes | yes | yes |
| Obs | 51,293,300 | 51,293,300 | 51,293,300 | 19,551,900 | 19,551,900 | 19,551,900 |
| R-squared | 0.06 | 0.06 | 0.07 | 0.61 | 0.61 | 0.61 |
This table presents OLS regressions using the change in worker log average quarterly pay as the dependent variable. In column 1, the change is calculated for year t-2 to year t-1, in column 2, it is calculated for year t-1 to t, in column 3, it is calculated for year t+1 to year t+2, and in column 4, it is calculated for year t+2 to year t+3. The key independent variable is the interaction of labor market size (Size) and the change in firm leverage ($$ \Delta$$Leverage). The change in firm profitability, market-to-book ratio, log sales, asset tangibility, and marginal tax rate are included as controls in column 1. Labor market size interacted with the change in firm profitability, market-to-book ratio, log sales, asset tangibility, and marginal tax rate are included as controls in columns 2 and 3. Worker controls include log average quarterly pay and indicator variables for worker age = 25-34, worker age = 35-44, worker age = 45-54, race, male, high school graduate, and college graduate. See Appendix Table A1 for variable definitions. The unit of observation is worker-firm-year. Standard errors are adjusted for clustering by firm and are reported in parentheses. *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively.
| (1) | (2) | (3) | (4) | |
|---|---|---|---|---|
| Size * $$ \Delta$$Leverage |
0.002 (0.005) |
-0.007 (0.006) |
0.002 (0.006) |
-0.002 (0.006) |
| Year | t-2 | t-1 | t+1 | t+2 |
| Firm-Year FE | yes | yes | yes | yes |
| MSA-Year FE | yes | yes | yes | yes |
| MSA-Industry FE | yes | yes | yes | yes |
| Obs | 40,419,900 | 45,590,800 | 45,028,300 | 39,601,400 |
| R-squared | 0.04 | 0.07 | 0.04 | 0.03 |
This table presents OLS regressions with three dependent variables. The dependent variable in columns 1 and 2 is the change in the firm-MSA value of shipments per worker, and the dependent variable in columns 3 and 4 is the change in the firm-MSA value added per worker. The key independent variable is the interaction of labor market size (Size) and the change in firm leverage ($$ \Delta$$Leverage). Labor market size interacted with the change in firm profitability, market-to-book ratio, log sales, asset tangibility, and marginal tax rate are included as controls in all specifications. Worker controls include indicator variables for worker age = 25-34, worker age = 35-44, worker age = 45-54, race, male, high school graduate, and college graduate. Columns 1 and 2 also include log firm-MSA value of shipments per worker, and column 3 and 4 also include log firm-MSA value added per worker. See Appendix Table A1 for variable definitions. The unit of observation is worker-firm-year. The sample is restricted to workers at firms with data in the CMF and ASM. Standard errors are adjusted for clustering by firm and are reported in parentheses. *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively.
| (1) | (2) | (3) | (4) | |
|---|---|---|---|---|
| Size * $$ \Delta$$Leverage |
-0.003 (0.018) |
-0.008 (0.020) |
-0.030 (0.030) |
-0.030 (0.033) |
| Size |
-0.002 (0.003) |
-0.003 (0.004) |
-0.008* (0.005) |
-0.008 (0.006) |
| Dep. Var. | $$ \Delta$$Labor Prod | $$ \Delta$$Labor Prod | $$ \Delta$$Value Add per Emp | $$ \Delta$$Value Add per Emp |
| Worker Controls | yes | yes | yes | yes |
| Firm-Year FE | yes | yes | yes | yes |
| MSA-Year FE | yes | yes | yes | yes |
| MSA-SIC3 FE | yes | yes | yes | yes |
| Worker FE | no | yes | no | yes |
| Obs | 18,791,000 | 18,791,000 | 18,791,000 | 18,791,000 |
| R-squared | 0.89 | 0.91 | 0.90 | 0.92 |
This table presents OLS regressions using firm-MSA growth rates as the dependent variable. The dependent variable in column 1 is employment growth, the dependent variable in column 2 is growth in the number of establishments, the dependent variable in column 3 is sales growth, the dependent variable in column 4 is growth in value added, and the dependent variable in column 5 is growth in capital expenditures. The key independent variable is the interaction of labor market size (Size) and the change in firm leverage ($$ \Delta$$Leverage). Labor market size interacted with the change in firm profitability, market-to-book ratio, log sales, asset tangibility, and marginal tax rate are included as controls in all specifications. The initial level of the dependent variable, in logs, is included in each specification. See Appendix Table A1 for variable definitions. The unit of observation is firm-MSA-year. The samples in columns 3 through 5 are restricted to firms with data in the CMF and ASM. Standard errors are adjusted for clustering by firm and are reported in parentheses. *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively.
| (1) | (2) | (3) | (4) | (5) | |
|---|---|---|---|---|---|
| Size * $$ \Delta$$Leverage |
0.012 (0.011) |
-0.009 (0.007) |
-0.002 (-0.223) |
-0.027 (-0.801) |
-0.012 (-0.287) |
| Size |
-0.013*** (0.002) |
-0.001 (0.001) |
-0.002 (-0.697) |
-0.010 (-1.274) |
-0.007 (-0.733) |
| Dep. Var. | $$ \Delta$$MSA Emp | $$ \Delta$$MSA Estab | $$ \Delta$$MSA Sales | $$ \Delta$$MSA Value Add | $$ \Delta$$MSA CapEx |
| Firm-Year FE | yes | yes | yes | yes | yes |
| MSA-Year FE | yes | yes | yes | yes | yes |
| MSA-SIC3 FE | yes | yes | yes | yes | yes |
| Obs | 372,300 | 372,300 | 29,300 | 29,300 | 29,300 |
| R-squared | 0.36 | 0.52 | 0.54 | 0.57 | 0.62 |