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«ABSTRACT We present evidence that financing frictions adversely impact investment in workplace safety, with implications for worker welfare and ...»

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— Insert Table X here — To do so, we compute LeadInjuries/Hour as next year’s firm-level injury rate, and include that in the regressions. The inclusion of LaggedInjuries/Hour and LeadInjuries/Hour in a regression requires that a firm be present three consecutive years in the data to be included in the sample, reducing the sample size from 4,469 to 2,843. Columns (3) and (4) present the results from the augmented regressions. Firm value continues to be negatively related to LaggedInjuries/Hour, and the magnitudes of these relations are similar to those shown in the first two columns. Firm value appears to be unrelated to future injury rates

–  –  –

Workplace injuries are not an i.i.d. process, however, and since injury rates exhibit significant persistence, one additional injury represents a persistent change in future injuries as well. Consequently, this estimate is not attributable to a single injury, but rather to the cumulative discounted cost of this and all future injuries. Estimates from an AR(1) model of injury rates indicate persistence of 0.65 at an annual horizon. Assuming a constant cost

–  –  –

where ρ = 0.65 is the rate of persistence and δ is a discount factor. As injury risk is likely to be largely idiosyncratic, future costs should be discounted at approximately the risk-free rate.

We therefore set δ to 1−0.0151, where 0.0151 is the mean five-year inflation-indexed Treasury rate over the sample period. This produces an estimated CostP erInjury of $98,924.

While it is difficult to attribute this cost to specific sources, existing research, primarily focusing on the 1970s and 1980s, finds an average compensating differential of $20,000 to $70,000 per additional expected injury (Viscusi and Aldy (2003)). Adjusting for inflation, this translates into between $50,000 and $300,000 per injury in 2005 dollars, where 2005 is the midpoint of our sample period. Thus, compensating wage differentials appear to explain a significant portion of our estimates of the total cost to the firm per injury. The remainder is likely attributable to increased downtime, higher insurance premia, and reduced productivity. While individual estimates of these cost components are not available, Danna and Griffin (1999) argue that the cost associated with reduced productivity in particular is likely to be even larger than the compensating wage differential. Ultimately, given all of the assumptions that are required to estimate the cost per injury, the estimates provided here should be taken with a grain of salt. Nevertheless, the estimated costs appear large enough to support the argument that higher expected injury risk represents a significant cost of financial policies that increase the likelihood that financing constraints bind in the future.

29 VI. Conclusion

In summary, this paper provides new evidence that injury rates increase with leverage and decrease with operating cash flow. We contribute to the literature studying the effect of financing on employee welfare by uncovering evidence of a novel and important channel through which financing can affect the well-being of employees. Our results also have implications for optimal financial policy, as firm value appears to decline with injury rates.

Further research in this area should yield additional insights into how firms internalize these costs and how the organizational form of the firm affects worker welfare.

30 Appendix A. Additional Tables

Table AI Workplace Injuries and Cash Flow Shocks - Other Oil Price Shock Specifications This table presents estimates of the effects of cash flow shocks arising from three quasi-natural experiments on injury rates.

Panel A reports estimates from OLS models where the dependent variable is Injuries/Hour. Panel B reports estimates from Poisson models where the dependent variable is Injuries and the exposure variable is HoursW orked. All models include establishment, industry-year, and state-year fixed effects. In the AJCA experiment, the sample is restricted to the years 2002, 2003, 2005, and 2006. T reatment equals one post-2004 and zero pre-2004, and Exposure equals one if the parent firm’s cumulative reported foreign profits in 2001-2003 were positive and zero otherwise. In the financial crisis experiment, the sample is restricted to the 2006 to 2008 period. T reatment equals one in 2008 and zero in 2006 and 2007, and Exposure equals one if the parent firm’s debt maturing within one year as a percentage of assets os of fiscal year-end 2007 exceeds 0.03064 (the 75th percentile for the sample). The oil price experiment uses all years in the sample (2002 to 2009). The sample consists of only non-oil producing establishments. T reatment equals the natural log of the average oil price for the year, and Exposure equals one if an establishment’s parent firm is in the oil business (either has an oil-producing establishment in the BLS data in any year in the sample or is identified by Capital IQ as being in the oil, gas, and consumable fuels business, excluding coal mining) and zero otherwise.

In each experiment, treated establishments (Exposure = 1) are matched with untreated establishments (Exposure = 0) using propensity score matching. See Table VII for information about the characteristics of treated and untreated establishments in each matched sample. Control variables Debt/Assets, Cash/Assets, Log(Assets), M arketT oBook, and T angibleAssetRatio, CashF low/Assets, Dividends/Assets, AssetT urnover, Capex/Assets, Log(Employees), and Hours/Employee are included in the regressions but omitted from the table for brevity. Standard errors clustered at the firm level are reported in parentheses below each point estimate. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% level, respectively, based on a two-tailed t-test.





–  –  –

31 Table AII Workplace Injuries and Cash Flow Shocks - Matching Within Industry This table presents estimates of the effects of cash flow shocks arising from three quasi-natural experiments on injury rates. Panel A reports estimates from OLS models where the dependent variable is Injuries/Hour. Panel B reports estimates from Poisson models where the dependent variable is Injuries and the exposure variable is HoursW orked. All models include establishment, industry-year, and state-year fixed effects. In the AJCA experiment, the sample is restricted to the years 2002, 2003, 2005, and

2006. T reatment equals one post-2004 and zero pre-2004, and Exposure equals one if the parent firm’s cumulative reported foreign profits in 2001 to 2003 were positive and zero otherwise. In the financial crisis experiment, the sample is restricted to the 2006 to 2008 period. T reatment equals one in 2008 and zero in 2006 and 2007, and Exposure equals one if the parent firm’s debt maturing within one year as a percentage of assets as of fiscal year-end 2007 exceeds 0.03064 (the 75th percentile for the sample). The oil price experiment uses all years in the sample (2002 to 2009). The sample consists of only non-oil producing establishments. T reatment equals the natural log of the average oil price for the year, and Exposure equals one if an establishment’s parent firm is in the oil business (either has an oil-producing establishment in the BLS data in any year in the sample or is identified by Capital IQ as being in the oil, gas, and consumable fuels business, excluding coal mining) and zero otherwise.

In each experiment, treated establishments (Exposure = 1) are matched with untreated establishments (Exposure = 0) using propensity score matching. See Table VII for information about the characteristics of treated and untreated establishments in each matched sample. Control variables Debt/Assets, Cash/Assets, Log(Assets), M arketT oBook, and T angibleAssetRatio, CashF low/Assets, Dividends/Assets, AssetT urnover, Capex/Assets, Log(Employees), and Hours/Employee are included in the regressions but omitted from the table for brevity. Standard errors clustered at the firm level are reported in parentheses below each point estimate. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% level, respectively, based on a two-tailed t-test.

–  –  –

To test whether employment grew in a firm’s establishments in safer industries relative to its establishments in more dangerous industries after 2004 if it had positive foreign profits, we form a sample of all establishments that are in the data at least once in both the preand the post-AJCA periods. For each establishment, we compute the annualized percent change in employment from the pre- to post-AJCA period. For pre-AJCA employment, we use an establishment’s 2003 employment if it is available and 2002 if is not. For the post-AJCA period, we use 2005 employment if it is available and 2006 if it is not. We then divide establishments into more or less dangerous establishments depending on whether an establishment’s industry mean injury rate is above or below the median industry mean injury rate for all of the parent firm’s establishments in the sample.29 Table BI presents the mean percent change in employment around the AJCA in more and less dangerous establishments separately for firms with and without cumulative foreign profits over the 2001 to 2003 period.

— Insert Table BI here — Firms with foreign profits appear to reduce employment in safer establishments more than in dangerous establishments after 2004, both in absolute terms and relative to firms without foreign profits, though the differences are not statistically significant. While this does not rule out the possibility of a differential shift in productive activities, such a shift would have to have occurred only within establishments and not (to a detectable degree) across establishments. The lack of an increase in employment overall is consistent with existing conclusions that, despite its intent, the AJCA failed to actually create jobs (e.g., Dharmapala, Foley, and Forbes (2011)).

–  –  –

Almeida, Heitor, Murillo Campello, Bruno Laranjeira, and Scott Weisbenner, 2012, Corporate debt maturity and the real effects of the 2007 credit crisis, Critical Finance Review 1, 3-58.

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Brody, David, 1960, Steelworkers in America: The Nonunion Era (Harvard University Press Urbana, IL).

Bronars, Stephen G., and Donald R. Deere, 1991, The threat of unionization, the use of debt, and the preservation of shareholder wealth, Quarterly Journal of Economics 106, 231-254.

Brown, Jennifer, and David Matsa, 2013, Boarding a sinking ship? An investigation of job applications to distressed firms, Working paper, Northwestern University.

Clark, Kim B., and Joshua Daniel Margolis, 2000, Workplace safety at Alcoa (A) (Harvard Business School Publishing).

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Denis, David, and Diane Denis, 1993, Managerial discretion, organizational structure, and corporate performance: A study of leveraged recapitalizations, Journal of Accounting and Economics 16, 209-236.

Dharmapala, Dhammika, C. Fritz Foley, and Kristin J. Forbes, 2011, Watch what I do, not what I say: The unintended consequences of the Homeland Investment Act, Journal of Finance 46, 753-787.

Dionne, Georges, Robert Gagné, François Gagnon, and Charles Vanasse, 1997, Debt, moral hazard and airline safety: An empirical evidence, Journal of Econometrics 79, 379-402.

–  –  –

Faulkender, Michael, and Mitchell Petersen, 2011, Investment and capital constraints:

Repatriations under the American Jobs Creation Act, Review of Financial Studies 25, 3351-3388.

Fazzari, Steven M., R. Glenn Hubbard, and Bruce C. Petersen, 1988, Financing constraints and corporate investment, Brookings Papers on Economic Activity, 141-206.

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Jensen, Michael, 1986, Agency costs of free cash flow, corporate finance, and takeovers, American Economic Review 76, 323-329.

Kini, Omesh, Jaideep Shenoy, and Venkat Subramaniam, 2014, Impact of financial leverage on the incidence and severity of product failures: Evidence from product recalls, Working paper, Georgia State University.

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–  –  –

Rose, Nancy L., 1990, Profitability and product quality: Economic determinants of airline safety performance, Journal of Political Economy 98, 944-964.



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