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The Impact of Corporate Welfare Policy on Firm-Level Productivity: Evidence from Unemployment Insurance

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Abstract

We study how changes in unemployment risk affect firms’ productivity and whether firm-initiated policies can mitigate the moral hazard problem created by increases in unemployment insurance benefits (UIBs) that might decrease workers’ incentives to work hard. We focus on state-specific changes in UIB levels as a quasi-natural experiment. While a large body of research has examined UIBs, including their effect on unemployed workers, few studies investigate whether UIBs have any impact on a firm’s overall productivity. Using data on firm-level total factor productivity and state-level UIBs, we find a negative association between productivity and UIBs. We also find that the negative association is weaker for firms with higher employee-welfare indices than for firms with lower indices, suggesting that the adverse effect of higher UIBs on productivity is mitigated by policies that benefit workers’ welfare. More specifically, we find that among policies that are under the umbrella of corporate social responsibility, a subset of employee-welfare policies (e.g., work/life benefits) are more effective in managing moral hazard problems than other policies.

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Notes

  1. For more details, see the US Department of Labor: Employment and Training Administration website (http://www.doleta.gov).

  2. See Holmlund (1998) for a survey of the economic analyses of unemployment insurance since the 1970s.

  3. In addition to inducing workers not to shirk, efficiency wages that are higher than market-clearing wages might be used to reduce worker turnovers, to attract more efficient workers, or to promote a high level of performance norm (Akerlof 1982; Yellen 1995).

  4. Focusing on labor turnover, Salop (1979) earlier provided a similar prediction, Schlicht (1978), and Stiglitz (1974) in the same model setup.

  5. UIBs are paid from state trust funds to which employers contribute unemployment insurance taxes. Every state uses an experience-rating system to calculate the tax rate of each employer. Thus, job rationing and tax rates can be interrelated. The 2014 tax rates vary across states from wages subject to an unemployment tax of $7000 at the rate of 0.02% in Arizona to $40,400 at 6.4% in Hawaii. New employers are subject to a fixed flat rat or industry average rates in some states. See http://www.taxpolicycenter.org/taxfacts/displayafact.cfm?Docid=541. While the experience-rating system implies that a firm’s tax payment and its layoffs are positively correlated, the correlation appears to be quite imperfect, partly because the maximum tax rate is bounded. Topel (1983) notes extensive cross subsidization across firms.

  6. Huselid (1995), Delaney and Huselid (1996), and Konrad and Mangel (2000), among others, also examine employee-welfare enhancing activities as a mechanism to mitigate the moral hazard problem by improving worker motivation and productivity.

  7. We present the definitions of these categories in Table 8 of Appendix 1.

  8. See Margolis et al. (2009) for a review of the earlier literature.

  9. Smith (1776, Vol. 1, Chapter 10) observed that wage differentials can be due to (1) “the agreeableness or disagreeableness of the employments,” (2) “the easiness and cheapness, or the difficulty and expense of learning them,” (3) “the constancy or inconstancy of employment in them,” (4) “the small or great trust which must be reposed in those who exercise them,” and (5) “the probability or improbability of success in them.”

  10. Being the residual, it is hard to define what total factor productivity measures. Empirical studies have attempted to identify the determinants of total factor productivity and, in particular, its growth rate. Variables that are often associated with TFP growth at the macro-level include the following: technology, institutions, infrastructure, and R&D investment. At the firm level, Imrohoroglu and Tuzel (2014) find that firm size, book-to-market, leverage, asset growth rate, and return on assets (ROA) are associated with firm-specific TFP.

  11. Note that the state political process sets the level of UIBs. Most firms are too small to influence the level of UIBs; thus, we assume UIBs to be exogenously set from each firm’s viewpoint.

  12. Equation (1) essentially assumes a Cobb–Douglas production of the form y = ωKβkLβl. A logarithmic transformation yields the regression equation. We do not impose constant returns to scale; i.e., βk need not equal 1 − βl.

  13. See the online appendix (http://www.bcf.usc.edu/~tuzel/TFP_InternetAppendix_July8.pdf) of Imrohoroglu and Tuzel (2014) for details.

  14. We obtain the Production Efficiency measure from Professor Peter Demerjian’s website: http://faculty.washington.edu/pdemerj/data.html. We thank Professors Demerjian, McVay, and Lev for making these data available for research.

  15. We normalize the average market capitalization of each industry to one every year (firm size/average firm size in that year) since the nominal size of firms has grown over the years.

  16. As a sensitivity test, we follow Petersen (2009) and Gow et al. (2010) and calculate two-way clustered standard errors based on industry and year; our inferences are unaffected.

  17. In a robustness test (untabulated), we also use an indicator variable (High welfare) equal to one when a firm’s EWI is equal to or above the year-industry EWI median level. The results are qualitatively similar and even stronger for TFP.

  18. This percentage is calculated as the coefficient on the interaction term divided by the coefficient of Log UI Generosity, i.e., 0.024/0.175.

  19. We calculate wage level at the industry level since many sample firms do not have wage data. We first calculate the average wage level for each firm in Compustat that has wage information and then calculate the average wage level at the industry level.

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Authors and Affiliations

  1. Baruch College, New York City, NY, USA

    Masako Darrough

  2. University of Maryland, College Park, MD, USA

    Heedong Kim & Emanuel Zur

Authors
  1. Masako Darrough
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  2. Heedong Kim
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  3. Emanuel Zur
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Corresponding author

Correspondence to Emanuel Zur.

Additional information

We are grateful to Professor İmrohoroğlu for supplying us with the data on total factor productivity.

Appendices

Appendix 1

See Table 8.

Table 8 Employee-welfare index (EWI) categories (KLD database)
Full size table

Appendix 2

See Table 9.

Table 9 Variable and its definitions
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Darrough, M., Kim, H. & Zur, E. The Impact of Corporate Welfare Policy on Firm-Level Productivity: Evidence from Unemployment Insurance. J Bus Ethics 159, 795–815 (2019). https://doi.org/10.1007/s10551-018-3817-2

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