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The Economic Consequences of Labor Unionization: Evidence from Stock Price Crash Risk

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Abstract

This study investigates the impact of labor unionization on stock price crash risk. We find that labor unionization is negatively associated with stock price crash risk. Such negative relation is more pronounced when firms can intimate more credible evidence on unfavorable prospects and when firms face more powerful labor unions. Our findings are consistent with the notion that firms take strategic actions to reduce the bargaining advantages enjoyed by labor unions and that labor unions force firms to take less risky investments and discontinue underperformed projects more timely, which leads to lower stock price crash risk.

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Notes

  1. Indeed, our empirical findings show a negative association between labor union and crash risk, which contradicts the strategic-bargaining story. Our findings instead are consistent with labor unions using their power to reduce risk-taking by managers. We thank one anonymous reviewer for pointing this out, which makes our story more complete.

  2. Please see Allen and Michaely (2003) for the review on the related literature.

  3. It’s also interesting to test how far the labor unionization can be associated with future crash risk. We calculate crash risk measurement (i.e., NCSKEW and DUVOL) in 2- and 3-year ahead windows and then replicate empirical tests in Table 2. The untabulated results show that labor unionization is significantly associated with crash risk in the future 2 years. However, we do not find significant association between labor unionization and crash risk in the future 3 years. This indicates that the ability of labor unionization to predict crash risk in future three or more years is limited. We’d like thank one of the anonymous reviewers for the suggestion of testing future crash risk in 2- and 3-year windows.

  4. We’d like to thank the reviewer for the suggestion of including labor adjustment costs in the subsample tests.

  5. Bushee (1998, p. 324) describes the detailed procedures.

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Acknowledgements

We appreciate the insightful comments from Wenlan Zhang and the participants at the 6th World Business Ethics Forum. We also thank Chuancai Zhang for his excellent research assistance. Jun Chen and Feida Zhang acknowledge financial support from the National Natural Science Foundation of China (Grant numbers: NSFC-71572181 and NSFC-71332004). Feida Zhang and Jamie Tong acknowledge the financial support from the MOE (Ministry of Education in China) Project of Humanities and Social Sciences (Grant No. 17YJA790012) and National Social Science Foundation of China (Grant No. 17BGL067). All authors make equal contribution to this study.

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  1. Jamie Y. Tong & Feida Zhang

    Present address: UQ Business School, University of Queensland, Brisbane, QLD, Australia

Authors and Affiliations

  1. Department of Accounting and Finance, School of Management, Zhejiang University, Hangzhou, Zhejiang, People’s Republic of China

    Jun Chen

  2. School of Accounting and Finance, University of Western Australia, Perth, WA, Australia

    Jamie Y. Tong

  3. School of Accountancy and Law, Hong Kong Baptist University, Kowloon Tong, Hong Kong

    Wenming Wang

  4. School of Business and Governance, Murdoch University, Perth, WA, Australia

    Feida Zhang

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Correspondence to Feida Zhang.

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Appendix A Variable Definitions

Appendix A Variable Definitions

Crash Risk Variables

NCSKEW is the negative skewness of firm-specific weekly returns over the fiscal year.

DUVOL is the log of the ratio of the standard deviations of down-week to up-week firm-specific weekly returns.

CRASH is an indicator variable that takes the value one for a firm-year that experiences one or more firm-specific weekly returns falling 3.2 standard deviations below the mean firm-specific weekly returns over the fiscal year, with 3.2 chosen to generate frequencies of 0.1% in the normal distribution during the fiscal-year period, and zero otherwise.

For all crash risk measures, the firm-specific weekly return (W) is equal to ln (1 + residual), where the residual is from the following expanded market model regression:

$$r_{j,\tau } = \alpha_{j} + \beta_{1j} r_{m,\tau - 2} + \, \beta_{2j} r_{m,\tau - 1} + \, \beta_{3j} r_{m,\tau } + \, \beta_{4j} r_{m,\tau + 1} + \, \beta_{5j} r_{m,\tau + 2} + \varepsilon_{j,\tau }$$

Union Related Variables

UNION is the percentage of employed workers in a firm’s primary Census Industry Classification (CIC) industry covered by unions in collective bargaining with employers.

FEMALE is the fraction of female workers in a firm’s CIC industry based on the Census Population Survey conducted by the Bureau of Census for the Bureau of Labor Statistics.

UNION_SI is the product of the percentage of employed workers in a firm’s primary CIC industry covered by unions in collective bargaining with employers multiplying that percentage in the state where the firm is incorporated.

Control Variables

DTURN is the average monthly share turnover over the current fiscal-year period minus the average monthly share turnover over the previous fiscal-year period, where monthly share turnover is calculated as the monthly trading volume divided by the total number of shares outstanding during the month.

SIGMA is the standard deviation of firm-specific weekly returns over the fiscal-year period.

RET is the mean of firm-specific weekly returns over the fiscal-year period, times 100.

SIZE is the log of the market value of equity.

MB is the market value of equity divided by the book value of equity.

LEV is total long-term debts divided by total assets.

ROA is income before extraordinary items divided by lagged total assets.

AVGACC is the average absolute discretionary accruals over the past 3 years, where discretionary accruals are estimated from the modified Jones model (Dechow et al. 1995) by each year and each 2-digit SIC code industry.

HOLDINGS_DED is the percentage of holdings of dedicated institutional investors at the fiscal year-end. Similar to prior studies (e.g., Ke and Ramalingegowda 2005; Ramalingegowda and Yu 2012), we obtain institutional investors’ trading classifications (transient, dedicated, and quasi-indexing) from Brian Bushee directly. According to Bushee (1998), the classification of institutional investors is based on a collection of nine variables that capture the past investment behavior of each institutional investor in terms of both portfolio diversification and turnover. Bushee (1998) then uses a principal factor analysis to produce a factor that captures the average size of an institution’s stake in its portfolio firms, and to develop another factor that captures the degree of portfolio turnover. Cluster analysis is then performed to group similar institutions into three clusters: transient, dedicated, and quasi-indexing.Footnote 5

ANALYST is the number of analysts with estimates of current-year EPS as reported in the IBES Summary File at the fiscal year-end.

EINDEX is a measure based on six provisions: classified boards, poison pills, golden parachutes, limit to amend bylaws, supermajority requirements for mergers, and supermajority requirements for charter amendments. Accordingly, each firm in each year will have an EINDEX between 0 and 6 (Bebchuk et al. 2009). For example, if a firm has a score of 5, then this firm has five provisions for its governance arrangements. Given that these six provisions are arrangements that protect incumbents from removal and are harmful to shareholders, a higher value of the EINDEX represents a weaker market for corporate control.

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Chen, J., Tong, J.Y., Wang, W. et al. The Economic Consequences of Labor Unionization: Evidence from Stock Price Crash Risk. J Bus Ethics 157, 775–796 (2019). https://doi.org/10.1007/s10551-017-3686-0

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