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The Effect of Human Capital on Stock Price Crash Risk

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Abstract

This paper studies how the human capital embedded in rank-and-file employees affects firms’ stock price crash risk. Employing a unique setting in China where we measure human capital using employee information at the firm level, we show that human capital quality improves firms’ internal information environments, curbs bad-news hoarding and overinvestment, leading to lower stock price crash risk. The findings are robust to instrumental variable regressions. Our study highlights the internal informational role of human capital and sheds light on its implications for capital markets and outside investors. Therefore, we contribute to the literature on the interaction between non-shareholding stakeholders and shareholders.

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Notes

  1. This includes an employee’s creativity, useful knowledge in specific areas, work skills in a particular field, social skills, personality, and work ethic.

  2. The RESSET database is a data platform that provides professional services for model testing and investment research. For more details, please refer to http://www.resset.cn/databases.

  3. Consistent with this view, Kim et al. (2012) find that CSR increases ethical standard and obligation of managers, leading to better earnings quality. Similarly, Kim et al. (2014) and Zhang et al. (2016) find that CSR refrains firms from bad-news hoarding and reduces crash risk.

  4. Call et al. (2017) show that firms headquartered in areas whose population has higher education levels are associated with better financial reporting outcomes. We directly measure the education level of employees at each firm and show that it is negatively related to stock price crash risk. We therefore have a more accurate measure and extend the effect of education to the capital market.

  5. Ben-Nasr and Ghouma (2018) find that high levels of employee welfare standards contribute to stock price crash risk, and Chen et al. (2019) document that labor unionization is negatively associated with stock price crash risk.

  6. Please note that some observations are dropped due to missing values of employee compensation.

  7. We also construct the demeaned HUMCAP by subtracting the mean level of HUMCAP of the same city from the firm level to test our hypothesis, and we find robust results.

  8. We do not employ the firm fixed-effects model for the main analysis because employee quality does not vary significantly within firms. In such cases where the regressor does not present much time-series variation, firm fixed-effect model results will be unreliable (Zhou, 2001).

  9. Please note that PROVEDU only provincial data are available. while for other demographic controls city-level data are available.

  10. The ten cities are Qiqihar, Maoming, Putian, Xianyang, Guigang, Shangqiu, Weifang, Zhongshan, Jieyang, and Zhoukou, which are all third-tier cities in China.

  11. We use the V–V test because our measurement of overinvestment (OVERINVEST) is a binary variable, and the Sobel test is only appropriate when the mediator is continuous.

  12. The internal control quality index is obtained from the DIB internal control database, which has been widely used in Chinese studies of internal control quality (e.g., Liu et al., 2017). The index is calculated based on the firm’s internal environment, risk assessment, internal control activities, and internal monitoring.

  13. Gallemore and Labro (2015) employ two additional proxies for internal information quality: management forecast accuracy and restatements caused by unintentional errors. We do not use management forecast accuracy because we believe it is confounded with managers’ unethical incentives related to bad-news hoarding. In addition, we do not use restatements caused by unintentional errors because we cannot disentangle the restatements caused by unintentional errors from those caused by intentional errors in the Chinese setting.

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Acknowledgements

We thank the editor and two anonymous referees for their insightful comments. We also appreciate the helpful comments from Jeong-Bon Kim, Chao Kevin Li, Liuchuang Li, Baolei Qi, Zheng Qiao, Xiao’ou Yu, and participants of seminars/workshops at University of Science and Technology of China, Xiamen University, and Xi’an Jiaotong University. Yi Si acknowledges the financial support from the National Natural Science Foundation of China (Grant Number: 72002165), Humanities and Social Science Youth Foundation of the Ministry of Education of China (Grant No.: 20YJC790116), and the Fellowship of China Postdoctoral Science Foundation (Grant No.: 2020M683519). Chongwu Xia acknowledges the financial support from the Fundamental Research Funds for the Central Universities (Grant No.: WK2040000029). All authors have contributed equally. Errors and omissions are our responsibility.

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  1. The School of Management, Xi’an Jiaotong University, No. 28 Xianning West Road, Xi’an, 710049, China

    Yi Si

  2. International Institute of Finance, School of Management, University of Science and Technology of China, 96 Jinzhai Road, Hefei, 230026, China

    Chongwu Xia

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Appendix: Variable Definitions

Appendix: Variable Definitions

Vars

Definitions

Panel A: dependent variables

 NCSKEW

Negative skewness of firm-specific weekly returns over the fiscal-year period

 DUVOL

Log of the ratio of the standard deviations of down-week to up-week firm-specific returns

 COUNT

COUNT is calculated as the difference between the frequency of instances where firm-specific weekly returns are 3.2 standard deviations below the mean return and the frequency of instances where firm-specific weekly returns are 3.2 standard deviations above the mean over a fiscal year, with 3.2 chosen to generate frequencies of 0.1% in the normal distribution during the fiscal-year period, and zero otherwise

 OVERINVEST

Overinvestment, equals one if the residual following Richardson (2006)’s investment model is positive, and zero otherwise

 HOARDING

Bad-news hoarding, measured as Roychowdhury and Sletten (2012)'s news ratio

 READBLTY

Average readability of annual reports and quarterly reports throughout the fiscal year. Dataset available at Wingo database: http://www.wingodata.com/#/dash/index

 ACCCOM

Financial reporting comparability, measured as the average value of the whole industry (De Franco et al., 2011)

 ICONTROL

Natural logarithm of the internal control quality index. Dataset available at DIB database: http://www.ic-erm.com/index.html

 EALAG

Natural logarithm of number of days between fiscal year-end date and earnings announcement date

Panel B: test variables

 HUMCAP

The proportion of employees who hold a bachelor’s degrees or higher for each firm in each year

 HUMSCORE

The weighted average education score of human capital, 10 for bachelor’s degrees, 11 for master degrees and 12 for doctoral degrees (Call et al., 2017)

 HUMCOM

Employee compensation, firm-level employee average compensation scaled by provincial-level average workers' compensation

 DISTANCE

Natural logarithm of the average distance (in kilometers) between each list firm and all universities in China (2740 universities accredited by the Ministry of Education)

 EDUINPUT

Educational expenditures scaled by fiscal expenditures for each city in each year

Panel C: control variables

 DTURN

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

Standard deviation of firm-specific weekly returns over the fiscal-year period

 RET

Mean of firm-specific weekly returns over the fiscal-year period, times 100

 SIZE

Firm size, log of the market value of equity

 MB

Market to book ratio, market value of equity divided by the book value of equity

 LEV

Financial leverage, total liabilities divided by total assets

 ROA

Return on assets, income before extraordinary items divided by average of total assets

 ABACC

Absolute value of discretionary accruals, where discretionary accruals are estimated from Kothari et al., (2009)

 PROVEDU

The proportion of local population with a bachelor’s degrees or higher for each province in each year

 GDPG

Gross domestic product (GDP) growth rate for each city in each year

 TERTIND

Proportion of GDP of tertiary industry to total GDP for each city in each year

 POPD

Natural logarithm of population density per square kilometer for each city in each year

 POPG

Population growth rate for each city in each year

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Si, Y., Xia, C. The Effect of Human Capital on Stock Price Crash Risk. J Bus Ethics 187, 589–609 (2023). https://doi.org/10.1007/s10551-022-05134-w

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  • DOI: https://doi.org/10.1007/s10551-022-05134-w

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