The Value of Employee Satisfaction in Disastrous Times: Evidence from COVID-19* Chenyu Shan Shanghai University of Finance and Economics Email: shan.chenyu@mail.sufe.edu.cn Dragon Yongjun Tang The University of Hong Kong Email: yjtang@hku.hk July 10, 2020 Abstract Employee treatment is a dilemma for many business owners and executives: while everyone prefers a pleasant working environment, satisfying employee needs can be costly to shareholders Given such costs, is it still worthwhile to make employees happy on a daily basis? This study provides evidence supporting a positive answer: firms with more satisfactory employees heading into the COVID-19 withstand the crisis better according to stock price reaction Such outperformance by high-employee-satisfaction firms is more pronounced for financially weaker firms, for knowledge-based industries, and for FinTech-developed cities Moreover, the result is not driven by state ownership, information asymmetry, or insider propping Our findings show the importance of employee morale during crisis period and that firms can well by doing good Keywords: Employee satisfaction, Shareholder value, Intangible assets, COVID-19 JEL classification: G32, G34 * We thank Rui Albuquerque, Alex Edmans, Hao Liang, Jun-Koo Kang, Alan Kwan, Gustavo Manso, Henri Servaes, Fei Xie, Quan Wen, Chendi Zhang, Jian Zhang, and Joe Zou for helpful comments We thank MioTech for providing some of the data Tang acknowledges the support of the General Research Fund (#17510016) of Hong Kong Research Grants Council Shan acknowledges the support of the Research Fund for Youth (# 71803122) of the National Natural Science Foundation of China Electronic copy available at: https://ssrn.com/abstract=3560919 The Value of Employee Satisfaction in Disastrous Times: Evidence from COVID-19 Abstract Employee treatment is a dilemma for many business owners and executives: while everyone prefers a pleasant working environment, satisfying employee needs can be costly to shareholders Given such costs, is it still worthwhile to make employees happy on a daily basis? This study provides evidence supporting a positive answer: firms with more satisfactory employees heading into the COVID-19 withstand the crisis better according to stock price reaction Such outperformance by high-employee-satisfaction firms is more pronounced for financially weaker firms, for knowledge-based industries, and for FinTech-developed cities Moreover, the result is not driven by state ownership, information asymmetry, or insider propping Our findings show the importance of employee morale during crisis period and that firms can well by doing good Keywords: Employee satisfaction, Shareholder value, Intangible assets, COVID-19 JEL classification: G32, G34 Electronic copy available at: https://ssrn.com/abstract=3560919 Introduction Is it worthwhile for a firm to treat its employees well on a daily basis? The answer is not obvious because it is costly to improve employee treatment In absence of regulation and government intervention, firms usually underinvest in employee welfare as labor cost reduces corporate profitability in a way similar to financing cost Indeed, there is large-sample evidence showing that businesses are more likely to fail when they provide safer workplace.1 Some believe that relentless firms with merely acceptable working conditions produce more profits for their shareholders However, there are also potential benefits associated with better employee treatment such as talent recruiting and retention as well as enhanced productivity In this paper, we examine stock price performance during COVID-19 to draw new inference on this issue This setting is interesting as many firms are at the edge of survival and employees are experiencing unprecedented challenges as well Employers may expect reciprocity when they treat employees well That is, firms hope that more satisfactory employees will work harder to increase firm value, especially when the firms are facing severe difficulties However, if employees feel they are already contributing to a good social cause, they may engage more in unethical behavior including shirking and cheating at work, due to the so-called “moral licensing” (List and Momeni, 2020) COVID-19 forced many people to work from home, making shirking a real concern It is not obvious what types of firms would perform better during such a crisis Employees from firms with hostile working environment are probably used to coordination troubles They may even get a productivity boost when they not need to go to office, which helps them avoid counterproductive office politics In contrast, firms with good employee morale may become See Pagell et al (2020), which is based on 386,179 organizations in Oregon, USA from 1989 to 2014 They find that the odds and length of survival are smaller for firms providing a safe workplace Electronic copy available at: https://ssrn.com/abstract=3560919 less productive under “social distancing” as bonding and collegiality play an important role for their work COVID-19 provides a rare opportunity to examine the value of employee satisfaction and generate implications on corporate employment policies The COVID-19 setting also helps circumvent endogeneity concerns as firm-employee relation is slow-moving and correlated with other firm attributes in normal times The big, direct, and exogenous shock brought by COVID-19 hence is useful for the empirical identification In this study, we use novel data to analyze the short-run reaction of stock prices to the outbreak of COVID-19 to quantify the value of employee satisfaction from a shareholder’s point of view Our employee satisfaction data come from MioTech, a leading FinTech company based in Hong Kong that specializes in environmental, social and governance (ESG) information for Chinese firms, covering all publicly listed Chinese companies (and many private companies) MioTech uses natural language processing to crowdsource employee satisfaction data from current and former rank-and-file employees.3 We examine stock price reactions to COVID-19 for all firms listed in the two stock exchanges in mainland China (‘A-shares’ in Shanghai and Shenzhen) We focus on February 3, 2020, the first trading day after the lockdown of Wuhan—the Chinese city where the first infected case was identified and the largest number of infected patients resided.4 We find that while Chinese stocks experienced record drops on that day, firms with higher employee satisfaction scores withstood the negative shock better than firms with lower employee satisfaction scores The economic magnitude of the result is meaningful: the high-low return MioTech is financed by Horizons Ventures, the private investment fund of Li Ka-shing (the richest man in Hong Kong) See media reports: https://www.scmp.com/tech/enterprises/article/2115417/li-ka-shing-shows-strong-backing-hong-kongs-fintech-sector-leading https://www.fintechfutures.com/2020/01/hong-kongs-sustainable-finance-start-up-miotech-closes-series-a-funding/ https://finance.yahoo.com/news/horizons-ventures-continues-lead-series-100000493.html The raw data is similar to the employer reviews on Glassdoor Figure shows the timeline for the outbreak of COVID-19 Electronic copy available at: https://ssrn.com/abstract=3560919 difference was 0.5 percentage points while the Shanghai Composite Index dropped 7.7 percentage points on that day This result is robust to alternative measures and sample selection The employee satisfaction effect remains strong even when we exclude firms located in the city of Wuhan from the sample This finding highlights the pervasive impact of COVID19 on the financial market and the profound moderating effect of employee satisfaction Our finding suggests that the effect of the observed rank-and-file employee satisfaction on stock returns is not limited to directly infected areas Instead, healthy people who continue to work for their employers drive the differences in firm performance, and such a difference is priced in by financial market investors when the coronavirus hit The stock price reaction suggests that firms that have regularly treated their employees well can weather negative economic shock better High morale is important for working from home arrangements or no pay leaves, which are meant to ease the financial burden on firms In contrast, firms that have treated their workers poorly in the past experience reciprocity from their workers at this disastrous time It is worth noting that even though corporate culture is known before COVID-19, its value is not revealed until difficult times when people pay more attention to such nuisances and when individuals are personally experiencing the impacts This point of investor awareness during crisis is also made by Servaes and Tamayo (2013) Supporting such human capital or employee morale channel, we find the employee satisfaction effect to be stronger in firms with more intangible assets and in knowledge-based industries Why does employee satisfaction make firms more resilient to an exogenous shock? We consider how firms are prepared for working from home arrangements Employees who feel satisfied with their firms may be more incentivized to work remotely Accordingly, the employee satisfaction effect should be more pronounced for firms whose employees are better able to work from home It is challenging to directly measure the productivity of working from Electronic copy available at: https://ssrn.com/abstract=3560919 home Thus, we exploit how differential technology development that facilitates workers’ remote work ability affects our results Using proprietary data from Ant Financial (the FinTech company affiliated with Alibaba), we find that, for firms located in areas with more usage of mobile payments, employee satisfaction has a stronger mitigating effect on price drop during the shock This finding corroborates the channel that employee satisfaction plays a role via employees’ maintaining productivity while working from home We also consider alternative explanations for the better stock price reaction of higher employee satisfaction firms such as government or societal support Firms that have treated their employees better in the past may derive economic benefits in crisis period due to “halo effects” Governments may bail out or provide guarantees to large corporations with political implications, such as state-owned enterprises Government support can also add value to the connected firms (Fisman, 2001), but through a channel different from employee morale However, we find a similar significant employee satisfaction effect for both state-owned and privately owned firms, suggesting that employee satisfaction effect is beyond the government support channel The second alternative explanation is that employees may have better information about the firm than outsiders The observed relation between stock returns and employee satisfaction could be due to information revelation of updated information about the firm disclosed by its employees However, we find similar results when we use the employee satisfaction score from former employees who may have left the firm several years ago The third alternative explanation hinges on inside ownership Insiders have incentives to support their share prices, reducing downward pressure on the stock during the selloff of COVID-19 We examine this possibility by adding inside ownership measure into our analysis, and find that the effect of employee satisfaction remains statistically significant and economically Electronic copy available at: https://ssrn.com/abstract=3560919 important The finding suggests that the incremental effect of employee satisfaction on shareholder value is on top of the effect of inside ownership Our study adds to the growing literature showing strategic firm considerations with respect to their employees (see, e.g., Ellul and Pagano, 2019) and the value of employee satisfaction to the firm (see, e.g., Edmans, 2011) Edmans (2011) finds that companies named as “best places to work” have better future stock returns However, there are also opposing views in the literature Mueller, Ouimet, and Simintzi (2017) show that firms with higher pay inequity (lower pay for rank-and-file employees relative to top executives) have higher valuation and better operating performance This finding suggests that firms are better off if they treat their non-executive employees less generously Our study complements this line of literature by focusing on an adverse shock to rank-and-file employees and their firms Our broad-based analysis from a major emerging market is complementary to existing studies using alternative employee data from the U.S (e.g., Green, Huang, Wen and Zhou, 2019) Our findings support the view that firms can well by doing good It is important to note that, according to Edmans (2011), the market does not fully recognize the value of employee satisfaction in a timely manner The value of employee satisfaction is incorporated into stock prices over the long run Our findings shed light on why employee satisfaction is not priced in the stock market in a timely fashion: the value of rankand-file employees could be hard to identify and thus ignored by the public in tranquil times However, when the tangible link between the firm and its employees is damaged by a crisis like COVID-19, the value of employee satisfaction as an important intangible link could be The interpretation of our results is different from Green, Huang, Wen, and Zhou (2019), which emphasizes the information content of employer reviews We find that scores given by current employees and past employees have similar effect on the firm’s stock price during the crisis, suggesting that what the employee satisfaction score reveals is related to firm background such as corporate culture Electronic copy available at: https://ssrn.com/abstract=3560919 realized and attracts investors’ attention Therefore, this study complements Edmans (2011) from two aspects First, we focus on stock performance during disastrous times Our findings explain why it takes long for investors to incorporate employee satisfaction into price We document that the effect may not be revealed in tranquil times but becomes materialized after negative shocks Second, we use employee review data from China, a considerably different labor market from the U.S Given that employee satisfaction is part of ESG, our findings are consistent with several other contemporaneous studies including Ding et al (2020) and Albuquerque et al (2020) that firms with better ESG perform better during COVID-19 The reminder of the paper is organized as follows Section introduces the background on COVID-19 and the related literature Section describes the dataset for our empirical analysis Section presents our main results and Section concludes Background on COVID-19 and Related Literature COVID-19 posed major disruptions to economic activities all over the world The coronavirus was first manifested by a cluster of pneumonia cases of unknown type in Wuhan, the capital of Hubei Province in China in December 2019 Although Chinese government officially acknowledged the infectious nature of the novel coronavirus on January 20, its severity was not immediately recognized In late evening of January 23, the city of Wuhan was declared to be in lockdown and restricted movement (effective 10am on January 24), but by January 29 the novel coronavirus had spread to all provinces in China On January 31, WHO declared the outbreak a “public health emergency of international concern”.6 The outbreak of COVID-19 was sudden, unexpected and dramatic The market closed from January 24 for the Chinese New Year holidays and the scheduled closure of the market https://www.bbc.com/news/world-51318246 Electronic copy available at: https://ssrn.com/abstract=3560919 was extended from January 31 to February due to the coronavirus outbreak The market reopened on February with a record loss and finished the day with 7.7% drop for the Shanghai Composite Index 3,527 (out of 3,859) stocks in the A share market saw price declines on that day and 3,177 stocks hit the trading halt limit after losing 10% Therefore, we choose February 3, 2020 as the event day for our study The impact of COVID-19 goes far beyond infected people.7 Healthy people have been severely inconvenienced by the lockdown, curfew and other anti-epidemic measures The economic impacts of the outbreak, although difficult to estimate thus far, have been deep and wide It presents a sudden challenge to firm-employee relations and provokes investors’ perceptions about employee satisfaction, which may not be revealed in more tranquil times While the economy was essentially frozen and many firms had zero revenues, firms were asked by the government to continue to pay their workers The large and sudden disruption caused by COVID-19 has thus far caused many firms, especially SMEs which did not have sufficient cash before the outbreak, to come under liquidity pressure Some firms had to cut wages and benefits while asking employees to contribute to avoid bankruptcy In this context, employeefirm relations become crucial to a firm’s stability Firms that have healthy relations with their employees may receive more support from them, while firms with weak relations with employees may find it hard to motivate its employees to work from home Hence, we expect that firms with different levels of employee satisfaction before the outbreak will vary in their stock market performance during the outbreak The two major national political meetings, usually in March, were cancelled The China International Conference in Finance (CICF), the major finance academic event, scheduled for July 2020 was also cancelled It is estimated that around million people in China lost their jobs amid the outbreak of the new coronavirus in January and February 2020 (https://www.cnbc.com/2020/03/16/china-economy-millions-lose-their-jobs-asunemployment-spikes.html) Electronic copy available at: https://ssrn.com/abstract=3560919 Unlike financial crises, the COVID-19 outbreak is exogenous to the practices of financial institutions and to the economic links between firms, making it even harder to predict the impacts from an economist’s perspective This study therefore reflects on the value of employee satisfaction when firms experience real disruptions, instead of a decline in trust and social capital within the financial sector used by corporate social responsibility (CSR) literature (Lins, Servaes, and Tamayo, 2017) Our setting is also different from the firm-specific shocks analyzed by Hong, Kubik, Liskovich, and Scheinman (2019), or industry-specific shocks used by Kim, Maug, and Schneider (2018) Evidence on how firms with different levels of ESG perform during crisis period is mixed On one hand, Lins, Servaes, and Tamayo (2017) find that high-CSR firms performed better during the 2008 financial crisis, as stakeholders were more willing to help high social capital firms weather a negative shock.9 On the other hand, Bansal, Wu, and Yaron (2019) argue that firms without negative ESG incidents (“good” stocks) generate lower abnormal returns than firms with incidents (“bad” stocks) during economic downturns Their explanation is that ESG is a luxury goods and investors pull back their concerns for socially responsible investment when they face more wealth constraints A growing literature demonstrates employee morale is an important intangible to firms, while its value is slowly incorporated into prices 10 Higher employee satisfaction leads to higher stock returns in the long run (Edmans, 2011) Better employees’ views on managerial integrity and ethics lead to higher firm valuation (Guiso, Sapienza, and Zingales, 2015) Employee-friendly policies are positively related to acquirer returns in domestic M&As (Liang, More generally, one strand of literature posits that firms can well by doing good, i.e., investing in corporate social 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indicator taking one if a firm's employee satisfaction score is above the sample median, and zero otherwise A dummy indicator taking one if a firm's employee satisfaction score given by the firm’s present employees is above the sample median, and zero otherwise A dummy indicator taking one if a firm's employee satisfaction score given by the firm’s past employees (employees who have left the firm) is above the sample median, and zero otherwise Percentage of shares held by members of the board of directors, board of supervisors, and executives of a firm out of total shares of the firm The natural logarithm of market capitalization of the firm Market-to-Book Market value of equity divided by book value of equity Employee SatisfactionHigh High Employee SatisfactionPresent High Employee SatisfactionFormer Inside Ownership Stock Return in 2019 Beta Idiosyncratic Risk Annual stock return in 2019, cumulated from daily stock returns Estimated from the market model using monthly returns in the past 60 months Residual variance from the market model estimated over the five-year period ending in December 2019 Cash Holdings Cash and cash equivalents/total assets Investment Capital expenditure/total assets Net cash flow Net cash flow/total assets Leverage Total book debt-to-total assets A dummy indicator taking one if the firm’s ultimate controller is central/local State-owned Assets Supervision and Administration Commission or other government agents, based on CSMAR information (Net sales in year t-net sales in year t-1)/net sales in year t-1 State-owned Sales Growth Net Income Growth Cash Conversion Cycle (CCC) (Net income in year t-net income in year t-1)/absolute value of net income in year t-1 CCC is calculated as the sum of days of inventory outstanding (DIO), days sales outstanding (DSO), and negative days payables outstanding (DPO) DIO is calculated as inventory/COGS*365 DSO is calculated as accounts receivable/revenue*365 DPO is calculated as accounts payable/COGS*365 29 Electronic copy available at: https://ssrn.com/abstract=3560919 Figure The timeline of outbreak of the coronavirus disease 2019 (COVID-19) in China This figure shows the major development of novel coronavirus (COVID-19) from December 2019 to March 2020 30 Electronic copy available at: https://ssrn.com/abstract=3560919 Panel A Raw Returns -7.60% -7.70% -7.80% -7.90% -8.00% -8.10% -8.20% -8.30% -8.40% -8.50% Low Employee Satisfaction High Employee Satisfaction Panel B Industry-adjusted Returns 0.30% 0.20% 0.10% 0.00% -0.10% Low Employee Satisfaction High Employee Satisfaction -0.20% -0.30% -0.40% -0.50% -0.60% Panel C FF5-adjusted Returns 0.40% 0.30% 0.20% 0.10% 0.00% Low Satisfaction High Satisfaction -0.10% -0.20% Figure Stock returns for firms with different levels of employee satisfaction We divide our sample firms into low-and high-groups based on the sample median of Employee Satisfaction, the average employee satisfaction score given by the firm’s employees in 2019 Panel A plots value-weight raw returns of firms within each group on February 3, 2020, with market capitalization as the weight In Panel B, the industry-adjusted return is calculated as the raw return subtracting the value-weighted industry average, based on the CSRC industry classification In Panel C, returns are adjusted by the Fama-French five factors (market, SMB, HML, RMW, CMA) 31 Electronic copy available at: https://ssrn.com/abstract=3560919 Table Summary Statistics This table reports the summary statistics of key variables used in this study Size is the natural logarithm of market cap in million Chinese Yuan Market-to-book is the market value of equity divided by book value of equity Stock return in 2019 is the cumulative stock return over January 1, 2019 to December 31, 2019 Beta is estimated from market model using monthly returns in the past 60 months Idiosyncratic Risk is the residual variance from the market model estimated over the five-year period ending in December 2019 Leverage is total book debt-to-total assets State-owned is a dummy indicator taking one if the firm’s ultimate controller is central/local State-owned Assets Supervision and Administration Commission or other government agents, based on CSMAR information All financial data are extracted as the end of year 2019 Employee Satisfaction is the satisfaction score given by individual employees in 2019 See Appendix A for variable definitions Mean 23.426 15.453 2.541 0.315 1.112 0.144 0.164 0.045 0.013 0.435 0.038 2.053 19.094 0.049 0.313 3.160 Total Assets (billion Chinese Yuan) Size Market-to-book Stock return in 2019 Beta Idiosyncratic Risk Cash Holdings Investment Net Cash Flow Leverage Return-on-Assets Sales Growth Firm Age (years) Net Income Growth State-owned Employee Satisfaction Median 4.131 15.221 1.910 0.311 1.094 0.120 0.137 0.031 0.023 0.421 0.050 0.000 19.000 0.058 0.000 3.136 32 Electronic copy available at: https://ssrn.com/abstract=3560919 StDev 84.051 0.971 2.468 0.279 0.196 0.096 0.111 0.045 0.112 0.210 0.108 4.894 5.425 0.826 0.464 0.980 Table Employee Satisfaction and Stock Returns This table examines the effect of employee satisfaction on stock returns The dependent variable is industryadjusted return on February 3, 2020 The independent variable is Employee Satisfaction In Columns and 2, the independent variable is Employee Satisfaction, the employee satisfaction score averaged across employees of a firm The scores are submitted in 2019 In Column 3, the independent variable of interest is Past 3-year Employee Satisfaction, the average employee satisfaction scores across employees who submitted score in 2016 to 2018 In all specifications we control for industry fixed effects based on the CSRC industry classification t-statistics calculated from standard errors clustered by firm are reported in the parentheses ***, ** and * denote statistical significance at 1%, 5% and 10% levels, respectively See Appendix A for variable definitions (1) 0.217*** (2.609) Employee Satisfaction (2) 0.197** (2.292) Past 3-year Employee Satisfaction Size Market-to-book Stock Return in 2019 Beta Idiosyncratic Risk Net Income Growth Cash Holdings Return-on-Assets State-owned Investment Leverage Log (Firm Age) Net Cash Flow Sales Growth Five-factor Loadings Industry FE R-squared (%) Observations Yes Yes 3.36 1343 0.144 (1.298) 0.113** (2.298) -0.117 (-0.377) 1.022** (1.968) -1.374** (-2.129) 0.067 (1.184) -0.313 (-0.352) -0.902 (-0.496) 0.510** (2.353) 1.129 (0.484) -0.129 (-0.247) -0.249 (-0.901) 0.796 (0.618) 0.015 (0.709) Yes Yes 4.72 1231 33 Electronic copy available at: https://ssrn.com/abstract=3560919 (3) 0.282** (1.993) 0.126 (1.133) 0.143*** (3.149) -0.069 (-0.223) 0.856* (1.677) -1.548** (-2.419) 0.067 (1.177) -0.161 (-0.183) -3.517*** (-3.566) 0.498** (2.311) -0.681 (-0.322) -0.268 (-0.592) -0.224 (-0.809) 0.902 (0.923) 0.007 (0.344) Yes Yes 4.74 1224 Table Employee Satisfaction and Stock Returns: Alternative Samples This table reports the estimation results for the impact of employee satisfaction on stock returns for alternative samples The dependent variable is industry-adjusted return on February 3, 2020 The independent variable is Employee SatisfactionHigh, a dummy taking one if a firm’s employee satisfaction score is above sample median We employ the sample of all firms listed in mainland China (‘A-shares’ listed in Shanghai and Shenzhen Stock Exchange) We include Missing Employee Satisfaction Dummy in all specifications, which takes one if the firm does not have an employee satisfaction score, and zero otherwise In Column 2, we exclude firms that are headquartered in the city of Wuhan from the sample Column excludes firms headquartered in Hubei province t-statistics calculated from standard errors clustered by firm are reported in the parentheses ***, ** and * denote statistical significance at 1%, 5% and 10% levels, respectively See Appendix for variable definitions Employee SatisfactionHigh Log (Market Capitalization) Market-to-book Stock Return in 2019 Beta Idiosyncratic Risk Net Income Growth Cash and Cash Equivalents/ Total Assets Return-on-Assets State-owned Capital Expenditure/ Total Assets Leverage Log (Firm Age) Net Cash Flow/Total Assets Sales Growth Five-factor Loadings Industry FE R-squared '(%) Observations Full Sample Excluding Wuhan (1) 0.408*** (3.356) 0.252*** (3.978) 0.101*** (3.978) -0.201 (-1.033) 1.261*** (4.355) -0.311 (-0.733) 0.028 (0.893) -0.174 (-0.366) -3.171*** (-4.490) 0.149 (1.366) 0.106 (0.092) 0.115 (0.449) -0.019 (-0.129) 0.726 (1.366) 0.002 (0.155) Yes Yes 4.74 3453 (2) 0.331*** (2.533) 0.268*** (4.289) 0.093*** (3.633) -0.225 (-1.154) 1.227*** (4.245) -0.464 (-1.155) 0.022 (0.688) -0.255 (-0.522) -2.951*** (-4.166) 0.137 (1.255) 0.194 (0.177) 0.108 (0.418) 0.01 (0.067) 0.955* (1.755) 0.001 (0.119) Yes Yes 4.55 3383 34 Electronic copy available at: https://ssrn.com/abstract=3560919 Excluding Hubei (3) 0.334*** (2.753) 0.274*** (4.378) 0.096*** (3.723) -0.234 (-1.198) 1.296*** (4.453) -0.428 (-1.062) 0.02 (0.633) -0.296 (-0.609) -2.974*** (-4.199) 0.132 (1.202) 0.162 (0.144) 0.108 (0.412) 0.002 (0.012) 0.958* (1.779) 0.001 (0.123) Yes Yes 4.46 3318 Table Employee Satisfaction and Stock Returns: Constrained vs Unconstrained Firms This table reports the estimates of regressions that examine the impact of employee satisfaction score on stock returns for financially-constrained and unconstrained firms The dependent variable is industry-adjusted return on February 3, 2020 We split the sample by median book asset size, return-on-assets, and cash conversion cycle (CCC) Values of these measures are extracted at the end of 2018 CCC is calculated as the sum of days of inventory outstanding (DIO), days sales outstanding (DSO), and negative days payables outstanding (DPO) DIO is calculated as inventory/COGS*365 DSO is calculated as accounts receivable/revenue*365 DPO is calculated as accounts payable/COGS*365 Firm controls are the same as we use in Table 2, Column t-statistics calculated from standard errors clustered by firm are reported in the parentheses ***, ** and * denote statistical significance at 1%, 5% and 10% levels, respectively See Appendix for variable definitions By Firm Size Small Employee SatisfactionHigh Five-factor Loadings Firm Controls Industry FE R-squared (%) Observations Difference in Coefficients Large (1) (2) 0.541*** 0.205 (2.735) (1.354) Yes Yes Yes Yes Yes Yes 5.19 5.46 1726 1689 0.336* (1.698) By Earnings Low High (3) (4) 0.838*** 0.229 (2.789) (1.203) Yes Yes Yes Yes Yes Yes 19.37 9.01 1742 1673 0.609** (1.982) 35 Electronic copy available at: https://ssrn.com/abstract=3560919 By Cash Cycle Long Short (5) (6) 0.359** 0.182 (1.22 (2.11) ) Yes Yes Yes Yes Yes Yes 6.24 3.89 1587 1674 0.177** (2.150) Table Employee Satisfaction, Human Capital, and Work from Home This table reports the estimated impact of human capital on the effect of employee satisfaction The dependent variable is industry-adjusted daily return on February 3, 2020 In Panel A, we split the sample into low- and highgroup based on the median intangible assets-to-total assets ratio of the sample measured at the end of 2018 In Column 3, the independent variable of interest is the interaction of Employee SatisfactionHigh and Intangible Employee SatisfactionHigh is a dummy taking one if the firm’s employee satisfaction score in 2019 is above the sample median, and zero otherwise Intangible is a dummy taking one if the firm’s intangible assets-to-total assets ratio is above the sample median, and zero otherwise In Panel B, we split the sample based on whether a firm belongs to a knowledge-based industry In Column 3, the independent variable of interest is Employee SatisfactionHigh and Knowledge-based, a dummy indicator taking one if the firm is in knowledge-based industries We define pharmaceutical (C27), IT and network service (I63-I65), and R&D service (M73-M75) as knowledgebased industries t-statistics calculated from standard errors clustered by firm are reported in the parentheses ***, ** and * denote statistical significance at 1%, 5% and 10% levels, respectively See Appendix A for variable definitions Panel A By Intangible Assets Employee SatisfactionHigh High (1) 0.444** (2.459) Low (2) 0.243 (1.509) Yes Yes Yes 5.06 1681 Yes Yes Yes 6.41 1633 Employee SatisfactionHigh*Intangible Intangible Five-factor Loadings Firm Controls Industry FE R-squared (%) Observations Difference in Coefficients (High-Low) 0.201* (1.897) ) Panel B By Knowledge-based Industry Employee SatisfactionHigh Knowledgebased Non-knowledge -based (1) 0.995** (2.323) (2) 0.191* (1.729) Yes Yes Yes 6.82 520 Yes Yes Yes 6.89 2794 Employee SatisfactionHigh *Knowledge-based Knowledge-based Five-factor Loadings Firm Controls Industry FE R-squared (%) Observations Difference in Coefficients (Knowledge-based – Non-knowledge-based) Full Sample (3) 0.246 (1.439) 0.549** (2.044) -0.076 (-1.043) Yes Yes Yes 4.86 3453 0.804*** (2.774) 36 Electronic copy available at: https://ssrn.com/abstract=3560919 Full Sample (3) 0.224** (1.920) 0.991** (2.355) -0.659 (-1.277) Yes Yes Yes 5.18 3453 Table Employee Satisfaction and Remote Work Ability We examine how the impact of employee satisfaction on stock returns for firms is affected by employees’ remote work ability We obtain city-level mobile payment rate, i.e., the percentage of people who use mobile payment in 2018 out of total population in a city In Panel A, we divide the sample into high- and low-mobile payment rate group depending on whether the firm is headquartered in a city with this percentage above sample median In Column 3, the independent variable of interest is the interaction of Employee SatisfactionHigh and High Mobile Payment Usage Rate High Mobile Payment Usage Rate is a dummy indicator taking one if the mobile payment rate in the city a firm is headquartered in is above sample median In Panel B, we divide the sample into low and high on-line sales group based on the dollar amount of online sales per capita of the province that the firm is headquartered in In Column 3, the independent variable of interest is the interaction of Employee SatisfactionHigh and High On-line Sales High On-line Sales is a dummy indicator taking one if the on-line sales per capita of the province where a firm is headquartered is above sample median Firm controls are the same as we use in Table 2, Column t-statistics calculated from standard errors clustered by firm are reported in the parentheses ***, ** and * denote statistical significance at 1%, 5% and 10% levels, respectively See Appendix for variable definitions Panel A By Mobile Payment Usage Rate Employee SatisfactionHigh High Low Full Sample (1) 0.840*** (2.748) (2) 0.315 (1.259) Yes Yes Yes 5.34 1943 Yes Yes Yes 5.74 1472 (3) 0.192** (2.349) -0.037 (-0.339) 0.509** (2.033) Yes Yes Yes 4.73 3415 High Mobile Payment Employee SatisfactionHigh *High Mobile Payment Usage Rate Five-factor Loadings Firm Controls Industry FE R-squared (%) Observations Difference in Coefficients (High-Low) 0.525** (1.995) Panel B By On-line Sales Employee SatisfactionHigh High (1) 0.749*** (2.685) Low (2) 0.389 (1.229) Yes Yes Yes 5.74 1303 Yes Yes Yes 5.74 2112 High On-line Sales Employee SatisfactionHigh *High On-line Sales Five-factor Loadings Firm Controls Industry FE R-squared (%) Observations Difference in Coefficients (High-Low) 0.360* (1.774) 37 Electronic copy available at: https://ssrn.com/abstract=3560919 Full Sample (3) 0.663*** (2.968) 0.074 (0.617) 0.443** (1.998) Yes Yes Yes 4.67 3314 Table State-owned vs Non-state-owned Firms This table examines the impact of employee satisfaction for state-owned and non-state-owned firms The dependent variable is industry-adjusted return on February 3, 2020 The independent variable is Employee SatisfactionHigh, a dummy taking one if the employee satisfaction score is above sample median We group firms into State-owned Enterprises and Non-state-owned Enterprises based on the ultimate controller information in CSMAR In Column 3, the independent variable of interest is the interaction of Employee SatisfactionHigh and State-owned, a dummy indicator taking one if the firm’s ultimate controlled is central State-owned Assets Supervision and Administration Commission of the State Council (SASAC), local SASAC, or other government agents We use the same firm controls as in Table 2, Column t-statistics calculated from standard errors clustered by firm are reported in the parentheses ***, ** and * denote statistical significance at 1%, 5% and 10% levels, respectively See Appendix for variable definitions Employee SatisfactionHigh State-owned (1) 0.572*** (2.609) Non-state-owned (2) 0.304** (2.011) Yes Yes Yes 10.85 962 Yes Yes Yes 4.29 2157 Employee SatisfactionHigh*State-owned State-owned Five-factor Loadings Firm Controls Industry FE R-squared (%) Observations Difference in Coefficients (State-owned – Non-state-owned) 0.268 (1.196) 38 Electronic copy available at: https://ssrn.com/abstract=3560919 Full Sample (3) 0.290** (1.998) 0.281 (1.077) 0.038 (0.312) Yes Yes Yes 4.70 3453 Table Effect of Employee Satisfaction: Present vs Former Employees This table reports the estimates of regressions that examine the impact of employee satisfaction score given present and past employees on the firm’s stock return on February 3, 2020 For each firm, we divide the review scores into those given by the firm’s present employees and those given by employees that have left the firm at the time they submitted the score, then take average within each group High Employee SatisfactionPresent is a dummy taking one if a firm’s average employee satisfaction score given by its present employees is above sample median High Employee SatisfactionFormer is a dummy taking one if a firm’s average employee satisfaction score given by its former employees is above sample median Firm controls are the same as we use in Table 2, Column t-statistics calculated from standard errors clustered by firm are reported in the parentheses ***, ** and * denote statistical significance at 1%, 5% and 10% levels, respectively See Appendix for variable definitions High Employee SatisfactionPresent (1) 0.290** (2.008) (2) High Employee SatisfactionFormer Five-factor Loadings Firm Controls Industry FE R-squared (%) Observations Difference in Coefficients (Present-Former) 0.286** (2.177) Yes Yes Yes 4.49 3453 Yes Yes Yes 4.49 3453 0.004 (0.028) 39 Electronic copy available at: https://ssrn.com/abstract=3560919 Table The Impact of Inside Ownership This table reports the estimates of regressions that examine how the impact of employee satisfaction score on the firm’s stock return on February 3, 2020 is affected by the firm’s inside ownership Inside Ownership is the percentage of shares held by a firm’s management team (including the board of directors, board of supervisors, and executives) out of total shares of the firm Columns 1, 2, and employ the full sample In Columns and 4, we divide the sample into high- and low-groups based on whether the firm’s inside ownership measured at the end of 2018 is above the sample median Firm controls are the same as we use in Table 2, Column tstatistics calculated from standard errors clustered by firm are reported in the parentheses ***, ** and * denote statistical significance at 1%, 5% and 10% levels, respectively See Appendix for detailed variable definitions Full Sample Employee SatisfactionHigh Insider Ownership Employee SatisfactionHigh* Insider Ownership Five-factor Loadings Firm Controls Control for Firm CSR Industry FE R-squared (%) Observations Difference in Coefficients (High-Low) (1) 0.369*** (2.998) 0.058 (0.219) Yes Yes Yes Yes 4.56 3314 High Ownership (2) 0.218 (1.325) Low Ownership (3) 0.551*** (2.988) Yes Yes Yes Yes 4.38 1646 Yes Yes Yes Yes 7.85 1668 -0.333* (-1.898) 40 Electronic copy available at: https://ssrn.com/abstract=3560919 Full Sample (4) 0.258** (1.998) 0.001 (0.109) 0.225 (0.953) Yes Yes Yes Yes 4.63 3314 ... pressure on the stock during the selloff of COVID- 19 We examine this possibility by adding inside ownership measure into our analysis, and find that the effect of employee satisfaction remains statistically... variable of interest is the interaction of Employee SatisfactionHigh and Intangible Employee SatisfactionHigh is a dummy taking one if the firm’s employee satisfaction score in 2 019 is above the sample... headquartered in In Column 3, the independent variable of interest is the interaction of Employee SatisfactionHigh and High On-line Sales High On-line Sales is a dummy indicator taking one if the on-line