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Do Labor Unions Affect Stock Price Crash Risk?

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This paper examines the influence of labor unions on stock price crash risk. Using a large sample of U.S. firms over the period 1984-2013, we provide the evidence that labor unions increase the likelihood to experience future stock price crashes. This finding is consistent with the argument that firms facing strong labor unions tend to report lower accounting information, in order to preserve bargaining power when negotiating contracts with labor unions. Further, we find that the adverse effects of labor unions on stock price crash risk are less pronounced for firms with strong external monitoring mechanisms, such as high institutional ownership and high analyst coverage.
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http://ijfr.sciedupress.com International Journal of Financial Research Vol. 6, No. 2; 2015
Published by Sciedu Press 11 ISSN 1923-4023 E-ISSN 1923-4031
Do Labor Unions Affect Stock Price Crash Risk?
Hamdi Ben-Nasr1, Abdullah Al-Dahmash2 & Hatem Ghouma3
1 Finance Department, College of Business Administration, King Saud University, Riyadh, Saudi Arabia
2 MSF program, College of Business Administration, King Saud University, Riyadh, Saudi Arabia
3 The Gerald Schwartz School of Business, St. Francis Xavier University, NS, Canada
Correspondence: Hamdi Ben-Nasr, Finance Department, College of Business Administration, King Saud University,
Riyadh, 71115, 11587, Saudi Arabia. Tel: 966-54-704-4326. E-mail: hbennasr@ksu.edu.sa
Received: December 31, 2014 Accepted: January 17, 2015 Online Published: March 9, 2015
doi:10.5430/ijfr.v6n2p11 URL: http://dx.doi.org/10.5430/ijfr.v6n2p11
Abstract
This paper examines the influence of labor unions on stock price crash risk. Using a large sample of U.S. firms over
the period 1984-2013, we provide the evidence that labor unions increase the likelihood to experience future stock
price crashes. This finding is consistent with the argument that firms facing strong labor unions tend to report lower
accounting information, in order to preserve bargaining power when negotiating contracts with labor unions. Further,
we find that the adverse effects of labor unions on stock price crash risk are less pronounced for firms with strong
external monitoring mechanisms, such as high institutional ownership and high analyst coverage.
Keywords: stock crash risk, extreme outcomes, labor unions
1. Introduction
The role of labor protection in corporate finance has recently drawn the interest of numerous scholars. One strand of
literature examines the role of labor protection in determining corporate decisions. For instance, De Angelo and De
Angelo (1991) examine the impact of labor union on corporate dividends. In the same vein, Klasa, Maxwell,
Ortiz-Molina (2006) explore the effect of labor union on cash holdings. Atanassov and Kim (2009) examine the
relative influence of labor vis-à-vis investors on the nature of the restructuring decisions made by poorly performing
firms. Acharya, Baghai, and Subramanian (2010) examine the impact of labor union on corporate innovation. More
recently, Chen and Chen (2013) study the link between labor union and the sensitivity of investment to cash-flow.
Several other studies (e.g., Hillary, 2006; Bova, 2013; Chung, Lee, Lee and Sohn, 2014) examine the impact of labor
union on the quality of financial reporting. Another strand of literature, investigate the economic outcomes of labor
protection. For example, Faleye, Mehrotra, and Morck (2006) find that when labor has a weight in the firm’s
corporate governance, workers might adopt strategies that push the firm policies away from stockholders’ value
maximization. In particular, their study documented lower new capital expenditures, less risk appetite, slower growth
and lower total factor productivity for firms where employees have a greater voice in corporate governance.
Consistent with this point of view, Chen, Kacperczyk, and Ortiz-Molina (2012) show that strong labor union is
associated with a lower cost of debt. Furthermore, Chen, Kacperczyk, and Ortiz-Molina, (2011) examine the impact
of labor union on the cost of equity. Strong labor protection is associated with higher labor adjustment costs (e.g.,
Serfling, 2013). Indeed, wages are sticky and layoffs are more costly when labor protection is strong. In such a case,
firing employees wouldn’t be an easy task even if it is economically optimal. Higher labor adjustment costs reduces
operating flexibility, hence increases the cost of equity. Consistent with this argument, Chen et al (2011) show that
firms from highly unionized industries are penalized by a higher cost of equity.
In this study, we extend the aforementioned researches by analyzing the relationship between labor unions and stock
price crash risk. We conjuncture that in firms where unions are strong, managers tend to reduce the bargaining power
of these unions by adopting a less transparent disclosure policies which might result in higher likelihood to
experience stock price crash.
Moreover, previous studies (e.g., An and Zhang, 2013; Callen and Fang, 2013; Kim and Zhang (2013), etc.) suggest
that large institutional ownerships and higher analyst coverage could mitigate information asymmetry and decrease
the probability to hoard bad news. Hence, we further explore the role that institutional ownership and analyst
coverage could play in the relationship between labor unions and stock price crash risk. To test our hypotheses, we
use a sample of 73,543 U.S. firm-year observations and a patent data covering the period from 1984 to 2013.
Specifically, we use firm-year unionization rate, calculated by multiplying the industry-level unionization rate by the
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number of employees deflated by total assets, as a proxy for labor union. In line with prior literature (e.g. Chen,
Hong and Stein, 2001; Jin and Myers, 2006), we use two proxies for stock price crash risk: (i) the negative
conditional skewness of weekly return (NCSKEW) and (ii) down-to-up volatility (DUVOL). Our results suggest that
stock price crash risk increases in labor union strength. This finding is consistent with the conjecture that firms
facing strong labor unions tend to report lower accounting information, in order to preserve bargaining power when
negotiating contracts. This behavior, resulting in more opaque financial reporting, increases the likelihood to
experience stock price crash. We also find that the adverse effects of labor union on stock price crash risk are less
pronounced for firms with strong external monitoring mechanisms, such as high institutional ownership and high
analyst coverage.
These findings contribute to the existing literature twofold. First, it contributes to the understanding or the drivers of
stock price crash risk by focusing on the role of non-financial and traditional stakeholders, namely labor unions.
Second, it contributes to the ongoing literature on labor unions (e.g., Hillary, 2006; Klasa et al., 2006; Chen,
Kacperczyk, and Ortiz-Molina 2011, 2012; Chen, Chen, and Liao 2011; Bova, 2013; Chung et al., 2014, among
others) by shedding more lights on the roles and objectives of these unions and their places within the firm’s overall
corporate strategies.
The rest of the paper is organized as follows. In section 2, we review the literature and outline our testable hypothesis.
Section 3 describes data and empirical design. Section 4 presents the empirical results and robustness tests. Section 5
concludes.
2. Related Literature and Hypotheses
Previous studies suggest that rents that unionized employees can extract from their firms are higher compared to
those that non-unionized employees can extract (Bova, 2013). In fact, unionized employees have greater bargaining
power when negotiating contracts (Hirsch 1991, 2008) mainly due to the threat to go on strikes, which might result in
disrupting production and damaging the reputation of the firm in the labor market if the agreements are not reached
(Hamm, Jung, and Lee, 2013). Given that, managers of firms with unionized employees have incentives to take
strategic actions in order to minimize the rents that these employees may extract. Prior research provides large
support for this point of view. For instance, De Angelo and De Angelo (1991) show that firms with unionized
employees tend to cut dividends in order to impede labor to extract more resources. Similarly, another strand of
literature (e.g., Bowen, Ducharme, and Shores, 1995; Cullinan and Knoblett, 1994; D’Souza, Jacob, and Ramesh,
2000) shows that firms with unionized labor strategically choose accounting methods that results in lower
transparency, hence reduces the ability of employees to extract more rents. In the same vein, Hillary (2006) shows
that strong labor unions are associated with higher bid-ask spread increases, hence higher information asymmetry.
This finding suggests that managers of firms with unionized employees are reluctant to share information on the
prospects of the firm with employees in order to preserver bargaining power when negotiating contracts. Klasa et al.
(2006) show that the managers of firms with unionized employees tend to hold more cash than non-unionized peers
in order to shelter firm resources form rent-seeking labor unions. More recently, research by Farber, Hsieh, Jung and
Yi (2012) suggests that higher levels of union strength are associated with lower level of accounting conservatism
(i.e., lower earnings quality). Hamm, Jung, and Lee (2013) argue that strong labor unions increase the incentives of
managers to smooth incomes. Specifically, they argue that firms with unionized employees tend to manage earnings
upwards (downwards) in bad (good) times in order reduce the ability of labor unions to extract firm resources.
Consistent with this argument, they show that income smoothing activities (i.e., discretionary income smoothing and
R&D investment adjustments) are positively associated with labor unions strength. Bova (2013) argues that firms
facing strong labor union may undertake actions that allow them to intentionally miss the expectations of analysts,
hence hide corporate resources that may be extracted by labor unions. To do so, they may manipulate either the
expectations of analysts or the reported earnings. Consistent with this view, the author shows that unionized firms are
more likely to miss the analyst earnings consensus. Chung et al. (2014) argue that disclosure frequency for Korean
firms is negatively related to labor union strength, also supporting the view that managers of unionized firms tend to
obscure accounting information in order to preserve bargaining power.
Poor disclosure quality, in our case due to unions’ pressures, might result in stock price crashes. Indeed, extant
empirical research provides empirical evidence suggesting opaque financial reporting is associated with higher stock
price crash risk. For example, Jin and Myers (2006) show that firms from countries with high financial reporting
opacity are more likely to experience stock price crash. Similarly, Hutton et al. (2009) show that stock price crash
risk may be affected by earnings management. In fact, he shows the tendency of firms to manage earnings upwards
in bad times to a point beyond which they can no longer do it, leads to stock prices to crash when a cascade of bad
information is revealed. In the same vein, DeFond, Hung, Li and Li (2012) show that the mandatory adoption of
International Financial Reporting Standards (IFRS) in the European Union, which reduces financial reporting opacity,
hence reduces stock price crash risk. Additionally, Kim and Zhang (2013) provide evidence that accounting
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conservatism (i.e., timelier recognition of economic losses versus economic gains) weakens the ability of managers
to conceal bad news, which reduces stock price crash risk.
The above arguments suggest that firms facing strong labor unions tend to report lower accounting information, in
order to preserve bargaining power when negotiating contracts, which lead to opaque financial reporting. Given that
financial reporting opacity increases the likelihood to experience stock price crash, we expect that stock price crash
risk increases in labor union strength.
3. Data Description and Empirical Design
3.1 Stock Price Crash Risk Proxy
Following previous researches (Kim and Zhang, 2011a, 2011b, and 2013; Kim, Li, and Li, 2014), we use two
different firm-specific crash risk proxies based on Jin and Myers’s (2006) market model. Specifically, we regress the
weekly stock return of each firm in our sample on the value weighted market return in current week as well as two
weeks forward and backward using the following model:
, 1, , 2, , 1 3, , 2 4, , 1 5, , 2 ,
i t i i mt i mt i mt i mt i mt i t
rrrrrr
   
 
  (1)
where ,it
r is the stock return for firm i in week t, and ,mt
r is the return of CRSP’s value-weighted market index
in week t, and it
is an error term. In line with previous studies, the lead and lag returns are introduced to account
for non-synchronous trading. Since stock prices reflect mixed information, including both firm-level and
market-level, we use the regression model (1) to decompose these information and only keep the firm-level
component ( ,it
)to analyze the crash risk. Economically, stock prices are more informative when stock returns
become less correlated with past, current, and future market returns. The natural logarithm of one plus the residual
from equation (1), i.e. Log (1+ ,it
) is our proxy for firm-specific weekly return for firm i in week t ( ,it
W).
Our first proxy for stock price crash risk is the negative conditional firm-specific skewness of weekly return
(NCSKEW). We calculate NCSKEW by dividing the negative of the third moment of firm firm-specific weekly
returns, ,it
W, for each sample year by the standard deviation of firm-specific weekly returns raised to the third power.
Following Kim et al. (2014), we calculate NCSKEW for each firm i at year tas:
3/2
3/2 3 2
,, ,
(1) /(1)( 2)
it it it
NCSKEW n n W n n W

  


(2)
where ,it
W is as previously defined and n is the number of weekly return observations during year t. A higher
negatively skewed return distribution (i.e., a higher value for NCSKEW) indicates a higher crash risk.
The second proxy for stock price crash risk is the down-to-up volatility (DUVOL) calculated as the natural logarithm
of the standard deviation of weekly-stock returns ,it
W, during the weeks in which ,it
W is lower than its annual
mean (“down” weeks) over the standard deviation of weekly-stock returns ,it
W, during the weeks in which ,it
W is
higher its annual mean (“up” weeks). Specifically, DUVOL for each firm i at year t is calculated as:
22
,,,
log ( 1) / ( 1)
it u it d it
DOWN UP
DUVOL n W n W
 
 
 
 


(3)
where u
n is the number of “up” weeks and d
n is the number of “down” weeks. The higher the DUVOL, the
higher the crash risk.
3.2 Labor Unions Proxy
Our labor unions proxy is the firm-year unionization rate (UNION ). This measure is widely used in the accounting
and finance literature (e.g., Bova, 2013; Chen and Kacperczyk, 2011; Chen, Kacperczyk, and Ortiz-Molina, 2012;
Chung, Lee, Lee and Sohn, 2014, among others). The firm-year unionization rate is calculated by multiplying the
industry-level unionization rate by the number of employees deflated by total assets. Industry-level unionization rates
come from Hirsch and Macpherson (2003)’s updated database of Union Membership and Coverage. (Note 1)
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3.3 The Sample
We collect firm and market stock returns from The Center for Research in Security Prices (CRSP). First, we calculate
our two proxies for crash risk for the period from 1983 to 2013. (Note 2) Then, we merge our estimates for stock
price crash risk with: (i) labor union data over the period from 1983 to 2013 from Hirsch and Macpherson (2003)’s
updated database of Union Membership and Coverage (ii) financial data from CO MP US TAT, (iii) analyst coverage
data from the Institutional Brokers' Estimate System (I/B/E/S) summary files and (iv) institutional ownership data
from Thomson Financial Institutional Holdings (13f) Database. Finally, we winsorize all firm-level variables at the
1st and the 99th percentiles to mitigate the effect of outlier observations. Thus, we end-up with a sample of 73,543
firm-year observations for the period from 1984 to 2013.
3.4 Empirical Models
To examine the impact of labor unions on stock price crash risk, we estimate several specifications of the following
regression model:
,01 ,12 ,1it it it t it
CRASH UNION CONTROLS
 

  (4)
Following the recent literature on stock price crash risk (e.g., Kim and Zhang, 2011a, 2011b, and 2013; Kim et al.,
2014), we include in CONTROLS the following variables that may affect crash risk: the natural logarithm of a
firm’s market value at year 1t ( ,1it
SIZE
) to control for firm size, the ratio of long-term debt for a firm i at
year 1t over total assets for firm i at year 1t
(,1it
L
EVERAGE
) to control for financial risk, the
market-to-book ratio ( ,1it
M
B) at year 1t
to control for growth opportunities, the ratio of net income at year
1t over total assets at year 1t,1it
R
OA
) to control for firm profitability, the change in turnover ratio
(,1it
DTURNOVER ) calculated as the difference between the average monthly turnover at 1t and the average
monthly turnover at 2t to control for the intensity of differences of opinion among investors, the average of
firm-specific weekly returns over the fiscal year to control for past returns ( ,1it
R
ET
). Chen et al. (2001) show that
firms with high past returns have more probability to crash. Thus, we include the standard deviation of the weekly
stock returns at year 1t(,1it
SIGMA ) to control for stock return volatility and the absolute value of Dechow and
Dichev’s (2002) measure of abnormal accruals at year 1t
( ,1it
A
Q
), as modified by Ball and Shivakumar (2005)
to control for earnings management. Moreover, we include industry and year dummies to control for the industry and
fixed effects in all the regressions. Finally, we adjust standard errors for the effect of non-independence by clustering
on each firm.
4. Empirical Results
4.1 Descriptive Statistics
Table 1 reports descriptive statistics on the variables used to estimate equation (4). The average (median) of
,1it
NCSKEW is equal to -0.006 (-0.096) and the average (median) of ,1it
DUVOL
is equal to -0.030 (-0.059).
These numbers are comparable to those reported in prior related literature (e.g., Kim and Zhang, 2011a, 2011b, and
2013; Kim et al., 2014). The average (median) of our proxy for labor union, the firm-year unionization rate (UNION)
is 0.094 (0.062).
Table 1. Descriptive statistics
Va r ia b l e N Mean Median Standard Q1 Q3
deviation
NCSKEWt 73,543 -0.006 -0.096 1.071 -0.554 0.366
DUVOLt 73,543 -0.030 -0.059 0.443 -0.309 0.200
UNIONt-1 73,543 0.094 0.062 0.098 0.021 0.131
SIZEt-1 73,543 5.127 5.020 2.113 3.548 6.606
NCSKEWt-1 73,543 -0.002 -0.104 1.301 -0.579 0.370
DUVOLt-1 73,543 -0.026 -0.063 0.527 -0.320 0.202
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LEVERAGE t-1 73,543 0.196 0.165 0.182 0.021 0.317
MBt-1 73,543 2.757 1.881 2.981 1.151 3.217
ROAt-1 73,543 -0.007 0.038 0.205 -0.034 0.090
DTURNOVERt-1 73,543 -0.021 -0.009 0.751 -0.247 0.206
RETt-1 73,543 -0.002 -0.001 0.006 -0.004 0.002
SIGMAt-1 73,543 0.037 0.032 0.022 0.021 0.047
AQt-1 73,543 0.144 0.082 0.215 0.040 0.161
This table presents descriptive statistics for the variables used in our multivariate regression analysis to examine the impact of labor
union on stock price crash risk for a sample of 73,543 firm-year observations for the 1984-2013 period. Descriptions and sources of
these variables are provided in the Appendix.
Table 2 reports Pearson correlation coefficients between the stock price crash risk proxies, our labor union proxy, and
the control variables. The correlation coefficients that are significant at the 1% level are highlighted in bold. For
instance, we find that UNIONi,t-1 are significantly and positively correlated at the 1% level with NCSKEWi,t,
suggesting a positive relationship between the negative skewness in stock returns and the unionization rate. We also
find that UNIONi,t-1 is significantly and positively correlated at the 1% level with DUVOLi,t, suggesting a positive
relationship between the two variables as well. As for the control variables, we report several significant correlations
which are consistent with prior related crash risk literature. In fact, both of NCSKEWi,t and DUVOLi,t, are positively
correlated at the 1% level with SIZEi,t-1, MBi,t-1, ROAi,t-1, DTURNOVERi,t-1, and RETi,t-1. Additionally, both NCSKEWi,t
and DUVOLi,t, are negatively correlated at the 1% level with SIGMAi,t-1, suggesting a significant relationship between
the volatility of the stock returns and the probability to experience stock price crash. We also report low correlation
coefficients between our labor union proxy and the control variables, thus mitigating multicollinearity concerns that
could affect our regression results.
Table 2. Pearson correlations
Va r ia b l e
NCSKEWt
DUVOLt
UNIONt-1
SIZEt-1
NCSKEWt-1
DUVOLt-1
LEVERAGE t-1
MBt-1
ROAt-1
DTURNOVERt-1
RETt-1
SIGMAt-1
DUVOLt 0.948
UNIONt-1 0.026 0.024
SIZEt-1 0.035 0.031 0.005
NCSKEWt-1 0.032 0.031 0.005 0.960
DUVOLt-1 0.048 0.035 0.019 0.083 0.064
LEVERAGE t-1 0.000 0.006 0.140 -0.008 -0.002 -0.035
MBt-1 0.038 0.029 -0.099 0.026 0.013 0.194 -0.049
ROAt-1 0.070 0.067 0.098 0.070 0.062 0.254 -0.019 -0.171
DTURNOVER
t-1 0.027 0.024 0.009 0.014 0.008 0.076 0.017 0.086 0.056
RETt-1 0.032 0.033 0.024 -0.395 -0.425 0.142 -0.019 0.032 0.153 0.074
SIGMAt-1 -0.052 -0.054 -0.111 0.154 0.137 -0.488 -0.003 0.038 -0.323 0.026 -0.238
AQt-1 0.003 0.004 -0.106 -0.014 -0.014 -0.081 -0.084 0.150 -0.183 0.028 -0.021 0.141
This table presents Pearson pairwise correlation coefficients between the regression variables. The full sample includes 73,543
firm-year observations for the 1984-2013 period. Bold face indicates statistical significance at the 1% level. Descriptions and data
sources for these variables are provided in the Appendix.
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4.2 Labor Unions and Stock Price Crash Risk
Table 3 reports the OLS regression results obtained by regressing our two proxies for stock price crash risk on
UNION. In both of the two models reported in this table, we control for industry fixed-effects and year fixed-effects.
We also cluster standard errors at the firm-level. The results reported in Model 1, in which we use the negative
skewness in stock returns (NCSKEW) as dependent variable to proxy for stock price crash risk, provide evidence that
supports our hypothesis, suggesting that highly unionized firms are more likely to experience stock price crash. As it
can be seen from the model, the coefficient of ,1it
UNION
is positive and statistically significant at the 1% level.
Moreover, the proxy for the reporting quality (AQ) is significantly and positively related to the crash risk. These two
combined results are consistent with the argument that more employee-unionized firms tend to report lower
accounting information, in order to preserve bargaining power when negotiating contracts with labor unions. This
behaviour would lead to higher likelihood of stock price crash.
Model (2) reports our results of the impact of labor union on stock price crash risk when we use the down-to-up
volatility as a proxy for stock price crash risk. As we can observe, the coefficient for UNION is also positive and
significant at the 1% level, corroborating our earlier finding. This finding suggests that highly unionized firms have
higher down-to-up volatility.
As for the control variables, we report several significant coefficients that are consistent with our predictions. The
coefficients for ,1it
SIZE , ,1it
R
OA ,,1it
M
B
, ,1it
DTURNOVER
, ,1it
R
ET
and ,1it
A
Q are positive and
significant at the 1% level, across all specifications, suggesting that larger firms, more profitable firms, firms with
higher growth opportunities, with higher changes in turnover ratio, and higher returns in the past and higher absolute
value of Dechow and Dichev’s (2002) measure of abnormal accruals (i.e., lower earnings quality) have higher stock
price crash risk. Additionally, we find a negative significant coefficient at the 1% level for ,1it
SIGMA , implying
that firms with higher stock return volatility have higher stock price crash risk.
Table 3. Multivariate results
Va r ia b l e NCSKEW DUVOL
Coefficient p-value Coefficient p-value
UNIONt-1 0.187*** <0.01 0.067*** <0.01
SIZEt-1 0.007** 0.01 -0.001
0.270
NCSKEWt-1 0.037*** <0.01
DUVOLt-1 0.037*** <0.01
LEVERAGE t-1 -0.021 0.37 0.000 0.970
MBt-1 0.016*** <0.01 0.006*** <0.01
ROAt-1 0.286*** <0.01 0.113*** <0.01
DTURNOVERt-1 0.027*** <0.01 0.011*** <0.01
RETt-1 5.426*** <0.01 2.344*** <0.01
SIGMAt-1 -1.599*** <0.01 -0.818*** <0.01
AQt-1 0.079*** <0.01 0.037*** <0.01
Intercept -0.138 0.130 -0.138
Year fixed effect Yes Ye s
Industry fixed effect Yes Ye s
Observations 73,543 73,543
R-squared 0.017 0.017
This table presents regression results of the impact of labor union on stock price crash risk. The full sample includes a sample of
73,543 firm-year observations for the 1984-2013 period. All regressions include industry and year dummies to control for industry
and year fixed-effects, respectively. Descriptions and data sources for the regression variables are provided in the Appendix. We
adjust standard errors for the effect of non-independence by clustering on each firm. ***, **, and * denote statistical significance at
the 1%, 5%, and 10% levels, respectively.
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4.3 Further Analysis on the Role of Institutional Ownership and Analyst Coverage
In this section, we control for additional control variables that may affect stock price crash risk. Particularly, we
investigate the role that the presumed external disciplinary mechanisms, namely institutional ownership and analyst
coverage, might have in the above documented relationship between labor union and crash risk. Prior literature
suggests that large stake holdings by long term institutional investors are associated with lower stock price crash risk
(e.g., An and Zhang, 2013; Callen and Fang, 2013). Moreover, the number of analysts following the company might
also mitigate information asymmetry and decrease the probability to hoard bad news (Kim and Zhang (2013)).
To test these hypotheses, we include in the regressions reported in table 4 the percentage of institutional ownership
(IO) and the number of analysts following a firm (ACOV) as additional explanatory variables.The results reported in
Models 1 and 3 where (IO) was added as an explanatory variable show that the coefficient for UNION remains
positive and significant at the 1% level, corroborating our earlier findings.
In Models (2) and (4) of Table 4, we control for analyst coverage using the natural logarithm of one plus the number
of analysts following a firm (ACOV) from I/B/E/S summary files. The results show that the coefficient for ACOV is
negative and significant (at 10% level only), in line with Kim and Zhang (2013), suggesting that higher analyst
coverage mitigates information asymmetry, hence decreases the probability to hoard bad news.
Table 4. Additional controls
Va ri a b l e
NCSKEW
DUVOL
IOt-1 ACOVt-1 IOt-1 ACOVt-1
Coefficient p-value Coefficient p-value Coefficient p-value Coefficient p-value
(1) (2) (3) (4)
UNIONt-1 0.187 <0.01 0.190*** <0.01 0.067*** <0.01 0.068*** <0.01
SIZEt-1 0.007** 0.01 0.010*** <0.01 0.000 0.26 -0.001 0.87
NCSKEWt-1 0.037*** <0.01 0.037*** <0.01
DUVOLt-1 0.038*** <0.01 0.037*** <0.01
LEVERAGE t-1 -0.022 0.37 -0.021 0.37 0.000 0.97 0.000 0.98
MBt-1 0.016*** <0.01 0.016*** <0.01 0.006*** <0.01 0.006*** <0.01
ROAt-1 0.286*** <0.01 0.287*** <0.01 0.113*** <0.01 0.113*** <0.01
DTURNOVERt-1 0.027*** <0.01 0.027*** <0.01 0.010*** <0.01 0.011*** <0.01
RETt-1 5.427*** <0.01 5.407*** <0.01 2.337*** <0.01 2.344*** <0.01
SIGMAt-1 -1.598*** <0.01 -1.582*** <0.01 -0.811*** <0.01 -0.817*** <0.01
AQt-1 0.079*** <0.01 0.079*** <0.01 0.037*** <0.01 0.037*** <0.01
IOt-1 0.000 0.47 0.000 0.18
ACOVt-1 -0.001* 0.09 -0.001* 0.08
Intercept -0.138 0.13 -0.148 0.11 -0.055 0.16 -0.051 0.13
Year fixed effect Yes Yes Ye s Ye s
Industry fixed effect Yes Ye s Ye s Ye s
Observations 73,543 73,543 73,543 73,543
R-squared 0.017 0.017 0.017 0.017
This table presents regression results of the impact of labor union on stock price crash risk while introducing additional control variables. The full
sample includes a sample of 73,543 firm-year observations for the 1984-2013 period. All regressions include industry and year dummies to control
for industry and year fixed-effects, respectively. Descriptions and data sources for the regression variables are provided in the Appendix. We adjust
standard errors for the effect of non-independence by clustering on each firm. ***, **, and * denote statistical significance at the 1%, 5%, and 10%
levels, respectively.
Evidence from Table 4 supports our original hypotheses that stock price crash risk is positively related to labor union.
However, the insignificant coefficient of the Institutional Ownership variable (Models 1 and 3 of the same table) and
the relatively low significance level of the Analyst Coverage variable (Models 2 and 4 of the same table) require
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further investigations particularly in light with the findings of prior researches supporting the disciplinary roles of
these two mechanisms. To this end, we propose to reconsider our analysis for two sub-samples, i.e. firms with high
percentage of institutional ownership versus firms with low percentage of institutional ownership (IO). We do the
same for the variable Analyst Coverage (ACOV).
We re-run our basic model (Model 1 of Table 3) separately for sub-samples based on the median of institutional
ownership. The results for NCSKEW regressions are reported in Models (1) and (2) of Table 5. As we can observe,
the coefficient for UNION is positive and significant at the 1% level only for the sub-sample of firms with low
institutional ownership, suggesting that the adverse effects of labor union on stock price crash risk are less
pronounced in firms with high institutional ownership. This finding is consistent with the monitoring hypothesis of
institutional ownership. The results for DUVOL regressions, which are reported in Models (1) and (2) of Table 6 also
support this hypothesis.
Table 5. Sub-sample analysis—Set 1
Va ri a b l e
NCSKEW
IOt-1 ACOVt-1
High Low High Low
Coefficient p-value Coefficient p-value Coefficient p-value Coefficient p-value
(1) (2) (3) (4)
UNIONt-1 0.034 0.66 0.356*** <0.01 -0.054 0.51 0.374*** <0.01
SIZEt-1 0.009** 0.02 0.011*** <0.01 0.001 0.24 -0.005 0.14
NCSKEWt-1 0.031*** <0.01 0.033*** <0.01 0.026*** <0.01 0.038*** <0.01
LEVERAGE t-1 -0.049 0.17 -0.007 0.82 -0.006 0.53 0.004 0.66
MBt-1 0.019*** <0.01 0.012*** <0.01 0.006*** <0.01 0.005*** <0.01
ROAt-1 0.400*** <0.01 0.207*** <0.01 0.168*** <0.01 0.081*** <0.01
DTURNOVERt-1 0.019** 0.02 0.034*** <0.01 0.008*** <0.01 0.014*** <0.01
RETt-1 5.574*** <0.01 5.369*** <0.01 2.666*** <0.01 2.288*** <0.01
SIGMAt-1 -0.294 0.49 -2.156*** <0.01 -0.111 0.97 -1.069*** <0.01
AQt-1 0.098*** <0.01 0.065*** <0.01 0.035*** <0.01 0.035*** <0.01
Intercept -0.193 -0.146 0.16 -0.021 0.96 -0.059 0.05
Year fixed effect Yes Yes Ye s Ye s
Industry fixed effect Ye s Ye s Ye s Ye s
Observations 36,771 36,772, 34,961 38,582
R-squared 0.017 0.022 0.018 0.023
This table presents results of sub-sample analysis of the impact of labor union on stock price crash risk. Models from 1 and 2 report results of
NCSKEW regressed on labor union for high and low IO. Models from 3 and 4 report results of NCSKEW regressed on labor union for high and low
ACOV. The full sample includes a sample of 73,543 firm-year observations for the 1984-2013 period. All regressions include industry and year
dummies to control for industry and year fixed-effects, respectively. Descriptions and data sources for the regression variables are provided in the
Appendix. We adjust standard errors for the effect of non-independence by clustering on each firm. ***, **, and * denote statistical significance at
the 1%, 5%, and 10% levels, respectively.
As for the second variable Analyst Coverage, we also re-run our basic model (Model 1 of Table 3) separately for
sub-samples based on the median of ACOV. The results for NCSKEW regressions are reported in Models (3) and (4)
of Table 5. They show that the coefficient for UNION is positive and highly significant only for the sub-sample of
firms with low ACOV, suggesting that the adverse effects of labor union on stock price crash risk are less pronounced
in firms with high analyst coverage. This result is consistent with the argument that analyst following helps
mitigating information asymmetry problems, hence reduces stock price crash risk. The results for DUVOL
regressions, which are reported in Models (3) and (4) of Table 6 also support this hypothesis.
Overall, these results suggest that the adverse effects of labor union on stock price crash risk are less pronounced in
firms with high institutional ownership and high analyst coverage, respectively.
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Table 6. Sub-sample analysis—Set 2
Va ri a b l e
DUVOL
IOt-1 ACOVt-1
High Low High Low
Coefficient p-value Coefficient p-value Coefficient p-value Coefficient p-value
(1) (2) (3) (4)
UNIONt-1 0.002 0.93 0.180*** <0.01 -0.043 0.19 0.192*** <0.01
SIZEt-1 0.002 0.35 -0.002 0.15 0.001 0.67 -0.005*** <0.01
DUVOLt-1 0.027*** <0.01 0.039*** <0.01 0.026*** <0.01 0.038*** <0.01
LEVERAGE t-1 -0.019 0.19 0.010 0.46 -0.006 0.68 0.004 0.76
MBt-1 0.007*** <0.01 0.005*** <0.01 0.006*** <0.01 0.005*** <0.01
ROAt-1 0.156*** <0.01 0.084*** <0.01 0.168*** <0.01 0.081*** <0.01
DTURNOVERt-1 0.007*** <0.01 0.014*** <0.01 0.008*** <0.01 0.014*** <0.01
RETt-1 2.068*** <0.01 2.486*** <0.01 2.666*** <0.01 2.288*** <0.01
SIGMAt-1 -0.191 0.27 -1.158*** <0.01 -0.111 0.55 -1.069*** <0.01
AQt-1 0.042*** <0.01 0.032*** <0.01 0.035*** <0.01 0.035*** <0.01
Intercept -0.098 0.05 -0.011 -0.021 0.64 -0.059 0.20
Year fix e d e f f e c t Yes Yes Ye s Ye s
Industry fixed
effect Yes Yes Ye s Ye s
Observations 36771 36772 34961 38582
R-squared 0.015 0.024 0.018 0.023
This table presents results of sub-sample analysis of the impact of labor union on stock price crash risk. Models from 1 and 2 report results of
DUVOL regressed on labor union for high and low IO. Models from 3 and 4 report results of DUVOL regressed on labor union for high and low
ACOV. The full sample includes a sample of 73,543 firm-year observations for the 1984-2013 period. All regressions include industry and year
dummies to control for industry and year fixed-effects, respectively. Descriptions and data sources for the regression variables are provided in the
Appendix. We adjust standard errors for the effect of non-independence by clustering on each firm. ***, **, and * denote statistical significance at
the 1%, 5%, and 10% levels, respectively.
5. Conclusion
In contributing to the literature on the importance of nonfinancial stakeholders for corporate decisions and economic
outcomes (e.g., Hillary, 2006; Klasa et al., 2006; Chen, Kacperczyk, and Ortiz-Molina 2011, 2012; Chen, Chen, and
Liao 2011; Bova, 2013; Chung et al., 2014, among others), we choose to focus on the relationship between labor
unions and stock price crash risk. Specifically, using a large sample of U.S. firms over the period 1984-2014, we
show that stock price crash risk is increasing in labor union strength. This finding is consistent with the conjecture
that firms facing strong labor unions tend to report lower accounting information, in order to preserve bargaining
power when negotiating contracts, which lead to opaque financial reporting and increases the likelihood to
experience stock price crash. We also find that the adverse effects of labor union on stock price crash risk are less
pronounced for firms with strong external monitoring mechanisms, such as high institutional ownership and high
analyst coverage.
Overall, our study sheds the light on the importance of the previously under-explored role of labor unions in relation
to the stock price crash risk. While the present paper highlights the importance of labor unions as nonfinancial player
for stock price crash risk, future researches can examine the impact of other nonfinancial shareholders such as
customers and suppliers on stock price crash risk.
Acknowledgement
We would like to thank the Deanship of Scientific Research at King Saud University, represented by the research
center at CBA, for supporting this research financially.
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Notes
Note 1. The database is available at http://www.unionstats.com. See Hirsch and Macpherson (2003) for a description
of the approach used to construct this database.
Note 2. Labor union data is available starting from 1983. That’s why, we estimate stock price crash risk proxies over
the period that starts in 1983. Our final sample covers the period between 1984-2013. We lose observations for 1983
because of the need to control in all our regression for our lagged labor union proxy.
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Appendix
Variables, Descriptions, and Sources
Variable Description Source
NCSKEW The negative coefficient of skewness calculated by taking the negative of the third moment
of firm firm-specific weekly returns for each sample year divided by the standard deviation
of firm-specific weekly returns raised to the third power. See equation (2) for details.
Authors'
calculation
DUVOL The down-to-up volatility calculated as the natural logarithm of the standard deviation of
weekly-stock returns during the weeks in which they are lower than their annual mean
(“down” weeks) over the standard deviation of weekly-stock returns during the weeks in
which they are higher than their annual mean (“up” weeks).
Authors'
calculation
UNION The firm-year unionization rate is calculated by multiplying the industry-level unionization
rate by the number of employees deflated by total assets. Industry-level unionization rates
come from Hirsch and Macpherson (2003)’s updated database of Union Membership and
Coverage.
Authors'
calculation
calculation
SIZE The natural logarithm of the firm’s market value. Authors'
calculation
LEVERAGE The ratio of long-term debt over total assets. Authors'
calculation
MB The market-to-book ratio. Authors'
calculation
ROA The ratio of net income over total assets. Authors'
calculation
DTURNOVE
R The difference between the average monthly turnover at the end of the year and the average
monthly turnover at the beginning of the year.
Authors'
estimation
RET The average of firm-specific weekly returns over the fiscal year. Authors'
calculation
SIGMA The standard deviation of the weekly stock returns over the fiscal year. Authors'
calculation
AQ The absolute value of Dechow and Dichev’s (2002) measure of abnormal accruals, as
modified by Ball and Shivakumar (2005).
Authors'
calculation
IO The fraction of the firm’s shares held by institutional investors. Authors'
calculation
ACOV The natural logarithm of one plus the number of analysts following a firm. I/B/E/S
... This crash-inducing effect of media coverage is due to extremely large reactions to information distribution among investors as a result of concentrated media coverage. Ben-Nasr, Dahmash & Ghouma (2015) examine the influence of labor unionization on crash risk, and find a positive association between the two. This finding is consistent with the argument that firms facing strong labor unions tend to report lower accounting information, in order to preserve bargaining power when negotiating contracts with labor unions. ...
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