Descriptive statistics
Firm-year average levels of wage theft by 2-digit NAICS industry and year are presented in Panels A and B of Table 2. In both panels, column (1) considers the proportion of sample firm-years in which wage theft occurred (regardless of whether or not a violation was detected by WHD in those years). Columns (2), (3), and (4) provide descriptive statistics for the other three wage theft proxies used in my analyses. The incidence of (detected) wage theft is relatively stable over time, although it exhibits a decline after 2013. In untabulated analyses, I verify that my results are robust to excluding firm-year observations from 2014 and 2015 from the sample. Panel C presents descriptive statistics for the three non-indicator measures of WageTheftit for the conditional sample of 1,573 firm-years where wage theft occurred. Panel C highlights the limitations of federal laws that set low maximum penalties; the average violation, scaled by the number of associated violation-years, results in $57,640.19 in penalties.
Table 2 Summary statistics on wage theft Summary statistics for all regression variables are presented in Table 3. Continuous variables are winsorized at the 1% and 99% levels. On average 11.6% of employees in firms’ industries are unionized, although this ranges from 2.0% to 51.3% within individual industry-years. The sample consists of large firms with average leverage ratios of 25.2%, which is low relative to the Compustat universe. The majority of firms (82%) are profitable in that they have positive ROA, reflecting the fact that my sample comprises larger firms. Approximately 2.9% of firm-years correspond to financial misconduct; because FinMisconductit = 1 when either the current or subsequent year is a misconduct year, this gives a slightly higher sample mean value of FinMisconductit of 4.0%.
Table 3 Summary statistics of regression variables Meet-or-beat incentives and wage theft
Results from estimating (1) are in Table 4. I present results separately for each of the four wage theft measures outlined in Section 4.1. In all four cases, I find a positive and significant coefficient on SUSPECTit, meaning that firms are more likely to engage in wage theft when they have incentives to meet or beat analyst forecasts. These effects are economically significant; the coefficient in column (1) suggests that the mean firm is 13.7% more likely to engage in wage theft relative to the case where it does not have meet-or-beat incentives and faces penalties that are 9.8% − 10.7% higher (based on the coefficients in columns (2) and (4)). These results are consistent with Hypothesis 1. To ensure that the results in Table 3 are unlikely to be driven by an omitted correlated variable, I follow Larcker and Rusticus (2010) and calculate the impact threshold of a confounding variable (ITCV). In untabulated tests I find that a potential omitted correlated variable would have to have an impact at least 7.6, 2.7, 4.3, and 9.4 times that of the most impactful control variable in columns (1)-(4), respectively, in order to invalidate my results. The results in Table 4 reflect meet-or-beat incentives driving the incidence, as well as the severity, of corporate wage theft: in untabulated analyses, I find that, within the set of wage theft firm-years observed in my sample, firms with meet-or-beat incentives face higher penalties per affected employee.
Table 4 Meet-or-beat incentives and wage theft With respect to control variables, I find that larger firms (based on the number of employees) are more likely to engage in wage theft. This result may simply reflect the fact that firms with more opportunities to engage in wage theft are more likely to do so. Interestingly, I do not find statistically significant results for union coverage or the industry-year violation rate. These results are likely driven by the fixed effects design and minimal within-firm, between-year variation in these measures; in untabulated analyses that use 2-digit NAICS industry fixed effects in lieu of firm fixed effects, I find a significant negative relation between union coverage and the likelihood of wage theft. In these analyses I also find that ROA is negatively associated with the likelihood of wage theft; i.e., better-performing firms have less of a need to engage in actions that harm their employees.
Extreme earnings observations
Prior research (Siriviriyakul 2014; Srivastava 2019) highlights that extreme-earnings firms may have unusual characteristics that could drive inferences related to real earnings management. Underlying this criticism is the assertion that financial statement-based measures of real earnings management (e.g., those constructed using disclosed production costs or SG&A) are likely to suffer from an omitted variables problem for extreme observations. This is unlikely to be a concern in my setting because, unlike prior studies such as Roychowdhury (2006) or Zang (2012), I observe wage theft directly rather than inferring it from firms’ financial statements. Nonetheless, to verify that my results are not driven by comparing just-meet-or-beat firms against extreme earnings observations, I restrict the sample by excluding extreme earnings observations. I construct three different subsamples based on excluded extreme observations.Footnote 19 Subsample 1 directly follows Siriviriyakul (2014) and Roychowdhury (2006) and excludes observations with earnings before extraordinary items (scaled by assets) that lie outside the interval (− 0.075,0.075). Results from estimating (1) on Subsample 1 are presented in column (1) of Table 5; the results in Table 4 continue to hold.
Table 5 Meet-or-beat incentives and wage theft, excluding extreme intervals Prior literature focuses on extreme intervals with respect to the zero-earnings benchmark. However, as an additional robustness test, I consider extreme intervals with respect to prior-year earnings and analyst forecasts as well. Because of differences in scaling, I do not use endpoints of (− 0.075,0.075) to construct these intervals; a cutoff of 0.075 will exclude substantially more firms on the basis of scaled earnings than on the basis of the change in scaled earnings. To ensure that my definitions of extreme earnings are as similar as possible across the three measures, I therefore define earnings interval endpoints on the basis of the number of observations included in these subsamples. Subsample 1, described above, contains 9,105 observations, relative to a total of 16,692 used in Table 4. By defining Subsample 2 as observations with the change in income before extraordinary items (scaled by assets) lying in the interval (− 0.03,0.03) and Subsample 3 as observations with analyst forecast error lying in the interval (-0.06, 0.06), I obtain 9,388 and 9,604 observations, respectively. Results from estimating Equation (1) on Subsamples 2 and 3 are presented in columns (2) and (3) of Table 5; my results continue to hold. In the analyses that follow, I use the full sample from Table 4 to maximize statistical power.
Managerial incentives and wage theft
Meet-or-beat behavior represents a response to firm-level financial incentives. However, managers’ personal incentives are also frequently cited as a cause of firms’ decisions to engage in misconduct (e.g., Armstrong et al. 2010). I therefore test whether managerial incentives play a role in firms’ decisions to engage in wage theft. I consider both positive and negative managerial incentives for wage theft. Following Coles et al. (2006) and Armstrong et al. (2013), I measure positive managerial incentives using CEO delta and vega. Delta measures the the sensitivity of executive compensation with respect to stock prices, while vega measures the sensitivity of executive compensation with respect to risk-related incentives. I obtain data on delta and vega from the authors of Coles et al. (2006) and include the natural logarithms of these quantities as additional variables in a modified version of Eq. 1.
To measure managerial disincentives I exploit five circuit court cases in the United States that shifted managerial liability for wage theft for subsets of my sample. Under the FLSA, certain employees can be held liable for wage theft. However, in order to be held personally liable for wage theft, an employee must have “significant control” over the establishment.Footnote 20 Courts across the United States have interpreted “significant control” differently and, as a result, the set of employees who are considered personally liable for wage theft has varied in a staggered fashion across geography and time. During my sample period, two notable circuit court cases had the effect of decreasing individual executives’ liability for firms headquartered in those circuits, and three other circuit court cases had the effect of increasing individual executives’ liability for firms headquartered in those circuits.
In 2008, the Eleventh Circuit Court of Appeals ruled in Alvarez Perez v. Sanford-Orlando Kennel Club that executives who were not regularly on site at a specific workplace did not have significant control and thus could not be held liable for wage theft at that location. Under similar logic, in 2012 the Fifth Circuit Court of Appeals handed down a similar ruling in Gray v. Powers. In contrast, in 2009 the Ninth Circuit Court of Appeals ruled in Boucher v. Shaw that executives exercised significant control over all company locations and could, as such, be held personally liable for wage theft. Rulings similar to Boucher v. Shaw were handed down in 2013 by both the First and Second Circuits, in Manning v. Boston Medical Center Corp. and Irizarry v. Catsimatidis, respectively. Finally, in 2014 the Supreme Court resolved the uncertainty created by these five circuit court rulings by affirming the decision in Irizarry v. Catsimatidis; in doing so, the Supreme Court established that individuals with “general control over corporate affairs” may be held personally liable for wage theft under the FLSA.
Using the court cases above, I conduct staggered difference-in-differences tests to examine whether these shifts in executives’ liability affect firms’ levels of wage theft. I construct two variables for use in these tests. The first, LiabDecreaseit, equals 1 for firms headquartered in the Eleventh Circuit from 2008-2014 or the Fifth Circuit from 2012-2014. The second, LiabIncreaseit, equals 1 for firms headquartered in the Ninth Circuit from 2009-2014 or the First or Second Circuits from 2013-2014. These two variables represent the product of the “treatment” and “post” variables in the differences-in-differences specifications. The main effect of the “post” variable is subsumed by year fixed effects, while, to account for the main effect of the “treatment” variable, I include headquarters state fixed effects.Footnote 21 I obtain headquarters information data from the Loughran and McDonald Augmented 10-X Header file.
I present results from tests of managerial incentives in Table 6. For brevity, I only tabulate results using the wage theft indicator. Results using the other three wage theft proxies outlined in Section 4.1 are qualitatively similar in terms of both directional effect and statistical significance. In column (1) I consider CEO compensation incentives alone; in columns (2) and (3) I consider the effects of liability-decreasing court cases and liability-increasing court cases, respectively; and in column (4) I consider all three measures. The sample is significantly smaller in columns (1) and (4) because of limited data coverage in ExecuComp that underlies the calculations of vega and delta. My findings in column (1) are consistent with 2020) results for labor violations more broadly: I find that CEO vega is positively associated with wage theft although, unlike in their study, I do not find that CEO delta has an effect on wage theft. This result implies that executives with greater incentives for risk-taking are more likely to engage in wage theft. The effect of the circuit court cases is also consistent with my expectation that wage theft is higher (lower) after court cases that decrease (increase) executives’ personal liability for wage theft.
Table 6 Managerial incentives and wage theft Wage theft and financial misconduct
Having documented that wage theft arises from similar financial incentives to other forms of real activities management, I next turn to the relation between wage theft and financial misconduct. Table 7 presents results from estimating (2) for each of the four wage theft proxies (indicator, total penalties assessed, number of sites at which wage theft occurred, and per capita penalties). In all cases, the dependent variable is FinMisconductit, an indicator for whether firm-year t or t + 1 resulted in either an AAER or a securities lawsuit.
Table 7 Wage theft and financial misconduct Control variable results are relatively standard with respect to prior literature. For example, firms that see year-over-year increases in ROA are less likely to be caught engaging in fraud. Higher R&D, which may signal more opaque financial statements, is also associated with higher levels of financial misconduct. Firms with a greater need for external financing are more likely to engage in financial misconduct, possibly due to the heightened incentive to obtain favorable financing terms.
Across all specifications in Table 7, the coefficient on the wage theft indicator is not statistically significant. However, this indicator does not differentiate undetected wage theft from detected wage theft and, accordingly, the results above could reflect countervailing forces. When wage theft is ongoing and undetected, firms may have less of a need to engage in financial misconduct. Conversely, after wage theft is caught, the costs of further wage theft increase due to WHD’s dynamic enforcement model, in which the severity of the penalty and future WHD scrutiny depend on both the severity of the underlying violation and the firm’s compliance history. Repeat violators (i) are more likely to be investigated by WHD in subsequent years and (ii) receive higher penalties than first-time offenders for the same types of violations. In addition, repeat violations may incur significant incremental litigation or arbitration risk. If the firm deems the increase in the costs of future wage theft to be material, it may shift from wage theft to other forms of misconduct subsequent to the detection of wage theft.
I am able to directly test this possibility because wage theft typically lasts for multiple years and, unlike in the case of financial misconduct, WHD typically catches wage theft while it is still ongoing and imposes sanctions immediately.Footnote 22 As a result, for many of the violations in my sample, there are two distinct periods: (i) a period in which the violation was occurring and undetected, and (ii) a period in which the violation was occurring and detected. Because I can identify these periods, I can separate the effect on financial misconduct of firms’ decisions to engage in wage theft from the effect of firms’ wage theft being detected.
To implement this empirically, I create a variable, WageTheftCaughtit, measured analogously to the main wage theft measures. Specifically, for each of the four measures of WageTheftit, the corresponding measure of WageTheftCaughtit considers only the portion of WageTheftit that is allocable to detection years. For example, if a firm engages in wage theft in 2008 and 2009, and is caught in 2009, then the indicator form of WageTheftit equals 1 for both 2008 and 2009 while the indicator form of WageTheftCaughtit is equal to 0 in 2008 but 1 in 2009. I then estimate the following modified version of Eq. 2:
$$ \begin{array}{@{}rcl@{}} FinMisconduct_{it} &=& {\upbeta}_{0} + {\upbeta}_{1} WageTheft_{it} + {\upbeta}_{2} WageTheftCaught_{it} \\&&+ {\upbeta}_{3} Controls_{it} + \gamma_{i} + \theta_{t} + \varepsilon_{it}. \end{array} $$
(3)
Results from estimating (3) are in Table 8. Each of columns (1)-(4) corresponds to one of the four wage theft measures. In all four cases, the coefficient on WageTheftCaughtit is positive and significant. Conversely, the coefficient on WageTheftit is negative, and statistically significant in all but column (3).Footnote 23 These results suggest that while wage theft is undetected, firms have less incentive to engage in financial misconduct; however, once firms have been caught engaging in wage theft, they shift toward engaging in financial misconduct. The coefficient of -0.018 on WageTheftit suggests that firms are nearly 45% less likely to engage in financial misconduct while engaging in undetected wage theft, relative to non-wage-theft firm-years. However, once these firms are caught, the coefficient of 0.021 on WageTheftCaughtit implies that their likelihood of engaging in financial misconduct nearly doubles (relative to the firms concurrently engaging in undetected wage theft). Collectively, and perhaps most relevant, the total effect of the two coefficients suggests that firms caught engaging in wage theft are 7.5% more likely to subsequently engage in financial misconduct. Because FinMisconductit reflects the incidence of financial misconduct in year t or t + 1, it is unlikely that my results merely reflect companies managing earnings in the year of detection in response to the imposition of financial penalties. In sum, my findings suggest that wage theft precedes financial misconduct. This result is consistent with Choi and Gipper (2019), who find that lower-paid employees’ wages decline significantly in the years immediately preceding financial fraud; the results in Table 8 suggest one mechanism (wage theft) by which this decline occurs.
Table 8 Wage theft detection and financial misconduct A potential alternate explanation for the result in Table 8 is that it reflects complementarities in the detection of misconduct rather than in the incidence of misconduct, i.e., that when one agency detects misconduct at a firm, other agencies also investigate that firm. However, this is unlikely with respect to financial and labor-related misconduct. The SEC’s enforcement manualFootnote 24 references collaboration with and referrals from agencies such as Treasury, the DOJ, and state securities regulators, but does not mention wage theft or the Department of Labor. Further, while securities lawsuits can arise from a variety of trigger events, I manually investigate all securities lawsuits in my sample, and none mention wage-related enforcement actions or lawsuits.