Download Opaque Financial Reporting due to Unemployment Concerns

Opaque Financial Reporting due to Unemployment Concerns*
Jeffrey Ng
Singapore Management University
Tharindra Ranasinghe
Singapore Management University
Guifeng Shi
Shanghai Jiao Tong University
Holly Yang
Singapore Management University
August 2014
Corporate reporting is a recapitulation process in which workers provide the inputs that are
consolidated by top executives into a financial report. Agency theory predicts that both workers
and top executives have incentives to hide information in face of unemployment concerns. This
paper examines the link between unemployment concerns and financial reporting opacity.
Following Agrawal and Matsa (JFE, 2013), who find that unemployment concerns affect
corporate leverage, we focus on state unemployment insurance as one important buffer that
mitigates unemployment concerns. We find that when unemployment concerns are lower, there
is less opaque financial reporting. This relation is stronger when workers face higher
unemployment risk, workers have greater bargaining power, and top executives have higher
equity incentives. Our study contributes to the literature by showing that labor market policies
have a significant, likely unintended, impact on corporate reporting.
*
Ng ([email protected]), Ranasinghe ([email protected]), and Yang ([email protected]) are at
Singapore Management University. Shi ([email protected]) is at Shanghai Jiao Tong University. We appreciate
financial support from the School of Accountancy Research Center (SOAR) at Singapore Management University.
1.
Introduction
In this study, we examine how concerns about unemployment affect financial reporting
opacity.1 Unemployment imposes significant economic, physiological, and psychological costs
on workers (e.g., Diamond, 1982; Harris and Holmstrom, 1982; Lazear, 2003; Mortensen, 1986;
Wanberg, 2012). Workers are likely to be less concerned about unemployment if they are
provided with more unemployment benefits in the event that they are laid off. Prior theories and
empirical evidence that link labor conditions to financial reporting choices typically focus on
how employment considerations of senior executives (e.g., bonus contracts and equity
incentives) affect these choices (e.g., Healy, 1995; Fischer and Verrecchia, 2000; Kirschenheiter
and Melumad, 2002; Cheng and Warfield, 2005; Goldman and Slezak, 2006). What is lacking in
the literature is how unemployment concerns, especially those of broader groups of rank-and-file
employees, impact these reporting choices. Filling this gap can lead to a more holistic
understanding of how labor conditions, and in particular labor frictions such as unemployment,
affect financial reporting outcomes.
Financial reporting involves consolidating the financial accounting data at various levels
of the organizations. The quality of a financial report is a function of the quality of the financial
accounting data provided by various reporting units within the firm, as well as decisions made by
top executives (e.g., see, Healy and Wahlen, 1999; Kothari, 2001). Opaque financial reporting
has important economic consequences such as higher stock price crash risk (Hutton et al., 2009)
and cost of capital (e.g. Botosan, 1997; Francis et al., 2004, 2005). Attempts to hide bad news or
store good news as cookie jar reserves lead to more opaque financial reports (Dechow, Ge, and
Schrand, 2010).
1
For parsimony, henceforth, we will use the term “opacity” to refer to financial reporting opacity.
1
Concerns over unemployment can affect firms’ opacity through at least two avenues. Job
security concerns can lead to greater opacity as workers who provide inputs into the financial
reporting process may have incentives to distort these inputs to mitigate the likelihood of job
loss. For example, one of the problems of setting earnings targets for division managers is that it
puts pressure on them and consequently other workers of the division to meet these targets, and
this could result in hiding bad news or storing good news as cookie jar reserves, which, in turn,
lead to opaque division reports (Bruns and Merchant, 1990). Oberholzer-Gee and Wulf (2012)
argue that managers down in the firm hierarchy are likely to also have incentives, and perhaps
even greater opportunity to manipulate earnings. They illustrate this point using a Harvard
Business School case of H.J. Heinz Company which is based on Heinz Form 8-K dated 27 April
1979.2 As division managers at Heinz received bonuses only when earnings increased from the
previous year, they engaged, without the knowledge of senior managers, in a long list of
improper accounting practices. These practices included manipulation of the timing of
shipments, falsification of sale invoice dates, and improper recognition of advertising expenses.
While extant empirical research primarily focuses on top executives’ incentives to manipulate
financial reports, perhaps due to the lack of availability of data about employees lower in the
hierarchy, it is important to recognize that financial reporting is a recapitulation process that
begins at the lower levels of the firm.3 Opacity at the lower levels is expected to lead to opacity
at the aggregated consolidated level.
Second, it is well established in labor economics literature that unemployment risk is
costly to the firm because employees concerned about the adverse effects of unemployment
2
Post, R. and K. Goodpaster, “H.J. Heinz Company: The Administration of Policy,” HBS Case #382-034. Source:
Heinz form 8-K, April 27, 1979, p. 2.
3
Presenting a somewhat parallel argument that rank-and-file workers can indeed influence corporate outcomes, a
recent paper by Bova et al. (2014) documents that non-executive employees are able to affect corporate risk taking.
2
require firms to provide a wage premium (“compensating wage differential”) for this risk
exposure. The outcome is an increase in compensation expenses of the firm (Abowd and
Ashenfelter, 1981; Li, 1986). This compensating wage differential is not trivial. For example,
Agrawal and Matsa (2013) conservatively estimate the cost of compensating wage differentials
to be over 150 basis points of firm value for a BBB rated firm when there are no unemployment
insurance benefits. Not surprisingly, workers pay attention to financial reporting numbers,
especially earnings, because these numbers can affect their employment outcomes, including
possible loss of employment arising from layoffs or non-renewal of periodic employment
contracts. Concerns about possible post-unemployment problems (e.g., need to seek new jobs
and financial difficulties) could also negatively affect morale and productivity in the current
employment. For example, workers might spend time looking for alternative jobs just in case
they are laid off. Therefore, we argue that top executives have incentives to manipulate the
financial reports in order to minimize compensating wage differentials and other costs.4
Following Agrawal and Matsa (2013), we use exogenous inter-state cross-sectional and
intra-state time-series variations in US unemployment insurance benefits to identify changes in
unemployment concerns (that is, more generous the benefits, lower the concerns) to investigate
whether firms’ opacity is affected by these concerns. Similar to Agrawal and Matsa (2013), we
argue that this approach enables us to identify the impact of shocks to concerns about
unemployment on corporate financial reports because legally mandated increases in
unemployment insurance payments by states reduce the costs workers face when unemployed.5
4
It is also possible that top executives (and lower-level managers) are not manipulating the numbers simply to
maximize their own welfare. These executives might also be concerned about the welfare of their workers who are
also their colleagues and/or friends. If so, they might also be more inclined to manipulate the numbers to protect the
workers being laid off due to cost-cutting pressures arising from poor earnings, especially when these workers are
less protected by unemployment insurance benefits.
5
Agrawal and Matsa (2013) show that increases in state unemployment insurance benefits are associated with
greater state unemployment insurance payouts.
3
While various measures of opacity exist, we rely on two widely-used measures that best capture
the notion of hiding bad news and/or storing good news (Dechow, Ge, and Schrand, 2010). The
first measure is Hutton et al.’s (2009) opacity measure (computed as the sum of absolute annual
abnormal accruals), which is shown to be associated with higher stock price crash risk. The
second measure is Dechow and Dichev’s (2002) accruals quality measure (computed as the
standard deviation of annual abnormal accruals), which has been shown to be associated with
lower market and liquidity risk, as well as cost of capital (e.g., Francis et al., 2004, 2005; Ng,
2011).
Using data on state-level unemployment insurance benefits from 1991 to 2012, we find
evidence that greater unemployment concerns (as indicated by lower benefits) increases opacity.
Following Agrawal and Matsa (2013), when we exclude firms that likely have a more
geographically dispersed workforce, the results become even stronger. For firms with a more
geographically dispersed workforce, we are more likely to measure eligible state unemployment
insurance benefits with some error because we assign these benefits to the state where the firm is
headquartered, whereas the unemployment insurance benefits are typically assessed at the state
in which the employee performs her work.
To further our understanding of the effects of unemployment concerns on opacity, we run
several cross-sectional analyses. The first set of analyses is based on the idea that expected
unemployment income is a function of the interaction between unemployment risk and
unemployment insurance benefits. In particular, workers have higher unemployment concerns
when unemployment risk is high and unemployment insurance benefits are low. We show that
the lower unemployment insurance benefits increase opacity even more for the subsample of
firms whose employees are likely to face higher employment risk. We proxy for this
4
employment risk in two ways: firm risk and number of state-level unemployment insurance
benefit claimants, as a proportion of state population. We show that lower unemployment
insurance increases opacity more for firms that are inherently riskier, i.e., growth firms, and
firms with higher bankruptcy risk and low cash flows. We also find that higher unemployment
insurance had a greater effect on reducing opacity when the number of claimants of
unemployment insurance benefits is higher. Overall, the results are consistent with the pressure
on workers and/or top executives to be more opaque when unemployment concerns are greater.
Second, we find a significant effect of unemployment concerns on opacity only for the
subsample of firms whose top executives have high equity incentives. As noted earlier, workers
will demand a higher wage premium when they are concerned about unemployment and
unemployment benefits are low. This wage premium leads to higher employment costs that
reduce the value of the firm. Hence, to maximize the value of their equity, top executives have
greater incentives to be opaque in their financial reporting and reduce the wage premium. Third,
we examine how the relation between unemployment insurance and opacity is affected by the
power of lower-level employees in terms of trade union membership and collective bargaining
power. Both theoretical and empirical studies suggest that unemployment concerns are especially
costly for unionized firms due to higher compensating wage differentials (Moore, 1995; Viscusi,
1980). If so, the benefits of generous UI programs should be greater for firms operating in highly
unionized environments. Consistent with this prediction we find that unemployment concerns
increase opacity even more for firms domiciled in states with high labor union power.
In additional analyses, we show that the negative relation between unemployment
insurance benefits and opacity remains significant after controlling for future unemployment
insurance benefits. In contrast, there is no significant relation between future unemployment
5
insurance and opacity. These results mitigate concerns about endogeneity. We also consider
potential nonlinearity between unemployment insurance benefits and opacity. In particular, we
conjecture that unemployment insurance is likely to have a more salient effect on financial
reporting decisions when there has been a recent increase in unemployment insurance because of
the attention that these increases attract. We find some evidence that the effect of unemployment
insurance on reducing opacity is higher when there is a recent increase in the insurance.
Our paper contributes to the literature in two key ways. First, even though the Financial
Accounting Standards Board (FASB) recognizes employees as a primary group of financial
statement users, few studies investigate whether financial reporting choices are influenced by
broad labor considerations (e.g., Hamm, Jung, and Lee, 2013).6 A large body of accounting
literature documents how the remuneration of top executives (typically, CEOs and CFOs)
influences firms’ financial reporting quality (e.g., see, Healy and Wahlen, 1999; Kothari, 2001).
Second, the extant literature on how corporate behavior is affected by unemployment insurance
investigates issues ranging from wage setting (for example, see, Abowd and Ashenfelter, 1981;
Hamermesh and Wolfe, 1990; Li, 1986; Topel, 1984) to lay-off decisions (Topel, 1983), and
corporate leverage decisions (Agrawal and Matsa, 2013). In this paper, we argue and find
support for the notion that the generosity of state UI benefits affects corporate financial reporting
decisions as well. Hence, we add to a growing literature about the positive externalities of
unemployment insurance (e.g., Hsu, Matsa, and Melzer, 2014).
The remainder of this paper is organized as follows. Section two discusses the
hypotheses. Section three describes the data and empirical design. Section four presents the
results and robustness tests. Section five concludes.
6
FASB Statement of Financial Accounting Concepts No. 8, OB2 and BC1.10
6
2.
Hypotheses Development
In the United States, the Federal-State Unemployment Insurance Program is an important
safety net that provides temporary income to eligible workers who are unemployed through no
fault of their own.7 Based on the guidelines under federal law, each state administers a separate
unemployment insurance program. State laws determine the eligibility for unemployment
insurance, benefit amounts, and the duration of benefits. Most states fund their programs from
taxation on employers, with three states requiring minimal employee contributions. The taxes
imposed on the firms vary based on past experiences; firms with more worker unemployment
claims in the past pay higher taxes.8 Claims for unemployment insurance benefits are paid by
state governments, which are allowed to tap federal funds after they use up their resources or hit
certain rates of aggregate employment. Benefits are typically based on a percentage of an
individual’s earnings over the recent 52-week period and are limited to a maximum amount
stipulated by the state. Most states allow for a maximum of 26 weeks of benefits.9
Many factors can lead to variation in unemployment insurance benefits across states and
times. Key factors include underlying economic conditions (e.g., higher average wages) and
political forces (e.g., bolstering of political support). The direct effect of unemployment
insurance program is on unemployed workers. Gruber (1997) argues the primary benefit of
unemployment insurance is to smooth consumption during periods of unemployment. In
particular, he argues that pooling unemployment risk through insurance leads to greater
efficiency and provides evidence that consumption would fall significantly in the absence of
unemployment insurance. Other studies have found that unemployment insurance is associated
7
http://workforcesecurity.doleta.gov/unemploy/uifactsheet.asp
See, for example, the determination of unemployment insurance tax rates in Washington state:
http://www.esd.wa.gov/newsandinformation/faq/tax-rate-update-6-10.php
9
See, Agrawal and Matsa (2013) for a more detailed discussion of the institutional background of UI programs in
the United States.
8
7
with workers’ searches for new employment and the durations of their unemployment spells,
labor productivity, savings, and stock market participation (e.g., Feldstein, 1978; Topel and
Welch, 1980; Moffitt, 1985; Meyer, 1990, 1995; Acemoglu and Shimer, 2000; Gormley, Liu,
and Shou, 2010; Engen and Gruber, 2001; Meyer and Mok, 2007).
As for the firms, there is a direct and an indirect effect of unemployment insurance
programs. The direct effect is the taxes that firms pay to fund the program. The indirect effect is
via firms’ consideration of the impact of unemployment insurance on their workers. Topel
(1983) find that firms are more willing to lay off workers when workers are more protected by
unemployment insurance. Agrawal and Matsa (2013) hypothesize that firms will choose
financial policies that decrease the risk of distress and costly layoffs when their workers are less
protected by unemployment insurance because workers will demand higher compensation for
potential job loss. Consistent with their hypothesis, they find that higher unemployment benefits
lead to increased corporate leverage.
2.1
The Relation between Unemployment Insurance and Opacity
Agency theory typically assumes that an agent is risk-averse and is likely to withhold bad
news, especially if the negative consequences of disclosing bad news (e.g., getting fired) is
perceived as being worse than the positive consequences (e.g., promotion) of disclosing good
news (Jensen and Meckling, 1976; Baiman, 1990; Kanodia et al., 1989). While there is a large
stream of literature that has focused on withholding bad news, the literature has also recognized
that agents also have incentives to withhold good news (e.g., Healy, 1995; Jones, 1991; Bartov,
Givoly, and Hayn, 2002; Cheng and Warfield, 2005; Kothari, Shu, and Wysocki, 2009). Reasons
for withholding good news include building up of cookie jar reserves (for hiding bad news in the
8
future) and avoiding political scrutiny. For example, Cheng and Warfield (2005) show that top
managers’ equity incentives lead them to either engage in upwards or downwards earnings
management, depending on the specific conditions facing the managers. Actions to hide bad or
good news typically lead to more opaque financial reports, especially if the actions involve
accruals that tend to reverse over time (Dechow and Dichev, 2002; Hutton et al., 2009).
While the prior literature typically focus on opacity driven by top managers, it is
important to recognize that financial reporting can be thought of as a recapitulation process
where information inputs from lower levels of the firm are progressively aggregated at
divisional, subsidiary, and holding company levels. Concerns over unemployment risk induce
more opaque financial reporting due to actions of both workers and senior management.
There are anecdotal examples of workers engaging in behavior to limit the risk of
unemployment. For example, Progress Rail, a California-based rail car repairing unit of
Caterpillar Inc. is currently under criminal investigation for creating “bogus repairs” to create
billable work. As reported by the Wall Street Journal (Hagerty and Tita, 2014), it is alleged that
workers have resorted to a practice called “green repairs” where parts were intentionally
damaged so that they can be repaired or replaced. It appears that this behavior is not driven by
explicit directions by the management. More interestingly, the fear of unemployment due to low
volume of work seems to have played a role. Rationalizing their actions, one worker told the
Wall Street Journal, “A lot of guys are in fear of losing their jobs because there’s no work in
California” (Hagerty and Tita, 2014).
Apart from work actions, unemployment concerns of workers are likely to affect inputs
into firms’ financial reporting system as well. The reluctance to transmit bad news is a problem
endemic to individuals, either in their daily personal lives or in their role as workers of an
9
organization (Miceli and Near, 1992; Tesser and Rosen, 1975; Keil et al. 2008). Just like top
executives who might have incentives to hide bad news or store good news, workers also face
similar, albeit not identical incentives. In particular, workers concerned about unemployment
have incentives to provide these information inputs in a manner that minimizes their
unemployment risk. For example, members of sales staff may be less forthright about their
assessments of credit worthiness of customers if they perceive provision of true information to
reveal deteriorating business conditions which may lead to layoffs. This in turn would impact
financial reporting through poor provisioning for write-offs. Moreover, many workers, especially
the lower-level workers, might face asymmetric incentives in that they do not benefit much (e.g.,
limited bonus) from reporting extreme good news but might expose themselves to significant
unemployment risk from reporting extreme bad news. In fact, incentives to report smoother
outputs are likely especially high for workers as their risk tolerance is arguably lower than that of
managers due to both lower wealth levels (endowment effect) and lack of high power incentives
(incentive effect). Hence, just like the executives, workers are also likely to have incentives to
create cookie jar reserves to store good news and release them during rainy days.
Unemployment concerns potentially impact managerial decisions on financial reporting
as well because both existing and prospective employees likely use firms’ accounting
information to assess unemployment risk. As higher risk of unemployment leads to higher
compensating wage differentials, firms have incentives to present the prospects of its employees
in a brighter manner. These compensating wage differentials can be quite substantial. For
example, employing conservative assumptions, Agrawal and Matsa (2013) estimate the cost of
compensating wage differentials to be 154 basis points of firm value for a BBB rated firm when
there are no unemployment insurance benefits. Chemmanur et al. (2013) find the incremental
10
labor costs associated with higher unemployment risk due to added leverage to be large enough
to offset tax shield benefits of debt. Hamm, Jung, and Lee (2013) argue that when faced with
strong labor unions, firms have incentives not only to manage earnings downwards to shelter
from unions’ rent seeking, but also to manage earnings upwards to avoid demands for higher
compensating wage differentials due to higher risk of unemployment.
In the United States, workers’ concerns over future unemployment are partially mitigated
by the presence of the unemployment insurance benefits programs, as these programa provide
unemployment income for workers in the event that they are laid off. A simple way of
illustrating the impact of the unemployment insurance program is to rely on the following
equation:
Expected unemployment income (inverse proxy for unemployment concerns)
= Unemployment risk x Unemployment insurance benefits
Eq. (1)
Note that when expected unemployment income is higher, future unemployment
concerns are less. Expected unemployment income is a function of the risk of unemployment and
unemployment insurance benefits. Following Agrawal and Matsa (2013), we use the exogenous
changes in state level unemployment insurance benefits to empirically capture exogenous
changes in unemployment concerns. As previously argued, if greater unemployment concerns
indeed result in more opaque financial reports because workers want to avoid job losses and/or
managers want to avoid paying high wage differentials, we would expect opacity to be
negatively associated with unemployment insurance benefits. Hence, our primary hypothesis is
as follows (alternative form):
Hypothesis H1: “Opacity is negatively associated with unemployment insurance
benefits.”
11
Next we turn to a number of cross sectional tests that provide us with further insights on
the relation between unemployment concerns and financial reporting.
2.2
The Role of Unemployment Risk
As expressed in equation (1), since expected costs of unemployment are increasing in risk
of unemployment, benefits of generous UI programs would be particularly high when the firm is
inherently riskier and therefore exposes its employees to higher levels of unemployment risk. For
example, according to Agrawal and Matsa’s (2013) estimates, presence of UI programs reduce
the costs of compensating wage differential by 269 and 97 basis points of firm value,
respectively, for B and BBB rated firms, but only by 10 and 1 basis point, respectively, for A and
AAA rated firms. Therefore, if unemployment insurance reduces opacity induced by
unemployment concerns, then this reduction should be more pronounced for riskier firms that
benefit more due to UI programs. Accordingly, we posit that;
Hypothesis H2a: “The negative association between opacity and unemployment
insurance benefits is stronger for riskier firms.”
An important feature of the Federal-State Unemployment Insurance Program is
that eligible unemployment insurance claimants have to contact their State
Unemployment Insurance agency as soon as possible after becoming unemployed. The
United States Department of Labor consolidates the claims from the various states and
produces a weekly nationwide jobless claims report that reflects the employment
situation in the United States.10 This report is important for macroeconomic analyses and
the financial markets rely on this report to make projections about government policies
10
These reports are disclosed to the public in the form of news releases and can be found at:
http://www.dol.gov/ui/data.pdf.
12
(Balduzzi, Elton, and Green, 2001). In this report, there is a breakdown of the claims filed
within each state. For states with significant increases or decreases in the claims, there is
a state supplied comment about the underlying reason for the significant changes. More
unemployment insurance claimants is an indicator of relatively higher unemployment risk
for the workers within the state, who might be more inclined to engage in behavior that
leads to more opaque financial reports. It is also an indicator that firms within the state
might have to contribute more towards unemployment insurance. Given that the taxes to
fund unemployment insurance are experienced-based (see earlier discussion), this creates
more incentives for firms to engage in behavior that results in more opaque financial
reporting. Hence, our hypothesis is:
Hypothesis H2b: “The negative association between opacity and the
unemployment insurance benefits is stronger in states with more unemployment
insurance claimants.”
2.3
The Role of Executive Equity Incentives
We expect top executives with greater equity incentives to be more wary of the firms’
costs of unemployment concerns. Due to high equity incentives, the wealth of these managers is
more closely tied to firm value (Hall and Liebman, 2000), and firm value is higher when costs,
including worker’s compensation, are lower. Moreover, prior research on executive equity
incentives suggests that executives have incentives to engage in misreporting when their equity
portfolios make them less averse to firm risk (Armstrong, Larcker, Ormazabal, and Taylor,
2013). Using both regression and matched-sample designs, Armstrong et al. (2013) find that
managers who are more sensitive to changes in equity risk (i.e. higher portfolio vega) are more
13
likely to use discretionary accruals and have earnings restatements, after controlling for the effect
of managers’ sensitivity to changes in equity value (i.e. portfolio delta), which prior studies also
find to be positively associated with misreporting.11
As discussed earlier, workers demand greater compensation when expected
unemployment income is lower and opaque financial reporting has the potential to reduce
unemployment concerns. Hence, to increase firm value, which, in turn, increases the value of
their equity incentives, managers with higher equity incentives, as measured by their portfolio
vega and delta, are more likely to rely on more opaque financial reporting to reduce worker’s
compensation.
Hypothesis H3: “The negative association between opacity and unemployment
insurance benefits is stronger for firms managed by executives with greater equity
incentives.”
2.4
The Role of Trade Unions
Viscusi (1980) argues that since trade unions promote the welfare of inframarginal
workers, the wage received per unit of risk should be greater in unionized contexts. In other
words, the compensation wage differentials should be higher in unionized firms because more
risk-averse employees enjoy greater power in unionized settings. Consistent with this prediction,
Viscusi (1980) finds compensating wage premium for risk of work place non-fatal and fatal
injuries to be substantially higher in unionized contexts. Extending this to employment risk in
terms of variations in hours worked, Moore (1995) finds a significantly larger compensating
wage differential for unionized firms. In fact, Moore (1995) estimates wages in unionized firms
11
See Armstrong et al. (2013) for a review of the extensive body of research examining the association between
managers’ sensitivity to stock price and misreporting.
14
to be about 19% higher than those of non-unionized firms and attribute more than 75% of this
difference to compensating wage differential on employment risk. Relatedly, Hamm, Jung, and
Lee (2013) finds that firms faced with strong labor unions are more likely to have stronger
incentives to manage earnings to project themselves as less risky.
If concerns over unemployment risk are indeed more costly for unionized firms, then
benefits of unemployment insurance programs – including the effect of reducing opacity –
should be greater for firms operating in highly unionized settings. Accordingly, we expect the
negative relation between unemployment insurance and opacity to be stronger for firms
operating more unionized environments.
Hypothesis
H4:
“The
negative
association
between
opacity
and
the
unemployment insurance benefits is stronger for firms operating in more
unionized environments.”
3.
Data and Empirical Framework
Unlike most other countries, the level (generosity) of unemployment insurance benefits in the US
is determined at the state, as opposed to federal government level. Moreover, there are time
series variations in UI benefits at the state level. We use these cross-sectional (across states) and
time-series (within state) variations in UI benefits generosity to test our conjecture of a link
between opacity and unemployment concerns.
3.1
Data
We obtain data on unemployment insurance from the US Department of Labor’s annual
issues of Significant Provisions of State Unemployment Insurance Laws and data on firm
15
financials from Compustat.12 We combine firm-level financial information with state-level
unemployment insurance based on the state in which the firm’s headquarter is located.13 We
begin our sample period in 1991, following prior studies on reporting opacity (Hutton et al.
2009). We exclude firms in financial services and utilities industries (SIC 6000-6999 and SIC
4900-4948). We also exclude firms with insufficient data to calculate our opacity measures and
the control variables. This results in a final sample of 63,186 firm-years over the sample period
from 1991 to 2012.
3.2
Unemployment Insurance Benefits
To analyze the impact of unemployment insurance on financial reporting, we use the
maximum amount of unemployment benefits (Unemployment Insurance) allowed for each state
in a given year, defined as the natural log of the maximum number of weeks that a state provides
benefits to claimants (Max Duration), times the maximum weekly benefit amount (Max Weekly
Benefit). This variable provides a proxy for the total unemployment insurance benefits that a
claimant can receive in a given year and has been shown to impact firms’ financial policies
(Agrawal and Matsa, 2013). Table 1 presents summary statistics on the maximum duration,
maximum weekly benefit, and total benefits for each of the fifty-one states in our sample. While
there is little variation in the maximum number of weeks a worker can claim unemployment
benefits for, the maximum amount of weekly benefits varies significantly, ranging from the
12
http://workforcesecurity.doleta.gov/unemploy/statelaws.asp#sigprouilaws
This matching criterion creates some measurement error with respect to variable of interest if some of the firm’s
workers are located outside of the headquarter-state, since employees are covered by the UI laws of the state in
which they are employed. Following Agrawal and Matsa (2013), we address this issue by reporting results excluding
industries with a dispersed workforce in our main analysis and conducting several cross-sectional analyses..
13
16
lowest of $195.91 in Mississippi to $692.86 in Massachusetts. The maximum total benefit is also
lowest in Mississippi ($5,093.64) and highest in Massachusetts ($20,785.91).
[Insert Table 1]
One might look at these weekly and total numbers and consider them to be small relative
to at least what some workers were used to getting as employment income prior to being laid off.
From a utility (or economic importance) perspective, it is important to note that the utility of a
dollar of employment income is likely to be lower than the utility of a dollar of unemployment
income because of the diminishing marginal utility of income. A key reason is that the earlier
and later dollars are likely to be spent on necessities and luxury items, respectively.
Unemployment insurance provides an important economic lifeline to many who have lost their
jobs. It is also important to note that involuntary unemployment tends to increase sharply during
period of economic crisis when the wealth of many individuals has fallen significantly and there
are few job opportunities. The marginal utility per dollar of income – in particular,
unemployment income – is likely to greater during these periods.
3.3
Financial Reporting Opacity
Accruals-based measures are commonly used to proxy for attempts to hide news
(Dechow, Ge, and Schrand, 2010). In particular, to the extent that abnormal accruals are driven
by attempts to hide bad news or store good news, they are likely to reverse over time. In other
words, positive (negative) abnormal accruals are likely to be succeeded by negative (positive)
abnormal accruals in future periods. Two commonly used accruals-based measures in the
literature rely on such patterns of abnormal accruals over time to capture the opacity of financial
17
reports: the opacity measure in Hutton et al. (2009) and the accruals quality measure in Dechow
and Dichev, 2002). We use these two measures as our measures of financial reporting opacity in
this paper.
Opacity, following Hutton et al. (2009), is the sum of the absolute abnormal accruals
from year t-1 to year t+1, where abnormal accruals are estimated based on the cross-sectional
modified Jones model (Jones, 1991; Dechow et al., 1995). Specifically, we estimate the
following cross-sectional regression in each two-digit SIC industry for each fiscal year.14
TotalAccruals,
1
∆ ,
""#,
= + + !
+ $, #%. (2)
Assets,
,
,
,
Abnormal accruals is then calculated based on the following equation, using the coefficient
estimates from Eq. (2).
AbnormalAccruals,
=
1
∆ , − ∆01234 ,
TotalAccruals,
/
/
− (β
+β
Assets,
,
,
/!
+β
""#,
)#%. (3)15
,
Opacityt = Sum of Abnormal Accrualsi,t-1, Abnormal Accrualsi,t, and Abnormal Accrualsi,t+1 Eq.
(4)
14
We require at least twenty observations for each industry-year regression.
The inclusion of ∆Receivablesi,t modifies the Jones (1991) model to account for the change in sales that is due to
the aggressive recognition of questionable sales (Dechow, Sloan, and Sweeney, 1995). Results remain qualitatively
the same when we simply use the residual in Eq. (2) to measure abnormal accruals.
15
18
As an alternative measure of opaque financial reporting, we use AQ, which is the
standard deviation of three firm-specific residuals from a cross-sectional regression of accruals
on the prior, current, and future cash flow from operations, as well as the changes in revenue and
PP&E (Dechow and Dichev, 2002; McNichols, 2002). Unlike Opacity, which requires three
years of data to compute, AQ requires five years of data. Specifically, to obtain AQ, we follow
Francis et al. (2005) and estimate the following cross-sectional regression for each of the Fama
and French (1997) 48 industry groups with at least 20 firms in fiscal year t.
TCAi ,t = φi0 + φi1CFOi ,t −1 + φi2CFOi ,t + φi3CFOi ,t +1 + φi4 ∆REVi ,t + φi5 PPEi ,t + vi ,t
Eq. (5)
where TCAi,t = ∆CAi,t - ∆CLi,t - ∆Cashi,t + ∆STDebti,t – Depni,t = total current accruals, CFOi,t =
NIBEi,t – TCAi,t = cash flow from operations, NIBEi,t = net income before extraordinary items,
∆CAi,t = change in current assets, ∆CLi,t = change in current liabilities, ∆Cashi,t = change in cash,
∆STDebti,t = change in debt in current liabilities, Depni,t = depreciation and amortization expense,
∆REVi,t = change in revenues, and PPEi,t = gross value of plant, property, and equipment. The
annual cross-sectional regression produces firm-year residuals. For each firm in each fiscal year,
the standard deviation of the residuals for fiscal years t-1 to t+1 is computed. Five years of data
are required to obtain the residuals because of the inclusion of cash flow from operations at t-1
and t+1. As a result, the sample size is smaller (i.e. 43,608 observations) when we use this
measure of reporting opacity in our specifications.
AQt = Standard Deviation of Residualsi,t-1, Residualsi,t, and Residualsi,t+1
3.4
Impact of Unemployment Risk on Reporting Opacity
19
Eq. (6)
We employ the following OLS model to examine the relation between unemployment
insurance benefits and corporate reporting:
Opacityt = b 0 + b1Unemployment Insurancet + b 2Cash Flow Volatilityt + b 3 Sales Volatilityt
+ b 4 Leveraget + b 5 Log Salest + b 6 Market to Bookt + b 7 Return on Assetst
+ b 8 Proportion of Fixed Assetst + b 9 Zscoret + b10Unemployment Ratet
+ b11GDP Growtht + Firm and Year Fixed Effects + e
Eq. (7)
The dependent variable, Opacity, represents one of the two opacity measures defined above. Our
main variable of interest, Unemployment Insurance, is the natural log of maximum
unemployment benefits available to claimants. We control for various firm-level variables that
could potentially influence firms’ decisions to provide opaque financial statements. Hribar and
Nichols (2007) show that it is important to control for operating volatility when one uses
absolute discretionary accruals to test for earnings management, as lack of fit in the discretionary
accruals models increases the value of absolute discretionary accruals and can bias tests in favor
of rejecting the null of no earnings management. As such, we control for both cash flow and
sales volatility, where Cash Flow Volatility and Sales Volatility are the standard deviation of
operating cash flows and revenue, respectively, deflated by assets over the current and prior four
years. We also control for leverage because Agrawal and Matsa (2013) show that firms use more
conservative financing policies when unemployment risk is high. Leverage is total debt divided
by market value. We also include variables that proxy for growth and performance, such as
Sales, Market to Book, and Return on Assets, as they are likely to be determinants of earnings
management. Following Agrawal and Matsa (2013), we also control for the probability of
bankruptcy (Z-score) and the importance of firm collateral (Proportion of Fixed Assets). In
addition, Unemployment Rate and GDP Growth are two state-level variables that capture local
economic conditions that may affect firms’ financial reporting decisions. For example, firms in
20
states with higher GDP growth may have better access to capital and higher investment
efficiency, which is associated with higher financial reporting quality (Biddle, Hilary, and Verdi,
2009). We also include firm fixed effects to ensure that the hypothesized relation is driven by
changes in unemployment insurance over time, rather than any time-invariant firm
characteristics, and year fixed effects to control aggregate macroeconomic conditions.16 We
cluster standard errors by state to correct for potential correlations among firms within the same
state.17
Table 2 reports summary statistics of the variables used in our analyses. Panel A provides
descriptive statistics for the main variables. The mean of Opacity is 0.232 with a standard
deviation of 0.249. For comparison, Hutton et al. (2009) report a mean of 0.243 and a standard
deviation of 0.222 for their sample period from 1991 to 2005. The mean of AQ is 0.046 with a
standard deviation of 0.041. This is also comparable to Biddle et al. (2009), who report a mean
of 0.06 and a standard deviation of 0.05 for their sample period from 1993 to 2005. The mean of
Unemployment Insurance is 9.083, equivalent to 8,804 dollars. Panel B provides Pearson
(Spearman) correlation coefficients among the variables in the upper (lower) diagonal. As
expected, the correlation between Opacity and AQ is positive and significant (p<0.01). The
correlations between Unemployment Insurance and Opacity and AQ are also negative and
significant (p<0.01).
[Insert Table 2]
16
We winsorize all control variables at the 1st and 99th percentiles, except for Proportion of Fixed Assets.
Agrawal and Matsa (2013) argue that it is appropriate to cluster at the state level because the variation in
unemployment insurance benefits is at the state level, and this controls for potential time-varying correlations in
unobserved factors that affect different firms within the same state. They also argue that this also corrects for withinfirm error term correlations over time, so it is more general than firm-level clustering.
17
21
4.
Results
4.1
Test of H1: Relation between Unemployment Insurance Benefits and Financial
Reporting Opacity
Table 3 presents results for tests of our main hypothesis that reporting opacity is
negatively associated with the generosity of state-level UI benefits. Columns (1) and (3) include
all firm-years in sample while columns (2) and (4) report results excluding industries with a
dispersed workforce (i.e. retail, wholesale, and transport). In columns (1) and (3), the negative
coefficients on Unemployment Insurance suggest that an increase in unemployment benefits
reduces reporting opacity (β=-0.031 and β=-0.008, p<0.05 and p<0.01). When we restrict our
sample to industries with less-dispersed workers (Columns (2) and (4)), the results are even
stronger (β=-0.044 and β=-0.012, p<0.01 and p<0.01), further supporting our hypothesis.
[Insert Table 3]
We now examine whether the relation between unemployment insurance and opacity
varies across multiple hypothesized dimensions, to provide further support for our hypothesis
that higher unemployment risk leads to more opaque reporting.
4.2
Test of H2: Effect of Unemployment Risk
Hypothesis 2 predicts that the relation between unemployment insurance and reporting
opacity should be stronger for riskier firms. Firm risk is likely to be higher for firms
experiencing high growth because these firms are likely to have riskier future cash flows
(compared to a firm that has reached a stable state), which, in turn, increases the likelihood of
22
unemployment for current employees of firms that fail to convert its growth opportunities into
long-term positive cash flows. Prior research also suggests that unemployment risk is higher for
firms with difficulty obtaining external funding, as firms respond to these negative shocks by
layoffs (John, Lang, and Netter, 1992; Ofek, 1993). Therefore, workers concerned about their job
security are likely to demand a higher wage premium to compensate for higher unemployment
risk, and managers will prefer to withhold news from its employees to reduce the firm’s overall
unemployment costs. To examine whether firms with higher unemployment risk are more likely
to use opaque financial reports, we group firms based on their growth characteristics, likelihood
of bankruptcy and operating cash flows. Following prior research, we use market-to-book and
the modified Altman’s z-score to proxy for growth and bankruptcy risks, respectively.18
Similarly, firms with low operating cash flows have more difficulty raising external capital due
to liquidity concerns. We separately estimate Eq. (7) for firms with high and low growth, zscores, and cash flow, where firms are stratified at the median of the sample values. We report
the results for Opacity (AQ) in Panel A (B) of Table 4. Consistent with our hypothesis that riskier
firms are more likely to take into account unemployment insurance benefits in their financial
reporting decisions, we only find a significant and negative relation between Unemployment
Insurance and Opacity in the high growth (p<0.01), high bankruptcy risk (p<0.01), and low cash
flow subsamples (p<0.1) in Panel A. The results reported in Panel B using the second measure of
reporting opacity are similar.
[Insert Table 4]
18
The modified Altman’s z-score is estimated using the following equation: 3.3
1.0
<8B7<
?8B8<<7<
+ 1.4
978:7@789::;<
?8B8<<7<
+ 1.2
E?9F:;G8H8B
?8B8<<7<
23
789::;<=7>?97:797<8:@8A7<
?8B8<<7<
+
Next, we examine whether the relation between unemployment insurance and opacity is
affected by changes in unemployment concerns. Unemployment risk for currently employed
workers is higher when the number of individuals collecting payments is high. Using
unemployment insurance payment rates, defined as the number of individuals collecting
unemployment benefits scaled by state populations, as a proxy for high unemployment, we
estimate Eq. (7) separately for firms with high and low UI payment rates based on the sample
median. We obtain data on unemployment collections and state populations from the Current
Population Survey. The results for this analysis are reported in Table 5. Consistent with our
hypothesis, we find that Unemployment Insurance is only significant for the high UI payment
subsamples (β=-0.080 and β=-0.013, p<0.01 and p<0.01).
[Insert Table 5]
4.3
Test of H3: Effect of Top Executives’ Equity Incentives
Since unemployment risk has a negative effect on firm value, managers with incentives
tied to firm value are more likely to be concerned about the indirect impact of unemployment
costs on her compensation. This suggests that the relation between unemployment insurance and
reporting opacity should be stronger for managers with high equity incentives. To test
Hypothesis 3, we group firms based on the pay-performance sensitivity (delta) and risk-taking
incentives (vega) of the firm’s top five executives. Vega and delta are calculated following the
procedures used in Core and Guay (2002). We then separately estimate Eq. (7) for firms with
high and low compensation incentives and report the results in Panel 5. Firms with both vega and
24
delta above (below) the sample median are classified as having high (low) equity incentives.19
Consistent with our hypothesis, we only find a significant and negative relation between
Unemployment Insurance and Opacity in the high equity incentives sub-samples (β=-0.059 and
β=-0.009, p<0.01 and p<0.1).
[Insert Table 6]
4.4
Test of H4: Effect of Trade Unions
To examine the effect of unionized labor on the relation between unemployment
insurance and opacity, we use two proxies that are likely to capture the level of union intensity
(both measured at state level). The first proxy is the percentage of employees who are union
members. The second proxy is the percentage of employees covered by collective bargaining
agreements based on figures reported. We obtain data on union membership from the US Census
Bureau website. We separately estimate Eq. (7) for firms with high and low union membership
and collective bargaining power and report the results in Table 7. Consistent with Hypothesis 4,
we find a significant and negative relation between Unemployment Insurance and Opacity (AQ)
only in the high union intensity sub-samples in Panel A (B). The negative coefficients on
Unemployment Insurance in Panel A (β=-0.038 and β=-0.035, p<0.05 and p<0.05) and Panel B
(β=-0.009 and β=-0.011, p<0.01 and p<0.01) provide support for Hypothesis 4 and suggest that
managers are more likely to conceal unemployment risks through opaque financial reporting as
unemployment insurance benefits decrease for firms with highly unionized employees.
19
If a firm either has high vega and low delta or low delta and high vega, it is not included in the analyses. In other
words, we are comparing firms with high vega and high delta against firms with low vega and low delta. Ex-ante,
the prediction for the mixed combinations is ambiguous.
25
[Insert Table 7]
4.5
Additional Analyses
To provide further identification on the causal relation between unemployment insurance
benefits and reporting opacity, we perform a robustness analysis in this section and control for
unemployment insurance in future years. If poor local economic conditions lead firms to engage
in earnings management, and state governments increase unemployment insurance payments
because of increase tax revenues or political pressure, then we can expect a negative relation
between Opacity and forward values of total benefits. Thus, we estimate the following
regression:
Opacityt = b 0 + b1Unemployment Insurancet + b 2Unemployment Insurancet+1
+ b 3Unemployment Insurancet+2 + b 4Cash Flow Volatilityt + b 5 Sales Volatilityt
+ b 6 Leveraget + b 7 Log Salest + b 8 Market to Bookt + b 9 Return on Assetst
+ b10 Proportion of Fixed Assetst + b11Zscoret + b12Unemployment Ratet
+ b13GDP Growtht + e
Eq. (8)
, where we include Unemployment Insurance measured in year t+1 and year t+2 to Eq. (7). The
results reported in Table 8 shows that there is no relation between forward values of
unemployment insurance and our opacity measures. Log Max Total Benefitt also remains
negative and significant in columns (1) through (4). Overall, this suggests that increases in
unemployment insurance are associated with increases in financial opacity, and not vice versa.
[Insert Table 8]
26
Next, we consider potential nonlinearity between unemployment insurance benefits and
opacity. In particular, we conjecture that that unemployment insurance is likely to have a more
salient effect of financial reporting decisions when there has been a recent increase in
unemployment insurance because of the attention that these increases attracts. To examine the
effect of recent increases in unemployment insurance benefits on the relation between
unemployment insurance benefits and opacity, we create two subsamples, one consisting of firms
in states with an increase in unemployment insurance benefits from the prior year and one
consisting of other firms. Since higher UI reduces unemployment costs for rank-and-file
employees, an increase in UI should lead to a stronger relation between Unemployment
Insurance and Opacity or AQ.
The first (next) two columns in Table 8 present the results with Opacity (AQ) as the
dependent variable. Columns (1) and (2) present the results of the subsample with and without
recent increases in unemployment insurance, respectively. We find a negative and significant
coefficient (β=-0.073 and β=-0.020, p<0.1 and p=0.22) in column (1) but not in column (2),
consistent with a recency effect. However, when reviewing the results of similar analyses with
AQ in Columns (3) and (4), we observe a significant negative coefficient in both cases ((β=-
0.008 and β=-0.008, p<0.1 and p<0.1). Hence, there is inconclusive evidence as to whether a
recent increase in unemployment insurance leads to more financial reporting opacity due to
unemployment concerns using our second measure.
[Insert Table 9]
27
5.
Conclusion
Hiding bad news is a typical human instinct, especially when the negative consequences
of the bad news are significant. It seems intuitive to expect workers to hide bad news if they have
more concerns about unemployment. It also seems intuitive to expect workers to store good news
to facilitate hiding bad news in the future. The labor theory on compensating wage differentials
also suggests that firms have incentives to reduce the actual and/or perceived unemployment risk
of workers because, ex-ante, this would lower the cost of compensating the workers. Given that
corporate reporting is a recapitulation process in which workers provide the inputs that are
consolidated by top executives into a financial report, it is important to examine whether the
unemployment concerns of workers affect financial reporting opacity.
Using exogenous inter-state cross-sectional and intra-state time-series variations in US
unemployment insurance benefits to identify changes in unemployment concerns, we show that
more generous state unemployment insurance benefits, which reduce workers’ concerns about
unemployment risk, result in less opaque financial reporting by firms. This effect of
unemployment insurance benefits on financial reporting opacity is interesting because it is likely
to be an unintended outcome of state-level labor policies. It is hard to imagine that policy
makers, in their deliberations about unemployment insurance policies, had considered financial
reporting consequences (which, in turn, could have other consequences such as crash risk and
cost of capital). To add richness to our study about the effect of unemployment insurance
benefits on financial reporting opacity, we show that relation is stronger when workers face
higher unemployment risk, workers have greater bargaining power, and top executives have
higher equity incentives. These additional results provide further support that incentives have an
important role to play in how unemployment concerns affect financial reporting behaviour.
28
Reverse causality and endogeneity are serious concerns that preclude the researcher from
making strong causal inferences in empirical studies in financial economics. While our study is
not completely devoid of these concerns, we believe that our institutional setting and research
design choices allay them to a great extent. First, since we capture unemployment insurance
benefits at the state level, it is unlikely that reverse causality explains our results. In other words,
it is difficult to conjecture a situation where unemployment insurance benefits at the state level
are affected by financial reporting quality at the firm level.
Second, while it is conceptually plausible to conjecture broader economic factors
affecting both UI benefits and the firms’ accounting quality, thereby raising concerns over
omitted correlated variables, it appears that from a practical stand-point, changes in UI benefit
generosity are driven more by political considerations rather than underlying economics. For
example, in the state of Florida, maximum UI benefits remained constant over the period of
1998-2011 despite notable fluctuations of the economy whereas states such as Connecticut and
Massachusetts increased their UI benefits almost annually during the same period. Lending
support to this notion Agrawal and Matsa (2013) report that in contrast to broader economic
indicators, there are no regional trends in UI benefit generosity. Moreover, in all our analyses we
control for state-level unemployment and GDP growth rates to capture state-wide economic
conditions and employ year fixed effects to control for broad time series trends.20 Third, as
expected, we obtain stronger results by removing industries with more dispersed workforces
from our analyses. Fourth, rendering support for a causal relation, we obtain expected results for
all our cross sectional tests. Fifth, allaying concerns over omitted correlated variables, we do not
find current period opacity to be associated with UI benefits in future years.
20
We also employ firm fixed effects to ensure that results are not driven by time invariant firm factors.
29
How firms’ financial reporting outcomes are shaped by concerns of rank-and-file
members of the workforce is an important, yet under-researched issue in the accounting
literature. Meanwhile, both labor economists and regulators alike are likely interested in the
broader, unintended effects of labor market interventions such as UI programs. In this light, we
believe this paper to be of interest to a broad array of audiences. While our paper indicates a link
between UI benefits and accruals-based financial reporting decisions, it plausible that UI benefits
influence earnings management decisions through real actions (real earnings management) as
well. We leave the exploration of this issue to future research.
30
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36
Table 1: Average Unemployment Insurance Benefits by State 1991-2012
State
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of Columbia
Florida
Georgia
Hawaii
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Max
Weekly
Benefit
204.32
332.36
208.64
330.73
328.18
361.77
474.00
304.32
336.27
264.77
263.64
410.23
291.09
411.82
293.45
343.55
327.45
314.55
227.86
400.55
298.05
692.86
325.05
423.59
195.91
238.64
Max Duration
Unemployment
Insurance
26.00
26.00
26.00
25.95
26.00
26.00
26.00
26.00
26.00
25.86
26.00
26.00
26.00
25.95
26.00
26.00
26.00
26.00
26.00
26.00
26.00
30.00
25.73
26.00
26.00
25.73
5312.27
8641.45
5424.55
8578.14
8532.73
9406.09
12324.00
7912.27
8743.09
6846.59
6854.55
10665.91
7568.36
10682.32
7629.82
8932.18
8513.82
8178.18
5924.45
10414.18
7749.18
20785.91
8352.45
11013.36
5093.64
6117.27
State
Max Weekly
Benefit
Max Duration
Unemployment
Insurance
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Dakota
North Carolina
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Dakota
South Carolina
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
295.77
240.91
298.86
310.14
450.82
306.86
360.45
305.77
378.09
407.50
295.09
378.82
440.05
513.36
229.09
259.73
246.36
313.23
339.55
308.05
281.45
436.18
339.95
307.23
297.05
26.73
26.00
26.00
26.00
26.00
26.00
26.00
26.00
26.00
26.00
26.00
26.00
26.00
26.00
26.00
25.73
26.00
26.00
26.00
26.00
26.00
28.55
26.00
26.00
26.00
7972.82
6263.64
7770.45
8063.55
11721.27
7978.45
9371.82
7950.09
9830.36
10595.00
7672.36
9849.27
11441.18
13347.45
5956.36
6664.00
6405.45
8143.91
8828.18
8009.18
7317.82
12311.45
8838.82
7987.91
7723.18
This table shows the average maximum weekly benefit and maximum number of weeks allowed by state. Unemployment Insurance is the average of the
maximum unemployment benefit allowed.
37
Table 2 Panel A: Summary Statistics
25th Percentile
Median
75th Percentile
0.232
0.046
Standard
Deviation
0.249
0.041
0.101
0.018
0.171
0.033
0.288
0.059
63,186
9.083
0.353
8.799
9.054
9.308
63,186
63,186
63,186
63,186
63,186
63,186
63,186
63,186
63,186
63,186
0.095
0.223
5.143
3.588
0.010
0.008
0.447
1.037
5.947
5.014
0.161
0.233
2.213
24.181
0.241
0.033
0.968
3.981
1.907
2.720
0.034
0.087
3.673
1.175
-0.012
0.000
0.007
0.769
4.629
3.530
0.058
0.155
5.158
1.969
0.067
0.000
0.131
1.859
5.444
5.130
0.103
0.273
6.655
3.397
0.121
0.000
0.438
2.693
6.846
6.910
Dependent Variables
Opacity
AQ
N
Mean
63,186
43,608
Unemployment Insurance Variable
Unemployment Insurance
Control Variables
Cash Flow Volatility
Sales Volatility
Log Sales
Market to Book
Return on Assets
Proportion of Fixed Assets
Leverage
Z-score
Unemployment Rate
GDP Growth
38
Table 2 Panel B: Summary Statistics
(1)
(2)
(3)
(4)
(5)
(6)
('7)
(8)
(9)
(10)
(11)
(12)
(13)
(1) Opacity
1.00
0.49
-0.02
0.25
0.20
-0.30
0.06
-0.32
-0.07
-0.04
-0.27
-0.02
0.01
(2) AQ
0.55
1.00
-0.05
0.31
0.28
-0.36
0.07
-0.33
-0.07
-0.04
-0.26
-0.02
0.03
(3) Unemployment Insurance
-0.05
-0.05
1.00
0.02
-0.07
0.08
0.00
-0.06
-0.19
-0.09
-0.10
0.10
-0.29
(4) Cash Flow Volatility
0.47
0.47
-0.02
1.00
0.24
-0.35
0.07
-0.43
-0.06
-0.08
-0.40
0.00
0.01
(5) Sales Volatility
0.28
0.33
-0.08
0.42
1.00
-0.14
0.03
-0.11
-0.05
0.01
-0.05
-0.05
0.05
(6) Log Sales
-0.39
-0.38
0.10
-0.53
-0.19
1.00
-0.04
0.50
0.02
0.09
0.43
0.03
-0.08
(7) Market to Book
0.04
0.02
0.05
0.09
0.00
0.04
1.00
-0.10
-0.01
-0.03
-0.10
0.01
0.01
(8) Return on Assets
-0.28
-0.29
-0.05
-0.36
-0.13
0.47
0.24
1.00
0.06
0.02
0.75
-0.01
0.02
(9) Proportion of Fixed Assets
-0.09
-0.07
-0.22
-0.11
-0.06
0.03
-0.06
0.07
1.00
0.06
0.07
0.11
0.02
(10) Leverage
-0.13
-0.10
-0.14
-0.25
-0.03
0.26
-0.37
-0.05
0.12
1.00
0.04
-0.05
-0.02
(11) Z-score
-0.23
-0.20
-0.09
-0.28
0.01
0.43
-0.06
0.69
0.11
-0.02
1.00
-0.04
0.03
(12) Unemployment Rate
-0.02
-0.02
0.07
-0.02
-0.04
0.02
-0.01
-0.03
0.18
-0.05
-0.03
1.00
-0.46
(13) GDP Growth
0.02
0.03
-0.30
0.05
0.04
-0.08
0.08
0.04
-0.01
0.01
0.01
-0.41
1.00
This table presents summary statistics of the variables used in the study. Panel A presents descriptive statistics and Panel B presents the Pearson (Spearman)
correlations in the upper (lower) diagonal. Opacity is from Hutton et al. (2009) and is the sum of absolute discretionary accruals from years t-1 to t+1. AQ is
from the Dechow-Dichev (2002) model and is the standard deviation of residuals from firm-specific regressions of working capital on past, present, and future
cash flows from years t-1 to t+1. Unemployment Insurance is the log of maximum total benefits. Cash Flow Volatility is the standard deviation of operating cash
flows. Sales Volatility is the standard deviation of sales. Log Sales is the log of sales. Market to Book is market value of equity divided by book value of equity.
Return on Assets is earnings before extraordinary items scaled by lag total assets. Proportion of Fixed Assets is the PP&E scaled by total assets. Leverage is debt
in current liabilities plus long-term debt scaled by total assets. Z-score is the modified Altman Z-score. Unemployment Rate is state-level unemployment rate.
GDP Growth is state-level growth in GDP.
39
Table 3: Impact of Unemployment Insurance
Unemployment Insurance
Cash Flow Volatility
Sales Volatility
Log Sales
Market to Book
Return on Assets
Proportion of Fixed Assets
Leverage
Z-score
Unemployment Rate
GDP Growth
Observations
R-squared
(1)
Opacity
(2)
Opacity
(3)
AQ
(4)
AQ
-0.031**
(0.014)
0.072***
(0.019)
0.079***
(0.012)
-0.006
(0.005)
0.000*
(0.000)
-0.125***
(0.023)
-0.013
(0.027)
-0.002
(0.002)
0.002
(0.001)
0.001
(0.002)
0.000
(0.001)
-0.044***
(0.013)
0.074***
(0.022)
0.100***
(0.013)
-0.004
(0.004)
0.000*
(0.000)
-0.121***
(0.022)
-0.008
(0.033)
-0.000
(0.002)
0.001
(0.001)
0.001
(0.002)
0.000
(0.001)
-0.008***
(0.003)
0.018**
(0.009)
0.020***
(0.002)
-0.001*
(0.001)
0.000**
(0.000)
-0.023***
(0.004)
0.016**
(0.007)
0.001
(0.000)
-0.000
(0.000)
0.000
(0.000)
0.000**
(0.000)
-0.012***
(0.003)
0.018*
(0.009)
0.022***
(0.003)
-0.001
(0.001)
0.000**
(0.000)
-0.020***
(0.004)
0.013
(0.009)
0.001*
(0.000)
-0.000
(0.000)
0.000
(0.000)
0.000**
(0.000)
Firm and Year Fixed Effects Included
53,614
43,608
0.404
0.501
63,186
0.405
36,735
0.500
This table presents regression results on reporting opacity. Opacity is from Hutton et al. (2009) and is the sum of
absolute discretionary accruals from years t-1 to t+1. AQ is from the Dechow-Dichev (2002) model and is the
standard deviation of residuals from firm-specific regressions of working capital on past, present, and future cash
flows from years t-1 to t+1. Unemployment Insurance is the log of maximum total benefits. Control variables are
defined in Table 2. Industries with a dispersed workforce are excluded in columns (2) and (4). We report coefficient
estimates and standard errors (in parentheses) based on robust standard errors clustered by state. *, **, and ***
indicate statistical significance at the 10%, 5%, and 1% levels (two-tailed), respectively.
40
Table 4 Panel A: Role of Firm Risk
Unemployment Insurance
Cash Flow Volatility
Sales Volatility
Log Sales
Market to Book
Return on Assets
Proportion of Fixed Assets
Leverage
Z-score
Unemployment Rate
GDP Growth
Observations
R-squared
High Growth
Low Growth
Low Z-score
High Z-score
Low Cash Flows
High Cash Flows
(1)
Opacity
(2)
Opacity
(3)
Opacity
(4)
Opacity
(4)
Opacity
(5)
Opacity
-0.051***
(0.015)
0.051**
(0.022)
0.069***
(0.019)
-0.007
(0.005)
0.000
(0.000)
-0.075***
(0.018)
-0.074
(0.046)
-0.002
(0.004)
-0.001
(0.002)
0.001
(0.003)
0.001**
(0.001)
-0.009
(0.017)
0.103***
(0.025)
0.081***
(0.019)
-0.005
(0.004)
0.012**
(0.006)
-0.210***
(0.047)
0.020
(0.035)
0.002
(0.002)
0.006**
(0.003)
-0.000
(0.002)
-0.001
(0.001)
-0.049***
(0.017)
0.043**
(0.017)
0.091***
(0.018)
-0.001
(0.006)
0.000
(0.000)
-0.154***
(0.022)
0.080
(0.066)
-0.001
(0.002)
0.002
(0.001)
0.001
(0.003)
-0.000
(0.001)
-0.012
(0.020)
0.204**
(0.076)
0.058***
(0.016)
-0.018***
(0.004)
0.001**
(0.000)
0.023
(0.031)
-0.033
(0.031)
-0.002
(0.002)
0.005*
(0.003)
0.002
(0.001)
0.001
(0.001)
-0.035*
(0.019)
0.046**
(0.019)
0.066***
(0.013)
-0.001
(0.005)
0.000
(0.000)
-0.110***
(0.026)
-0.004
(0.059)
-0.008***
(0.002)
0.001
(0.002)
0.004
(0.004)
0.001
(0.001)
-0.014
(0.023)
0.195*
(0.105)
0.063***
(0.016)
-0.015***
(0.005)
0.000**
(0.000)
-0.147**
(0.059)
-0.007
(0.025)
0.001
(0.004)
0.002
(0.008)
-0.000
(0.001)
0.000
(0.001)
31,492
0.542
31,694
0.270
31,605
0.519
Firm and Year Fixed Effects Included
31,405
31,781
0.495
0.338
31,581
0.336
41
Table 4 Panel B: Role of Firm Risk
Unemployment Insurance
Cash Flow Volatility
Sales Volatility
Log Sales
Market to Book
Return on Assets
Proportion of Fixed Assets
Leverage
Z-score
Unemployment Rate
GDP Growth
Observations
R-squared
High Growth
Low Growth
Low Z-score
High Z-score
Low Cash Flows
High Cash Flows
(1)
AQ
(2)
AQ
(3)
AQ
(4)
AQ
(5)
AQ
(6)
AQ
-0.011***
(0.003)
0.013*
(0.008)
0.019***
(0.003)
-0.001
(0.001)
0.000*
(0.000)
-0.017***
(0.005)
0.007
(0.014)
0.002**
(0.001)
-0.000
(0.000)
-0.000
(0.000)
-0.000
(0.000)
-0.004
(0.003)
0.036***
(0.009)
0.018***
(0.002)
-0.000
(0.001)
0.001
(0.001)
-0.032***
(0.005)
0.019**
(0.009)
0.000
(0.000)
-0.000
(0.000)
0.000
(0.001)
0.000
(0.000)
-0.015***
(0.004)
0.011
(0.010)
0.020***
(0.003)
0.000
(0.001)
0.000*
(0.000)
-0.017***
(0.004)
0.014
(0.021)
-0.000
(0.000)
-0.000
(0.000)
-0.000
(0.001)
0.000
(0.000)
-0.004
(0.003)
0.057***
(0.018)
0.016***
(0.002)
-0.002**
(0.001)
0.000
(0.000)
-0.029***
(0.007)
0.024***
(0.008)
0.001**
(0.001)
0.001
(0.001)
0.000
(0.000)
0.000*
(0.000)
-0.010***
(0.004)
0.011
(0.010)
0.017***
(0.003)
-0.001
(0.001)
0.000
(0.000)
-0.020***
(0.004)
0.015
(0.014)
0.000
(0.000)
-0.000
(0.000)
0.000
(0.001)
0.000
(0.000)
-0.008**
(0.004)
0.033**
(0.013)
0.021***
(0.003)
-0.003**
(0.001)
0.000
(0.000)
-0.025***
(0.005)
0.018**
(0.007)
0.000
(0.001)
0.001
(0.000)
0.000
(0.000)
0.000
(0.000)
20,060
0.474
23,548
0.504
22,202
0.545
Firm and Year Fixed Effects Included
19,633
23,975
0.508
0.463
21,406
0.486
42
This table presents regression results on firm risk. Panel A (B) presents results for Opacity (AQ). Opacity is from Hutton et al. (2009) and is the sum of absolute
discretionary accruals from years t-1 to t+1. AQ is from the Dechow-Dichev (2002) model and is the standard deviation of residuals from firm-specific
regressions of working capital on past, present, and future cash flows from years t-1to t+1. Unemployment Insurance is the log of maximum total benefits. We
split the sample into high and low growth, z-score, and cash flows based on the sample median of Market to Book, Z-score, and Operating Cash Flows. Control
variables are defined in Table 2. We report coefficient estimates and standard errors (in parentheses) based on robust standard errors clustered by state. *, **, and
*** indicate statistical significance at the 10%, 5%, and 1% levels (two-tailed), respectively.
43
Table 5: Impact of the Level of Unemployment Insurance Claimants within State
Unemployment Insurance
Cash Flow Volatility
Sales Volatility
Log Sales
Market to Book
Return on Assets
Proportion of Fixed Assets
Leverage
Z-score
Unemployment Rate
GDP Growth
Observations
R-squared
High UI Payment
Rates
Low UI Payment
Rates
High UI Payment
Rates
Low UI Payment
Rates
(1)
Opacity
(2)
Opacity
(3)
AQ
(4)
AQ
-0.080***
(0.015)
0.072**
(0.028)
0.072***
(0.010)
0.005
(0.004)
0.000***
(0.000)
-0.129**
(0.049)
0.000
(0.000)
0.003
(0.007)
0.001
(0.002)
-0.000
(0.002)
-0.003
(0.002)
0.015
(0.026)
0.058*
(0.033)
0.107***
(0.024)
-0.008
(0.008)
0.000*
(0.000)
-0.148***
(0.024)
-0.027
(0.346)
-0.002
(0.001)
0.004*
(0.002)
0.000
(0.003)
0.001
(0.001)
-0.013***
(0.004)
0.033***
(0.006)
0.019***
(0.003)
-0.000
(0.001)
0.000***
(0.000)
-0.017**
(0.008)
0.000
(0.000)
0.000
(0.001)
-0.000
(0.001)
0.001
(0.001)
0.000
(0.000)
-0.004
(0.005)
0.009
(0.013)
0.018***
(0.003)
-0.000
(0.001)
0.000
(0.000)
-0.021***
(0.006)
-0.278***
(0.015)
0.000
(0.001)
-0.000
(0.000)
-0.000
(0.001)
0.000
(0.000)
24,987
0.230
Firm and Year Fixed Effects Included
23,508
17,008
0.534
0.511
15,628
0.512
This table presents regression results on changes in unemployment risk. Opacity is from Hutton et al. (2009) and is
the sum of absolute discretionary accruals from years t-1 to t+1. AQ is from the Dechow-Dichev (2002) model and is
the standard deviation of residuals from firm-specific regressions of working capital on past, present, and future cash
flows from years t-1 to t+1. Unemployment Insurance is the log of maximum total benefits. We split the sample into
high and low UI payments based on the sample median of UI payment rates. UI payment is the number of
unemployment insurance claims scaled by state population. Control variables are defined in Table 2. We report
coefficient estimates and standard errors (in parentheses) based on robust standard errors clustered by state. *, **, and
*** indicate statistical significance at the 10%, 5%, and 1% levels (two-tailed), respectively.
44
Table 6: Role of Executive Equity Incentives
Unemployment Insurance
Cash Flow Volatility
Sales Volatility
Log Sales
Market to Book
Return on Assets
Proportion of Fixed Assets
Leverage
Z-score
Unemployment Rate
GDP Growth
Observations
R-squared
High Equity
Incentives
Low Equity
Incentives
High Equity
Incentives
Low Equity
Incentives
(1)
Opacity
(2)
Opacity
(3)
AQ
(4)
AQ
-0.059***
(0.021)
0.456***
(0.123)
0.039**
(0.015)
0.001
(0.010)
0.000**
(0.000)
-0.135
(0.097)
0.147
(0.132)
-0.004
(0.010)
-0.000
(0.001)
-0.000
(0.002)
-0.000
(0.001)
-0.019
(0.040)
0.332***
(0.070)
0.063***
(0.016)
-0.010**
(0.005)
0.001
(0.001)
-0.170***
(0.033)
0.001
(0.074)
0.000
(0.003)
-0.001
(0.005)
-0.003
(0.002)
0.001
(0.001)
-0.009*
(0.006)
0.060***
(0.020)
0.015**
(0.006)
0.001
(0.002)
0.000
(0.000)
-0.028***
(0.007)
0.002
(0.019)
0.004
(0.002)
0.002***
(0.000)
-0.001
(0.000)
0.000
(0.000)
-0.013
(0.009)
0.047***
(0.014)
0.020***
(0.005)
-0.000
(0.001)
0.000
(0.000)
-0.023***
(0.005)
0.014
(0.014)
0.001
(0.001)
-0.000
(0.000)
-0.000
(0.001)
0.000***
(0.000)
7,764
0.526
Firm and Year Fixed Effects Included
13,908
6,253
0.465
0.416
10,552
0.446
This table presents regression results on executive equity incentives. Opacity is from Hutton et al. (2009) and is the
sum of absolute discretionary accruals from years t-1 to t+1. AQ is from the Dechow-Dichev (2002) model and is
the standard deviation of residuals from firm-specific regressions of working capital on past, present, and future cash
flows from years t-1 to t+1. Unemployment Insurance is the log of maximum total benefits. We split the sample into
high and low equity incentives, where equity incentives is defined as the sensitivity to stock price (i.e. delta) and
stock volatility (i.e. vega) for the firm’s top five executives. The high equity incentives sample includes firm-years
with vega and delta above the median. Firm-years with delta and vega below the sample median are classified as
low equity incentives. Control variables are defined in Table 2. We report coefficient estimates and standard errors
(in parentheses) based on robust standard errors clustered by state. *, **, and *** indicate statistical significance at
the 10%, 5%, and 1% levels (two-tailed), respectively.
45
Table 7 Panel A: Role of Labor Unions
Unemployment Insurance
Cash Flow Volatility
Sales Volatility
Log Sales
Market to Book
Return on Assets
Proportion of Fixed Assets
Leverage
Z-score
Unemployment Rate
GDP Growth
Observations
R-squared
High Union
Membership
(1)
Opacity
Low Union
Membership
(2)
Opacity
-0.038**
(0.016)
0.079***
(0.019)
0.068***
(0.011)
0.000
(0.004)
0.000
(0.000)
-0.098***
(0.034)
0.001
(0.033)
-0.003*
(0.002)
-0.002
(0.002)
-0.000
(0.002)
-0.001
(0.001)
-0.009
(0.024)
0.067*
(0.035)
0.085***
(0.025)
-0.011
(0.007)
0.000**
(0.000)
-0.140***
(0.023)
-0.033
(0.048)
-0.002*
(0.001)
0.003
(0.002)
0.002
(0.002)
0.001*
(0.001)
32,238
0.361
High Collective
Low Collective
Bargaining Power Bargaining Power
(3)
(4)
Opacity
Opacity
-0.035**
(0.015)
0.072***
(0.017)
0.067***
(0.012)
-0.002
(0.003)
0.000
(0.000)
-0.083***
(0.029)
0.006
(0.033)
-0.003*
(0.002)
-0.003*
(0.001)
-0.001
(0.002)
-0.001
(0.001)
Firm and Year Fixed Effects Included
30,948
31,590
0.513
0.348
46
-0.032
(0.022)
0.080*
(0.040)
0.083***
(0.024)
-0.009
(0.008)
0.000**
(0.000)
-0.170***
(0.032)
-0.027
(0.048)
-0.003**
(0.001)
0.005***
(0.002)
0.002
(0.003)
0.001
(0.001)
31,596
0.518
Table 7 Panel B: Role of Labor Unions
Unemployment Insurance
Cash Flow Volatility
Sales Volatility
Log Sales
Market to Book
Return on Assets
Proportion of Fixed Assets
Leverage
Z-score
Unemployment Rate
High Union
Membership
Low Union
Membership
High Collective
Bargaining Power
Low Collective
Bargaining Power
(1)
AQ
(2)
AQ
(3)
AQ
(4)
AQ
-0.009***
(0.003)
0.012
(0.011)
0.022***
(0.003)
-0.001
(0.001)
0.000
(0.000)
-0.027***
(0.005)
0.028***
(0.008)
0.000
(0.001)
-0.000
(0.000)
-0.000
(0.000)
-0.002
(0.005)
0.025***
(0.005)
0.016***
(0.002)
-0.001
(0.001)
0.000***
(0.000)
-0.015**
(0.007)
-0.006
(0.018)
0.001
(0.001)
-0.000
(0.000)
0.000
(0.001)
0.000
(0.000)
-0.011***
(0.003)
0.010
(0.012)
0.022***
(0.003)
-0.001
(0.001)
0.000
(0.000)
-0.028***
(0.005)
0.028***
(0.008)
0.000
(0.000)
-0.000
(0.000)
-0.000
(0.000)
0.000
(0.000)
-0.003
(0.003)
0.027***
(0.006)
0.016***
(0.002)
-0.000
(0.001)
0.000
(0.000)
-0.017***
(0.005)
-0.005
(0.018)
0.001
(0.001)
-0.000
(0.000)
0.000
(0.000)
0.000
(0.000)
GDP Growth
Observations
R-squared
23,021
0.519
Firm and Year Fixed Effects Included
20,587
22,602
0.508
0.519
21,006
0.503
This table presents regression results on union intensity. Panel A (B) presents results for Opacity (AQ). Opacity is
from Hutton et al. (2009) and is the sum of absolute discretionary accruals from years t-1 to t+1. AQ is from the
Dechow-Dichev (2002) model and is the standard deviation of residuals from firm-specific regressions of working
capital on past, present, and future cash flows from years t-1 to t+1. Unemployment Insurance is the log of
maximum total benefits. We split the sample into high and low union membership and collective bargaining power
based on the sample median of union membership and employee bargaining power. Union membership is the
percentage of employees that are union members. Collective bargaining power is the percentage of employees
covered by collective bargaining agreements. Control variables are defined in Table 2. We report coefficient
estimates and standard errors (in parentheses) based on robust standard errors clustered by state. *, **, and ***
indicate statistical significance at the 10%, 5%, and 1% levels (two-tailed), respectively.
47
Table 8: Controlling for Future Unemployment Insurance
Unemployment Insurance (t)
Unemployment Insurance (t+1)
Unemployment Insurance (t+2)
Cash Flow Volatility
Sales Volatility
Log Sales
Market to Book
Return on Assets
Proportion of Fixed Assets
Leverage
Z-score
Unemployment Rate
GDP Growth
Observations
R-squared
(1)
Opacity
(2)
Opacity
(3)
AQ
(4)
AQ
-0.055**
(0.021)
0.029
(0.020)
-0.005
(0.009)
0.073***
(0.018)
0.078***
(0.012)
-0.007
(0.005)
0.000
(0.000)
-0.122***
(0.021)
-0.010
(0.027)
-0.002
(0.002)
0.002
(0.001)
0.002
(0.002)
0.000
(0.001)
-0.063***
(0.017)
0.023
(0.022)
-0.006
(0.009)
0.075***
(0.022)
0.098***
(0.013)
-0.004
(0.004)
0.000
(0.000)
-0.118***
(0.021)
-0.005
(0.033)
-0.000
(0.002)
0.001
(0.001)
0.001
(0.002)
0.000
(0.001)
-0.007**
(0.003)
-0.002
(0.005)
0.001
(0.002)
0.018**
(0.009)
0.020***
(0.002)
-0.001*
(0.001)
0.000**
(0.000)
-0.023***
(0.003)
0.016**
(0.007)
0.001
(0.000)
-0.000
(0.000)
0.000
(0.000)
0.000*
(0.000)
-0.010***
(0.003)
-0.003
(0.006)
0.001
(0.002)
0.018*
(0.009)
0.022***
(0.003)
-0.001
(0.001)
0.000**
(0.000)
-0.020***
(0.004)
0.013
(0.009)
0.001*
(0.000)
-0.000
(0.000)
0.000
(0.000)
0.000**
(0.000)
61,196
0.387
Firm and Year Fixed Effects Included
51,880
43,608
0.378
0.500
36,735
0.499
This table presents regression results controlling for future unemployment benefits. Opacity is from Hutton et al.
(2009) and is the sum of absolute discretionary accruals from years t-1 to t+1. AQ is from the Dechow-Dichev
(2002) model and is the standard deviation of residuals from firm-specific regressions of working capital on past,
present, and future cash flows from years t-1 to t+1. Unemployment Insurance is the log of maximum total benefits.
Industries with a dispersed workforce are excluded in columns (2) and (4). Control variables are defined in Table 2.
We report coefficient estimates and standard errors (in parentheses) based on robust standard errors clustered by
state. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels (two-tailed), respectively.
48
Table 9: Impact of Recent Increases in Unemployment Insurance
Unemployment Insurance
Cash Flow Volatility
Sales Volatility
Log Sales
Market to Book
Return on Assets
Proportion of Fixed Assets
Leverage
Z-score
Unemployment Rate
GDP Growth
Observations
R-squared
Increase
No Increase
Increase
No Increase
(1)
Opacity
(2)
Opacity
(3)
AQ
(4)
AQ
-0.073*
(0.043)
0.043*
(0.025)
0.070***
(0.013)
-0.009*
(0.005)
0.000
(0.000)
-0.112***
(0.036)
-0.002
(0.038)
-0.001
(0.003)
0.001
(0.003)
-0.001
(0.003)
0.001
(0.001)
-0.020
(0.016)
0.129***
(0.027)
0.086***
(0.021)
-0.007
(0.006)
0.000
(0.000)
-0.122***
(0.020)
-0.016
(0.033)
-0.002
(0.002)
0.004**
(0.001)
0.001
(0.003)
-0.000
(0.001)
-0.008*
(0.005)
0.017**
(0.006)
0.019***
(0.003)
-0.001
(0.001)
0.000***
(0.000)
-0.019***
(0.006)
0.012
(0.007)
0.000
(0.001)
-0.000
(0.000)
-0.000
(0.001)
0.000**
(0.000)
-0.008*
(0.004)
0.017
(0.017)
0.023***
(0.004)
-0.002**
(0.001)
0.000***
(0.000)
-0.025***
(0.005)
0.006
(0.016)
0.001*
(0.001)
0.000
(0.000)
-0.000
(0.001)
0.000
(0.000)
35,518
0.365
Firm and Year Fixed Effects Included
27,668
24,924
0.518
0.525
18,684
0.500
This table presents regression results on changes unemployment insurance. Opacity is from Hutton et al. (2009) and
is the sum of absolute discretionary accruals from years t-1 to t+1. AQ is from the Dechow-Dichev (2002) model
and is the standard deviation of residuals from firm-specific regressions of working capital on past, present, and
future cash flows from years t-1 to t+1. Unemployment Insurance is the log of maximum total benefits. We split the
sample based on whether unemployment insurance increased from the prior year. Control variables are defined in
Table 2. We report coefficient estimates and standard errors (in parentheses) based on robust standard errors
clustered by state. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels (two-tailed),
respectively.
49