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Policy Uncertainty and Earnings Management*
Sadok El Ghoul
Campus Saint-Jean, University of Alberta
8406, Rue Marie-Anne-Gaboury (91 Street), Edmonton, AB T6C 4G9, Canada
Omrane Guedhami
Moore School of Business, University of South Carolina
1014 Greene Street, Columbia, SC 29208, U.S.A.
Yongtae Kim
Leavey School of Business, Santa Clara University
500 El Camino Real, Santa Clara, CA 95053, U.S.A.
Hyo Jin Yoon
Moore School of Business, University of South Carolina
1014 Greene Street, Columbia, SC 29208, U.S.A.
* The authors thank Amanda Badger, Ye Cai, Chuck Kwok, Siqi Li, Mujtaba Mian, Carrie Pan,
and Marc Van Essen for their helpful comments and suggestions. Sadok El Ghoul acknowledges
financial support from Canada’s Social Sciences and Humanities Research Council. Yongtae Kim
acknowledges financial support from the Robert and Barbara McCullough Family Chair
Professorship.
Policy Uncertainty and Earnings Management
This version: January 2018
Abstract
We examine how policy-induced economic uncertainty affects earnings management. While
uncertainty may make it easier for managers to conceal earnings management, more intense
scrutiny during turbulent times can limit their ability to manage earnings. Using a sample of 27,888
unique firms from 19 countries over the 1990–2015 period, we find that firms engage in less
earnings management as policy uncertainty increases. Strong institutions allow market participants
to monitor management more effectively, and managers have greater incentives to meet investor
demand for transparency when they need to access external capital. Thus, we predict the negative
relation between policy uncertainty and earnings management to be more pronounced in countries
with strong institutions and when the firm has a greater need for external capital. The results are
consistent with these predictions. We find that the negative relation between policy uncertainty
and earnings management is more pronounced for firms in countries with stronger legal institutions,
a stronger reporting environment, and greater press freedom. The results are also more pronounced
for firms in industries with higher growth opportunities and firms with a greater need for external
capital.
Key words: policy uncertainty, earnings management, monitoring, corporate governance, country-
level institutions, press freedom
1
1. Introduction
Earnings management occurs when managers use judgment in financial reporting
and in structuring transactions to alter financial reports to either mislead some
stakeholders about the underlying economic performance of the company or to
influence contractual outcomes that depend on reported accounting numbers
(Healy and Wahlen 1999).
Research on earnings management dates back at least to the early work on income
smoothing (e.g., Ronen and Sadan 1975) and the introduction of positive accounting theory
(Watts and Zimmerman 1978). Since then, for more than four decades, the literature has
examined various determinants of earnings management, including firm characteristics,
corporate governance, internal controls, auditors, and capital market incentives (Dechow, Ge,
and Schrand 2010). While a survey of practitioners identifies industry- and economy-wide factors
as important determinants of earnings quality (Dichev, Graham, Harvey, and Rajgopal 2013),
relatively few studies examine how changes in macroeconomic conditions affect earnings
management.
Liu and Ryan (2006) find that banks managed earnings upward during the pre-1990 bust
period and accelerated provisions for loan losses to manage income downward during the 1990s
boom. Aboody, Barth, and Kasnik (1999) further find that upward revaluations of fixed assets by
UK firms show an upward trend before 1990 but a downward trend after 1990 and that such a shift
coincides with increased volatility in UK economic conditions in the 1990s. While these studies
provide some evidence on the relation between economic cycle and earnings management, notably
absent in the literature is evidence on how changes in macroeconomic conditions caused by policy
decisions and regulatory outcomes affect managerial incentives for earnings management and their
ability to mislead users of accounting information. Pointing to a lack of research on how
macroeconomic conditions influence earnings quality, Dechow et al. (2010) call for more research
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in this area. In particular, they encourage research that examines how firms behave during periods
of regulatory scrutiny. Our study answers this call and makes an early attempt to fill this gap in
the literature by examining the relation between policy uncertainty and earnings management in a
cross-country setting.
Fiscal, regulatory, and monetary policies influence economic activities (Federal Open
Market Committee 2009; IMF 2012, 2013), and hence uncertainty around these policies is
detrimental to the economy (Friedman 1968; Rodrik 1991; Higgs 1997; Hassett and Metcalf 1999).
Uncertainty around healthcare, tax, and environmental policies also influences business activities,
as does uncertainty related to noneconomic policy matters such as military actions and national
security policies (Baker, Bloom, and Davis 2016). Investors and firms adjust their actions when
they face a significant amount of uncertainty regarding the timing, content, and impact of policy
decisions by politicians and regulators. Concerns about policy uncertainty have intensified in
recent years in the wake of rising political polarization and the changing economic role of the
government.
Policy-induced economic uncertainty is different from firm-level uncertainty. Firm-
specific uncertainty may arise from factors unique to a firm such as new product development,
acquisitions, and management turnover, and is thus diversifiable. Macroeconomic uncertainty
affects a board range of firms, and hence is relatively difficult to diversify and largely stems from
factors beyond managers’ control such as oil-price shocks, terrorist attacks, a subprime mortgage
crisis, and policy and regulatory changes. Recent studies pay special attention to uncertainty
attributable to economic policy. At the macro level, studies find that policy uncertainty influences
capital flows, the business cycle, and the speed of economic recovery (Bloom, Floetotto,
Kaimovich, Sapoera-Eksten, and Terry 2012; Baker et al. 2016; Julio and Yook 2016). Studies
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that examine how policy uncertainty impacts firm-level decisions, however, is still in its infancy.1
Taking advantage of the aggregate policy uncertainty index developed by Baker et al. (2016), we
examine how policy uncertainty relates to managerial incentives to engage in earnings
management.
On the one hand, a high level of uncertainty may make it easier for managers to conceal
earnings management because investors and other market participants may be distracted by
unpredictable policy changes. On the other hand, more intense scrutiny from investors, the media,
and regulators during turbulent times may limit managers’ ability to manage earnings without
getting caught. Theoretical work suggests that as uncertainty rises, economic agents take
precautionary actions to protect themselves (Mitton 2002; Boubakri, Guedhami, and Mishra 2010).
For instance, outside investors become more prudent and demand greater transparency (Mitton
2002). Managers must respond to this demand, especially when they need to access external capital.
Which of these two effects dominates is an open empirical question.
Using 243,554 firm-year observations representing 27,888 unique firms from 19 countries
over the 1990–2015 period, we examine the relation between policy uncertainty and earnings
management. Our regressions include both firm and time fixed effects to control for unobservable
heterogeneity across firms and time. Controlling for factors previously shown to affect earnings
management, we find that firms reduce earnings management as policy uncertainty rises. Our
results are robust to controlling for the level of capital investment, suggesting that our evidence is
not driven by a decline in investment under increased policy uncertainty (Gulen and Ion 2016).
Our findings also remain when we control for the confounding effects of macroeconomic
1 Notable contributions to this literature include Gulen and Ion (2016), who estimate the effect of policy uncertainty
on corporate investments, and Bonaime, Gulen, and Ion (2017), who relate policy uncertainty to merger and
acquisition activity at the macro and firm levels.
4
conditions and other types of uncertainty. Our results are not limited to accrual-based earnings
management. We find that firms also reduce real earnings management when policy uncertainty
increases.
Julio and Yook (2012) show that investment drops significantly during election years.
While elections may be good exogenous indicators of heightened uncertainty, studies based on
election indicators implicitly assume that policy uncertainty does not change during nonelection
years (Gulen and Ion 2016). In contrast, a study based on the aggregate policy uncertainty index
of Baker et al. (2016) does not need to make such an assumption because the index is a continuous
variable and available for all years. We find that the relation between election indicators and
earnings management is statistically insignificant. More importantly, even after controlling for the
effect of elections, the negative relation between policy uncertainty and earnings management
remains significant.
To address the potential endogeneity arising from omitted correlated variables, we
implement a two-stage instrumental variables analysis. We use political fractionalization as our
instrument. Aghion, Alesina, and Trebbi (2004) find that legislative actions are often blocked in
countries with high political fractionalization. While policy uncertainty is high in countries with
high political fractionalization, the instrumented policy uncertainty variable is negatively and
significantly associated with the level of earnings management. Our results are therefore robust to
addressing endogeneity using the instrumental variables approach.
Our cross-country setting also allows us to study the mechanisms through which policy
uncertainty influences financial reporting quality. In particular, we conduct a cross-sectional
analysis based on country-level characteristics and examine whether country-level institutions,
reporting environment, and freedom of the press influence the relation between policy uncertainty
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and earnings management. In the absence of strong institutions and legal enforcement, a firm’s
stakeholders are limited in their ability to scrutinize the firm’s information quality. A more
transparent reporting environment also helps market participants monitor financial reporting
quality. Thus, the relation between policy uncertainty and earnings management should be more
pronounced in countries with stronger institutions and a more transparent reporting environment.
Consistent with this prediction, we find that the negative relation between policy uncertainty and
earnings management is more pronounced in countries with stronger legal institutions (as proxied
by the anti–self-dealing index, the level of securities regulation, and the strength of public
enforcement) and greater reporting transparency (as captured by the degree of country-level
opacity and the quality of accounting standards). When freedom of the press is limited, the role of
media scrutiny in limiting managerial incentives to engage in earnings management is relatively
weak. The relation between policy uncertainty and earnings management should thus be less
pronounced in countries with low press freedom. The empirical results support this prediction.
We further examine whether the need for accessing external capital has implications for
the relation between policy uncertainty and financial reporting quality. Firms with more growth
opportunities need more external capital (Gopalan and Jayaraman 2012). These firms have
incentives to improve transparency to lower their cost of capital. If policy uncertainty increases
investor scrutiny, then firms with a greater need for external capital are more likely to respond to
investor demand for higher quality earnings. Using an industry-level measure of growth
opportunities (Gopalan and Jayarman 2012) and Rajan and Zingales’s (1998) measure of external
finance dependence, we find that the negative relation between policy uncertainty and earnings
management is more pronounced for firms with more growth opportunities and for firms with
greater external financing needs.
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Our study makes two important contributions to the literature. First, we contribute to the
earnings management literature by providing initial evidence on how policy uncertainty, an
important macroeconomic factor, affects financial reporting quality. Our study differs from earlier
studies that focus on the effect of firm-level uncertainty on earnings quality. Graham, Harvey, and
Rajgopal (2005) note that firms guide analysts to the earnings per share target but missing
previously guided targets breeds uncertainty about firms’ future prospects. The survey evidence
suggests that managers engage in earnings management or income smoothing to avoid uncertainty
arising from missing the earnings benchmarks. Stein and Wang (2016) find that firms report more
negative discretionary accruals when firm-level uncertainty, measured by option implied volatility,
standard deviation of analyst forecasts, and standard deviation of stock returns, is high. They
reason that managers shift earnings from uncertain to more certain times because stock price
responses to earnings surprises are moderated in the period of high uncertainty (Imhof and Lobo
1992; Kinney, Burgstahler, and Martin 2002). The effect of policy-induced economic uncertainty
on managerial incentives to manage earnings cannot be easily inferred from the relation between
firm-level uncertainty and earnings management because policy uncertainty persists over a longer
horizon and is more difficult to diversify relative to firm-level uncertainty (Bonaime et al. 2017).
Our study also differs from studies that examine other types of macroeconomic uncertainty (e.g.,
Kim, Pandit, and Wasley, 2016) because we focus on political and regulatory systems as particular
sources of aggregate uncertainty, while accounting for the effects of other sources of economic
uncertainty.
Second, our study adds to the growing literature on the effect of policy uncertainty (e.g.,
Gulen and Ion 2016; Bonaime et al. 2017) by studying the relation between policy uncertainty and
earnings management. The results suggest that increased public scrutiny during periods of high
7
policy uncertainty limits opportunities for earnings management.
The rest of the paper is organized as follows. In Section 2, we develop our main hypothesis.
In Section 3, we discuss the data, sample and variable construction, and descriptive statistics.
Section 4 presents our empirical analysis. Finally, in Section 5, we conclude the paper.
2. Hypothesis Development
Government actions and policies shape the contractual environment in which firms operate,
which in turn affects corporate performance as well as financial and operating decisions. A growing
literature documents the economic consequences of policy-induced uncertainty. Following the
financial crisis of 2008, for instance, confusion due to uncertainty about the fiscal, regulatory, and
monetary policies of governments was a major cause of the sluggish recovery. At the macro level,
policy uncertainty hinders economic recovery (Bloom 2014). At the industry level, local and global
political risks affect return volatility (Boutchkova, Doshi, Durnev, and Molchanov 2012). And at
the firm level, policy uncertainty is associated with a higher cost of corporate debt (Waisman, Ye,
and Zhu 2015), lower stock prices (Pastor and Veronesi 2012), and a decrease in bank credit growth
(Bordo, Duca, and Koch 2016) and liquidity creation (Berger, Guedhami, Kim, and Li 2017).
In response to the various consequences of uncertainty, managers become more prudent in
making decisions. For example, firms reduce investment expenditures and increase cash holdings
(Julio and Yook 2012), reduce capital investment (Gulen and Ion 2016), avoid mergers and
acquisitions (Bonaime et al. 2017; Nguyen and Phan 2017), and cut back on hiring (Hansen,
Sargent, and Tallarini 1999; Ilut and Schneider 2014). Other economic agents also become more
wary. Consumers, for instance, increase precautionary savings (Bansal and Yaron 2004), while
other market participants, such as investors, creditors, auditors, and the media, evaluate firm
8
performance more closely and place more emphasis on corporate governance (e.g., Mitton 2002).
As scrutiny of firms rises due to increased uncertainty, earnings management entails
increased costs (McInnis and Collins 2011). In line with this view, Boubakri et al. (2010) show
that following the Asian financial crisis, investors recognized and priced the risk of expropriation
by corporate insiders. The increased costs associated with expropriation lead managers to decrease
the extent to which they seek to extract rents through earnings management (Bagnoli and Watts
2000). Thus, managers are likely to refrain from engaging in earnings management during
turbulent times with greater policy uncertainty.
A high level of uncertainty, however, may provide an opportunity for managers to conceal
earnings management because market participants can be distracted by unpredictable policy
changes. When policy-induced economic uncertainty makes it more difficult for market
participants to predict future prospects of a firm, information asymmetry between managers and
market participants is likely to be greater. Schipper (1989) argues that the absence of full
communication, together with asymmetric information, makes it possible for managers to manage
earnings. Thus, a high level of policy uncertainty may lead to more earnings management. We
therefore present our hypothesis in a null form and turn to data to find out which of the two effects
dominates and leads to a positive or negative relation between policy uncertainty and earnings
management.
H1: Earnings management is unrelated to policy uncertainty.
3. Data, Variables, and Descriptive Statistics
3.1. Data and Sample Construction
We first obtain financial data for all firms in Compustat North America and Compustat
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Global. We then merge Compustat data with the policy uncertainty index from Baker et al. (2016)
(henceforth BBD), which covers 19 countries.2 We exclude firms in financial industries (SIC codes
6000-6999) because the operating decisions of financial firms differ significantly from those of
nonfinancial firms and the nature of their accruals differs from that of industrial firms. We also
omit firm-years if the SIC code or data necessary for our empirical analyses are missing. To
mitigate the influence of outliers, we winsorize all continuous variables at the 1st and 99th
percentiles. Our final sample consists of 243,554 firm-year observations representing 27,888
unique firms from 19 countries over the 1990–2015 period.3
We obtain proxies for macroeconomic conditions, including the real GDP growth rate, real
GDP growth forecasts, the consumer confidence index, and composite leading indicators, from the
World Bank’s World Development Indicators (WDI) database and the Organisation for Economic
Co-operation and Development (OECD) database. Data on elections and a government’s political
orientation come from the World Bank’s Database of Political Institutions (DPI). Country-level
institutional environment indexes (anti–self-dealing, public enforcement, and securities regulation)
and reporting environment indexes (accounting standards and opacity) come from various sources,
including La Porta, Lopez-de-Silanes, Shleifer, and Vishny (1998), La Porta, Lopez-de-Silanes,
and Shleifer (2006), and Kurtzman, Yago, and Phumiwasana (2004). The Appendix provides
definitions and data sources for all the variables used in our analyses.
2 These countries are Australia, Brazil, Canada, Chile, China, France, Germany, India, Ireland, Italy, Japan, Korea,
Netherlands, Russia, Singapore, Spain, Sweden, U.K., and U.S. Our core findings are not sensitive to sequentially
excluding these countries one at a time. 3 Our inferences are not affected by excluding firms cross-listed in the U.S.
10
3.2. Variables
3.2.1. Earnings Management
Following the literature, we use discretionary accruals to measure earnings management.4
As in Kothari, Leone, and Wasley (2005), we augment the modified Jones model (Jones 1991; as
modified by Dechow, Sloan, and Sweeney 1995) with contemporaneous return on assets (ROA) to
avoid potential misspecification due to the impact of profitability on accruals. For firm i in year t,
discretionary accruals (DAit) is estimated as the difference between actual total accruals (TAit) and
the normal, or predicted, level of total accruals (Predicted TAit):
𝐷𝐴𝑖𝑡 = 𝑇𝐴𝑖𝑡 − 𝑃𝑟𝑒𝑑𝑖𝑐𝑡𝑒𝑑 𝑇𝐴𝑖𝑡, (1)
where TAit is earnings before extraordinary items and discontinued operations minus operating
cash flows reported in the statement of cash flows, all deflated by lagged total assets (Hribar and
Collins 2002).
To estimate discretionary accruals, we first estimate the following performance-adjusted
modified Jones model in the cross-section for 2-digit SIC industry-years with more than 15
observations:
𝑇𝐴𝑖𝑡 = 𝑘11
𝐴𝑠𝑠𝑒𝑡𝑖𝑡−1+ 𝑘2
∆𝑆𝑎𝑙𝑒𝑠𝑖𝑡
𝐴𝑠𝑠𝑒𝑡𝑖𝑡−1+ 𝑘3
𝑃𝑃𝐸𝑖𝑡
𝐴𝑠𝑠𝑒𝑡𝑖𝑡−1+ 𝑘4
𝐼𝐵𝑋𝐼𝑖𝑡
𝐴𝑠𝑠𝑒𝑡𝑖𝑡−1+ 𝑒𝑖𝑡, (2)
where ΔSalesit is the change in sales in year t from year t-1, PPEit is gross property, plant, and
equipment in year t, IBXIit is income before extraordinary items in year t, and Assetsit-1 is lagged
total assets.
We then estimate the predicted (normal) level of accruals based on the coefficients estimated
from equation (2):
4 We also examine real earnings management (Roychowdhury 2006; Zang 2012) in additional analyses below.
11
𝑃𝑟𝑒𝑑𝑖𝑐𝑡𝑒𝑑 𝑇𝐴𝑖𝑡 = �̂�11
𝐴𝑠𝑠𝑒𝑡𝑖𝑡−1+ �̂�2
∆𝑆𝑎𝑙𝑒𝑠𝑖𝑡− ∆𝐴𝑅𝑖𝑡
𝐴𝑠𝑠𝑒𝑡𝑖𝑡−1+ �̂�3
𝑃𝑃𝐸𝑖𝑡
𝐴𝑠𝑠𝑒𝑡𝑠𝑖𝑡−1 + �̂�4
𝐼𝐵𝑋𝐼𝑖𝑡
𝐴𝑠𝑠𝑒𝑡𝑖𝑡−1, (3)
where ΔARit is the change in receivables in year t from year t-1.
Because earnings management can involve both income-increasing and income-decreasing
accruals (Warfield, Wild, and Wild 1995; Healy and Wahlen 1999), we use the absolute value of
DAijt (henceforth, AbsDAijt) in our analyses, where higher values of AbsDAijt indicate higher levels
of earnings management.
3.2.2. Economic Policy Uncertainty (EPU) Index
Measuring the economic uncertainty generated by regulatory and political systems is a
challenge for two reasons. First, it is not clear which events should be classified as causing policy-
induced uncertainty, nor is it clear how to measure the degree of policy uncertainty that an event
may cause. Second, it is difficult to disentangle policy change–induced uncertainty from general
macroeconomic uncertainty. To overcome these challenges, we employ the index of aggregate
policy uncertainty developed by BBD.5
Using a computer-automated search of newspapers, BBD measure policy uncertainty by
counting the number of articles in a country’s major newspapers that contain the terms “uncertain”
or “uncertainty,” “economic” or “economy,” and at least one policy-relevant term such as
“Congress,” “deficit,” “Federal Reserve,” “legislation,” “regulation,” or “White House” in the
newspaper’s native language. Differences in the supervising agency’s name (e.g., “Bank of Japan”
for Japan) as well as terms specific to a nation (e.g., “customs duties” for India) are accounted for,
as well as abbreviations and term variants such as “uncertainties” and “regulatory.” After obtaining
raw monthly counts by newspaper, BBD scale the counts by the total number of articles in each
5 We focus on the news-based aggregate BBD index as the measure of economic policy uncertainty because other
index components related to other policy categories (e.g., monetary, fiscal) are not available for the international
sample.
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newspaper-month to control for differences in the volume of articles over time and across
newspapers. They then standardize each newspaper’s monthly scaled series of counts to unit
standard deviation and take the average of the numbers across newspapers so that each country
has one representative monthly series. Each country’s series is then normalized to have a mean of
100 over a given period specific to each country. BBD show that the resulting index captures clear
spikes around important policy-relevant events such as the Gulf Wars and the debt ceiling dispute
in the summer of 2011. The index is not necessarily correlated with all political events that have
mild economic consequences.
Given the concern that their newspaper-based measure could be associated with potential
bias in terms of accuracy and reliability, BBD conduct various validation tests and show that their
index captures the overall level of policy-induced uncertainty. First, they employ human audits of
newspapers under close supervision and training and verify that their computer-automated search
is strongly correlated with the results of the human-generated index. Second, BBD ensure that a
newspaper’s political slant does not significantly affect the reliability of their index. Using the
media slant index of Gentzkow and Shapiro (2010), they divide newspapers based on inclinations
towards left versus right political slants and compare the “left” and “right” versions of the index.
BBD find that regardless of the political slant, their index does not distort the variation in policy
uncertainty over time. Third, BBD compare their index to other reasonable measures of economic
uncertainty such as the Chicago Board Options Exchange Volatility Index and indicators based on
analysis of the Beige Book and 10-K filings. They confirm that their index is distinct in scope from
other indicators and that it contains information about policy-related economic uncertainty as
opposed to general financial uncertainty and stock market events.
Commercial data providers such as Bloomberg, Haver Analytics, and Reuters carry the
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BBD index, suggesting that the BBD index is relevant to entities (e.g., banks, hedge funds, and
policy makers) that subscribe to these data services. Following Gulen and Ion (2016), we define
economic policy uncertainty (EPU) as the natural logarithm of the average of the BBD index over
the 12 months of a given firm’s fiscal year.
3.2.3. Control Variables
To isolate the impact of policy uncertainty on earnings management, in our multivariate
analysis we control for a comprehensive set of variables previously shown to impact the quality of
accounting information. Given evidence that corporate decisions are influenced by aggregate
economic conditions, we first include real GDP growth rate (GDP_GR) to control for the effect of
the general economic cycle. Following Dechow (1994) and Dechow and Dichev (2002), we also
control for firm size (SIZE), measured as the natural logarithm of total assets in millions of U.S.
dollars, and a firm’s operating cycle (OPT_CYCLE), calculated as the natural logarithm of the sum
of days in receivables and days in inventory. Hribar and Nichols (2007) and Liu and Wysocki
(2017) recommend controlling for operating volatility to reduce potential bias in measures of
accruals quality. Accordingly, we further control for cash flow volatility (CF_VOL), computed as
the standard deviation of cash flows to total assets over the past five years, and sales volatility
(SALES _VOL), measured as the standard deviation of sales to total assets over the past five years.
In addition, we control for sales growth volatility (SG_VOL), defined as the standard deviation of
sales growth over five years, and leverage (LEV), defined as the ratio of long-term debt to total
assets, because Sweeney (1994) shows that debt covenant provisions provide an incentive for
earnings management. We also control for annual sales growth (SALES_GR), as in Chaney, Faccio,
and Parsley (2011), and both days payable (DAY_PAYABLE) and an indicator of whether a firm
reported a loss in net income (LOSS), as in Gopalan and Jayaraman (2012). Finally, we control for
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financial performance using return on assets (ROA), as suggested by McNichols (2002) and
Kothari et al. (2005).
3.3. Descriptive Statistics
Table 1 reports descriptive statistics of our key variables by country. On average, the firms
in our sample engage in a considerable degree of earnings management: the sample mean of
AbsDA is 0.18. Australia has the highest mean AbsDA at 0.27, followed by Canada (0.22) and India
(0.22), while Chile and Italy have the lowest mean 𝐴𝑏𝑠𝐷𝐴 at 0.13. The level of EPU (the natural
logarithm of the BBD index) is highest in France (5.03), followed by the UK (5.00) and Russia
(4.91), and lowest in Sweden (4.49). We omit discussion of other variables for brevity.
Figure 1 provides preliminary evidence on the relation between policy uncertainty and
earnings management practices. For each sample country-year, we calculate the average EPU and
AbsDA, and then we plot the two over time. In general, the figure shows that EPU and AbsDA are
inversely associated, with one observing a low (high) when the other is at its peak (trough). In the
next section, we use a multivariate framework to further examine this relation.
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Insert Table 1 and Figure 1 here
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4. Empirical Analysis
4.1 Main Analysis
To test our prediction about the effect of policy uncertainty on earnings management, we
estimate the following model:
𝐴𝑏𝑠𝐷𝐴𝑖𝑡 = 𝛽0𝐸𝑃𝑈𝑖𝑡−1 + 𝛽1𝑋𝑖𝑡 + 𝛼𝑖 + 𝜇𝑡 + 𝜀𝑖𝑡, (4)
15
where 𝑋 is a vector that comprises the firm-level control variables and GDP_GR, as defined above.
To address concerns of potential unobserved heterogeneity, we include firm (𝛼𝑖) and year (𝜇𝑡)
fixed effects in all of our regressions. The inclusion of both time and firm fixed effects in the panel
regressions is a generalization of the difference-in-differences approach that allows for a causal
interpretation in a regression setting, as noted in Bertrand and Mullainathan (2003), Angrist and
Pischke (2009), and Khan, Serafeim, and Yoon (2016). We cluster standard errors by firm in all
regressions.6
Table 2 reports the results. We present the results without control variables in column (1)
and with control variables in columns (2). A negative (positive) coefficient on EPU indicates a
negative (positive) relation between policy uncertainty and earnings management. We find that
AbsDA is negatively associated with policy uncertainty, which suggests that an increase in policy
uncertainty induces firms to decrease earnings management activities. In particular, in column (2),
we find that a 100% increase in EPU leads on average to a 0.044 reduction in AbsDA.7 This is
equivalent to firms decreasing AbsDA by 24.4% (=0.044/0.18) of the sample mean.8
*******************************
Insert Table 2 here
*******************************
6 The main results are qualitatively similar when we cluster standard errors at the country level. 7 As noted in Section 3.2.2, EPU is the natural logarithm of the BBD index. Thus, the coefficient on EPU is interpreted
as the change in AbsDA as policy uncertainty increases by 100%. 8 Owens, Wu, and Zimmerman (2017) suggest that idiosyncratic shocks affect accrual-generating processes and bias
inferences of tests based on discretionary accruals. Following the suggestions in Owens et al. (2017), we augment our
discretionary accrual models in equations (2) and (3) with a proxy for idiosyncratic shock, computed as the mean
squared error from a regression of monthly firm returns on monthly industry and market returns using two years of
monthly data (years t and t-1). The results (untabulated) based on discretionary accruals estimated from this model
are consistent with those in Table 2. We tabulate the results based on discretionary accruals estimated from the model
without idiosyncratic shock as an additional regressor because policy uncertainty may affect firm-level idiosyncratic
shock and thus discretionary accruals estimated from the model with idiosyncratic shock as a determinant.
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4.2. Robustness Tests
Uncertainty tends to be countercyclical (Bloom et al. 2012), and both our earnings
management proxy and the measure of policy uncertainty potentially reflect underlying economic
factors. For example, it could be the case that our results reflect management’s reluctance to
deviate from the normal operating level due to a poor economic outlook or changes in investment
behavior in response to policy uncertainty. To address concerns arising from the confounding
effects of macroeconomic conditions and to ensure that our results are not driven by shrinking
growth and investment opportunities, we control for several macroeconomic variables. Following
Gulen and Ion (2016), we obtain the forecasted real GDP growth rate (R_GDP_F), the consumer
confidence index (CCI), and composite leading indicators (CLI) from the OECD database. These
macroeconomic variables capture market participants’ expectations regarding the economic
outlook, with higher values indicating better prospects. In addition, we control for capital
investment (CAPITAL_INV), and research and development intensity (R&D), defined as capital
expenditures scaled by lagged sales and research and development expenditures scaled by lagged
sales,9 respectively, as well as an indicator for missing R&D (R&D DUMMY), to mitigate the
concern that the negative relation between investment and policy uncertainty (Gulen and Ion 2016)
could drive our results since both lower investment and lower earnings management may reflect a
firm’s overall tendency to avoid risk.
The results including these additional controls are reported in column (1) of Table 3. Here,
the number of observations is smaller because of the availability of the additional control variables.
The results show that the effect of policy uncertainty on earnings management remains significant
after including the additional controls, suggesting that policy uncertainty has a distinct and
9 We replace missing R&D values with zero.
17
persistent negative effect on the incentive to manage earnings. Interestingly, all of the additional
macroeconomic control variables load negatively, implying that firms engage in less earnings
management as economic prospects improve. One possible interpretation of this result is that firms
anticipate better financial performance with improvements in macroeconomic conditions, thus
reducing their incentives to mislead market participants about their performance. We also find that
capital investment and R&D are significantly associated with AbsDA, but their inclusion does not
affect the negative relation between policy uncertainty and earnings management.
*******************************
Insert Table 3 here
*******************************
A number of studies use elections as an exogenous shock that increases political
uncertainty (e.g., Julio and Yook 2012). While elections serve as indicators of high uncertainty,
policy uncertainty can also change during nonelection years (Gulen and Ion 2016). In column (2)
of Table 3, we examine whether our results hold after controlling for elections. As shown, the
coefficient on the election indicator is statistically insignificant. More importantly, even after we
control for elections, the negative relation between policy uncertainty and earnings management
remains negative and statistically significant, suggesting that our results are not driven by
uncertainty during election years.
Prior research suggests that different types of uncertainty may influence the reporting
quality of firms. For instance, Kim et al. (2016) find that macroeconomic uncertainty negatively
affects managers’ tendency to issue earnings forecasts. Stein and Wang (2016) show that firms
report more income-decreasing discretionary accruals as firm-level uncertainty rises, which
reflects managerial motivation to shift earnings from high- to low-uncertainty period. To address
18
the concern that the negative relation between policy uncertainty and earnings management may
reflect different sources of uncertainty, we include additional controls – both sequentially and
altogether – for firm-, industry-, and macroeconomic-level uncertainty. Following Kim et al.
(2016), we use earnings volatility (EARNVOL) as a measure of firm-specific uncertainty,
calculated as the standard deviation of annual earnings over five years. Additionally, we control
for return volatility (RETVOL), defined as the standard deviation of the past 12 monthly returns
for each firm-year. Following Harford (2005), we capture industry-level uncertainty using industry
economic shock (INDUSTRY_SHOCK), measured as the first principal component from the
industry-year medians of seven economic shock variables (profitability, asset turnover, R&D,
capital expenditures, employee growth, ROA, and sales growth). Finally, following Bonaime et al.
(2017), we capture macroeconomic uncertainty using the cross-sectional standard deviation of
sales growth (SD_SALES_GR), calculated for each country-year, and the cross-sectional standard
deviation of cumulative returns from the past twelve months (SD_RET), calculated for each
country. The results, which are reported in columns (3) to (6) of Table 3, show that the coefficient
on EPU remains negative and significant at the 1% level even after controlling for firm-level,
industry-level, and other macroeconomic uncertainty. These results suggest that the effect of
economic policy uncertainty on earnings management is distinct from the effects of other types of
uncertainty.
We next examine the robustness of our results to alternative proxies of earnings
management. In columns (7) and (8) of Table 3, we estimate financial reporting quality using two
alternative measures of accruals quality that are estimated based on the model in Dechow and
Dichev (2002) as modified by McNichols (2002). Specifically, we estimate the following equation
for each industry-year combination with more than 15 observations:
19
∆𝑊𝐶𝑖𝑡 = 𝛽0 + 𝛽1𝐶𝐹𝑂𝑖𝑡−1 + 𝛽2𝐶𝐹𝑂𝑖𝑡 + 𝛽3𝐶𝐹𝑂𝑖𝑡+1 + 𝛽4∆𝑆𝑎𝑙𝑒𝑠𝑖𝑡 + 𝛽5𝑃𝑃𝐸𝑖𝑡 + 𝜀𝑖𝑡, (5)
where ∆𝑊𝐶𝑖𝑡 is (change in accounts receivable + change in inventory – change in accounts
payable – change in taxes payable – change in other assets) and 𝐶𝐹𝑂𝑖𝑡 is operating cash flows from
the statement of cash flows.
The residual from the above estimation, DA—DD, is our measure of abnormal accruals. As
with AbsDA, we take the absolute value of the residual to account for both income-increasing and
income-decreasing accruals and use AbsDA—DD as an alternative dependent variable. Following
Francis, LaFond, Olsson, and Schipper (2005), we also use accruals quality, AQ, defined as the
standard deviation of the residual from equation (5) over years t-4 to t, as an alternative proxy for
accruals quality.
For both the results based on AbsDA—DD and the results based on AQ, the coefficient on
EPU is negative and statistically significant at the 1% level. These results are consistent with the
results in Table 2 and suggest that firms reduce earnings management activities as policy-induced
economic uncertainty increases.
Prior literature documents that firms engage in earnings management not only through
accruals but also through real operating decisions (Cohen, Dey, and Lys 2008; Cohen and Zarowin
2010; Roychowdhury 2006). Given that executives engage in both real and accrual-based earnings
management (Graham et al. 2005), we next examine the effect of policy uncertainty on real
earnings management. Following Kim, Kim, and Zhou (2017), we proxy for real earnings
management using abnormal cash flows from operations. Specifically, we estimate the following
equation for each industry-year combination:
𝐶𝐹𝑂𝑖𝑡
𝐴𝑠𝑠𝑒𝑡𝑖𝑡−1= 𝑘1
1
𝐴𝑠𝑠𝑒𝑡𝑖𝑡−1+ 𝑘2
𝑆𝑎𝑙𝑒𝑠𝑖𝑡
𝐴𝑠𝑠𝑒𝑡𝑖𝑡−1+ 𝑘3
∆𝑆𝑎𝑙𝑒𝑠𝑖𝑡
𝐴𝑠𝑠𝑒𝑡𝑖𝑡−1+ 𝑒𝑖𝑡. (6)
20
The normal level of cash flows from operations is expressed as a linear function of sales
and the change in sales. The residual from the above regression is the abnormal cash flows from
operations (AbnCFO). Managers can accelerate the timing of sales by offering price discounts or
lenient credit terms, but such actions boost sales only temporarily, resulting in abnormally lower
operating cash flows. Alternatively, managers can reduce discretionary expenditures such as R&D,
advertising, and maintenance to increase current-period earnings. A decrease in discretionary
expenditures will reduce cash outflows, resulting in abnormally higher operating cash flows.
Regardless of the direction, deviations from the predicted level of operating cash flows indicate
earnings management. To account for the fact that deviations from the predicted level of operating
cash flows can be negative or positive, we use the absolute value of abnormal cash flows from
operations (AbsAbnCFO) to proxy for real earnings management. Higher levels of AbsAbnCFO
thus indicate more real earnings management.
The results, reported in column (5) of Table 3, show a negative and significant coefficient
on EPU when we replace AbsDA with AbsAbnCFO as the dependent variable. 10 Given that
earnings management through real activities involves real operational decisions that are more
difficult for outside stakeholders to challenge (Graham et al. 2005), the negative effect of EPU on
AbsAbnCFO suggests that during periods of high policy uncertainty, the pressure exerted by
increased public scrutiny is strong enough that managers even refrain from harder-to-challenge
forms of earnings management.
4.3. Endogeneity
Potential endogeneity could spuriously drive our results (Roberts and Whited 2013). First,
10 While we tabulate the results of subsequent analyses with AbsDA as a dependent variable for parsimony, using
AbsAbnCFO as a proxy for earnings management yields qualitatively the same results as those obtained with AbsDA
throughout the analyses (not tabulated).
21
bias from reverse causality could falsely identify the results as coming from one direction rather
than the other, or even bilaterally. It is unlikely, however, that firm-level earnings management
impacts the aggregate level of policy uncertainty present in the economy. While a firm-level crisis
(e.g., Enron scandal) could induce changes in regulatory policies, implementing such changes
takes a long time, and thus the reverse causality is unlikely in our context. Furthermore, Williamson
(2000) shows that corporate policies and actions are shaped by the formal institutions that govern
them, which supports the view that the government influences the environment in which firms
reside and, in turn, firms respond to changes in their environment. In the absence of a theoretical
link suggesting that firm-specific activities drive changes in economic policies, we rule out the
possibility of simultaneity bias.
Another potential endogeneity problem relates to measurement error in our main variable
of interest, BBD’s policy uncertainty index. Although being well constructed, this index may
capture economic uncertainty unrelated to policy. In Section 3.2.2, we note that BBD take
extensive precautions to ensure that their measure captures the policy-induced component of
uncertainty. To further address measurement concerns, we show that our findings are robust to the
inclusion of a number of macroeconomic proxies and an election indicator. Nonetheless, care
should be taken when interpreting our results because EPU may still be subject to unknown
measurement error.
Potential bias may also arise from omitted explanatory variables. Although our analyses
include firm and year fixed effects to control for unobserved heterogeneity, and an extensive set
of control variables as well as various proxies to capture the effects of economic cycles, additional
analysis is warranted to ensure that the results are not driven by other sources of economic
uncertainty unrelated to policy.
22
To address any potential endogeneity remaining in our analysis, we employ the
instrumental variables approach. As an instrument, we use the political fractionalization index. A
suitable instrument should satisfy both the relevance and the exclusion restrictions: that is, it should
be strongly correlated with our policy uncertainty measure from both a theoretical and a statistical
perspective, and it should have little relation with earnings management other than through the
channel provided by the relation between the instrument and the policy uncertainty variable.
Political fractionalization satisfies both conditions. Political fractionalization is defined as the
probability that two deputies picked from the legislature at random will be of different parties.
Higher values of this measure indicate greater policy uncertainty (relevance restriction) as deputies
from different parties have conflicting views on policy and hence there is more room for
disagreement (i.e., policy uncertainty). Moreover, this measure is unlikely to have a direct relation
to any of the firm-level variables (exclusion restriction) as firm policies and the partisan
distribution of legislative deputies are not linked.
To implement the instrumental variables approach, we first regress EPU on the political
fractionalization index (Political Fractionalization) and the control variables in vector X from
equation (4). Column (1) of Table 4 reports the first-stage regression results. We find that a higher
level of political fractionalization is associated with greater policy-induced uncertainty. The F-test
in the first-stage regression rejects the null that the instrument does not capture changes in policy
uncertainty at the 1% significance level, which suggests that the relevance condition of our
instrument is satisfied. Additionally, we perform the Kleibergen-Paap rk LM test to check the null
hypothesis that our regression is under-identified. The chi-square value rejects the null at the 1%
significance level, confirming that the model is well identified and the instrument is correlated with
EPU.
23
Next, we use the fitted value from the first-stage regression to replace the original value of
EPU in equation (4). The regression results, reported in column (2) of Table 4, confirm the
negative effect of policy uncertainty on earnings management. Specifically, the coefficient on
Predicted EPU loads negatively and is significant at the 1% level. Thus, our results are robust to
controlling for potential endogeneity through the instrumental variables approach.
*******************************
Insert Table 4 here
*******************************
4.4. Additional Analyses
Our results thus far show that policy uncertainty decreases earnings management. In this
section, we explore potential mechanisms through which policy uncertainty influences earnings
management decisions at the firm level. Specifically, we examine how country-level legal
institutions, financial reporting environment, and media scrutiny, as well as industry-level growth
opportunities, affect the relation between earnings management and policy uncertainty.
4.4.1. The Role of Legal Institutions
We first examine whether cross-country differences in legal institutions can explain the
negative relation between policy uncertainty and earnings management. Previous literature
emphasizes the importance of regulations and legal enforcement in curbing agency conflicts and
documents a positive impact of a strong institutional environment on earnings quality (e.g., Ball,
Kothari, and Robin 2000; Burgstahler, Hail, and Leuz 2006; Leuz, Nanda, and Wysocki 2003). To
test whether legal institutions also affect the uncertainty-earnings management relation, we split
the sample into strong and weak country-level legal institutions using the median value of each
index and compare the impact of policy uncertainty on earnings management between the two
24
subsamples. If policy uncertainty attracts market participants’ attention and public scrutiny as we
predict, we should find that the negative relation between earnings management and policy
uncertainty concentrates in countries with stronger institutions.
Drawing from prior literature, our proxies for country-level legal institutions include the
anti–self-dealing index of Djankov, La Porta, Lopez-de-Silanes, and Shleifer (2008), the composite
securities regulation index of Hail and Leuz (2006), and the public enforcement index of La Porta et
al. (2006). The anti–self-dealing index measures the extent to which minority shareholders are
protected against expropriation by insiders and is constructed such that higher values of the index
indicate lower self-dealing by insiders (i.e., greater protection of minority shareholders). The
composite securities regulation index is the arithmetic average of the disclosure requirement index,
the liability standard index, and the public enforcement index (La Porta et al. 2006) and measures
the strength of security laws mandating and enforcing disclosure, with higher values indicating
stronger enforcement or stricter standards. Lastly, the public enforcement index measures the power
of the supervising authority (such as government agency or central bank) to regulate and enforce
securities laws. Higher values of this index indicate greater power vested in the supervising authority.
We present the results in Table 5. Across all proxies for the strength of legal institutions, the
coefficient on EPU is negative and statistically significant at the 1% level for the subsample of firms
in countries with stronger legal institutions (columns (2), (4), and (6)). Although the coefficient on
EPU is also negative and statistically significant in two out of three specifications for countries with
weaker legal institutions, the magnitude of the coefficient is considerably lower than that for countries
with stronger legal institutions. In fact, in all specifications the difference between the coefficients on
EPU across the strong- and weak-institution subsamples is significant at the 1% level. These findings
are consistent with the argument that as policy uncertainty increases, market participants become more
25
prudent and in countries with stronger legal institutions it is easier for outside stakeholders to demand
greater transparency. The findings are also consistent with previous studies suggesting that the legal
environment influences managers’ ability to mislead shareholders (Leuz et al. 2003).
*******************************
Insert Table 5 here
*******************************
4.4.2. The Role of the Financial Reporting Environment
We next turn to the role of the country-level financial reporting environment, which we
measure using the opacity index of Kurtzman et al. (2004) and the accounting standards index of
La Porta et al. (1998). These indices capture the extent to which the reporting environment ensures
that firms disclose clear and accurate information to investors and regulators. We compare firms
in countries with strong and weak reporting environments. Under a stronger reporting environment,
it is easier for market participants to induce managers to adopt transparent financial reporting. We
therefore expect the negative relation between policy uncertainty and earnings management to be
more pronounced in countries with a stronger reporting environment.
Table 6 reports the results. We find that the negative relation between policy uncertainty
and earnings management is more pronounced for the subsample of firms in countries with a
stronger reporting environment (columns (2) and (4)) than for the subsample of firms in countries
with a weaker reporting environment (columns (1) and (3)). This evidence further supports the
idea that where the reporting environment is strong, increased public scrutiny arising from policy
uncertainty curtails managerial opportunism in financial reporting.
*******************************
Insert Table 6 here
26
*******************************
4.4.3. The Role of the Media
In addition to legal and regulatory institutions, the media can influence managerial
incentives for expropriation or unethical behavior as revelations by the press of wrongdoing can
have significant reputation costs (Zingales 2000). The monitoring role of the media should be
stronger in countries with greater press freedom. If policy uncertainty leads to increased
monitoring incentives, we should find the effect of policy uncertainty to be more pronounced in
the subsample of firms in countries with greater press freedom. To test this prediction, we use the
Freedom of the Press Index (FPI) from Freedom House. The FPI measures the degree of print,
broadcast, and digital media freedom in terms of legal, political, or economic pressure that may
influence the reporting of news. We divide our sample into high and low press freedom subsamples
using a country’s median FPI and compare the effect of policy uncertainty on earnings
management across subsamples.
The results, presented in Table 7, are consistent with our prediction. The negative relation
between policy uncertainty and earnings management is more pronounced for the subsample of
firms in countries with more press freedom than for the subsample of firms in countries with less
press freedom. The results provide additional support for the argument that firms decrease earnings
management during periods of high policy uncertainty due to increased monitoring incentives in
such times.
*******************************
Insert Table 7 here
*******************************
4.4.4. The Role of Growth Opportunities and the Need for External Financing
27
In this section, we examine the implications of capital market incentives for the relation
between policy uncertainty and earnings management. Firms have greater incentives to meet
investor demand for transparency when they seek a lower cost of capital. Prior studies show that
firms subject themselves to a stronger regulatory environment to benefit from a lower cost of
capital. 11 If policy uncertainty increases investor scrutiny, firms with more investment
opportunities and a greater need for external financing are more likely to respond to investor
demand for high quality earnings: that is, the negative effect of policy uncertainty on earnings
management should be more pronounced in the sample of firms with more investment
opportunities and a greater need for external financing.
To test the prediction for growth opportunities, we follow Gopalan and Jayaraman (2012)
and use industry-level growth opportunities. Specifically, for each year, we rank industries
according to their growth opportunities, as proxied by the market-to-book value of assets (MTB),
and divide the sample into high and low growth opportunity groups.12 We then compare the effect
of policy uncertainty on earnings management across the two subsamples.
To test the prediction for external financing need, we follow Rajan and Zingales (1998) and
construct the external finance dependence measure as capital expenditures minus funds from
operations, scaled by capital expenditures. While this measure allows a more direct test of the role
of the desire to lower the cost of capital, the current level of firms’ external financing needs might
be influenced by changes in policy uncertainty. To address this concern, we lag the variable by one
year so that it reflects dependence on external capital prior to the actual rise or fall of policy-induced
11 Doidge, Karolyi, and Stulz (2004), Doidge et al. (2009), Hail and Leuz (2009), Lang, Lins, and Miller (2003), and
Reese and Weisbach (2002) find that firms from weak–investor protection countries cross-list in the U.S. to realize
the reputation benefits associated with a more demanding regulatory environment. 12 Our results continue to hold when we instead use annual sales growth as a proxy for growth opportunities, as in
Gopalan and Jayaraman (2012).
28
uncertainty. As above, we partition the sample into high and low external finance dependence and
then compare the effect of policy uncertainty on earnings management across the two subsamples.
The results for growth opportunities are presented in columns (1) and (2) of Table 8, while
the results for external finance dependence are presented in columns (3) and (4). The results show
that the negative relation between policy uncertainty and earnings management is more
pronounced for firms with more growth opportunities and greater external finance dependence
than for firms with fewer growth opportunities and less need for external financing. These results
are consistent with the idea that firms are motivated to meet investor demand for higher quality
financial reporting under elevated policy uncertainty when they need external financing.
*******************************
Insert Table 8 here
*******************************
5. Discussion and Concluding Remarks
An increase in policy uncertainty calls for more prudence. In response to an increase in
policy-induced economic uncertainty, investors call for greater transparency. Given the increase
in public scrutiny during turbulent times, firms abstain from earnings management and report more
informative earnings. Our results are robust to controlling for the effect of macroeconomic
conditions and elections and using alternative proxies of earnings management. Moreover, the
results are not limited to accrual-based earnings management – we find that firms also refrain from
real earnings management during periods of high policy uncertainty.
In additional analyses, we show that the negative relation between policy uncertainty and
earnings management is more pronounced for firms in countries with stronger legal institutions,
29
a better reporting environment, and greater press freedom. These results suggest that the country-
level institutional environment can serve as a corporate governance mechanism that reduces the
extent of managerial opportunism. The negative relation between policy uncertainty and
earnings management is also more pronounced for firms with more growth opportunities and a
greater need for external capital. These results are consistent with the idea that as policy-induced
economic uncertainty rises, investors become more prudent and demand greater transparency
and, in turn, managers refrain from managing earnings, especially when they need access to
external capital.
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Appendix
VARIABLE DESCRIPTION SOURCE
AbsDA Absolute value of abnormal accruals estimated based on the
modified Jones model adjusted for performance
Compustat
EPU Natural logarithm of the moving average of the monthly
policy uncertainty index over the 12 months ending in the
month of the fiscal year-end
Baker et al. (2016)
GDP_GR Real GDP growth rate for year t WDI
SIZE Natural logarithm of total assets in millions of U.S. dollars Compustat
OPT_CYCLE Natural logarithm of the sum of days in receivable and days
in inventory
As above
CF_VOL 5-year standard deviation of cash flow to total assets As above
SALES_VOL 5-year standard deviation of sales to total assets As above
SG_VOL 5-year standard deviation of the annual sales growth rate As above
LEV Ratio of long-term debt to total assets As above
SALES_GR The annual sales growth rate As above
DAY_PAYABLE 360 divided by the ratio of average accounts payable to cost
of goods sold.
As above
LOSS Indicator variable equal to 1 if a firm reports a loss, and 0
otherwise
As above
ROA Ratio of operating income to total assets As above
R_GDP_F Real GDP growth rate forecast based on an assessment of the
economic climate in individual countries and the world
economy using a combination of model-based analyses and
expert judgment. This indicator is measured as a year-over-
year growth rate.
OECD
CCI Consumer confidence index based on households’ plans for
major purchases and their economic situation, both currently
and in the immediate future. Opinions compared to a
“normal” state are collected, with the difference between
positive and negative answers providing a qualitative index
on economic conditions.
OECD
CLI Composite leading indicator of turning points in business
cycles showing a fluctuation of economic activity around its
OECD
36
long-term potential level. The index shows short-term
economic movements in qualitative rather than quantitative
terms.
CAPITAL_INV Capital expenditures scaled by lagged sales Compustat
R&D Research and development expenditures scaled by lagged
sales. We replace missing R&D values with zero.
As above
R&D_DUMMY Indicator that equals 1 if research and development
expenditure is missing (and set to zero), and 0 otherwise
As above
EARNVOL 5-year standard deviation of firm’s annual earnings from year
t-4 to t
As above
RETVOL Standard deviation of the past 12 monthly returns for each
firm-year
As above
INDUSTRY_SHOCK First principal component from seven economic shock
variables (profitability, asset turnover, R&D, capital
expenditures, employee growth, ROA, and sales growth),
calculated for each industry-year. For each year, we take the
industry median of the absolute change in each of the seven
variables.
As above
SD_SALES_GR Cross-sectional standard deviation of sales growth,
calculated for each country-year, using the entire Compustat
universe
As above
SD_RET Cross-sectional standard deviation of cumulative returns
from the past 12 months, calculated for each country
As above
AbsDA—DD Absolute value of the residuals from the Dechow and Dichev
(2002) model as modified by McNichols (2002)
As above
AQ Accruals quality estimated as the standard deviation of the
residuals from the Dechow and Dichev (2002) model as
modified by McNichols (2002) over five years
As above
AbsAbnCFO Absolute value of abnormal cash flows from operations
estimated following Roychowdhury (2006)
As above
Political
Fractionalization
Fractionalization index from the Database of Political
Institutions. The index gives the probability that two deputies
picked at random from the legislature will be of different
parties.
DPI
Anti–Self-Dealing Measure of legal protection of minority shareholders against
expropriation by corporate insiders. The index is calculated
based on the legal rules prevailing in 2003 and focuses on
private enforcement mechanisms, such as disclosure,
approval, and litigation, that govern a specific self-dealing
transaction.
Djankov et al. (2008)
Securities Regulation Measure of securities regulation mandating and enforcing
disclosures, as in Hail and Leuz (2006), calculated as the
Hail and Leuz (2006)
37
arithmetic mean of the disclosure index, the liability standard
index, and the public enforcement index of La Porta et al.
(2006)
Public Enforcement Public enforcement index of La Porta et al. (2006), calculated
as the arithmetic mean of (1) the supervisor characteristics
index, (2) the rule-making power index, (3) the investigative
powers index, (4) the orders index, and (5) the criminal index
La Porta et al. (2006)
Opacity Index measuring the degree to which there is a lack of clear,
accurate, easily discernible, and widely accepted practices
governing the relationships among businesses, investors, and
governments
Kurtzman et al. (2004)
Accounting Standards Accounting standards index of La Porta et al. (1998), rating
firms’ 1990 annual reports on their inclusion or omission of
90 items
La Porta et al. (1998)
Freedom of the Press
Index
Index of country-level print, broadcast, and digital media
independence. The index evaluates the legal environment for
the media, political pressures that influence reporting, and
economic factors that affect access to news and information.
Freedom House
MTB Market value of assets divided by book value of assets Compustat
External Finance
Dependence
Capital expenditures less funds from operations, divided by
capital expenditures. When funds from operations is missing,
it is defined as the sum of income before extraordinary items,
depreciation and amortization, deferred taxes, equity in net
loss/earnings, sale of property, plant, and equipment and
investments – gain/loss, and funds from operations – other,
as in Rajan and Zingales (1998).
As above
38
Figure 1. AbsDA and EPU over time
39
Table 1. Descriptive Statistics
This table reports summary statistics for our main sample. We report the mean values of the key variables by country. The sample comprises
243,554 firm-year observations from 19 countries over the 19902015 period. We winsorize all continuous variables at the 1% level in both tails
of the distribution. The Appendix provides variable definitions and data sources.
N AbsD
A
EP
U
GD
P_G
R
SIZ
E
OP
T_C
YC
LE
CF
_V
OL
SA
LE
S_V
OL
SG
_V
OL
LE
V
SA
LE
S_G
R
DA
Y_P
AY
AB
LE
LO
SS
RO
A
Australia 7,615 0.27 4.55 2.92 4.35 0.21 0.23 0.27 1.96 0.12 0.28 1.93 0.33 -0.06
Brazil 2,602 0.15 4.85 2.81 6.43 0.05 0.10 0.15 0.48 0.19 0.10 0.30 0.11 0.06
Canada 9,189 0.22 4.71 2.36 5.38 0.07 0.15 0.20 0.83 0.17 0.17 0.61 0.23 -0.02
Chile 1,838 0.13 4.57 3.88 5.85 0.05 0.07 0.11 0.34 0.17 0.09 0.32 0.05 0.06
China 25,561 0.18 4.83 9.17 5.87 0.02 0.09 0.17 0.47 0.06 0.20 0.65 0.11 0.04
France 5,317 0.15 5.03 1.03 6.07 0.03 0.09 0.14 0.38 0.14 0.08 0.22 0.12 0.04
Germany 5,916 0.19 4.80 1.29 5.73 0.03 0.11 0.21 0.40 0.13 0.09 0.25 0.13 0.03
India 25,374 0.22 4.57 7.62 3.82 0.15 0.11 0.22 0.73 0.17 0.17 1.27 0.11 0.07
Ireland 371 0.15 4.73 2.68 6.59 0.05 0.09 0.18 0.51 0.20 0.11 0.30 0.13 0.04
Italy 1,917 0.13 4.65 -0.35 6.48 0.03 0.08 0.12 0.44 0.15 0.05 0.05 0.14 0.03
Japan 40,338 0.16 4.59 0.76 6.16 0.01 0.05 0.11 0.19 0.10 0.04 0.03 0.05 0.04
Korea 7,449 0.16 4.74 3.74 6.04 0.01 0.08 0.17 0.34 0.10 0.08 0.04 0.10 0.04
Netherlands 1,098 0.17 4.55 1.11 6.88 0.01 0.08 0.19 0.34 0.17 0.08 0.04 0.06 0.06
Russia 1,518 0.17 4.91 2.24 6.95 0.03 0.11 0.25 0.75 0.16 0.07 0.11 0.10 0.07
Singapore 5,472 0.16 4.65 5.66 4.74 0.05 0.13 0.23 0.62 0.08 0.14 0.34 0.19 0.02
Spain 1,084 0.15 4.57 0.71 7.17 0.02 0.07 0.11 0.31 0.21 0.07 0.09 0.09 0.05
Sweden 3,723 0.19 4.49 2.13 4.74 0.15 0.13 0.22 0.79 0.13 0.16 1.38 0.24 -0.02
UK 8,439 0.19 5.00 1.37 5.32 0.10 0.13 0.21 0.71 0.13 0.12 0.84 0.17 0.02
USA 88,733 0.17 4.64 2.60 5.28 0.06 0.13 0.23 0.52 0.20 0.13 0.53 0.20 -0.00
All countries 243,554 0.18 4.67 3.47 5.40 0.06 0.11 0.20 0.54 0.15 0.13 0.50 0.15 0.02
40
Table 2. Policy Uncertainty and Earnings Management
This table reports regression results relating earnings management to policy uncertainty. The dependent variable is accrual-based earnings management, AbsDA, calculated from the performance-augmented modified Jones model as in Kothari et al. (2005). EPU is the natural logarithm of the average BBD policy uncertainty index over the 12-month period ending in the month of the fiscal year-end. We winsorize all continuous variables at the 1% level in both tails of the distribution. The Appendix provides variable definitions and data sources. All regressions include firm and year fixed effects. t-statistics from robust standard errors clustered at the firm level are reported in parentheses. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.
Dependent variable = AbsDA (1) (2)
EPU -0.047*** -0.044***
(-9.77) (-8.89) GDP_GR -0.371*** (-6.32) SIZE 0.002
(1.11) OPT_CYCLE -0.012
(-0.60) CF_VOL 0.230***
(16.99) SALES_VOL 0.074***
(9.94) SG_VOL 0.000
(0.06) LEV 0.011
(1.13) SALES_GR 0.062***
(20.10) DAY_PAYABLE 0.006**
(2.45) LOSS 0.008**
(1.97) ROA -0.050***
(-3.83) Constant 0.304*** 0.239*** (13.18) (9.44) Firm fixed effects Yes Yes Year fixed effects Yes Yes Observations 243,554 243,554 Adjusted R2 5.5% 6.6%
41
Table 3. Robustness Checks
This table reports regression results relating earnings management to policy uncertainty using additional controls and alternative dependent variables.
The dependent variable in columns (1) to (6) is accrual-based earnings management, AbsDA, calculated from the performance-augmented modified
Jones model as in Kothari et al. (2005). As an alternative measure of accruals quality, in column (7) we use AbsDA—DD, calculated as the absolute
value of the residuals from the model developed by Dechow and Dichev (2002) as modified by McNichols (2002), and in column (8) we use AQ, the
standard deviation of the residuals over years t-4 to t following Francis et al. (2005). The dependent variable in column (9) is AbsAbnCFO, which
measures real earnings management based on abnormal cash flows from operations following Kim et al. (2017). EPU is the natural logarithm of the
average BBD policy uncertainty index over the 12-month period ending in the month of the fiscal year-end. We winsorize all continuous variables at
the 1% level in both tails of the distribution. The Appendix provides variable definitions and data sources. Firm and year fixed effects are included but
not reported. t-statistics from robust standard errors clustered at the firm level are reported in parentheses. ***, **, and * indicate significance at the
1%, 5%, and 10% levels, respectively.
Additional Controls
Alternative proxies for
earnings management
Dependent variable AbsDA AbsDA—DD AQ AbsAbnCFO (1) (2) (3) (4) (5) (6) (7) (8) (9)
EPU -0.055*** -0.028*** -0.050*** -0.045*** -0.042*** -0.049*** -0.007*** -0.016*** -0.016***
(-9.86) (-5.62) (-9.16) (-9.04) (-8.49) (-9.00) (-2.63) (-4.86) (-3.23)
R_GDP_F -0.393*
(-1.81)
CCI -0.503***
(-4.57)
CLI -0.164
(-1.17)
CAPITAL_INV 0.058***
(6.02)
R&D 0.062***
(2.90)
R&D_DUMMY -0.006
(-1.55)
ELECTION -0.002
(-0.69)
EARNVOL -0.000 -0.000
(-0.12) (-0.37)
RETVOL 0.060** 0.054**
(2.56) (2.33)
42
INDUSTRY_SHOCK 0.025*** 0.025***
(9.08) (8.21)
SD_SALES_GR 0.000*** 0.000***
(5.90) (5.03)
SD_RET -0.014*** -0.005
(-2.88) (-0.87)
GDP_GR 0.162 -0.003 -0.502*** -0.387*** -0.339*** -0.490*** -0.310*** -0.113*** -0.477***
(0.71) (-0.05) (-7.48) (-6.59) (-5.76) (-7.29) (-9.19) (-4.07) (-9.01)
SIZE 0.002 0.000 0.008*** 0.001 0.002 0.007*** -0.009*** 0.004** -0.012***
(0.98) (0.09) (3.46) (0.69) (0.99) (2.94) (-6.94) (1.98) (-5.64)
OPT_CYCLE -0.026 -0.019 0.013 -0.012 -0.012 0.013 -0.001 -0.007 -0.037***
(-0.86) (-0.92) (0.46) (-0.57) (-0.61) (0.47) (-0.04) (-0.32) (-2.71)
CF_VOL 0.234*** 0.226*** 0.194*** 0.229*** 0.230*** 0.194*** 0.113*** 0.184*** 0.237***
(16.06) (15.92) (12.37) (16.95) (17.01) (12.37) (13.40) (14.60) (16.10)
SALES_VOL 0.063*** 0.076*** 0.047*** 0.071*** 0.074*** 0.043*** 0.062*** 0.099*** 0.123***
(7.81) (9.53) (6.13) (9.57) (9.94) (5.71) (12.65) (13.37) (14.51)
SG_VOL -0.001 -0.001 0.001 0.000 0.000 0.001 0.000 0.014*** 0.002**
(-0.57) (-0.90) (0.76) (0.00) (0.07) (0.78) (0.34) (10.41) (1.96)
LEV 0.016 0.007 -0.008 0.013 0.011 -0.007 0.002 0.005 -0.024**
(1.49) (0.68) (-0.78) (1.28) (1.08) (-0.67) (0.34) (0.64) (-2.46)
SALES_GR 0.052*** 0.060*** 0.058*** 0.062*** 0.062*** 0.058*** 0.045*** 0.016*** 0.080***
(14.13) (17.75) (16.59) (20.06) (20.11) (16.55) (25.36) (11.28) (24.79)
DAY_PAYABLE 0.009** 0.006*** 0.000 0.005** 0.006** 0.000 0.003* 0.002 0.009***
(2.57) (2.81) (0.08) (2.41) (2.45) (0.06) (1.66) (0.89) (5.26)
LOSS 0.005 0.004 0.018*** 0.007* 0.008** 0.017*** 0.003 0.007*** -0.006
(1.13) (0.83) (4.26) (1.86) (1.98) (4.19) (1.29) (2.78) (-1.59)
ROA -0.044*** -0.052*** -0.032** -0.047*** -0.049*** -0.027** -0.026*** 0.026*** -0.010
(-3.17) (-3.84) (-2.30) (-3.64) (-3.77) (-1.98) (-3.45) (3.04) (-0.71)
Constant 0.946*** 0.167*** 0.256*** 0.251*** 0.241*** 0.271*** 0.115*** 0.089*** 0.201*** (5.13) (6.43) (9.04) (9.98) (9.53) (9.62) (8.24) (4.68) (7.93)
Firm fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes
Year fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes
Observations 208,948
212,521
200,222
243,181
243,554 199,949
217,193
200,291
243,554
Adjusted R2 6.5% 6.3% 6.6% 6.7% 6.6% 6.7% 5.7% 6.2% 6.5%
43
Table 4. Endogeneity
This table reports results of regressions addressing endogeneity of policy uncertainty using instrumental
variable analysis. In column (1), we report the results of the first-stage regression using Political
Fractionalization as an instrument. Specifically, we regress EPU on Political Fractionalization, all the
control variables, as well as firm and year fixed effects. Column (2) provides the results of the second-stage
regression, which uses the Predicted EPU estimates from the first-stage regression. EPU is the natural
logarithm of the average BBD policy uncertainty index over the 12-month period ending in the month of
the fiscal year-end. Political Fractionalization is the probability that two deputies picked at random from
the legislature will be of different parties. We winsorize all continuous variables at the 1% level in both
tails of the distribution. The Appendix provides variable definitions and data sources. Firm and year fixed
effects are included but not reported. z-statistics from robust standard errors clustered at the firm level are
reported in parentheses. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.
First Stage Second Stage
Dependent variable = EPU AbsDA
(1) (2)
Political Fractionalization 0.128***
(8.95)
Predicted EPU -0.340**
(-2.02)
GDP_GR -1.310*** -0.760***
(-37.43) (-3.35)
SIZE 0.017*** 0.007**
(14.63) (2.08)
OPT_CYCLE -0.006 -0.014
(-0.60) (-0.67)
CF_VOL 0.037*** 0.241***
(6.84) (16.08)
SALES_VOL -0.008** 0.071***
(-2.28) (9.24)
SG_VOL -0.000 0.000
(-0.17) (0.03)
LEV -0.018*** 0.007
(-4.12) (0.64)
SALES_GR -0.019*** 0.057***
(-18.91) (12.82)
DAY_PAYABLE -0.001 0.005**
(-0.56) (2.31)
LOSS 0.003* 0.009**
(1.83) (2.16)
ROA -0.013*** -0.053***
(-3.06) (-4.04)
Firm fixed effects Yes Yes
Year fixed effects Yes Yes
Observations 241,241 241,241
F-statistic 80.15
44
Table 5. Analyzing the Role of Legal Institutions
This table reports regression results of subsample analyses based on the strength of legal institutions. For each institutional variable (Anti–Self-Dealing, Securities Regulation, and Public Enforcement), we divide the sample into weak (below-median) and strong (above-median) institutional subsamples based on country-level median and examine the relation between earnings management and policy uncertainty in each subsample. In columns (1), (3), and (5), we provide the results using firm-year observations belonging to countries with weak legal institutions. Columns (2), (4), and (6) report the results using firm-year observations belonging to countries with strong legal institutions. Differences in the coefficients on EPU between the strong and weak subsamples are provided in the last row of the table. The dependent variable is accrual-based earnings management, AbsDA, calculated from the performance-augmented modified Jones model as in Kothari et al. (2005). EPU is the natural logarithm of the average BBD policy uncertainty index over the 12-month period ending in the month of the fiscal year-end. We winsorize all continuous variables at the 1% level in both tails of the distribution. The Appendix provides variable definitions and data sources. All regressions include firm and year fixed effects. t-statistics from robust standard errors clustered at the firm level are reported in parentheses. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.
Anti–Self-Dealing Securities Regulation Public Enforcement
Weak Strong Weak Strong Weak Strong
Dependent variable = AbsDA (1) (2) (3) (4) (5) (6)
EPU -0.008 -0.096*** -0.032*** -0.062*** -0.025*** -0.088***
(-1.29) (-9.94) (-5.56) (-6.45) (-4.25) (-8.74) GDP_GR -0.147** -0.594*** 0.029 -1.020*** -0.097 -0.811*** (-2.20) (-6.26) (0.42) (-9.41) (-1.46) (-7.63) SIZE 0.001 0.002 -0.006* 0.007** -0.001 0.004*
(0.23) (0.90) (-1.82) (2.51) (-0.26) (1.65) OPT_CYCLE 0.009 -0.028 0.000 -0.022 -0.003 -0.023
(0.39) (-0.93) (0.02) (-0.62) (-0.14) (-0.66) CF_VOL 0.160*** 0.251*** 0.153*** 0.266*** 0.155*** 0.259***
(6.69) (15.56) (7.37) (15.41) (7.20) (15.37) SALES_VOL 0.094*** 0.064*** 0.083*** 0.069*** 0.085*** 0.069***
(7.97) (6.80) (7.26) (7.06) (7.61) (7.11) SG_VOL -0.000 0.000 0.001 -0.001 -0.001 0.000
(-0.15) (0.13) (0.47) (-0.38) (-0.44) (0.27) LEV 0.009 0.013 0.001 0.019 0.012 0.013
(0.55) (1.06) (0.05) (1.50) (0.74) (1.07) SALES_GR 0.058*** 0.064*** 0.062*** 0.063*** 0.056*** 0.066***
(11.49) (16.29) (13.06) (15.00) (11.78) (15.92) DAY_PAYABLE 0.000 0.009*** 0.001 0.010** 0.001 0.009**
(0.01) (2.76) (0.50) (2.56) (0.62) (2.34) LOSS 0.014** 0.006 0.014** 0.008 0.014** 0.007
(2.30) (1.17) (2.25) (1.48) (2.26) (1.31) ROA -0.009 -0.057*** 0.010 -0.066*** 0.007 -0.064***
(-0.35) (-3.79) (0.44) (-4.20) (0.32) (-4.13) Constant 0.049 0.494*** 0.154*** 0.328*** 0.138*** 0.456*** (1.39) (10.66) (3.87) (7.05) (3.93) (9.43) Firm fixed effects Yes Yes Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes Yes Yes
45
Observations 107,363 136,191 113,081 128,955 114,655 127,381 Adjusted R2 6.3% 7.1% 6.2% 7.4% 6.1% 7.4% Difference in the coefficients on EPU -0.088*** -0.029*** -0.063*** (Strong – Weak) (-7.80) (-2.61) (-5.44)
46
Table 6. Analyzing the Role of Opacity and Accounting Standards
This table reports the regression results of subsample analyses based on opacity and accounting
standards. For each variable, we divide the sample into below- and above- median country-level reporting
environment subsamples and examine the relation between earnings management and policy uncertainty
in each subsample. In columns (2) and (3), we provide results using firm-year observations belonging to
countries with weaker reporting environments. Columns (1) and (4) report the results using firm-year
observations belonging to countries with stronger reporting environments. Differences in the coefficients
on EPU between the above- and below-median subsamples are provided in the last row of the table. The
dependent variable is accrual-based earnings management, AbsDA, calculated from the performance-
augmented modified Jones model as in Kothari et al. (2005). EPU is the natural logarithm of the average
BBD policy uncertainty index over the 12-month period ending in the month of the fiscal year-end. We
winsorize all continuous variables at the 1% level in both tails of the distribution. The Appendix provides
variable definitions and data sources. All regressions include firm and year fixed effects. t-statistics from
robust standard errors clustered at the firm level are reported in parentheses. ***, **, and * indicate
significance at the 1%, 5%, and 10% levels, respectively.
Opacity Accounting Standards
Low High Weak Strong
Dependent variable = AbsDA
(1) (2) (3) (4)
EPU -0.039*** -0.019*** 0.001 -0.039***
(-4.56) (-2.88) (0.11) (-4.44) GDP_GR -0.389*** -0.438*** -0.155** -0.390***
(-2.93) (-5.44) (-1.97) (-2.89) SIZE -0.000 0.002 -0.003 -0.000
(-0.04) (0.49) (-0.71) (-0.02) OPT_CYCLE -0.035 0.012 0.017 -0.037
(-1.13) (0.46) (0.71) (-1.19) CF_VOL 0.239*** 0.187*** 0.149*** 0.239***
(14.76) (8.02) (5.84) (14.74) SALES_VOL 0.065*** 0.079*** 0.102*** 0.065***
(6.60) (7.27) (8.53) (6.63) SG_VOL -0.002 0.004*** 0.001 -0.002
(-1.49) (2.68) (0.66) (-1.52) LEV 0.010 0.016 0.019 0.009
(0.77) (1.02) (1.14) (0.73) SALES_GR 0.061*** 0.063*** 0.055*** 0.062***
(14.98) (13.13) (9.84) (15.06) DAY_PAYABLE 0.010*** -0.001 -0.001 0.010***
(2.91) (-0.29) (-0.47) (2.97) LOSS 0.003 0.019*** 0.014** 0.003
(0.63) (3.32) (2.15) (0.58) ROA -0.057*** 0.016 0.037 -0.058***
(-3.79) (0.66) (1.40) (-3.85) Constant 0.236*** 0.081* -0.015 0.233*** (5.52) (1.88) (-0.34) (5.40)
Firm fixed effects Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes Observations 124,269 119,285 92,933 123,171 Adjusted R2 6.2% 7.7% 7.0% 6.2%
47
Difference in the coefficient on EPU -0.020* -0.040*** (Strong – Weak) (-1.86) (-3.59)
48
Table 7. Analyzing the Role of Freedom of the Press
This table reports the regression results of subsample analyses based on press freedom. We divide the
sample into above and below country-level median subsamples and examine the relation between
earnings management and policy uncertainty in each subsample. In column (1), we provide the results
using firm-year observations belonging to countries with below-median (Low) freedom of the press.
Column (2) reports the results using firm-year observations belonging to countries with above-median
(High) freedom of the press. The difference in the coefficients on EPU between the High and Low
subsamples is provided in the last row of the table. The dependent variable is accrual-based earnings
management, AbsDA, calculated from the performance-augmented modified Jones model as in Kothari
et al. (2005). EPU is the natural logarithm of the average BBD policy uncertainty index over the 12-
month period ending in the month of the fiscal year-end. We winsorize all continuous variables at the
1% level in both tails of the distribution. The Appendix provides variable definitions and data sources.
All regressions include firm and year fixed effects. t-statistics from robust standard errors clustered at the
firm level are reported in parentheses. ***, **, and * indicate significance at the 1%, 5%, and 10% levels,
respectively.
Press Freedom
Dependent variable = AbsDA Low High
(1) (2)
EPU -0.019** -0.063***
(-2.57) (-7.12)
GDP_GR -0.633*** 0.250
(-6.87) (1.42)
SIZE -0.008** 0.004
(-2.34) (1.38)
OPT_CYCLE 0.016 -0.019
(0.65) (-0.59)
CF_VOL 0.170*** 0.242***
(7.13) (13.94)
SALES_VOL 0.093*** 0.061***
(7.76) (5.86)
SG_VOL 0.004*** -0.003
(2.58) (-1.57)
LEV 0.016 0.012
(1.01) (0.88)
SALES_GR 0.065*** 0.061***
(13.65) (13.67)
DAY_PAYABLE -0.001 0.010***
(-0.46) (2.66)
LOSS 0.023*** 0.003
(3.79) (0.55)
ROA 0.030 -0.061***
(1.25) (-3.82)
Constant 0.218*** 0.311***
(5.24) (7.21)
49
Firm fixed effects Yes Yes
Year fixed effects Yes Yes
Observations 98,494 133,299
Adjusted R2 8.5% 5.9%
Difference in the coefficient on EPU -0.044***
(High – Low) (-3.81)
50
Table 8. Analyzing the Role of Growth Opportunities and External Finance Dependence
This table reports the regression results of subsample analyses based on growth opportunities and external
finance dependence. For each variable, we divide the sample into below-median (Low) and above-
median (High) subsamples and examine the relation between earnings management and policy
uncertainty in each subsample. In columns (1) and (3), we provide the results using firm-year
observations belonging to countries with low growth opportunities and external finance dependence.
Columns (2) and (4) report the results using firm-year observations belonging to countries with high
growth opportunities and external finance dependence. Differences in the coefficients on EPU between
the High and Low subsamples are provided in the last row of the table. The dependent variable is accrual-
based earnings management, AbsDA, calculated from the performance-augmented modified Jones model
as in Kothari et al. (2005). EPU is the natural logarithm of the average BBD policy uncertainty index
over the 12-month period ending in the month of the fiscal year-end. We winsorize all continuous
variables at the 1% level in both tails of the distribution. The Appendix provides variable definitions and
data sources. All regressions include firm and year fixed effects. t-statistics from robust standard errors
clustered at the firm level are reported in parentheses. ***, **, and * indicate significance at the 1%, 5%,
and 10% levels, respectively.
MTB
External Finance
Dependence
Low High Low High
Dependent variable = AbsDA
(1) (2) (3) (4)
EPU 0.000 -0.089*** -0.010 -0.062***
(0.07) (-10.77) (-1.57) (-7.29) GDP_GR -0.153*** -0.229** 0.169** -0.322***
(-4.15) (-2.29) (2.55) (-3.20) SIZE -0.004** 0.007** -0.003 0.005
(-1.97) (2.33) (-1.12) (1.46) OPT_CYCLE 0.015 -0.042* -0.000 -0.026
(0.64) (-1.83) (-0.01) (-1.59) CF_VOL 0.167*** 0.247*** 0.231*** 0.233***
(9.09) (14.08) (13.09) (10.52) SALES_VOL 0.055*** 0.090*** 0.060*** 0.098***
(6.37) (8.05) (6.77) (7.28) SG_VOL 0.002 0.000 -0.002 0.002
(1.35) (0.01) (-1.20) (1.22) LEV -0.002 0.018 0.024* -0.009
(-0.20) (1.24) (1.75) (-0.56) SALES_GR 0.058*** 0.064*** 0.061*** 0.062***
(14.64) (15.10) (15.65) (12.87) DAY_PAYABLE 0.001 0.009*** 0.004 0.007***
(0.43) (3.67) (0.95) (3.49) LOSS 0.013*** 0.006 0.007 0.016**
(3.56) (1.07) (1.36) (2.42) ROA -0.010 -0.070*** -0.081*** -0.004
(-0.51) (-4.26) (-5.04) (-0.22) Constant 0.054*** 0.445*** 0.111*** 0.285*** (3.12) (10.87) (3.47) (6.62)
Firm fixed effects Yes Yes Yes Yes
Year fixed effects Yes Yes Yes Yes
51
Observations 95,208 148,346 127,246 116,308 Adjusted R2 6.1% 10.3% 10.9% 12.5% Difference in the coefficient on EPU -0.089*** -0.053*** (High – Low) (-10.15) (-5.01)