Political Uncertainty and Cost Stickiness: Evidence from National
Elections around the World

Woo-Jong Lee
Seoul National University
woojong@snu.ac.kr
Jeffrey Pittman
Memorial University
jpittman@mun.ca
Walid Saffar
The Hong Kong Polytechnic University
walid.saffar@polyu.edu.hk

Abstract
Using a large panel of elections in 56 countries for the period 1989-2012, weshow that political uncertainty surrounding elections can affect asymmetriccost behaviors to activity changes (i.e., cost stickiness). The asymmetry incost behaviors is stronger during election years than in the non-election
years, even after controlling for other firm-level and country-level
determinants. We further document that the difference in cost stickinessduring election years is conditioned by formal political and legal
institutions and informal institutions (culture). The results are consistent
with the view that managers retain slack resources when politicaluncertainty is high but to be resolved soon.

October 2015

* We gratefully acknowledge the helpful comments from Sabri Boubaker, Jeffrey Callen, C.S. Agnes
Cheng, Mujtaba Mian, Kevin C. Park, Joshua Ronen, and Kam Ming Wan. Our paper has also benefited
from comments from seminar participants at Korea University-KAIST joint workshop, The Hong Kong
Polytechnic University, Yonsei University, the 2015 Korean Accounting Association International
Conference, and the 5th Biennial Conference of the World Accounting Frontier Series.

Political Uncertainty and Cost Stickiness: Evidence from National Elections around
the World

1. Introduction
Political uncertainty has attracted considerable attention in both research and public
policy circles in recent years.

Uncertain political events stemming from possible shifts ingovernment policy or national leadership can engender policy uncertainty that disrupts firms’
regularly planned activities and alters economic outcomes. Political uncertainty usually reaches
its peak during election periods when competing parties formulate their regulatory andeconomic policies and outline their platforms for stimulating growth.

Uncertainty shapes

corporate behavior (Bernanke, 1983; Bloom et al., 2007). Recent studies in finance focus onnational elections around the world to study how political uncertainty influences corporate
decisions and outcomes (Julio and Yook, 2012; Durnev, 2014). We extend this line of researchby analyzing the importance of political uncertainty to managers’ operating decisions that drive
cost behaviors.

More specifically, we examine whether political uncertainty influences

managers’ resource-retaining decisions and hence asymmetric sensitivity of costs to activity
changes labeled as “cost stickiness” in prior accounting research.

Developing a greater

understanding of what determines the degree of cost stickiness represents an important step in
improving cost analysis. Our paper also responds to Kelly et al.’s (2015) call for research on theeffect of political uncertainty. More specifically, they stress that despite the salience of politicaluncertainty, our understanding of its impact on the economy and financial markets remains
minimal.
Cost stickiness is rooted in asymmetric cost behaviorcosts decline less in response to asales decrease than they rise for an equivalent sales increase (Anderson et al., 2003). The key tounderstanding cost stickiness is to identify whether and in what circumstance managers are
more or less willing to retain slack resources when there is a need to do so (i.e., activity declines).
However, in focusing on cross-sectional determinants of cost stickiness, extant research
generally pays less attention to inter-temporal changes in cost stickiness. In their seminal work,
Anderson et al. (2003) stress that managers respond to uncertainty by purposely delaying
reductions to committed resources until the permanence of a decline in demand is better known.
1Consequently, cost stickiness under this argument may reverse in a subsequent period when
uncertainty is resolved. Although they do not analyze when uncertainty rises and subsides,
Anderson et al. (2003) validate this argument by demonstrating that the degree of cost stickinessdecreases with the aggregation of estimation periods. This reconciles with theory that the
impact of demand uncertainty, which grows with the length of the time horizon, depends onthe characteristics of firms’ adjustment costs (e.g., Pindyck, 1982).
Although Anderson et al. (2003) emphasize the inter-temporal pattern in cost stickiness,
empirical evidence on this front remains scarce. However, in an important exception, Banker etal. (2014) report that the direction of the prior period sales change is another determinant of cost
stickiness. Specifically, their research implies that managers’ expectations become optimistic
after a prior sales increase, leading to increased stickiness; in the other direction, a prior sales
decrease leads to pessimism, translating into anti-stickiness. Our analysis diverges from Banker
et al. (2014) by focusing on managers’ forward-looking activity projection under uncertainty
instead of excessively anchoring to past information (i.e., prior period sales change).
Accordingly, we contribute to extant research by considering whether uncertainty amounts toanother determinant of cost stickiness.
National elections provide an opportune testing ground for studying the impact ofpolitical uncertainty on cost stickiness for three reasons. First, election outcomes are highly
relevant to corporate decisions as they have broad implications for industry regulation,monetary and trade policy, and taxation, among other issues (Julio and Yook, 2012). Second,
elections around the world provide a natural experimental framework that alleviates
endogeneity concerns. 1 The timing of elections is beyond the control of any individual firm andusually set at fixed intervals according to constitutional rules. In addition, elections around theworld take place at different points in time, allowing us to net out any global trends incorporate cost stickiness (Julio and Yook, 2012). National elections are exogenous politicalepisodes that are well distributed across countries and over time, affording us with a powerful
Political uncertainty is likely determined by several factors, including macroeconomic
uncertainty. Kelly et al. (2015) conclude that the difficulty in assessing the impact of political uncertainty
largely stems from the complexity involved in isolating exogenous variation in this uncertainty. They
isolate political uncertainty by exploiting its variation around major political events, namely, national
elections and global summits. Our identification of national elections as a source of political uncertainty
therefore conforms well to Kelly et al.’s (2015) assertion.
12

econometric setting for examining our research questions (Durnev, 2014).

Third, national

elections provide an ideal context for isolating to what extent managers retain resources under
uncertainty that will be resolved shortly afterward. Given that cost stickiness is an outcome ofinter-temporal shifts in uncertainty, national elections enable researchers to directly observe
how cost stickiness varies with uncertainty associated with national elections.
The intuition underlying the links between electoral uncertainty and cost stickiness isstraightforward.

Prior evidence suggests that cost stickiness becomes stronger when theadjustment cost is greater or the assessed probability for a permanent demand decline is lower(Anderson et al., 2003; Banker et al., 2013). During election years characterized by high politicaluncertainty, managers might perceive the sales decline as temporary. It follows that possible
changes in government policies or national leadership could motivate managers to retain
resources that would otherwise be divested in response to an activity decline. 2 Analyticalresearch holds that the higher uncertainty surrounding political events induces firms totemporarily suspend their investment and hiring behavior when these decisions are at least
partially irreversible (Bloom, 2009).3 Pastor and Veronesi (2012) show numerically that firms
respond to policy uncertainty by reducing investment. In the presence of adjustment costs,
firms experiencing higher uncertainty become more cautious in responding to shifts in businessconditions according to theory.
Further, incumbent politicians eager to be reelected routinely strive to leave theirconstituents with the impression that a brighter future is in store (Nordhaus, 1975).

If

candidates on the campaign trail are persuasively adamant that economic conditions relevant to
the firm are on the verge of improving, then the real option value of waiting to restore
committed resources rises. In this situation, the firm may rationally delay incurring adjustment

2 Government policies, including tax provisions, law enforcement, and regulations, affect a broad

range of firms’ operations. Governments change policies after weighing both economic and noneconomic objectives. Although governments are generally eager to maximize citizens’ welfare, they also
consider the political implications (both costs and benefits) accompanying a policy shift. Politicalincentives are unknown to managers, which makes it difficult for them to gauge the likelihood that policychanges will occur (Pastor and Veronesi, 2012).
3 Prior research on the importance of uncertainty to firm behavior extends to policy uncertainty(e.g., Adda and Cooper, 2000).

3

costs until some or all of the policy uncertainty is resolved.4 The option value of inaction has
direct implications for the extent of cost stickiness. For example, managers facing a downturnin sales may wait to obtain information to ensure that they are in a better position to gauge thepermanence of the demand reduction before cutting resources. Such delays lead to sticky costs
because unutilized resources are maintained during the interval between the reduction involume and the adjustment decision (Anderson et al., 2003). Prior research on the political
economy documents that the presence of policy-induced uncertainty usually introduces a bias
toward the status quo (e.g., Fernandez and Rodrik, 1991). In the same vein, Bernanke (1983)
holds that events with uncertain outcomes generate managerial decision cycles (e.g., investment)
by increasing the option value of waiting for new information, particularly when theuncertainty will be resolved over time.

Similarly, a temporary increase in uncertainty

surrounding national elections creates incentives to delay any adjustments to resources, which
would be evident in cost stickiness.
For a large number of firms, we test five predictions using national elections in 56
countries between 1989 and 2012. First, we examine changes in cost stickiness behavior aspolitical uncertainty shifts surrounding elections by comparing corporate behavior in thenational election year with that in non-election years.5 We posit that managers will further
delay resource-divestment decisions during election periods, implying that the cost stickinesswill be magnified during election periods relative to non-election periods. Our results lend
strong, robust support to this prediction.
Second, we analyze whether the relation between electoral uncertainty and cost
stickiness varies with the soundness of country-level political institutions.

Authoritarian

4 Thereare two types of uncertainty to consider. The first type relates to uncertainty about
whether the existing policy will change after election. The second type stems from the impact, if any, thata new government policy will have on the economic environment. More precisely, there is uncertainty
about what the government will do, as well as uncertainty about the impact of its action. Pastor andVeronesi (2012) explicitly argue that both types matter in financial markets.
5 Our empirical setting considers the election year to test the political uncertainty hypothesis.

However, it is important to stress that the timing of elections may not be a direct measure of politicaluncertainty (Julio and Yook, 2012). Accordingly, an important identification assumption is that politicaluncertainty is higher, on average, in the election years compared to other years (Bialkowski et al., 2008;
Boutchkova et al., 2012).

4

governments undermine firm operations through, for example, over-regulation, solicitation ofbribes, confiscatory taxation, and outright expropriation of firm assets (Stulz, 2005). To sustain
their unjustifiable influence, such governments usually suppress political freedom and, as aresult, the elections typically involve fewer unexpected outcomes. It follows that managers incountries with weak political institutions experience less uncertainty associated withgovernment replacement or policy shifts during elections, leading to lower cost stickiness; i.e.,in this situation, elections are almost irrelevant to firms’ operating decisions given the minimal
additional uncertainty. Consistent with this prediction, we find that the greater cost stickinesssurrounding elections is concentrated in countries with relatively sound political institutions
and in cases in which greater uncertainty accompanies the national election.
Third, we examine whether and to what extent cross-sectional variation in country-level
rigidity in employee protection laws affects the relation between elections and cost stickiness. It
is important to note that Banker et al.’s (2013) study country-level variation in employee
protection regulations given that it captures the degree of adjustment costs across countries.
However, rather than focus on whether this legislation affects cost stickiness itself, we analyze
whether it motivates managers to retain resources (i.e., labor force), particularly during election
years. We find that, in countries more protective of labor, cost stickiness is greater in election
years relative to non-election years, implying that high adjustment costs reinforce managers’
incentives to delay resource-divesting decisions when uncertainty is greater. Additionally, we
find that the impact of political uncertainty on cost stickiness hinges on countries’ legal origin,which drives several cost stickiness determinants (La Porta et al., 1997; 2000), and the efficiencyof the judicial system (Banker et al., 2012).
Fourth, we investigate whether the link between political uncertainty and cost stickinessis sensitive to country-specific disclosure standards. Prior studies argue that managers’ empirebuilding incentives or earnings management incentives influence the degree of cost stickiness(e.g., Chen et al., 2012; Dierynck et al., 2012; Kama and Weiss, 2013). We extend this line ofresearch by analyzing whether the positive relation observed between political uncertainty andcost stickiness varies with countries’ disclosure requirements. Our findings suggest that the
impact of election-driven uncertainty on cost stickiness is more salient in countries with stricter

5

disclosure institutions, implying that sound disclosure infrastructure constrains managers’
opportunism in the form of manipulating resource adjustment as a means of avoiding losses.
Last, besides examining the conditioning role of formal legal and political institutions on
the connection between political uncertainty and cost stickiness, we analyze the moderating
impact of an informal institution, countries’ culture. This involves evaluating whether thechanges in cost stickiness during elections is more intense in countries that exhibit stronger
uncertainty avoidance. Prior research suggests that uncertainty-avoiding people require formal
structures and rules to reduce their anxiety about ambiguity. In countries with high uncertainty
avoidance, managers facing an activity decline might prefer to immediately adjust resources
and conform to the traditional cost modelwhich implies a symmetric mechanical relation
between changes in activity and changes in coststo better cope with uncertain outcomes after
the election. In contrast, managers operating in countries characterized by low uncertainty
avoidance would be less sensitive to activity changes in resource adjustment. In these countries,
it follows that managers would more gradually divest resources when activity declines.
Consistent with this prediction, we find that the higher cost stickiness that occurs during
election years is more pronounced in countries with low uncertainty avoidance.
Our study contributes to emerging evidence on cost behavior by demonstrating that thedegree of cost stickiness varies across countries and over time. Extant research identifies a set offirm-level determinants of asymmetric cost behavior, including the magnitude of adjustment
costs (Anderson et al., 2003), managerial opportunism (Dierynck et al., 2012; Chen et al., 2012)and managerial optimism or pessimism (Anderson et al., 2003). However, empirical researchseldom analyzes whether, given that firms interact with the environment of the country inwhich they operate, country-level characteristics matter to managers’ operating decisions,leading to differences in asymmetric cost behavior across countries. In investigating the “legalview” of cost stickiness, recent evidence implies that employment protection legislation (Banker
et al., 2013) as well as judicial efficiency and shareholder protection laws (Banker et al., 2012)
shape cost stickiness behavior. We extend recent research by providing initial evidence on the“political view” of cost stickiness. Our analysis lends empirical support to the narrative thatpolitical institutions play a major role in several firm decisions and outcomes (e.g., Rajan andZingales, 2003; Roe, 2006; Qi et al., 2010; Boubakri et al., 2013; 2014). Moreover, against the6backdrop of extensive evidence on why costs are sticky (Chen et al., 2012; Banker et al., 2013), we
directly examine when costs are sticky. A fuller understanding of cost behavior in general andof cost stickiness in particular requires careful analysis not only of the firm- and country-specificfactors, but also of the importance of political cycle forces.
Set against extensive prior evidence implying that politics shape economic outcomes,
asset prices, and financial risk, we also contribute to extant research by analyzing theimplications of political uncertainty.

Although empirical research primarily examines theimpact of political uncertainty on financial markets (e.g., Erb et al., 1996; Boutchkova et al., 2012;
Kelly et al., 2015), we share our focus with some studies estimating the real effects on corporate
decisions (Fernandez-Villaverde et al., 2012; Julio and Yook, 2012; Baker et al., 2013; Gulen and
Ion, 2013; Durnev, 2014). In particular, cost stickiness reflects operational resource management
decisions. Although election outcomes affect corporate policies in various ways according toprior evidence, research on the importance of elections to firms’ operating decisions remains
scarce. By taking advantage of the inter-temporal changes in cost stickiness, we help close thisgap by exploring the links between political cycles and corporate operational decisions.
The rest of this paper is organized as follows. Section 2 reviews prior research todevelop the testable hypotheses. Section 3 outlines our data and reports descriptive statistics on
the regression variables. Section 4 covers the empirical evidence and Section 5 concludes.
2. Motivation
2.1. Research on Cost Stickiness
The theory of asymmetric cost behavior builds on two key observations about costs(Anderson et al., 2013). First, many costs arise because managers make a deliberate decision tocommit resources. Second, although many resource commitments can be changed in the shortrun, they usually incur resource adjustment costs such as installation and disposal costs forcapital equipment. The interaction of deliberate managerial discretion and resource adjustment
costs introduces complex dynamics in the choice of resource levels. In particular, managers
have to consider not only current activity, but also past resource levels given that they affectadjustment costs incurred in the current period, along with expected future sales, which affect7

future adjustment costs.

Additionally, resource commitment decisions are sensitive tomanagers’ incentives and behavioral biases.
In the wake of Anderson et al.’s (2013) seminal work, a number of studies document
factors determining the degree of asymmetry in cost stickiness. For example, in cross-country
research, Banker et al. (2013) analyze the adjustment cost explanation outlined in Anderson et al.
(2003) by relying on the strength of countries’ employment protection legislation to estimate
labor adjustment costs. Although adjustment costs are hard to reliably measure, Banker et al.
(2013) exploit cross-country variation in adjustment costs in terms of labor costs. Another lineof research examines cost stickiness in an agency framework.

Chen et al. (2012) show

empirically that observed cost stickiness partly reflects value-destroying empire-building withpoorly governed firms more likely to suffer from this misbehavior. Dierynck et al. (2012) andKama and Weiss (2013) find that cost stickiness is less severe when managers face incentives tomeet an earnings target set by financial analysts.
Relevant to our purposes, Banker et al. (2014) argue that a major determinant of cost
asymmetry is the direction of the prior period sales change. Specifically, after a prior sales
increase, managerial expectations become optimistic, leading to increased stickiness; conversely,a prior sales decrease engenders pessimism that translates into anti-stickiness. The practicalconstraints stemming from resource slack also magnifies this impact, which contributes to cost
stickiness, reinforcing the role that optimism plays. Grounded in Banker et al. (2014), we positthat political uncertainty associated with elections could lead to managerial optimism arisingfrom political propaganda spread by the incumbents. In sum, we expect that managers will
deliberately make resource adjustment decisions due to their biased perceptions surrounding
political elections, resulting in greater cost stickiness.
2.2. Hypotheses
Empirical research reflects that political uncertainty has been prominently featured in
the economic landscape in recent years. Fernandez-Villaverde et al. (2012) find that changes in
uncertainty about future fiscal policy, as measured by time-varying volatility of tax andspending processes, have a negative impact on economic activity. Similarly, Baker et al. (2013)provide evidence that policy uncertainty, as measured by their index, increases unemployment
8and reduces investment. Gulen and Ion (2013) find that this negative effect on investment isstronger for firms with a higher degree of investment irreversibility and firms that are morefinancially constrained. The literature on the real effects of electoral uncertainty includes Julioand Yook (2012), who document that firms reduce their investment before national elections;Jens (2013), who finds that firms reduce their investment before U.S. gubernatorial elections;
Julio and Yook (2013), who find that U.S. firms cut FDI flows to foreign affiliates before
elections in recipient countries; and Durnev (2014), who reports that corporate investment isless sensitive to stock prices during election years.
Set against prior research focusing on firm performance and investment effects
stemming from political uncertainty, we examine the association between political uncertainty
and the operating decisions of managers that lead to changes in cost structure. We expect thatfurther resource commitment in adjusting operating facilities would remain pending during
election years. Since demand is stochastic, managers are eager to assess the permanence of any
demand decline since they are responsible for adjusting committed resources downward.
Further, political business cycle theory also suggests that incumbents manipulate fiscal andmonetary policy instruments to generate a positive outlook on the economy in an attempt tomaximize the probability that they will be reelected (Nordhaus, 1975). This will play a majorrole in shaping managerial optimism in general, and further delay resource-divesting decisions
for firms suffering a sales decline, in particular. This discussion leads to our first prediction (allhypotheses are stated in alternate form).
H1: Cost stickiness will be greater in election years than in non-election years.
The relative difference in cost stickiness between election years and non-election years is
not necessarily uniform across countries. Initially, we consider whether the inter-temporal
pattern in cost stickiness varies with the soundness of political institutions. Indeed, politicalinstitutions are tied to both the degree of corruption (Lederman et al., 2005) and the general
information environment (Bushman et al., 2004), implying that managers in countries withhigh-quality political institutions are in a better position to make sensible resource adjustment
decisions.

In comparison, firms located in countries with weak political institutions (lessdemocratic countries), in which wide government policy swings are scarce, are likely to be less9

sensitive to election outcomes, resulting in smaller changes in cost stickiness during election
years relative to firms in countries with sound political institutions.
Similarly, we also examine whether the impact of political uncertainty on cost stickinessvaries across elections within and across countries.

If the outcome of an election andsubsequent policy shifts are anticipated well in advance, there should be little uncertainty
associated with the election and therefore we expect that cost stickiness does not increasesignificantly during the election year. In contrast, for elections fraught with greater uncertainty,
we expect to observe that the impact on cost stickiness will be larger.
H2: The relative difference in cost stickiness between elections and non-election years is related to thecountry’s political environment.
Apart from its uncertainty-driven nature, cost stickiness also stems from the presence ofadjustment costs (Anderson et al., 2003). Exploiting country-level variation in labor adjustment
costs across 19 OECD countries, Banker et al. (2013) document that cost stickiness is greater incountries with more rigid employee protection laws where corporate employment more likely
stays at a sub-optimal level. Stricter employment protection laws lead to greater downward
adjustment costs for labor, and firms in countries with stringent labor regulation will exhibit
greater cost stickiness. In a similar vein, we also expect that in countries more protective oflabor (stricter labor regulation), cost stickiness is greater in election years relative to in nonelection years. In our analysis, we explore whether high adjustment costs reinforce managers’
incentives to further delay resource-divesting decisions during election years when uncertainty
is greater.
In addition, we test whether the impact of political uncertainty on cost stickiness varies
with the efficiency of the judicial system. Judicial efficiency is commonly recognized to haveimportant impacts on a country’s business activities. Firms from countries with high judicial
efficiency operate with greater assurance that contracts will be fairly and promptly enforced (LaPorta et al., 1997, 1998). It follows that this assurance makes managers of such firms more
willing to accept major commitments of resources despite the accompanying high adjustment
costs, which, in turn, increases cost stickiness (Anderson et al., 2003). Banker et al. (2012) report
evidence that higher judicial efficiency at the country level is associated with greater cost10

stickiness at the firm level. Hence, we expect that the impact of political uncertainty on cost
stickiness will be concentrated in countries with higher judicial efficiency.
Similarly, we also test whether the impact of political uncertainty on cost stickinessvaries with the country’s legal origin. Extensive prior research stresses the importance of jointly
considering the effects of political and legal institutions on firm decisions and outcomes (Qi etal., 2010; Boubakri et al., 2013; 2014). The legal origin of a country is one of the primary driversof cross-country differences in corporate governance, access to external financing, businessregulation and other outcomes, all of which should play a role in shaping firm-level costbehaviors (e.g., La Porta et al., 1997; 2000). Indeed, Banker et al. (2013; 2014) and Calleja et al.
(2006) find that legal origin affects cost behavior. For instance, common law countries exhibit
greater cost stickiness given that their legal environment is more conducive to long-term
resource commitments (La Porta et al., 1998; Banker et al., 2012). Accordingly, we set thefollowing hypothesis.
H3: The relative difference in cost stickiness between elections and non-election years is related to thecountry’s legal environment.
A natural extension to analyzing the moderating impact of countries’ legal institutions
in general is to narrow the focus to specific regulations that are particularly salient to therelation between political uncertainty and cost stickiness. Against the backdrop of extensive
prior research implying that accounting transparency deters managers’ opportunism, weconsider the role that countries’ disclosure requirements play given that these institutions
govern corporate financial reporting.

Additionally, disclosure regulations are relevant in thecontext of cost stickiness because prior studies hold that agency-driven incentives shape cost
stickiness with tough monitoring mechanisms enabling firms to constrain managers’
opportunistic behaviors (e.g., Chen et al., 2012; Dierynck et al., 2012; Kama and Weiss, 2013).
For example, Anderson et al. (2003) suggest that managers’ empire-building incentives induce
cost stickiness because entrenched managers tend to maintain slack resources even in bad times
in order to defend the current scale of business. In measuring agency problems with free cash
flow as well as CEO horizon, tenure, and compensation structure, Chen et al. (2012) provide
empirical support for Anderson et al.’s (2003) argument by documenting that cost stickinessincreases with managers’ empire-building incentives. In a similar spirit, Kim et al. (2014) show
11

that SG&A cost asymmetry is exacerbated for firms with weak internal controls. This researchhelps motivate our analysis on whether the role that political uncertainty plays in cost stickinesshinges on country-level disclosure standards.
However, extant research remains mixed on the impact of agency conflicts on cost
stickiness. On one hand, some evidence suggests that agency-driven motives lead to sticky costbehaviors (Anderson et al., 2003; Chen et al., 2012; Kim et al., 2014). On the other hand, somerecent studies claim that earnings management incentives lower cost stickiness. For example,
Kama and Weiss (2013) argue that managers tend to expedite the downward adjustment ofslack resources when sales fall in an attempt to reduce unnecessary costs in order to avoid anearnings decrease. Similarly, Dierynck et al. (2012) in analyzing a sample of Belgian privatecompanies find that cost stickiness is lower for firms that report small profits (i.e., firms that are
more likely to manage earnings). Diverging from Anderson et al.’s (2003) claim about the
impact of the empire-building incentives, the evidence in Kama and Weiss (2013) and Dierynck
et al. (2012) implies a negative association between earnings management incentives and cost
stickiness.

Given that empire-building managers may be more apt to undertake earnings

management, it becomes difficult to predict ex ante the association between agency-driven
incentives and cost stickiness.
In linking the difference in cost stickiness between election and non-election years
within an agency framework, we isolate whether cross-country variation in disclosure
regulation influences the association between political uncertainty and cost stickiness.
Entrenched managers eager to accelerate discretionary cuts to slack resources in the hope of
avoiding losses may take advantage of uncertain environments during elections to execute suchself-serving resource adjustment decisions. If disclosure requirements effectively prevent selfinterested managers from doing so, it follows that the impact of political uncertainty on cost
stickiness would be concentrated in countries with stricter disclosure standards. In contrast, if
disclosure requirements suppress empire-building incentives of entrenched managers and thus
attenuate per se cost stickiness, the relation between political uncertainty and cost stickinesswould be concentrated in countries with relatively lax institutions governing disclosure.

The

following hypothesis reflects that it is difficult to form a directional prediction given the12competing prior evidence on the role that agency problems play. We therefore set the following
hypothesis in null form.
H4: The relative difference in cost stickiness between elections and non-election years is related to thecountry’s disclosure standards.
Besides testing the role of formal institutionscountries’ political, legal, and disclosure
infrastructure under the predictions in H2 to H4on the importance of political uncertainty tocost behavior, we investigate the moderating impact of an informal institution, the country’s
culture. Formal institutions and culture have been shown to work both independently andcomplementarily in determining financial and accounting decisions (e.g., Hope, 2003; Li et al.,2013). Previous studies show that even when corporate decisions are made by sophisticatedprofessional managers in a globalized environment, culture still matters (Shao et al., 2010; Li etal., 2013). Next, we evaluate whether the impact of political uncertainty on cost stickiness varies
systematically with the uncertainty avoidance dimension of the national culture. This involves
analyzing whether the link between political uncertainty and cost stickiness hinges on countries’
culture.

Among the four cultural dimensions suggested by Hofstede (2001), namely,

individualism/collectivism,masculinity/femininity,

power

distance,anduncertainty

avoidance, the latter, which captures the extent to which people are averse to uncertainty, is themost relevant to our purposes.
Uncertainty-avoiding people shun ambiguous situations and tend to require formal
structures and clear rules of conduct to alleviate their anxiety about ambiguity. We therefore
expect that the severity of uncertainty avoidance would reinforce the impact of politicaluncertainty on cost stickiness.

Specifically, we expect that managers belonging to highuncertainty avoidance societies would prefer to immediately adjust resources when confronting
an activity decline to conform to the traditional cost model and to better cope with uncertain
outcomes after the elections. In contrast, managers belonging to low uncertainty avoidancesocieties are more comfortable dealing with unpredictable outcomes.

Consequently, theywould exert less effort in mitigating the impact of uncertainty, which would be evident in amore gradual divestiture of slack resources. This leads to our final hypothesis.
H5: The relative difference in cost stickiness between elections and non-election years is more salient incountries with lower uncertainty avoidance.
13

3. Research Design
3.1. Sample Construction
We obtain data for the analysis from Compustat (Global and North America) for
publicly listed non-financial firms. After Banker et al. (2013), we exclude firm-year observations
with missing sales or assets, missing or negative operating costs over the past two years, andextreme operating costs (i.e., operating costs which are less than 50% or greater than 200% ofsales over the past two years). We also delete firm-years in a non-native currency form. We
further exclude firm-years with extreme sales (i.e., when sales increased by more than 50% ordecreased by more than 33% in the past two years) since these large shifts in sales likely capture
unusual events such as mergers or divestitures. From this sample, we drop countries with oneparty systems (e.g., China) and countries in which the chief executive is a monarch (e.g., Saudi
Arabia) given the minimal political uncertainty surrounding elections there. Additionally, we
omit from the analysis countries with fewer than 100 firm-year observations. 6 We rely on
country-specific GDP deflators to control for inflation.

The final sample has 266,538

observations for 32,892 unique firms in 56 countries for the period 1989–2012. We begin thesample period in 1989 because Compustat Global covers non-North American firms starting in
1987, and two prior years are needed to calculate the growth rates.
3.2. Measuring Political Uncertainty
Drawing valid inferences on whether there is a causal link from political uncertainty tocost behaviors depends on the availability of an adequate empirical proxy for politicaluncertainty due to difficulties in measurement and possible endogeneity (Rodrik, 1991). Since
most political events are likely correlated with economic conditions which, in turn, affect costbehaviors, we should identify a proxy for variation in uncertainty that is orthogonal to costbehaviors. In search of a valid proxy, we follow the identification strategy applied by Julio andYook (2012) and Durnev (2014), among others, by using the timing of national elections aroundthe world as a measure of variation in political uncertainty.

This involves specifying an

indicator variable for election timing which is equal to one for election years, and zero
6 Later in the paper, we report that our core evidence is almost identical when we return thesecountries to the sample.

14

otherwise.

In constructing the election dummy, we appeal to the fact that the timing ofelections is out of the control of the firms (Julio and Yook, 2012). Implicit in relying on election
timing to proxy for the degree of political uncertainty is that political uncertainty increases inelection periods (Bialkowski et al., 2008; Boutchkova et al., 2012). For instance, Baker et al. (2013)
document that their index of economic policy uncertainty spikes upward around U.S.
presidential elections, validating the premise that political uncertainty rises during election
years.
3.3. Election Data
This study examines 248 national elections in 56 countries held between 1989 and 2012
in which the outcome determined the national leader directly or indirectly. Detailed election
information is obtained from a variety of sources. We begin by collecting information on
countries’ political system and elections from the World Bank’s 2012 Database of Political
Institutions (Beck et al., 2001). We cross-check the election data with data reported in the Polity
IV database maintained by the Center for International Development and Conflict Management
at the University of Maryland. We supplement the election data with various other sources like
Elections around the World, Election Guide, and The CIA World Factbook for cases in which election
information is missing from the Polity IV database or the Database of Political Institutions.
Initially, we identify the chief executive of each country and the national elections for thechief executive.

We follow Julio and Yook (2012) by analyzing presidential elections forcountries with presidential systems because the president normally holds the supreme
executive power under a presidential system.

In contrast, in a parliamentary system, thecabinet responsible to parliament is vested an executive power. We thus consider legislativeelections for countries with parliamentary systems as the outcome of such elections has theforemost influence over the appointment of prime minister or premier. Some countries use ahybrid system such that a president and a prime minister coexist with both presidential andlegislative elections held nationally.

In these cases, we select for the study the election

associated with the leader who exerts more power over executive decisions after Julio and Yook(2012).7

7 Dropping these countries from the analysis does not change the tenor of our results.

15

3.4. Descriptive Statistics
Table 1 presents descriptive statistics sorted by country. In particular, the table reports
for each sample country the total number of firm-year observations, the legal system origin(common law or civil law), the total number of elections, the average GDP growth, average firm
log-changes in operating costs and sales, and average log assets intensity. Table 1 confirms that
our sample covers a comprehensive set of geographical regions which is important when
examining the interplay between political uncertainty and firm-level cost behavior. The U.S.
and Japan contribute the largest share of the sample at 31% and 18%, respectively, followed byIndia (6.2%), and U.K. (6%).8 Firms from other countries comprise less than 4% of the sample.
The average annual GDP growth over the sample period ranges from -0.22% for Greece to 7.13%
for India. The sample captures, on average, five election cycles. For identification purposes,
this instills confidence in using the election dummy as a proxy for political uncertainty since thesample covering a fairly large number of election cycles ensures that our evidence is less likely
to spuriously stem from unique economic conditions; i.e., the analysis reflects pervasive
phenomena rather that a heavy concentration of a certain period that otherwise could
directionally influence firms’ cost behaviors.
[Insert Table 1 about here]
In Table 2, we report that nearly 58% of the firms in our sample come from countries
with a common law origin. The average annual growth rate for firm-level deflated log-sales is0.03. Similarly, the average annual growth rate for firm-level deflated log-operating costs is0.032. These statistics are broadly consistent with those reported in prior research (e.g., Banker
et al., 2012; 2013). The average value of the main variable of interest, ElectionYear, is 0.26.
[Insert Table 2 about here]

8 As

a robustness test, we drop these four countries from the sample and confirm that ourfindings are not driven by countries with large number of observations.

16

3.5. Empirical ModelIn testing our hypotheses, we rely on a model based on the standard model developedby Anderson et al. (2003) and extended by Banker et al. (2013). This model incorporates country
characteristics along with a set of firm-level controls:∆lnXOPR= β0+ (β1+ β2 ElectionYear + β3 COMMON + β4 GDPGROWTH + β5 lnAINT)∆lnSALE +(β6+ β7 ElectionYear + β8 COMMON + β9GDPGROWTH + β10lnAINT+ β11 LAGDEC)
DEC*∆lnSALE+ ε (1)where ∆lnXOPR is the log-change in operating costs; ∆lnSALE is the log-change in sales;
ElectionYear is a dummy variable equal to one for election years, and zero otherwise; COMMON
is a dummy variable equal to one for common-law countries, and zero otherwise;
GDPGROWTH is the real GDP growth rate; lnAINT reflects asset intensity (log-ratio of assets tosales); DEC is a dummy variable equal to one if sales decreased in year t, and zero otherwise;and LAGDEC is a dummy variable equal to one if sales decreased in year t-1, and zerootherwise; and ε is an error term. We estimate the model for the pooled sample with standard
errors clustered by firms.
Specifically, we include two firm-level determinants of cost stickiness to consider thatthe distribution of the known firm-level factors could differ across countries; failing to control
for these determinants could lead to a spurious relation between the political uncertainty andcost stickiness. We expect that asset intensity (AINT) captures firm-level adjustment costs(Anderson et al., 2003), and an indicator for prior sales decline LAGDEC reflects managers’
biased expectation on future sales (i.e., managerial optimism or pessimism) (Banker et al., 2013).
We also include the common law dummy COMMON, expecting more severe cost stickiness incommon law countries where legal environments are more conducive to long-term resource
commitments (La Porta et al. 1998). Last, we control for GDP growth GDPGROWTH to further
capture managers’ expectations (Anderson et al., 2003).
Extending Noreen and Soderstrom (1997), Anderson et al. (2003) provide acomprehensive empirical framework for examining the asymmetric response of costs to activity
changes. Specifically, they include ∆lnSALE and its interaction with sales decline dummy(DEC). The coefficient on ∆lnSALE (i.e., the sum of β1 to β5lnAINT in equation (1)) represents the17percentage change in costs in response to a one percent increase in sales. The coefficient on
DEC*∆lnSALE (i.e., the sum of β6 to β11LAGDEC in equation (1)) captures the incremental costchanges in response to sales changes when sales decline. Accordingly, a negative coefficient on
DEC*∆lnSALE implies that costs respond less to sales changes when sales decline. Extending
the baseline model of Anderson et al. (2003), the above equation (1) allows both coefficients tovary with the control variables – ElectionYear, COMMON, GDPGROWTH, and lnAINT where weare particularly interested in the coefficient on ElectionYear (i.e., β7). We predict under H1 thatthe coefficient β7 is negative, consistent with the degree of cost stickiness increasing in politicaluncertainty (ElectionYear).
4. Empirical Results
In a multivariate regression framework, we begin by estimating the impact of politicaluncertainty on firms’ cost stickiness using a sample of firms from 56 countries to examine theprediction in H1. Next, we analyze the prediction in H2 that the impact of political uncertainty
on cost stickiness is greater in countries with sound political institutions and when the elections
outcome is more uncertain. For the predictions in H3 to H5, we test whether the importance ofpolitical uncertainty to cost stickiness hinges on the quality of a country’s legal and informal
institutions.
4.1. Political Uncertainty and Cost Stickiness4.1.1. Main Evidence
In Table 3, we start in Column 1 with the model of cost behavior that links annualchanges in operating costs (∆lnXOPR) to contemporaneous changes in sales revenue (∆lnSALE)
following the sticky costs models developed by Noreen and Soderstrom (1997) and Anderson etal. (2003):∆lnXOPR= β0+ α1 ∆lnSALE + α2 DEC*∆lnSALE+ ε(2)

In this specification, the slope α1 approximates the percentage change in costs for a 1% increase(decrease) in sales, and the cost stickiness coefficient α2 captures the degree of asymmetry in costbehavior (stickiness if α2 is negative and anti-stickiness if α2 is positive). In Columns 2 and 3, we

18

specify the firm-level slopes in equation (2) as a function of country-level explanatory variables
and firm-level control variables after prior research (Anderson et al., 2003 and Banker et al.,2013). In our main model (Column 3), we specify the slope coefficients as:
α1 = β1+ β2 ElectionYear + β3 COMMON + β4 GDPGROWTH + β5 lnAINT(3)

α2 = β6+ β7 ElectionYear + β8 COMMON + β9 GDPGROWTH + β10 lnAINT+ β11 LAGDEC(4)In Column 1, we find that the cost stickiness model by Anderson et al. (2003) persists for
an international sample of firms from 56 countries, which is evident in the coefficient on
DEC*∆lnSALE loading negatively at the 1% level. Moreover, the coefficient on ∆lnSALE loads
positively at the 1% level, suggesting a systematic increase in operating costs for an increase insales. In Column 2, after including the different firm and country–level variables identified inequations (3) and (4)except our variable of interest, ElectionYearwe continue to find that thecost stickiness evidence holds. Additionally, we find that cost stickiness is higher in common
law countries.

Cost stickiness is increasing with GDP growth and assets intensity, anddecreasing with prior sales decline. This evidence is consistent with Banker et al. (2013). Afteradding our variable of interest in Column 3, we find that the coefficient β7 on DEC*∆lnSALE*
ElectionYear is negative and statistically significant at the 1% level, suggesting that cost
stickiness is higher during election years than in non-election years. This result lends support
to the prediction in H1 that cost stickiness is increasing in political uncertainty.9 Finally, we findthat the remaining control variables enter significantly consistent with the earlier regressions.
[Insert Table 3 about here]
4.1.2. Additional Tests
We report additional evidence on the prediction in H1 in Table 4. In Column 1, we
present the results after restricting our sample to U.S. firms that comprise 31% of the full sample.
9 We do not find evidence that our results perceptibly hinge on whether the firm belong to apolitically sensitive industry (tobacco products, pharmaceuticals, health care services, defense, petroleum
and natural gas, telecommunications, and transportation industries), as identified by Herron et al. (1999).
Julio and Yook (2012) explain that it is difficult to classify industries as being politically sensitive or not,and report marginal evidence implying that investments during elections years varies across industries
according to their level of political sensitivity.

19

By considering firms from a single country, we help dispel the concern that the observed results
spuriously stem from omitted country characteristics (e.g., Ke et al., 2014). In addition, electionsin the U.S. are highly competitive with results often very close and outcomes uncertain in
general, reinforcing that this is a high-power testing ground for our analysis. The U.S. sample
contains 82,574 firm-year observations, covering six presidential elections from 1989 to 2012.
The results show that the coefficient β7 on DEC*∆lnSALE* ElectionYear is negative andstatistically significant at the 1% level, providing some assurance that our earlier results are notdriven by omitted country characteristics.
In Column 2 of Table 4, we include country dummies instead of the common law
variable to capture the different country fixed effects. One drawback of this test is that asignificant number of country dummies will be included in the analysis and hence thecoefficients would be biased with possible multicollinearity problems. However, in an upside,
the country fixed effects capture the different country-level time-invariant characteristics thatwould not be observable otherwise. The results reported in Column 2 confirm that omitted
country-level fixed effects do not materially affect our earlier inferences.
In constructing our final sample, we omit countries with fewer than 100 firm-year
observations. Given the small number of firms domiciled in these countries, we may not be tovalidly attribute our inferences to these countries, especially if the number of elections during
the sample period is small.

Nevertheless, in the interest of completeness, we return thepreviously excluded countries to the sample. We find in Column 3 that the negative coefficienton DEC*∆lnSALE* ElectionYear remains highly statistically significant in this regression,
providing additional evidence consistent with the prediction in H1.10
Another threat to reliable inference in this setting is that the U.S., Japan, India and the
U.K. contribute an inordinate number of firms to the sample: collectively, these four countries
account for 61% of the observations.

Prior research implies that small shifts in country

sampling can have a material impact on the results (e.g., Miller, 2004). To determine whether
our results in Table 3 are driven by these countries, we re-estimate the regression in Column 3

10 Our main evidence persists when we exclude from the analysis observations from the recent

financial crisis period (2007-2009).

20

of Table 3 after excluding observations from the U.S., Japan, India and the U.K. In Column 4 of
Table 4, we report that the negative coefficient on DEC*∆lnSALE* ElectionYear remains
statistically significant in this regression, helping to dispel the concern that our results reflect a
few large countries dominating the data.
In Column 5 of Table 4, we consider an alternative proxy for political uncertainty.
Consistent with the election years, the alternative political uncertainty proxy must be forward
looking and should reflect political uncertainty in a narrow sense, as opposed to acomprehensive country measure that also embeds macro-economic factors. After Bekaert et al.
(2014), we rely on the ICRG database to specify this proxy. We consider the opposite of thegovernment stability dimension (GOVINSTABILITY) of the ICRG as an alternative proxy forpolitical uncertainty.11 The government stability dimension assesses the government’s ability tocarry out its declared programs and its ability to stay in office. 12 The results in Column 5 areconsistent with our previous evidence with the coefficient on DEC*∆lnSALE* GOVINSTABILITY
loading negatively at the 1% level.
Finally, in Column 6 of Table 4, we consider an alternative definition of the election year.
After Julio and Yook (2012), we set the dummy variable ElectionYear equal to one for any firmyear in which an election is held no earlier than 6o days prior to the fiscal year-end in year t andno more than 274 days after the fiscal year-end of year t.13 Reinforcing our earlier evidence,DEC*∆lnSALE* ElectionYear loads negatively at the 1% level.
Overall, the results reported in Table 4 suggest that our evidence is robust to different
sample composition, additional control variables, and alternative definitions of the politicaluncertainty proxy.
[Insert Table 4 about here]
11 The results of subsample analyses on the predictions in H to H are qualitatively similar when2
5

we use the government instability measure as a proxy of political uncertainty in lieu of the election years.
The rating is based on the insights of various analysts, ranging from 0 to 12, and is the sum of
three subcomponents, each with a maximum score of four points. The subcomponents are: government
unity, legislative strength, and popular support.
12

13 Please see the Internet Appendix of Julio and Yook (2012) for more detail on their procedure forclassifying the election years.

214.1.3. Alternative Model
Banker et al.’s (2013; 2014) recent research implies that managerial optimism shapes thedegree of cost stickiness in that managers with overly optimistic future prospects tend to keep
slack resources even when divestment of the resources is necessary. Conversely, pessimistic
managers tend to limit resource expansion when sales rise but cut resources when sales fall,even resulting in anti-stickiness. Likewise, manager optimism or pessimism plays a differential
moderating role in determining cost stickiness.

To address this issue in their empirical

specifications, Banker et al. (2013; 2014) introduce two indicator variables for a prior saleincrease or decrease given that positive (negative) changes in prior sales induce manager
optimism (pessimism). We also interact both indicator variables in assessing the impact ofElectionYear on cost stickiness. The estimation model is as follows:∆lnXOPR= β0+ (

+

∗

+

+

∗

∗

+ β8

+ β3 COMMON + β4 GDPGROWTH + β5 lnAINT) ∆lnSALE +
+

(

+

∗

+

COMMON + β9GDPGROWTH + β10lnAINT) DEC*∆lnSALE+ ε (5)where LAGINC (LAGDEC) is a dummy variable for prior sales increases (decreases), which
proxies for managers optimism (pessimism). The other variables are defined in section 3.5. The(

parameter) captures the degree of stickiness (or anti-stickiness) under optimism (orpessimism). The main parameters of interest are
both

and

and

. Hypothesis 1 implies thatwill be negative because we expect that political uncertainty leads to anincrease in cost stickiness under manager optimism but to a decrease in anti-stickiness undermanager pessimism.
In Table 5, we extend the Banker et al. (2014) framework by examining the relation
between political uncertainty and cost stickiness in consideration of differential impacts ofmanagers’ optimism or pessimism.

This involves estimating the average degree of cost

stickiness or anti-stickiness, and its relation with political uncertainty separately for optimism
and pessimism.

Consistent with the Banker et al. (2013; 2014), we find that, on average,

operating costs are sticky in the optimistic case (pessimistic case (0.172 , t= 19.028).

= -0.112, t= -14.652) and anti-sticky in the

As expected, both parameters of our interest
22

(

and

) are negative and significant at the conventional levels (= -0.025, t= -1.889;

0.062, t= -4.116). This evidence suggests that political uncertainty is associated withsignificantly higher stickiness under optimism and significantly lower anti-stickiness underpessimism.14 This result offers additional validation to our Hypothesis 1. In the rest of Table 5,
we report similar evidence when we analyze the US sample only (Model 2) and when we
consider government instability as a proxy of political uncertainty (Model 3).
[Insert Table 5 about here]
4.2. Do Political Institutions Influence the Relationship between Political Uncertainty and Cost
Stickiness?
So far in the analysis, we implicitly assume that the relation between politicaluncertainty and cost stickiness is uniform across countries despite that this may be hard tojustify as implausible. A related concern is that, although a national election represents an
exogenous surge of political uncertainty, it would accompany a series of political events
surrounding elections that we have not perfectly isolated. It is therefore important to ensurethat our earlier findings are actually driven by political uncertainty imposed by national
elections, not other factors that would also influence cost behavior. Accordingly, analyzing themoderating impact of the following variables helps empirically clarify whether the importanceof national elections to cost stickiness indeed stems from political uncertainty. In this section,
we examine whether the importance of political uncertainty to cost stickiness varies
systematically with a set of country-level conditioning variables.
First, we expect that the soundness of political institutions play a role in shaping therelation between political uncertainty and cost stickiness. Wide government policy swings areless prevalent in countries with weak political institutions, putting managers in a better position
to cope with election results. Second, it follows that political uncertainty during elections rises
when election outcomes are less predictable. Specifically, election-driven political uncertainty
14 Similar to Banker et al. (2013), we find that cost stickiness is stronger with manager pessimism

than with manager optimism. This appears reasonable because for the same sales decrease, pessimistic
managers should be inclined to reduce costs more than optimistic managers, and thus the adjustment
costs should have a larger impact on their choices.

23

would have been greater before elections under rational expectations if the election results
turned out to be close afterwards. We are interested in isolating whether the closeness of theelection outcomes affects the relation between political uncertainty and cost stickiness.
However, it is important to stress that these two moderating variables (i.e., the strength ofpolitical institutions and the closeness of election results) are naturally related to the degree of
uncertainty during elections. From an identification standpoint, analyzing these two variables
provides some assurance that our earlier inference that political uncertainty, rather than otheromitted variables during elections, influences managers’ resource adjustment decisions.
To capture a country’s political institutions and the closeness of the election outcomes,
we employ four variables. 15 We follow Qi et al. (2010) in measuring the strength of theprevailing political institutions with the political rights index (POLRIGHTS) developed by
Freedom House. This index reflects the freedom, competitiveness, and fairness of elections andthe strength of competing political and minority groups in constraining the government. We
also specify Henisz’ (2012) political constraints index (PCON) as a measure of the soundness ofpolitical institutions, where PCON ranges from 0 to 1, with higher scores indicating greater
political constraints and hence stronger political institutions. Finally, the soundness of politicalinstitutions is measured by CHECKS, a proxy for the degree of political constraints within thegovernment. This variable, which is drawn from the Database of Political Institutions (DPI), is
calculated as the number of veto players in a political system, adjusting for whether these vetoplayers are independent of each other, as determined by the level of electoral competitiveness inthe system, their respective party affiliations, and the electoral rules. In gauging the closenessof the election outcome, we follow Julio and Yook (2012) by coding a dummy variable(MARGIN) equal to one if the margin of victory is smaller than the first quartile value of themargin of victory distribution over the sample of countries under consideration, where themargin of victory is defined as the difference between the fraction of votes received by the
victor and that garnered by the runner-up.
15 In

examining the predictions in H2 to H5, we rely on multiple measures of the country
constructs under study since country-level regression variables can suffer from severe measurement error,
admitting bias that can call into question the inferences drawn in these settings (e.g., Bushman and Smith,
2001; Miller, 2004). Moreover, several of these conditioning variables are derived from surveys thatamount to subjective rankings, casting some doubt on the veracity of the underlying perceptions (e.g.,
Djankov et al., 2008).

24

In Table 6, we find across all the proxies that the coefficient β7 on DEC*∆lnSALE*
ElectionYear is only negative and statistically significant in the subsample of firms located incountries with sound political institutions (Columns 2, 4, and 6) and amidst highly competitiveelections (Column 8), consistent with the prediction in H2.

Reinforcing this evidence, thedifference in the β7 coefficients between the samples of weak and sound political institutions isstatistically significant at conventional levels for three out of the four country-level politicalconditioning variables.16[Insert Table 6 about here]
4.3. Do Legal Institutions Influence the Relationship between Political Uncertainty and Cost Stickiness?
Another important issue to consider when evaluating the sensitivity of cost stickiness tocountry-level characteristics is that the cost stickiness is a function of resource adjustment costs.
In this section, we provide evidence on the prediction in H3 by examining whether the link
between political uncertainty and cost stickiness hinges on countries’ legal infrastructure.
Banker et al. (2013) report evidence on this front by investigating the role that employee
protection laws play in cost stickiness across countries. They posit and find that more rigid
employee protection laws impose higher adjustment costs, resulting in more sticky costbehaviors. In a similar spirit, we expect that the difference in cost stickiness between election
years and non-election years varies with the severity of costly resource adjustment in eachcountry. Specifically, we analyze whether cross-country differences in legal institutions shape
the association between political uncertainty and cost stickiness.

This involves examining

whether the impact of political uncertainty on cost stickiness is more pronounced in countries
with a more effective judicial efficiency or more rigid labor protection laws.

Relatedly,

grounded in prior research documenting that legal origins affect cost behavior (Banker et al.,2013; Calleja et al., 2006; Kim and Wang, 2014), we explore how the relation between politicaluncertainty and cost stickiness varies according to countries’ legal origin.

16 In an additional test, we do not find evidence that our results differ between presidential andparliamentary systems.

25

To measure the strength of a country’s legal institutions, we employ three measures that
capture different dimensions of its institutional infrastructure. These measures include thecountry’s legal origin (COMMON), labor regulation (LABOR REG), and judicial efficiency(JUDEFF). We follow La Porta et al. (1998) by setting the indicator variable COMMON to one
for English Common Law countries, and zero otherwise. La Porta et al. (1998) show that
common law countries are associated with higher investor protection than other countries.
LABOR REG, obtained from the Economic Freedom of the World, captures the labor regulation
along different dimensions.

Among the more prominent are minimum wages, dismissal

regulations, centralized wage setting, extension of union contracts to nonparticipating parties,
and conscription.

Derived from La Porta et al. (1998), JUDEFF assesses the “efficiency andintegrity of the (country’s) legal environment as it affects businesses.”
In Table 7, we find across all proxies that the coefficient β7 on DEC*∆lnSALE* ElectionYear
is negative and statistically significant at the 5% level or better in the subsample of firms locatedin countries with strong legal institutions (Columns 2, 4, and 6), suggesting that strong legal
institutions intensity the cost stickiness exhibited during election years. In contrast, we find thatthe coefficient on DEC*∆lnSALE* ElectionYear is statistically insignificant in the subsample ofcountries with weak legal institutions (Columns 1, 3, and 5). The difference in the β7 coefficients
between the samples of weak and strong legal institutions is statistically significant at the 1%
level for all three of the country-level conditioning variables. Consistent with the prediction in
H3, these findings support the intuition that managers’ suboptimal resource adjustment
decisions under high political uncertainty (i.e., during elections) are worse when they have tocope with greater adjustment costs stemming from the country’s legal institutions.
[Insert Table 7 about here]
4.4. Does Disclosure Infrastructure Influence the Relationship between Political Uncertainty and Cost
Stickiness?
Next, we evaluate whether the link between political uncertainty and cost stickiness issensitive to countries’ disclosure institutions. If disclosure requirements effectively prevent
self-interested managers from accelerating discretionary cuts to slack resources in order toavoid incurring losses, then we expect to observe that the impact of political uncertainty on cost26

stickiness is more pronounced in countries that impose stricter disclosure standards. In contrast,
if disclosure requirements suppress empire-building incentives of entrenched managers and, inturn, attenuate cost stickiness, then the relation between political uncertainty and cost stickinesswill be less pronounced in countries with strict disclosure requirements. Given that the impact
of disclosure standards could run in either direction, we leave this as an empirical question that
we help resolve in analyzing H4.
To capture a country’s disclosure infrastructure, we employ three different measures.
These measures include a disclosure requirements index (DISCLOSE), Center for Financial
Analysis and Research (CIFAR) index, and Kurtzman et al.’s (2004) measure of financial
reporting quality (ACC).

DISCLOSE measures the strength of stock exchange-mandated

disclosure requirements according to La Porta et al. (2006). CIFAR represents the average
number of 90 items included in the 1995 annual reports of a sample of domestic firms; i.e., itreflects corporate voluntary disclosure largely stemming from countries’ accounting
standards.17 Kurtzman et al.’s (2004) measure of financial reporting quality (ACC) is one of thesubindices of their opacity index.
In Table 8, we find across all conditioning proxies that the coefficient β7 on
DEC*∆lnSALE* ElectionYear is only negative and statistically significant in the subsample offirms located in countries with strict disclosure requirements (Columns 2, 4, and 6), suggestingthat strict disclosure requirements magnify the higher levels of cost stickiness firms exhibit
during election years. The difference in the β7 coefficients between the samples of weak andstrict disclosure requirements is statistically significant at conventional levels for all three
country-level disclosure variables.

This evidence implies that disclosure requirements are

effective in preventing firms from using cost cuts as a means of earnings management.
[Insert Table 8 about here]
4.5. Does Culture Influence the Relationship between Political Uncertainty and Cost Stickiness?

17 We

acknowledge that prior studies raise concerns on the use of CIFAR as a measure ofdisclosure requirements in an economy (e.g., Bushman and Smith, 2001; Hail and Leuz, 2006). They
argue that CIFAR index also captures voluntary disclosures by firms.

27

Cultural backgrounds have gained significant attention in cross-country analyses
because it represents an important informal institution governing behavior. Complementing
the analysis of formal institutions in the previous sections, we shift gears in this section byfocusing on whether countries’ cultural attributes influence the association between politicaluncertainty and cost stickiness. Among various layers of cultural variables, we are particularlyinterested in the role that uncertainty avoidance plays given its natural link to politicaluncertainty. We expect that the impact of political uncertainty on cost stickiness is less salientin countries where people tend to be eager to expedite downward resource adjustment whensales decline in order to avoid uncertainty (i.e., in countries with high uncertainty avoidance
tendency).
To capture a country’s uncertainty avoidance tendency, we rely on three different
measures. These measures include Hofstede (2001)’s index (UAI_HF), Tang and Koveos’ (2008)
updated version of Hofstede’s index (UAI_TK) and House et al.’s (2001) proxy of the country’s
uncertainty avoidance index (UAI_HOUSE). The Hofstede value dimensions were derived
from a sample of IBM employees in the 1970s. Although we agree with Hofstede (1983) that
culture is extremely stable over time and that his culture dimensions indicate the relative
position of one country compared to another that rarely shifts even if culture changes, we test
whether our findings are robust to Tang and Koveos’ (2008) updated Hofstede index on
uncertainty avoidance (UAI_TK) that is based on economic mutation within a country. We also
employ the uncertainty avoidance index (UAI_HOUSE) constructed by House et al. (2001) tocapture a country’s institutional uncertainty avoidance. This dimension reflects the degree towhich societal institutions are rooted in avoidance uncertainty (House et al., 2001).
In Table 9, we find across all proxies that the coefficient β7 on DEC*∆lnSALE* ElectionYear
is negative and statistically significant at the 5% level or better in the subsample of firms locatedin countries with a lower uncertainty avoidance tendency (Columns 2, 4, and 6). This evidence
implies that a lower uncertainty avoidance tendency magnifies the cost stickiness that firms
experience during election years. The difference in the β7 coefficients between the samples ofhigh and low uncertainty avoidance tendency is statistically significant at the 1% level for twoof the three country-level uncertainty avoidance indices (the lone exception is the comparison
between Columns 1 and 2). Lending support to the prediction in H5, These results suggest that28

managers in low uncertainty avoidance countries are less willing to undertake adjustments inresources in response to a sales decline in order to avoid possible negative outcomes associatedwith uncertainty, which is evident in the higher cost stickiness exhibited during election years
in such countries.
[Insert Table 9 about here]
5. Conclusion
For a large panel of firms representing 56 countries, we provide initial evidence on therole that political uncertainty, which we measure with national elections that engender an
exogenous surge in political uncertainty, plays in shaping asymmetry in cost behaviors
stemming from activity changes. In regressions that control for other firm-level and countrylevel determinants, we find that cost stickiness is greater in election years relative to nonelection years, consistent with expectations. In another series of tests, we report strong, robust
evidence supporting the predictions that the importance of political uncertainty to cost
stickiness is concentrated in countries with sound political, legal, and disclosure institutions andcountries whose populations are known to exhibit low uncertainty avoidance. These results
collectively imply that the impact of political uncertainty on cost stickiness varies with countries’
formal and informal institutions.
To the best of our knowledge, we provide the first broad sample empirical evidence on
the links between political uncertainty and firms’ cost behavior. In particular, we contribute to
the extant literature by integrating two seemingly distant research streams.

First, despite

Anderson et al.’s (2003) intuitive argument that managers respond to uncertainty bydeliberately postponing reductions to committed resources until they have a better handle on
the permanence of a decline in demand, prior research seldom examines inter-temporal changes
in cost stickiness, which stands in sharp contrast to the extensive evidence from cross-sectional
analyses. Analyzing national election data enables us to help narrow this gap by providing an
opportune inter-temporal research setting where uncertainty rises steeply during elections andfalls steeply afterwards. Second, a growing stream of prior studies on the implications ofpolitics for corporate behaviors has been largely silent on accounting issues. We add to this lineof the literature by evaluating how firms respond to political events (i.e., national elections) in29

terms of their asymmetric cost behaviors.

Given that the interplay between politics andbusiness becomes critical for firms’ long-term survival, we look forward to further research on
how politics bring differences in accounting practices.

30

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34

APPENDIX
Variable definitions and sources
Variable

Definition

SourceCountry-level variables
ElectionYear
COMMON
GDPGROWTHPOLRIGHTS

PCON

Dummy variable takes a value of one for the election years, and zero Beck et al. (2001)
otherwise.
Dummy variable equal to one for firms from English Common Law La Porta et al. (1998)
countries, and zero otherwise.
World
Development
GDP growth over the year.
Indicators
An index of political rights from 1980 to 2010. These ratings rely Freedom House
upon the following criteria: free and fair elections take place; therulers are elected; there are competitive parties or other competitive
political groupings; the opposition has real power and plays asignificant role; and minority groups have moderate selfgovernment powers or can participate in the government throughinformal consensus. The criteria are then grouped into three subcategories: electoral process (three criteria), political pluralism (fourcriteria), and functioning of the government (three criteria). For
each criterion, 0 to 4 points are granted, where 0 denotes the lowest
degree and 4 the largest degree of rights. These scores are then
combined to construct the political rights index. The index goesfrom 1 (weak political rights) to 7 (strong political rights).
Measures the degree of political constraints of a country. Derived Henisz (2012)
from a model of political interaction that incorporates informationon the number of independent branches of governments with vetopower and the distribution of preferences across and within thosebranches. Government branches considered are chief executives,
lower house of legislature, higher house of legislature, judiciary, andsub-federal branches. Higher scores indicate stronger politicalconstraints and sound political institutions.

CHECKS

Number of checks and balances in the country.

MARGIN

Dummy variable set to one if the vote difference between thelargest government party and the largest opposition party is lessthan the first quartile value of the sample distribution.
12 minus the government stability index. The government stability
index assigns numbers between 1 and 12, where higher values
indicate more stable governments. The index is updated on amonthly basis and assesses the government’s ability to carry out its
declared programs and to stay in office.
Many types of labor-market regulations infringe on the economic
freedom of employees and employers. Among the more prominent
are minimum wages, dismissal regulations, centralized wage
setting, extension of union contracts to nonparticipating parties,
and conscription. The index goes from 1 (weak regulated labormarket) to 10 (strong regulated labor market).

GOVINSTABILITYLABOR REG

The
Databaseof
Political Institutions
The
Databaseof
Political Institutions
The
International
Country Risk Guide

Economic Freedom of
the World35

JUD_EFF

UAI_HF
UAI_TK

An index of judicial efficiency, with higher value indicating a more
efficient judicial system
An assessment of disclosure requirements relating to: (1)prospectus; (2) compensation of directors and key officers; (3)ownership structure; (4) inside ownership; (5) contracts outside theordinary course of business; and (6) transactions between the issuer
and its directors, officers, and/or large shareholders. The index
ranges from 0 to 1, with higher values indicating more extensive
disclosure requirements.
Index created by examining and rating companies’ 1995 annual
reports on their inclusion or omission of 90 items. These items fall
into seven categories: general information, income statements,
balance sheets, funds flow statement, accounting standards, stock
data, and special items. A minimum of 3 companies in eachcountry were studied.
An assessment of the quality of countries’ corporate accounting
standards.
Hofstede’s cultural index on Uncertainty Avoidance.
Tang & Koveos’ updated cultural index on Uncertainty Avoidance.

UAI_HOUSEHouse et al.’s (2002) cultural index on Uncertainty Avoidance.

DISCLOSE

CIFAR

ACC

La Porta et al. (1998)
La Porta et al. (2006)

La Porta et al. (2006)

Kurtzman et al. (2004)
Hofstede (2001)
Tang
andKoveos(2008)
House et al. (2002)

Firm-level variables∆lnXOPR

log-change in deflated operating costs∆lnSALE

log-change in deflated sales

lnAINT

log-ratio of total assets to sales

DEC

Dummy variable equal to one if deflated sales decreased inyear t, zero otherwise
Dummy variable equal to one if deflated sales decreased inyear t-1, zero otherwise
Dummy variable equal to one if deflated sales increased inyear t-1, zero otherwise

LAGDECLAGINC

Compustat Global andNorth America
Compustat Global andNorth America
Compustat Global andNorth America
Compustat Global andNorth America
Compustat Global andNorth America
Compustat Global andNorth America

36

TABLE 1
Descriptive Statistics by Country

Country
ARGENTINA
AUSTRALIA
AUSTRIABANGLADESHBELGIUM
BRAZIL
BULGARIA
CANADACHILECOLOMBIACROATIACYPRUSCZECH REPUBLIC
DENMARKEGYPTFINLANDFRANCEGERMANYGREECEHUNGARY
INDIA
INDONESIAIRELANDISRAEL
ITALY
JAPAN
KENYA
KOREA, SOUTHLATVIA
LITHUANIALUXEMBOURG
MALAYSIA
MAURITIUS
MEXICONETHERLANDSNEW ZEALANDNIGERIA

%
0.12
2.14
0.34
0.05

Average∆lnXOPR
0.038
0.032
0.037
0.082

Average∆lnSALE
0.034
0.03
0.034
0.079

AveragelnAINT
0.48
-0.03
0.07
0.272

AverageGDP
Growth
3.356
3.207
2.038
6.269

COMMON
0
1
0
1

1,108
2,420
103
8,643
1,469
239
208
185

0.42
0.91
0.04
3.24
0.55
0.09
0.08
0.07

0.02
0.033
0.01
0.036
0.037
0.039
0.024
0.016

0.018
0.035
-0.002
0.032
0.033
0.04
0.019
0.009

0.018
0.283
0.312
0.064
0.377
0.737
0.334
0.533

1.632
3.328
2.873
2.321
4.314
3.817
1.381
1.77

0
0
0
1
0
0
0
1

187
1,630
175
1,419
7,122
7,058
1,420
217

0.07
0.61
0.07
0.53
2.67
2.65
0.53
0.08

0.012
0.029
0.024
0.034
0.036
0.03
0.015
0.004

0.02
0.028
0.023
0.033
0.034
0.03
0.003
0.003

0.298
-0.1040.446-0.133-0.002-0.159
0.404
0.074

2.947
1.405
4.759
1.901
1.427
1.454
-0.22
2.351

0
0
0
0
0
0
0
0

16,606
2,431
338
1,483
2,202
48,324
167
5,226

6.23
0.91
0.13
0.56
0.83
18.13
0.06
1.96

0.053
0.019
0.042
0.046
0.027
0.019
0.031
0.048

0.049
0.015
0.04
0.044
0.021
0.02
0.024
0.044

0.13
0.099
0.024
0.199
0.311
-0.013
0.087
0.091

7.132
5.033
2.388
3.906
0.393
1.057
4.215
3.98

1
0
1
1
0
0
1
0

174
212
226
7,703
135
1,171
1,859
935

0.07
0.08
0.08
2.89
0.05
0.44
0.7
0.35

0.007
0.007
0.039
0.025
0.051
0.029
0.028
0.028

0.006
0.005
0.041
0.018
0.045
0.028
0.029
0.028

0.294
0.075
0.325
0.357
0.468
0.248
-0.258-0.014

2.917
3.567
2.884
5.082
4.333
2.694
1.952
2.291

0
0
0
1
0
0
0
1

336

0.13

0.027

0.027

-0.1937.004

1

Number of
Observations
308
5,696
911
124

Number ofElections
3
8
7
1
6
4
3
7
5
5
4
3
4
7
3
6
4
6
6
4
5
3
3
7
6
8
3
4
3
4
4
5
3
4
7
8
4

37

%
0.55
0.51
0.25
0.36
0.79
0.19
0.4
1.79

Average∆lnXOPR
0.028
0.025
0.052
0.01
0.049
0.011
0.012
0.04

Average∆lnSALE
0.028
0.024
0.054
0.009
0.045
0.015
0.012
0.034

AveragelnAINT
0.134-0.0970.422
0.662-0.084
0.327
0.157
0.228

AverageGDP
Growth
2.172
4.168
5.663
4.68
3.966
1.058
4.124
5.857

COMMON
0
1
0
0
0
0
0
1

186
2,384
1,429
860
3,215
2,725
9,398
3,659

0.07
0.89
0.54
0.32
1.21
1.02
3.53
1.37

0.025
0.032
0.038
0.016
0.04
0.025
0.019
0.038

0.025
0.03
0.037
0.024
0.04
0.024
0.01
0.034

0.265
-0.2130.316
0.253-0.133
0.093
0.157
0.091

1.846
3.22
2.15
6.414
2.354
1.609
1.409
3.748

0
1
0
1
0
0
0
1

1,036
15,97882,574
266,538

0.39
5.99
30.98
100

0.029
0.033
0.036
0.032

0.022
0.031
0.034
0.030

0.085
-0.129-0.076
0.012

3.898
2.11
2.601
2.690

0
1
1
0.577

Country
NORWAYPAKISTANPERUPHILIPPINESPOLANDPORTUGALRUSSIASINGAPORE

Number of
Observations
1,474
1,360
668
960
2,100
519
1,071
4,772

SLOVENIA
SOUTH AFRICASPAIN
SRI LANKASWEDENSWITZERLAND
TAIWANTHAILAND
TURKEYUNITED KINGDOMUNITED STATES
Total

Number ofElections
5
5
4
4
3
7
4
5
4
4
6
3
6
6
5
6
6
5
6
4.840

38

TABLE 2
Descriptive Statistics∆lnXOPR∆lnSALElnAINTDEC
LAGDECGDPGROWTHCOMMONElectionYear
GOVINSTABILITYPOLRIGHTPCON
CHECKSMARGINLABOR REG
JUD_EFFCIFAR
DISCLOSE
ACC
UAI_TK
UAI_HF
UAI_HOUSE

Mean
0.032
0.030
0.012
0.390
0.370
2.690
0.577
0.262
3.777
6.581
0.757
4.374
0.173
7.146
9.085
73.1970.824
26.12452.06959.143
4.261

Median0.033
0.032-0.023
0.000
0.000
2.553
1.000
0.000
3.500
7.000
0.773
4.000
0.000
7.500
10.00076.0000.833
22.00046.00046.0004.110

Standard
Deviation
0.150
0.146
0.643
0.488
0.483
2.791
0.494
0.440
1.773
1.006
0.156
2.427
0.378
1.521
1.623
6.741
0.178
8.569
19.05422.4430.470

Min-1.387-0.415-6.1720.000
0.000-17.955
0.000
0.000
0.500
1.000
0.000
1.000
0.000
2.800
2.500
56.0000.250
0.000
34.000
8.000
3.160

Max
1.306
0.405
8.216
1.000
1.000
14.781
1.000
1.000
9.000
7.000
0.894
18.0001.000
9.300
10.00085.0001.000
63.00086.000112.000
5.610

39

TABLE 3
Estimates of the relationship between election years and cost stickiness
Dependent Variable: ∆lnXOPR

Sign

Model 1Model 2Model 3∆lnSALE+

0.908***(308.212)

0.895***(244.048)-0.071***(-12.921)-0.017***(-4.587)
0.008***(8.934)-0.021***(-4.658)-0.082***(-12.241)

Constant

0.002***(5.624)-0.021***(-3.527)-0.017***(-12.937)-0.059***(-8.304)
0.131***(18.829)0.002***(6.016)

0.893***(230.431)
0.008*(1.956)-0.017***(-4.390)0.008***(8.857)-0.021***(-4.683)-0.071***(-9.679)-0.034***(-3.677)-0.026***(-4.272)-0.017***(-12.813)-0.059***(-8.324)
0.132***(18.949)
0.002***(6.007)

Observations
R-squared

266,538
0.756

266,538
0.759

266,538
0.760∆lnSALE*ElectionYear∆lnSALE*COMMON∆lnSALE*GDPGROWTH∆lnSALE *lnAINT
DEC*∆lnSALE-

DEC*∆lnSALE*ElectionYear-

DEC*∆lnSALE*COMMON-

DEC*∆lnSALE *GDPGROWTH-

DEC*∆lnSALE *lnAINT-

DEC*∆lnSALE*LAGDEC+

The estimation equation is:∆lnXOPR= β0+ (β1+ β2 ElectionYear + β3 COMMON + β4 GDPGROWTH + β5 lnAINT)∆lnSALE +(β6+ β7 ElectionYear + β8 COMMON + β9GDPGROWTH + β10lnAINT++ β11 LAGDEC) DEC*∆lnSALE+ ε
where ∆lnXOPR is the log-change in operating costs; ∆lnSALE is the log-change in sales;
GDPGROWTH is the real GDP growth rate; lnAINT is asset intensity (log-ratio of assets to sales);
COMMON is a dummy variable equal to one for common-law countries; ElectionYear is a dummy
variable equal to one for election years; DEC is a dummy variable equal to one if sales decreased inyear t and LAGDEC is a dummy variable equal to one if sales decreased in year t-1; ε is an error term.
Robust t-statistics clustered at the firm level are reported beneath each estimate. ***, **, and * denotestatistical significance at the 1%, 5%, and 10% levels, respectively. Definitions and data sources forthe variables are provided in the Appendix.

40

TABLE 4
Robustness Tests

Dependent Variable: ∆lnXOPR

Sign∆lnSALE+∆lnSALE*ElectionYear

U.S.
sample(1)

Add country
fixed effects(2)

Add countries
with less than100
observations(3)

0.863***(118.011)
0.010*(1.869)0.941(0.001)0.010**(2.291)0.893***(230.434)
0.008*(1.956)

Drop large
economies(4)
0.906***(170.508)-0.002(-0.314)∆lnSALE*GOVINSTABILITY∆lnSALE*COMMON∆lnSALE*GDPGROWTH∆lnSALE *lnAINT
DEC*∆lnSALE-

DEC*∆lnSALE*ElectionYear-

DEC*∆lnSALE*GOVINSTABILITY-

DEC*∆lnSALE*COMMON-

DEC*∆lnSALE *GDPGROWTH-

DEC*∆lnSALE *lnAINT-

DEC*∆lnSALE*LAGDEC+

0.011***(5.280)-0.012**(-2.075)-0.081***(-7.100)-0.050***(-3.740)0.007***(5.191)-0.022***(-4.793)-0.090***(-3.117)-0.036***(-3.851)-0.017***(-4.391)0.008***(8.857)-0.021***(-4.683)-0.071***(-9.679)-0.034***(-3.677)∆lnSALE * COUNTRYEFFECTS
DEC*∆lnSALE * COUNTRYEFFECTS-0.028***(-10.854)-0.074***(-8.121)0.154***(16.350)
0.002***(5.916)NONO-0.020***(-11.839)-0.065***(-8.835)
0.134***(19.104)
0.002***(5.787)
YES
YES-0.026***(-4.272)-0.017***(-12.813)-0.059***(-8.324)
0.132***(18.949)
0.002***(6.007)
NO
NO

Observations
R-squared

82,574
0.738

266,5380.760

267,489
0.760

Constant-0.010(-1.532)
0.007***(6.392)-0.040***(-6.451)-0.051***(-4.982)-0.028**(-2.184)

Alternative
political
uncertainty
proxy
(5)

Alternativeelection year
definition(6)

0.869***(144.597)

0.894***(227.593)
0.006(1.334)0.006***(5.604)-0.014***(-3.659)
0.009***(9.527)-0.021***(-4.576)-0.023*(-1.944)0.001(0.059)-0.008***(-4.463)-0.056***(-5.548)0.074***(7.810)0.003***(8.011)NONO-0.013***(-6.305)-0.036***(-5.287)-0.018***(-13.527)-0.060***(-8.372)
0.132***(18.963)
0.002***(5.895)
NONO

103,056
0.730

266,402
0.760-0.017***(-4.566)
0.009***(8.960)-0.021***(-4.681)-0.072***(-9.921)-0.036***(-3.658)-0.025***(-4.113)-0.017***(-12.597)-0.059***(-8.299)
0.131***(18.838)
0.002***(5.967)
NONO
266,538
0.760

The estimation equation is:∆lnXOPR= β0+ (β1+ β2 ElectionYear + β3 COMMON + β4 GDPGROWTH + β5 lnAINT)∆lnSALE +(β6+ β7 ElectionYear + β8 COMMON + β9GDPGROWTH + β10lnAINT++ β11 LAGDEC) DEC*∆lnSALE+ ε
where ∆lnXOPR is the log-change in operating costs; ∆lnSALE is the log-change in sales; GDPGROWTH is the real
GDP growth rate; lnAINT is asset intensity (log-ratio of assets to sales); COMMON is a dummy variable equal to
one for common-law countries; ElectionYear is a dummy variable equal to one for election years; DEC is a dummy
variable equal to one if sales decreased in year t and LAGDEC is a dummy variable equal to one if sales decreased41

in year t-1; ε is an error term. Model 1 considers only U.S. firms. Model 2 controls for the country fixed effects.
Model 3 includes countries with less than 100 observations. Model 4 drops large economies from the sample.
Model 5 considers an alternative proxy of political uncertainty. Robust t-statistics clustered at the firm level arereported beneath each estimate. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels,
respectively. Definitions and data sources for the variables are provided in the Appendix.

42

TABLE 5
Alternative Model

Sign

Total sample(1)

U.S. sample(2)∆lnSALE*LAGINC+∆lnSALE*LAGDEC+

0.934***(233.495)
0.784***(124.056)
0.000(0.040)0.905***(126.635)
0.730***(68.980)
0.005(0.978)

Dependent Variable: ∆lnXOPR∆lnSALE*LAGINC*ElectionYear∆lnSALE*LAGINC*GOVINSTABILITY

0.035***(3.242)0.043***(2.867)∆lnSALE*LAGDEC*GOVINSTABILITY∆lnSALE*COMMON∆lnSALE*GDPGROWTH∆lnSALE*lnAINT
DEC*∆lnSALE*LAGINC-

DEC*∆lnSALE*LAGDEC+

DEC*∆lnSALE*LAGINC*ElectionYear-

DEC*∆lnSALE*LAGINC*GOVINSTABILITY-

DEC*∆lnSALE*LAGDEC*ElectionYear-

DEC*∆lnSALE*LAGDEC*GOVINSTABILITY-

DEC*∆lnSALE*COMMON-

DEC*∆lnSALE*GDPGROWTH-

DEC*∆lnSALE*lnAINT-

Observations
R-squared

0.913***(150.154)
0.749***(59.259)

0.005***(4.146)∆lnSALE*LAGDEC*ElectionYear

Constant

Alternative
political
uncertainty
proxy
(3)

-0.025***(-6.511)0.009***(9.380)-0.016***(-3.603)-0.112***(-14.652)
0.172***(19.028)-0.025*(-1.889)0.009***(4.713)-0.008(-1.425)-0.120***(-10.285)
0.206***(15.876)-0.051***(-2.651)

0.011***(3.923)-0.020***(-5.415)0.009***(9.929)-0.015***(-3.518)-0.081***(-5.662)0.243***(14.251)-0.007**(-2.311)-0.062***(-4.116)-0.078***(-3.632)-0.019***(-3.013)-0.017***(-13.166)-0.064***(-9.096)0.002***(6.533)-0.027***(-10.413)-0.078***(-8.582)
0.003***(6.601)-0.022***(-6.085)-0.030***(-4.380)-0.019***(-13.897)-0.064***(-9.077)
0.002***(6.448)266,538
0.762

82,5740.741

266,402
0.762

43

The estimation equation is:∆lnXOPR= β0+ (++∗GDPGROWTH+ β5 lnAINT) ∆lnSALE + (+++ β8 COMMON + β9GDPGROWTH + β10lnAINT) DEC*∆lnSALE+ ε

∗

+
∗

+

+ β3 COMMON + β4∗

where ∆lnXOPR is the log-change in operating costs; ∆lnSALE is the log-change in sales; GDPGROWTH is the real
GDP growth rate; lnAINT is asset intensity (log-ratio of assets to sales); COMMON is a dummy variable equal to
one for common-law countries; ElectionYear is a dummy variable equal to one for election years; DEC is a dummy
variable equal to one if sales decreased in year t, LAGINC is a dummy variable equal to one if sales increased inyear t-1; and LAGDEC is a dummy variable equal to one if sales decreased in year t-1; ε is an error term. Model 1
considers the total sample. Model 2 considers only U.S. firms. Model 3 considers an alternative proxy of politicaluncertainty. Robust t-statistics clustered at the firm level are reported beneath each estimate. ***, **, and * denotestatistical significance at the 1%, 5%, and 10% levels, respectively. Definitions and data sources for the variables areprovided in the Appendix.

44TABLE 6
Subsample Analysis: Political Variables
Dependent Variable: ∆lnXOPR

Sign∆lnSALE+∆lnSALE*ElectionYear∆lnSALE*COMMON∆lnSALE*GDPGROWTH∆lnSALE *lnAINT
DEC*∆lnSALE-

DEC*∆lnSALE*ElectionYear-

DEC*∆lnSALE*COMMON-

DEC*∆lnSALE *GDPGROWTH-

DEC*∆lnSALE *lnAINT-

DEC*∆lnSALE*LAGDEC+

Constant

CHECKS
Low(5)

CHECKSHigh(6)MARGINHigh(7)

MARGINLow(8)

0.909***(234.310)
0.001(0.370)-0.024***(-7.025)
0.007***(7.001)-0.024***(-6.210)-0.078***(-11.612)-0.017**(-2.136)-0.025***(-4.186)-0.015***(-10.924)-0.070***(-11.019)
0.116***(19.945)0.001***(6.506)

0.896***
0.916***(244.168) (142.196)-0.006
0.007(-1.532)(1.480)-0.031***0.024***(5.408)(-5.749)
0.011***
0.002***(3.677)(7.640)-0.054***-0.017***(-3.699)(-11.523)-0.065***-0.072***(-10.214)(-5.741)0.020**-0.033***(2.430)(-3.080)0.001-0.019*(0.075)(-1.906)-0.003***-0.020***(-2.685)(-10.344)-0.068***-0.022***(-2.920)(-8.826)
0.128***
0.067***(10.568)(16.166)
0.002***
0.002***(6.792)(6.401)0.00

0.915***(192.569)
0.006(0.998)0.004(0.596)0.004***(4.005)-0.041***(-7.212)-0.066***(-7.343)0.000(0.012)0.001(0.088)-0.006***(-4.103)-0.053***(-5.967)0.084***(10.225)
0.003***(8.551)0.895***(213.421)-0.003(-0.852)-0.006(-1.605)
0.008***(11.269)-0.032***(-8.305)-0.063***(-7.980)-0.016*(-1.785)-0.031***(-4.527)-0.007***(-6.417)-0.039***(-6.203)0.101***(16.183)
0.002***(6.136)0.26

0.917***(222.455)-0.009**(-1.995)-0.019***(-4.828)0.006***(6.151)-0.030***(-7.241)-0.093***(-12.094)
0.014(1.558)-0.014*(-1.899)-0.009***(-6.130)-0.064***(-8.980)
0.113***(16.903)
0.002***(6.146)0.880***(92.124)0.008(1.190)0.005(0.508)0.006***(5.016)-0.031***(-3.871)-0.062***(-3.373)-0.051***(-3.106)-0.080***(-4.882)
0.006***(2.698)-0.030**(-2.148)0.116***(9.012)0.003***(5.503)0.00

209,6120.756

139,626
0.788

76,3180.807

190,2020.737

163,159
0.751

46,178
0.738

POLRIGHTS
High(2)0.905***(125.355)-0.004(-0.612)0.044***(6.119)0.001(1.118)-0.067***(-10.249)-0.046***(-3.327)0.008(0.592)-0.012(-1.013)-0.001(-0.899)
0.004(0.353)0.045***(4.357)0.003***(6.561)0.10

p-value for difference in the β7
Observations
R-squared

PCON
High
(4)

POLRIGHTS
Low(1)56,741
0.754

PCON
Low
(3)

126,9120.723

The estimation equation is:∆lnXOPR= β0+ (β1+ β2 ElectionYear + β3 COMMON + β4 GDPGROWTH + β5 lnAINT)∆lnSALE +(β6+ β7 ElectionYear + β8 COMMON + β9GDPGROWTH + β10lnAINT++ β11 LAGDEC) DEC*∆lnSALE+ ε
where ∆lnXOPR is the log-change in operating costs; ∆lnSALE is the log-change in sales; GDPGROWTH is the real GDP
growth rate; lnAINT is asset intensity (log-ratio of assets to sales); COMMON is a dummy variable equal to one forcommon-law countries; ElectionYear is a dummy variable equal to one for election years; DEC is a dummy variable equal
to one if sales decreased in year t and LAGDEC is a dummy variable equal to one if sales decreased in year t-1; ε is anerror term. Robust t-statistics clustered at the firm level are reported beneath each estimate. ***, **, and * denotestatistical significance at the 1%, 5%, and 10% levels, respectively. Definitions and data sources for the variables areprovided in the Appendix.

45

TABLE 7
Subsample Analysis: Legal Variables
Sign

COMMON=0(1)

COMMON=1(2)+

0.915***(228.397)-0.003(-0.544)0.886***(199.683)
0.000(0.053)0.004***(4.099)-0.054***(-9.593)-0.076***(-10.387)
0.008(0.894)0.008***(11.027)-0.027***(-6.574)-0.084***(-10.635)-0.023**(-2.328)-0.006***(-4.702)-0.028***(-3.142)0.082***(11.890)0.001***(4.840)-0.006***(-5.451)-0.048***(-7.249)
0.104***(14.931)0.002***(8.135)

Dependent Variable: ∆lnXOPR∆lnSALE∆lnSALE*ElectionYear∆lnSALE*COMMON∆lnSALE*GDPGROWTH∆lnSALE *lnAINT
DEC*∆lnSALE-

DEC*∆lnSALE*ElectionYear-

DEC*∆lnSALE*COMMON
DEC*∆lnSALE *GDPGROWTH-

DEC*∆lnSALE *lnAINT-

DEC*∆lnSALE*LAGDEC+

Constant

Labor
Regulation
Stringent(4)

Judicial
Efficiency
Low(5)

Judicial
Efficiency
High
(6)

0.910***(195.902)-0.006(-1.277)-0.030***(-6.899)
0.008***(10.328)-0.025***(-5.472)-0.080***(-8.210)0.012(1.114)0.003(0.318)-0.010***(-7.522)-0.044***(-5.595)
0.107***(14.095)
0.001***(4.493)0.907***(224.105)
0.003(0.726)0.010**(2.311)0.003***(4.355)-0.041***(-8.816)-0.061***(-8.202)-0.027***(-2.924)-0.056***(-7.257)-0.002*(-1.778)-0.042***(-5.512)0.086***(12.835)
0.002***(8.835)0.920***(186.138)-0.012**(-2.132)
0.015**(2.414)0.003***(3.017)-0.057***(-9.488)-0.063***(-6.662)0.011(1.044)0.022**(2.182)-0.004**(-2.431)-0.007(-0.774)
0.053***(6.398)0.003***(9.049)0.897***(211.150)
0.008**(2.054)-0.007*(-1.704)
0.006***(6.088)-0.025***(-6.142)-0.071***(-9.399)-0.024***(-2.703)-0.039***(-6.092)-0.012***(-8.323)-0.068***(-10.154)
0.122***(18.963)0.001***(5.499)0.01

p-value for difference in the β7

Observations
R-squared

Labor
Regulation
Weak
(3)

112,7350.802

0.00
153,803
0.729

120,161
0.753

0.01
146,107
0.756

78,6640.771

182,746
0.749

The estimation equation is:∆lnXOPR= β0+ (β1+ β2 ElectionYear + β3 COMMON + β4 GDPGROWTH + β5 lnAINT)∆lnSALE +(β6+ β7 ElectionYear + β8 COMMON + β9GDPGROWTH + β10lnAINT++ β11 LAGDEC) DEC*∆lnSALE+ ε
where ∆lnXOPR is the log-change in operating costs; ∆lnSALE is the log-change in sales; GDPGROWTH is thereal GDP growth rate; lnAINT is asset intensity (log-ratio of assets to sales); COMMON is a dummy variableequal to one for common-law countries; ElectionYear is a dummy variable equal to one for election years; DEC
is a dummy variable equal to one if sales decreased in year t and LAGDEC is a dummy variable equal to one if
sales decreased in year t-1; ε is an error term. Robust t-statistics clustered at the firm level are reported beneatheach estimate. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.
Definitions and data sources for the variables are provided in the Appendix.

46

TABLE 8
Subsample Analysis: Disclosure Variables
Dependent Variable:∆lnXOPR

Sign∆lnSALE+∆lnSALE*ElectionYear∆lnSALE*COMMON∆lnSALE*GDPGROWTH∆lnSALE *lnAINT
DEC*∆lnSALE-

DEC*∆lnSALE*ElectionYear-

DEC*∆lnSALE*COMMON-

DEC*∆lnSALE *GDPGROWTH-

DEC*∆lnSALE *lnAINT-

DEC*∆lnSALE*LAGDEC+

Constant
p-value for difference in the β7
Observations
R-squared

DISCLOSE
Low(1)

DISCLOSE
High
(2)

CIFAR
Low
(3)

CIFAR
High
(4)

ACC
Low
(5)

ACC
High
(6)

0.916***(224.214)-0.002(-0.357)-0.013(-1.410)0.004***(3.700)-0.049***(-8.902)-0.080***(-10.763)
0.011(1.141)
0.016(0.948)-0.006***(-4.314)-0.030***(-3.252)
0.079***(11.179)0.001***(4.395)0.792***(17.659)
0.002(0.350)0.092**(2.069)0.008***(11.472)-0.026***(-6.177)
0.046(0.955)-0.030***(-2.979)-0.132***(-2.753)-0.006***(-5.375)-0.050***(-7.426)
0.108***(15.174)
0.003***(8.498)0.912***(233.282)-0.006(-1.304)-0.001(-0.232)
0.006***(6.280)-0.058***(-10.878)-0.070***(-9.671)
0.018**(2.081)
0.005(0.465)-0.005***(-4.201)-0.032***(-3.620)0.070***(10.507)0.001***(5.075)0.907***(131.066)
0.005(0.978)-0.019***(-2.942)0.007***(7.046)-0.017***(-3.852)-0.079***(-6.524)-0.032***(-2.952)-0.016(-1.416)-0.010***(-6.178)-0.055***(-7.864)0.122***(15.965)
0.002***(7.571)0.920***(201.368)-0.013**(-2.399)
0.013**(2.513)0.003***(4.059)-0.044***(-8.716)-0.069***(-7.760)
0.011(1.029)0.001(0.089)-0.001(-0.564)-0.027***(-3.467)
0.057***(7.302)0.003***(8.253)0.893***(194.698)
0.011***(2.868)-0.019***(-4.879)
0.008***(7.350)-0.026***(-5.844)-0.062***(-7.757)-0.038***(-4.387)-0.037***(-5.324)-0.019***(-12.127)-0.066***(-8.961)0.129***(19.317)0.001***(5.427)143,279
0.737

121,6390.794

136,330
0.728

102,377
0.752

0.00
118,131
0.786

0.00

0.00
158,504
0.762

The estimation equation is:∆lnXOPR= β0+ (β1+ β2 ElectionYear + β3 COMMON + β4 GDPGROWTH + β5 lnAINT)∆lnSALE +(β6+ β7 ElectionYear + β8 COMMON + β9GDPGROWTH + β10lnAINT++ β11 LAGDEC) DEC*∆lnSALE+ ε
where ∆lnXOPR is the log-change in operating costs; ∆lnSALE is the log-change in sales; GDPGROWTH is thereal GDP growth rate; lnAINT is asset intensity (log-ratio of assets to sales); COMMON is a dummy variableequal to one for common-law countries; ElectionYear is a dummy variable equal to one for election years; DEC isa dummy variable equal to one if sales decreased in year t and LAGDEC is a dummy variable equal to one if
sales decreased in year t-1; ε is an error term. Robust t-statistics clustered at the firm level are reported beneatheach estimate. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. Definitions
and data sources for the variables are provided in the Appendix.

47

TABLE 9
Subsample Analysis: Cultural Variables
Dependent Variable: ∆lnXOPR∆lnSALE

Sign

UAI_HFHigh(1)

UAI_HF
Low(2)

UAI_TKHigh(3)

UAI_TK
Low(4)

UAI_HOUSEHigh(5)

UAI_HOUSE
Low
(6)

+

0.911***(224.312)
0.002(0.487)-0.016**(-2.534)
0.005***(5.611)-0.050***(-9.358)-0.066***(-8.805)-0.003(-0.334)-0.022**(-1.998)-0.010***(-6.856)-0.045***(-5.097)0.081***(11.576)0.001***(5.049)0.864***(71.493)-0.002(-0.406)
0.025**(2.120)0.007***(10.401)-0.024***(-5.487)-0.056***(-3.265)-0.021**(-2.053)-0.037**(-2.313)-0.005***(-4.041)-0.043***(-6.353)
0.110***(15.111)0.003***(8.259)0.913***(232.357)-0.005(-1.162)0.014***(2.613)0.004***(5.066)-0.051***(-10.203)-0.073***(-9.980)0.026***(3.024)-0.023**(-2.525)-0.002(-1.576)-0.025***(-3.049)
0.072***(10.643)0.001***(5.449)0.881***(158.493)
0.005(0.921)0.915***(232.627)-0.007(-1.563)
0.025***(4.475)0.004***(4.307)-0.063***(-11.417)-0.067***(-9.086)
0.025***(2.923)0.018*(1.907)-0.007***(-5.291)-0.012(-1.431)0.061***(8.754)0.002***(5.982)0.907***(156.310)
0.006(1.211)-0.021***(-3.999)
0.006***(5.953)-0.017***(-4.076)-0.082***(-7.663)-0.039***(-3.689)-0.022**(-2.372)-0.011***(-6.667)-0.070***(-9.788)
0.125***(16.870)0.002***(7.060)∆lnSALE*ElectionYear∆lnSALE*COMMON∆lnSALE*GDPGROWTH∆lnSALE *lnAINT
DEC*∆lnSALE-

DEC*∆lnSALE*ElectionYear-

DEC*∆lnSALE*COMMON-

DEC*∆lnSALE *GDPGROWTH-

DEC*∆lnSALE *lnAINT-

DEC*∆lnSALE*LAGDEC+

Constant

0.18

p-value for difference in the β7

Observations
R-squared

130,1240.770

0.008***(6.541)-0.017***(-3.710)-0.084***(-8.833)-0.032***(-2.739)-0.015***(-7.988)-0.059***(-7.918)0.126***(15.269)
0.002***(6.588)0.00

0.00
133,776
0.743

133,8440.786

116,6300.722

113,003
0.796

144,875
0.729

The estimation equation is:∆lnXOPR= β0+ (β1+ β2 ElectionYear + β3 COMMON + β4 GDPGROWTH + β5 lnAINT)∆lnSALE +(β6+ β7 ElectionYear + β8 COMMON + β9GDPGROWTH + β10lnAINT++ β11 LAGDEC) DEC*∆lnSALE+ ε
where ∆lnXOPR is the log-change in operating costs; ∆lnSALE is the log-change in sales; GDPGROWTH is the real
GDP growth rate; lnAINT is asset intensity (log-ratio of assets to sales); COMMON is a dummy variable equal to one
for common-law countries; ElectionYear is a dummy variable equal to one for election years; DEC is a dummy variableequal to one if sales decreased in year t and LAGDEC is a dummy variable equal to one if sales decreased in year t-1; εis an error term. Robust t-statistics clustered at the firm level are reported beneath each estimate. ***, **, and * denotestatistical significance at the 1%, 5%, and 10% levels, respectively. Definitions and data sources for the variables areprovided in the Appendix.

48