Download WHEN DOES FLEXIBILITY IN TARIFF RATES HURT TRADE? THE

W HEN D OES F LEXIBILITY IN T ARIFF R ATES H URT T RADE ?
T HE P OLITICAL S OURCES OF P OLICY U NCERTAINTY∗
Eric Arias†
May 16, 2016
Abstract
What are the political sources of trade policy uncertainty? To answer this question I examine when flexibility in tariffs under the World Trade Organization hurts international
trade. Uruguay Round commitments established maximum tariffs, often set at higher levels than applied tariffs, the difference being defined as the tariff overhang. While recent
research shows that tariff overhang reduces trade because of the uncertainty it creates,
we know little about its political sources. I argue that tariff overhang only generates uncertainty when the government’s coalition of supporters is volatile. This happens in two
situations. First, the penalizing effect of overhang should be greatest in hybrid regimes.
The reason is that the coalition of supporters is less stable than autocracies and democracies, but strong enough so to still successfully demand protection. Second, within democracies, overhang will be most likely to generate uncertainty and reduce trade under centrist
governments, as their coalition of supporters is less predictable than right- or left-wing
governments. Evidence at the industry- and firm-level from WTO members over the 1995–
2013 period supports these claims.
∗
I thank Michaël Aklin, Sean Ehrlich, Michael Gilligan, Shoaib Jillani, Peter Rosendorff, Shanker Satyanath,
Pedro Silva, Alastair Smith, and participants at the NYU IPE Workshop and at the MPSA conference for their
suggestions and comments.
†
Department of Politics, New York University. Contact: [email protected]
1
1
Introduction
Uncertainty over economic policy is a key obstacle for economic development, deterring
investment and precluding growth. In particular, uncertainty about future conditions is key
to firms and investors when deciding on costly irreversible investments. In uncertain environments, such economic agents are reluctant to commit resources in projects characterized
by large sunk cost such as enter a new market, adopt a new technology, or restructure its
labor force (Dixit and Pindyck, 1994). And while the theoretical literature analyzing uncertainty on firms’ investment is well developed, less is known about the political sources of such
uncertainty and its economic consequences.
Early contributions emphasized that convincingly measuring and quantifying the economic effects of policy uncertainty is inherently difficult (Rodrik, 1991). As such, earlier
studies relied on different proxies and levels of analysis. For example, investors’ perceptions
of investment risk has been used a proxy for policy uncertainty and linked to country-level
aggregate outcomes such as investment and growth (Jensen, 2003; Frye, 2002).1 Recent work
on trade policy arguably addresses these challenges by relying on precise measures of uncertainty and outcomes at the product-level. Here, uncertainty over applied tariffs has a negative
impact on international trade (Pelc, 2013; Handley, 2014; Handley and Limão, 2015). Yet, in
order to fully understand policy uncertainty and its consequences, one also needs to take into
account the politics underlying such policy. In this direction, some studies analyze the impact
of elections on firms’ and individuals’ behavior in developed countries (Julio and Yook, 2012;
Canes-Wrone and Park, 2012, 2014). Others have examined variation over regime types showing that hybrid regimes are indeed associated with higher perceptions of uncertainty (Petrova
and Bates, 2012; Kenyon and Naoi, 2010).2 However, by relying on country-wide (aggregated)
measures of policy uncertainty and outcomes, one would fail to leverage the fact that many
key policies can be and actually are targeted, such as trade policy. When this is the case,
policy outcomes (and their specific uncertainty) are determined by the political process itself
–but overall we know little about the political sources of this type of policy uncertainty.
To fill this gap, I focus on trade policy uncertainty. Specifically, I examine uncertainty over
tariffs under the World Trade Organization (WTO) and its effects on international trade at
the industry level, exploiting variation between and within countries and industries over time.
This is an important and well suited case for studying the political sources and economic consequences of policy uncertainty. While policy commitment and credibility are crucial factors
for inducing economic agents to make any type investments, they are particularly important
for trade policy (Limão and Maggi, 2015; Maggi and Rodriguez-Clare, 1998). Indeed, a founding principle of the WTO is to establish predictability of trade policy. Pursuing such objective
1
Other research proxies economic uncertainty with stock market volatility and shows that it leads to delay in
firms’ and individuals’ investments (Bloom, Bond, and Van Reenen, 2007; Hassler, 2001).
2
Hybrid regimes are also often referred to as competitive authoritarian regimes (Levitsky and Way, 2002) or
electoral authoritarianism (Diamond, 2002).
1
at the inception of the WTO, its member states negotiated maximum tariffs –defined as bound
rates– on almost all traded goods. These upper bounds were often set at levels higher than a
state had ever applied in the past (Ingco and Croome, 2004) and while tariffs are applied at
roughly comparable levels across countries, the bound rates vary dramatically (Bagwell and
Staiger, 2005; Pelc, 2013). The difference between the two –i.e., bound and applied rates–
is called tariff overhang.3 For example, while China, Kenya and Bangladesh all levied 25%
applied tariffs on apricots in 2008, China could have not raise its tariffs beyond that without
violating its WTO commitments, while Kenya and Bangladesh could have raised their tariffs overnight to their respective bound rates of 100% and 200% and remain fully complaint.4
As such, overhang can be better understood as “unused protection” (Walhenhorst and Dihel,
2003). This flexibility comes with a cost, however. The fact that leaders can use such protection at any given time creates uncertainty about trade policy, inducing economic agents to
adjust their consumption and investment decisions. Pelc (2013) estimates that an one-point
increase in overhang corresponds to a 0.8% decrease in imports for that product. However,
such average effect could mask substantial variation if uncertainty costs are also a function of
the political environment. This is likely to be the case because policy uncertainty is arguably
a function of the likelihood that certain groups receive such ‘unused’ trade protection, should
they demand it. This raises the following question: to what extent does the uncertainty cost
created by the flexibility given by tariff overhang vary by political environment? In other
words, what are the political sources of trade policy uncertainty?
The answer, I argue, lays in the extent to which economic agents can identify with enough
certainty who are the groups that can successfully demand protection, which in turns depends on domestic politics. As such, my argument specifies the policy uncertainty cost as a
function of the political environment. Here, I explore a key feature of the political environment, namely regime type. I argue that overhang will have the most trade-deterrent effect
in hybrid regimes. On the one hand, established democracies enjoy a set of institutions that
enhance the certainty of their policies. Not only they have transparent bureaucracies but also
it is often clear who the key supporters of the leader are. In addition, democrat leaders have
greater incentives to provide public goods, which consequently decreases the potential use of
overhang. On the other hand, full autocracies have incentives to provide a large set of private
goods, but to a smaller group of supporters. However, once the identity of these groups is
known, the optimal policy that an autocrat would carry out is also certain –providing substantial protection to their core supporters without acting for outsiders. That is, the ‘unused’
protection should arguably remain largely ‘unused.’ This, in turn, reduces the uncertainty
cost created by the tariff overhang. Instead, those political regimes that face a balanced mix
of these incentives, namely hybrid regimes, are the ones who face the larger uncertainty costs.
This is for two key reasons. One, leaders in these regimes are not free from accountability
3
4
Sometimes it is also referred as “binding overhang” or “water”.
These rates correspond to 2008 ad valorem import barriers on “Apricots, Dried” HS 081310.
2
pressures from the mass public, thus facing consumer pressures for keeping tariffs low. Two,
these leaders still rely heavily on private goods provision –in this case, trade protection– to
remain in power, and crucially the identity of the potential beneficiaries of such demand is
less clear than in autocratic regimes (Levitsky and Way, 2002; Diamond, 2002).
The logic of this argument can be extended to another key political feature, namely the
ideological type within the subset of democratic countries. Here, the political (ideological) orientation of the leader provides a similar type of information to firms. Arguably both left wing
or right wing governments often have very clear and stable supporters. Center governments,
instead, might need rely on a broader set of groups at different times, thus potentially inducing a greater uncertainty in policy and consequently differentially impacting the cost of tariff
overhang. As such, if democracies suffer any uncertainty costs from tariff overhang, then it
ought to be under ideologically centered incumbents.
I test these claims using both industry- and firm-level data. First, I rely on industrylevel imports from WTO members on the 1995–2013 period. Specifically, the estimate for
a one-point increase in overhang corresponds to approximately 1.5% drop in imports on the
product in question within hybrid regimes, whereas no average effect is found in neither full
autocratic nor full democratic regimes. Similarly, within the democratic sample, a one-point
increase in overhang is associated with almost 2% drop in imports when the incumbent is
ideologically centered, but no effect when the incumbent is either left- or right-wing oriented.
Second, I find further support for the main argument by analyzing industry-level exporter’s
rate of entry. Substantively, the estimate for a one-point increase in overhang is associated
with approximately a 2.5 percentage points decrease in the firm entry rate in hybrid regimes,
while no effect is found is full autocracies nor full democracies. Third, I rely on firm-level
survey data to further support the underlying mechanism. In line with the main argument,
the evidence suggests that tariff overhang is associated with perceptions of policy uncertainty
but only in hybrid regimes.
This paper contributes to at least three different literatures. The first one is to the research
on the economic consequences and on the political sources of policy uncertainty (Rodrik, 1991;
Kenyon and Naoi, 2010). By so doing, this study also adds to the literature on hybrid regimes
(Levitsky and Way, 2002; Diamond, 2002). Lastly, it also contributes to the literature –and
policy debate– on the design and consequences of international organizations, particularly
to the discussion regarding the role of trade agreements and flexibility provisions in general
(Rosendorff and Milner, 2001; Johns, 2014; Koremenos, 2001, 2005; Baccini, Dür, and Elsig,
2015; Bearce, Eldregde, and Jolliff, 2016) and of the WTO in particular (Pelc, 2009, 2013,
2011a,b; Rosendorff, 2005; Reinhardt and Kucik, 2008).
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2
Policy Uncertainty and its Consequences
Uncertainty is a key element when understanding growth and investment. Canonical
models show how firms may optimally delay investment and hiring decisions when uncertainty is high (Bernanke, 1983; Dixit, 1989; Cukierman, 1980). When exploring the underlying mechanisms, a specific strand of this literature focuses explicitly on policy uncertainty,
mainly analyzing macroeconomic factors. Here, uncertainty arising from monetary policy, fiscal policy, and regulatory policy is modeled to have the detrimental effects on the economy
(Friedman, 1968; Rodrik, 1991; Hasset and Metcalf, 1999).
Building upon this literature, the link between (economic and policy) uncertainty and investment has been put at test by several scholars. A first generation of studies discusses
the extent to which political instability (as a proxy for uncertainty) deters investment on
the aggregate level (Barro, 1991; Alesina and Perotti, 1996). Recent evidence links aggregate uncertainty shocks, proxied by stock market volatility, to delays in firm-level investment
(Bloom, Bond, and Van Reenen, 2007; Bloom, 2009). Similar findings arise from individuallevel evidence. Economic uncertainty, arising from personal labor income uncertainty or even
aggregate stock market volatility, can induce individuals to delay both costly-to-undo investments (e.g., homes) and spending on durables goods (e.g., automobiles) (Carroll and Dunn,
1997; Hassler, 2001; Romer, 1990). While proxies for economic and policy uncertainty such as
news-based indices and stock market volatility have pushed the literature forward, they do
not offer the necessary within country and between firms variation to test further hypotheses
(see Baker, Bloom, and Davis, 2015; Handley and Limão, 2014).
While Rodrik (1991) had emphasized the difficulty in convincingly measuring a specific
policy uncertainty and directly link it to a specific economic outcome, recent research examining the international trade setting has arguably addressed these issues by examining the case
of international trade and bound and applied tariff rates under the WTO. Pelc (2013) provides
the first empirical examination of the uncertainty cost related to tariff overhang. Analyzing
WTO member countries, he shows that the availability of overhang is indeed costly because of
the trade policy uncertainty it generates.5 Along these lines, Handley and Limão (2015) find
increased entry into the export market for Portuguese exporters as a result of the reduction
in uncertainty from European Community accession. Handley (2014) finds that product level
uncertainty has negative consequences for exports to the Australian market while also reducing the responsiveness to applied rate reductions. Handley and Limão (2014) examine the
impact of uncertainty faced by Chinese exporters to the United States in a general equilibrium framework prior to the granting of Permanent Normal Trade Relations to China in 2001
while Pierce and Schott (Forthcoming) provide further evidence that the consequent reduction
in tariff uncertainty contributed to an increase in Chinese exports as well as a reduction in
US manufacturing employment in sectors with larger reductions in tariff uncertainty. Over5
In his estimates, all else equal, an increase of one standard deviation from the mean level of binding overhang
leads to an average 17.6 percent drop in imports for that product.
4
all, while convincingly showing a link between uncertainty and behavioral outcomes, this
research does not shed light on the politics underneath such policy uncertainty.
When trying to understand politics as a source of uncertainty, studies have relied on
country-wide aggregated measures of uncertainty, such as major political events. For example, Bittlingmayer (1998) links stock market volatility to the transition from from Imperial
to Weimar Germany. Similarly, Bloom (2009) shows that major wars and acts of terrorism
increase stock market volatility. When trying to understand the economic consequences of
politically driven policy uncertainty, the literature has mainly focused on a particular type of
political event, namely elections. The notion that pre-election uncertainty induces a decline in
investment over that period is supported by evidence at the aggregate level (Canes-Wrone and
Park, 2012), firm-level (Julio and Yook, 2012) and individual-level (Canes-Wrone and Park,
2014). Moreover, while many theories in political science and economics rely on the regime
type as an explanation, it is surprising that few studies explicitly and systematically analyze
the link between regime type and policy uncertainty. In line with the arguments of this paper,
Canes-Wrone and Ponce de Leon (2015) argue that electorally-induced uncertainty is higher
in least developed democracies. In a similar vein, relying on firm-level surveys and including the full range of regime types, Petrova and Bates (2012) argue that hybrid regimes are
politically and economically more volatile than either more authoritarian regimes or liberal
established democracies, showing higher levels of variation in assessments of political risk
and economic performance, while Kenyon and Naoi (2010) show that firms in hybrid regimes
report higher levels of concern over policy uncertainty than their counterparts in other more
authoritarian or democratic polities. These studies, while rich in theory and evidence, rely
on a level of aggregation that provides challenges to inference. Using tariff overhang as a
product-level measure of uncertainty enables me to leverage within and between country
variation to further analyze the political roots of policy uncertainty.
3
The Political Sources of Trade Policy Uncertainty
The case of trade policy uncertainty and firms’ international trading decisions is a perfectly
suited case to analyze the political sources of uncertainty for several reasons. In a setup
where firms must incur a fixed cost to export, uncertainty over future profits will decrease the
incentive of firms to enter into the export market. Similarly, in a setup where firms must incur
a fixed cost to adopt a new technology or hire labor that requires foreign inputs, uncertainty
over future costs of that intermediate-good will decrease the incentive of firms to rely on the
import market. While uncertainty over any number of factors related to import and export
decisions (such as demand, productivity, exchange rate, etc.) may affect firms’ incentives
to trade, focusing on trade policy uncertainty arising from the tariff rate scheme has some
advantages. First, policy uncertainty is directly measured by observing both the policies that
firms face (i.e., applied tariffs) as well as the real counterfactual worst-case values (i.e., bound
5
rates). That is, tariff overhang –the difference between the bound and applied– is able to
precisely capture the trade policy uncertainty that firms face at the product-level. Second,
these policies can be directly tied with firms’ behavior by focusing on product-level outcomes in
an attempt to directly test the link between them. Indeed, recent evidence suggests that tariff
overhang creates uncertainty for key economic agents (Pelc, 2013; Handley, 2014; Handley
and Limão, 2015). This is because leaders have the flexibility to increase their tariffs, even
overnight, up to the bound level, and still be fully compliant with WTO stipulations.
However, there are reasons to expect that such uncertainty cost should vary systematically
according to domestic political configurations. For instance, in 2012, exporters of beans faced
similar rates on diverse countries such as Bahrain, Venezuela and Uruguay with overhang
(applied) rates of 35 (0), 32.5 (7.5) and 35 (0), respectively.6 In these cases, while facing similar
tariff profiles, firms also faced substantial variation in political institutions, with Bahrain
being a full autocracy (Polity2 score of -10), Venezuela a hybrid regime (Polity2 of -3) and
Uruguay an establish democracy (Polity2 of 10). Arguably, one should not expect exporters to
‘fear uncertainty’ equally between these countries. This insight highlights the importance of
understanding the political sources of trade polity uncertainty.
Specifically, the uncertainty cost associated with tariff overhang is one of trade protection threat. That is, the availability of overhang implies that industries can demand such
protection. However, it is well known that this type of trade policy, such as any policy with
distributional consequences, is a function of the political landscape. An office-seeking leader
will provide such protection only to those groups that are part of its coalition of supporters
–or members of the winning coalition as in Bueno de Mesquita et al. (2003). It is precisely
because not all leaders weight different domestic groups and voters in the same way that the
overall uncertainty created around the availability of overhang should not be the same across
political environments.
More specifically, I argue that these uncertainty costs show a non-monotonic relation –
inverse U-shape– with respect to regime type. In full democratic regimes, the expectation is
that leaders will provide public goods. This should make the overall ‘threat’ of overhang use
small. Analogously, when the regime is autocratic, the expectation is that leaders will provide
private goods but only to a small and highly loyal number of groups. This implies that once
those groups are known, the uncertainty about the overall use of overhang should also be
small. In contrast, hybrid regimes combine such incentives in a way that leads to a higher
uncertainty. On the one hand, leaders in such regimes provide a relatively more balanced
bundle of private and private goods. As a result, the pool of groups that can successfully
demand for protection is higher. However, unlike full democracies, leaders in such regimes are
obliged to provide such targeted policies or otherwise face higher risk of removal from office.
On the other hand, the identity of these groups is less clear over time. Unlike supporters in a
6
These rates correspond to 2012 ad valorem import barriers on ”Beans (vigna Spp., Phaseolus Spp.), Fresh Or
Chilled,” HS 070820.
6
full autocracy, members of the winning coalition in hybrid regimes are less stable (Bueno de
Mesquita et al., 2003).
If these implications are correct, economic actors should adjust their behavior to these
expectations. This is the main hypothesis of the paper:
Hypothesis 1 (Uncertainty by Regime Type). The uncertainty cost –measured by the negative effect of binding overhang on trade– is higher in hybrid regimes than in democratic and
autocratic regimes.
This argument is based on the potential exchange of benefits –in this case, trade protection–
between a particular leader and its coalition of potential supporters given their preferences.
Crucially, the extent to which a given coalition of supporters is directly linked to a given leaders also varies by regime type. Moreover, institutional variation within democracies can also
be relevant as well. In particular, one can exploit a specific source of variation about the identity of members of the winning coalition within democracies, namely party ideology. Indeed,
partisan preferences ideology have been linked to economic policy (Pinto, 2013; Stasavage,
2003; Weymouth and Broz, 2013; Weymouth and Pinto, 2015). When it comes to trade policy
in particular, Dutt and Mitra (2005) argue that, vis-à-vis right-wing governments, left-wing
leaders adopt more protectionist trade policies in capital-rich countries, but adopt more protrade policies in labor-rich countries. Similarly, Milner and Judkins (2004) find that rightwing parties take more free trade stances than do left ones in developed countries. I argue,
however, that the ideological orientation of the leader ought to be systematically associated
with the uncertainty faced by firms when taking into account tariff overhang. This is because
both left wing and right wing governments have a relatively more stable, and consequently
easier to identify, set of supporters. Center governments, instead, might rely on a broader set
of groups at different times, thus inducing a greater uncertainty over future policy, therefore
differentially impacting the cost induced by tariff overhang.
In short, the political orientation of the incumbent is informative about the likelihood that
given groups receive protection (i.e., exploit the tariff overhang) should a surge in demand for
such protections occurs. Hence, as a secondary hypothesis, I posit the following:
Hypothesis 2 (Uncertainty by Political Ideology). The uncertainty cost –measured by the
negative effect of binding overhang on trade– in the democratic sample is higher for ideologically center governments than for left-wing and right-wing governments.
Below, I empirically test my argument in three ways. The first and main analysis examines import flows at the industry-level. Secondly, I complement that evidence by analyzing
exporter’s rate of entry at the industry-level as well. Finally, to test the purported mechanism,
I explore firm-level perceptions of policy uncertainty using survey data.
7
4
Empirical Strategy and Data
4.1
Uncertainty by Regime Type
For the first set of evidence, I rely on a monadic model of imports where the unit of analysis
is country-year-product at the HS-4 digit level, covering WTO members over the 1995–2013
period.7 Specifically, I examine the extent to which the uncertainty cost associated with tariff
overhang varies as a function of regime type via OLS as follows:
Importsict =β1 Overhangict + β2 Democracyct + β3 Democracy2ct
+ β4 (Overhangict × Democracyct ) + β5 (Overhangict × Democracy2ct )
(1)
+ γXict + ξZct + φc + δt + ict
where the dependent variable is the natural logarithm of imports of product i by country c in year t (drawn, along with the tariff data, from the World Integrated Trade Solution
(WITS) hosted by the World Bank). The first key variable is the Tariff Overhang at that
level. The vector Xict controls for the Applied Tariff rate as well as its Volatility (defined as
|Applied Tariffict − Applied Tariffict−1 |) and its Dispersion (i.e., its standard deviation). This
is crucial as controlling for the effectively applied tariffs (and its movements) enables the estimation to isolate those effects from the availability of overhang in and of itself. (Pelc, 2013).
The second key variable of interest is regime type. In this specification, Democracy is
operationalized using the 21-point scale of polity2, scaled to range between 0 and 1. (The
Appendix shows that results are robust to different operationalizations and sources of political
regime data.) As the theory predicts a non-monotonic relation, the model also includes its
squared term, Democracy2 .
I also control for other country level variables. The vector Zit incorporates standard economic variables, namely the natural logarithm of both GDP and GDP per capita, and annual
GDP growth (measured in % GDP change) from World Development Indicators (WDI). I also
control for the natural logarithm of the cumulative number of PTA’s signed by the country at
any given year, from the DESTA database (Dür, Baccini, and Elsig, 2014). Importantly, I control for domestic veto players using the Political Constraints Index (Henisz, 2002). Summary
statistics are shown in the Appendix on Table A1
To account for unobservables that do not change over time, the specification includes
country-fixed effects (φc ). As such, the estimation leverages within country variation between
industries. Additionally, to account for time effects, year-fixed effects (δt ) are also included.
Finally, the error term ict is allowed to be arbitrarily correlated within countries but inde7
European Union (EU) individual countries are excluded since the EU has the Common Customs Tariff (CCT)
which implies a common trade policy and trade schedule. The product level is HS-4 as it is the highest level of
disaggregation that is fully consistent across different HS nomenclatures, thus minimizing measurement error
(see Pelc, 2013).
8
pendent otherwise, and as such, I cluster the standard errors at the country-level.
Since the main interest is exploring the conditional effects of tariff overhang, the specification includes its interaction with the political regime variables. As such, the coefficient of
interest are β1 , β4 and β5 . Hypothesis 1 predicts β1 = 0, β4 < 0 and β5 > 0, with the coefficients
β4 and β5 being similar in absolute value.8
Results are shown in Table 1. For the purposes of comparison, the first column shows the
estimation with year and country fixed effects but without the interactions. It is reassuring
that the coefficient estimate on overhang (0.008) is estimated to be the same as the one shown
in Pelc (2013). Column 2 introduces the interaction terms of interest using only year fixed
effects while Column 3, the preferred specification, also incorporates country fixed effects. As
expected, the overhang coefficient, β1 , is a precisely estimated 0. Moreover, the interactions
with Democracy and Democracy2 are negative and positive, respectively, with similar absolute
values, thus providing strong support for Hypothesis 1.
To aid the interpretation of the results, I estimate marginal effects of tariff overhang on
imports conditional on the political regime type. These results are shown in Figure 1.
Figure 1: Effects of Tariff Overhang on Imports by Regime type (HS-4 level)
-.02
Effects on Linear Prediction
-.01
0
.01
.02
Polity2 [0-1]
0
.1
.2
.3
.4
.5
.6
Polity2 Score
.7
.8
.9
1
Note: Lines represent 95% C.I
Results show the predicted non-monotonic relation with respect to regime type size. Ad8
Nonetheless, all hypotheses are tested with two-sided tests.
9
Table 1: Effect of Tariff Overhang by Regime Type
Tariff Overhang
Democracy
Democracy2
Overhang × Democracy
Overhang × Democracy2
Applied Tariff
Tariff Volatility
Tariff Dispersion
GDP (Ln)
GDPpc (Ln)
GDP Growth (annual %)
PTAs (Ln)
Veto Players
Year FE
Country FE
Observations
Countries
R2
Imports (Ln)
(1)
(2)
(3)
-0.008∗∗∗
0.001
0.004
(0.002)
(0.005)
(0.007)
1.109
3.461∗∗
2.751∗∗
(1.055)
(1.377)
(1.314)
∗∗∗
-1.101
-3.447
-2.644∗∗
(0.902)
(1.259)
(1.129)
∗∗∗
-0.076
-0.075∗∗
(0.023)
(0.035)
0.078∗∗∗
0.071∗∗
(0.020)
(0.033)
-0.017∗
-0.019∗∗
-0.017∗
(0.009)
(0.009)
(0.009)
-0.057∗∗∗ -0.048∗∗∗ -0.057∗∗∗
(0.012)
(0.013)
(0.013)
∗∗
∗∗∗
0.072
0.075
0.073∗∗
(0.029)
(0.028)
(0.029)
1.302∗
0.820∗∗∗
1.301∗
(0.724)
(0.034)
(0.721)
-0.295
0.166∗∗
-0.292
(0.771)
(0.073)
(0.769)
0.011∗∗∗
-0.001
0.010∗∗∗
(0.003)
(0.008)
(0.004)
∗
0.142
0.021
0.138∗
(0.072)
(0.071)
(0.071)
∗∗∗
0.322
0.199
0.338∗∗∗
(0.115)
(0.198)
(0.118)
X
X
X
X
X
528615
528615
528615
98
98
98
0.38
0.37
0.38
Standard errors clustered at the country level in parentheses.
∗
p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01
10
ditionally, I estimate two F-tests of equality of coefficients. When comparing the marginal
estimate of hybrid regimes (i.e., (rescaled) polity2 = 0.5) to full autocracies (i.e., (rescaled)
polity2 = 0) and full democracies (i.e., (rescaled) polity2 = 1), they are indeed statistically
different from each other (p-values of 0.03 and 0.05, respectively). Substantively, a one-point
increase in tariff overhang is associated with a 1.53 [95% C.I: -2.18 – -0.89] percentage drop
in imports in hybrid regimes (polity2=0) whereas these effects are estimated to be 0 and not
statistically significant at conventional levels for either autocracies (95% C.I: -1.01 – 1.87) or
democracies (95% C.I: -1.01 – 1.14).
4.2
Uncertainty by Political Ideology
In order to analyze how political ideology influences the impact of tariff overhang I build
upon the analysis from the previous section. Here, I restrict the sample to democratic countries.9 I rely on standard measures of political ideology of the party in power, namely the
ideological orientation of the executive coded from the Database of Political Institutions (DPI)
(Cruz, Keefer, and Scartascini, 2016), and include dummies of whether the orientation is Left
Wing or Right Wing, making Center ideology the omitted category.10 As before, I estimate this
model via OLS, as follows:
Importsict =β1 Overhangict + β2 Left Wingct + β3 Right Wingct
+ β4 (Overhangict × Left Wingct ) + β5 (Overhangict × Right Wingct )
(2)
+ γXict + ξZct + φc + δt + ict
Once again, I make use of interactions between overhang and the political variables of
interest, in this case, indicators for left- and right-wing ideology. As such, the coefficients of
interest are β1 , β4 and β5 . With Center ideology being the omitted category, β1 can be interpret
as the coefficient estimate for Center, for which Hypothesis 2 predicts β1 < 0. Moreover, as
Left Wing and Right Wing are expected to take that estimate to 0, Hypothesis 2 predicts β4 > 0
and β5 > 0, with all coefficients (β1 , β4 and β5 ) being similar in absolute value.
Results are shown in Table 2. The first column shows the result for the estimation without
country-fixed effects, while column two shows the results with country fixed effects.
To aid the interpretation of the results, I estimate marginal effects of tariff overhang on
imports conditional on the political political ideology of the incumbent. These results are
shown in Figure 2.
The evidence points in one clear direction. Neither right-wing nor left-wing incumbents
induce uncertainty costs associated with overhang. The estimated effects are not statisti9
For this results, I operationalize a democratic country if its polity2 score is equal or larger than 5.
As such, the final sample drops cases where DPI has ‘no information’, or there is ‘no executive’. For example,
Colombia from 2003 onwards is coded as ‘no information,’ and hence dropped from the analysis.
10
11
Table 2: Effect of Tariff Overhang by Ideology
Tariff Overhang
Left wing
Right wing
Overhang × Left wing
Overhang × Right wing
Applied Tariff
Tariff Volatility
Tariff Dispersion
GDP (Ln)
GDPpc (Ln)
GDP Growth (annual %)
PTAs (Ln)
Veto Players
Year FE
Country FE
Observations
Countries
R2
Imports (Ln)
(1)
(2)
-0.025∗∗∗
-0.022∗∗∗
(0.005)
(0.008)
-0.512∗∗∗
-0.375∗∗
(0.166)
(0.184)
∗∗
-0.446
-0.361∗∗
(0.175)
(0.171)
∗∗∗
0.022
0.019∗∗
(0.006)
(0.008)
0.023∗∗∗
0.020∗∗
(0.006)
(0.008)
-0.028∗∗∗
-0.024∗∗∗
(0.007)
(0.007)
-0.077∗∗∗
-0.086∗∗∗
(0.020)
(0.019)
∗∗∗
0.098
0.094∗∗∗
(0.031)
(0.032)
0.796∗∗∗
2.669∗∗∗
(0.045)
(0.782)
0.038
-1.017
(0.073)
(0.799)
-0.000
0.006
(0.010)
(0.005)
0.102
0.298∗∗∗
(0.079)
(0.105)
0.316
0.564∗∗∗
(0.267)
(0.207)
X
X
X
331155
331155
47
47
0.37
0.38
Standard errors clustered at the country level in parentheses.
∗
p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01
12
-.04
Effects on Linear Prediction
-.03
-.02
-.01
0
.01
Figure 2: Effects of Tariff Overhang on Imports by Political Ideology
Left wing
Center
Party Orientation
Right wing
Note: Lines represent 95% C.I
cally significant different from zero –the 95% C.I. for Left and Right-Wing respectively are:
[-1.4,0.8] and [-1.1, 0.7]. Uncertainty costs, however, are accrued by ideologically centered
incumbents. In this case, a one-point increase in overhang is associated with a 2.19 [95%
C.I: -3.79 – -0.06] percentage points drop in imports. Indeed, F-tests of equality of coefficients
show that the marginal estimate of center governments to left-wing and right-wing are statistically different from each other (p-values of 0.04 and 0.02, respectively). These results are
in line with the overall claim that these costs are driven by the uncertainty surrounding the
identity of supporters.
4.3
Plausibility Check: Evidence from Exporter Dynamics
While the evidence on industry level import flows presented above provides support to the
theoretical claims advanced here, because of the level of aggregation they do not provide direct
evidence of (potential) exporters not investing in a given country or (potential) importers not
acquiring new technology and labor as a result of trade policy uncertainty. In this section I
aim to check the plausibility of the exporter argument, which is the main focus of the extant
literature, by analyzing the extent to which exporters’ entry rate into a given country-industry
is systematically associated with the tariff profiles of such industry and regime type of such
13
country. Below I discuss the data and its limitations and then present the empirical strategy
and results.
4.3.1
Data & Limitations
To further examine exporters’ behavior, I analyze foreign firms’ rate of entry to the importing market of a given country-industry-year by relying on the Exporter Dynamics Database
(EDD) (Fernandes, Freund, and Pierola, 2016). The EDD provides information on the microstructure of trade flows between countries by covering the universe of export transactions
obtained directly from customs agencies. It contains aggregated measures on export-sector
characteristics and dynamics (chiefly among them, firms’ entry rate) at the country–HS 2digit product–destination–year level. For the purposes of this paper, I transform the data
by aggregating it at the destination–HS 2-digit–year because the destination, not the origin
country, is the political unit of interest.11 The average Entry Rate in the sample is approximately 60%, and while the country-specific means are not extremely different to each other,
there are relatively more differences in their respective standard deviations. For instance,
while countries like the United States or Saudi Arabia have an entry rate mean (s.d.) of 57.7
(11.4) and 61 (13.8), others like Uganda and Venezuela exhibit values of 59.4 (20.4) and 55.5
(21.2).
The main limitation the EDD presents is the level of aggregation. The EDD relies on HS
2-digit level codes to identify industries, and as a result, tariffs averaged at the HS 2-digit
level are likely to be a noisy approximation of the actual tariffs and overhang rates any given
firm actually faces.12
4.3.2
Empirical Strategy & Results
To examine the extent to which firm entry rate is linked to the tariff overhang and the
regime type exporters face, the analyses below relies on firm entry rate data from 101 WTO
member countries in the 1998–2012 period. The econometric specification is estimated via
OLS as follows:
Entry Rateict =β1 Overhangict−1 + β2 Democracyct−1 + β3 Democracy2ct−1
+ β4 (Overhangict−1 × Democracyct−1 ) + β5 (Overhangict−1 × Democracy2ct−1 )
+ γXict−1 + ξZct−1 + φc + δt + ict
(3)
where the dependent variable is the firm entry rate for industry i (HS-2 digit) in country
11
While arguably insightful, analyzing bilateral dynamics is beyond the scope of this paper.
Indeed, the combination of loss of power as a result of level of aggregation and missing political ideology data
precludes a precise estimation of those tests. As a result, I restrict the analysis to the main hypothesis, namely
regime type.
12
14
c at year t. The first key variable is the Tariff Overhang, averaged at that level which, along
with the other covariates, is lagged by one year. As before, the vector Xict controls for the
Applied Tariff rate as well as its Volatility and its Dispersion. In addition, I also control for
the natural logarithm of the number of exporting firms. The country level variables are the
same as before: Democracy is operationalized using the 21-point scale of polity2, scaled to
range between 0 and 1, and it is also modeled with its squared term, Democracy2 , while the
vector Zit incorporates the same variables as before. Summary statistics are shown in the
Appendix on Table A3.
Once again, the specification includes country-fixed effects (φc ) and year-fixed effects (δt ),
and standard errors are clustered at the country-level.
Since the main interest is exploring the conditional effects of tariff overhang, the specification includes its interaction with the political regime variables. As such, the coefficient of
interest are β1 , β4 and β5 . Hypothesis 1 predicts β1 = 0, β4 < 0 and β5 > 0, with the coefficients
β4 and β5 being similar in absolute value.13
Results are shown in Table 3. The first column shows the baseline estimation while the
second column controls for the number of exporters. As expected, the interactions between
Overhang with Democracy and Democracy2 are positive and negative, respectively, with similar absolute values,
To aid the interpretation of the results, I estimate marginal effects of tariff overhang on
firms entry rate conditional on the regime type. These results are shown in Figure 3.
While the results are not as precisely estimated as before, they nonetheless suggests the
expected non-monotonic relation with respect to regime type size. Firms’ rate of entry to
autocratic regimes is not associated to tariff overhang. In contrast, overhang seems to be
associated with a lower rate of entry in democracies, although that effect is not precisely
estimated, loosing significance as democracy scores increases. However, hybrid regimes see
the strongest negative association between overhang and exporters rate of entry in line with
mechanisms behind Hypothesis 1. Substantively, a one-standard deviation increase in tariff
overhang is associated with a 2.53 [95% C.I: -4.18 – -0.89] percentage point decrease in the
entry rate in hybrid regimes (polity2=0) whereas these effects are substantively smaller and
not statistically significant at conventional levels for either autocracies (95% C.I: -2.55 – 2.93)
or democracies (95% C.I: -2.98 – 0.14).
4.4
Testing the Mechanisms: Firm-Level Survey Evidence
The industry-level results discussed up until this point represent behavioral outcomes
fully in line with the theoretical expectations. However, a more direct test of the purported
mechanism would demonstrate a link between overhang and private-sector perceptions of
policy uncertainty, and that such effect is moderated by political institutions. This happens
in two situations. First, (potential) exporting firms should express higher uncertainty when
13
Nonetheless, all hypotheses are tested with two-sided tests.
15
Table 3: Effect of Tariff Overhang on Exporters’ Entry Rate by Regime Type (HS-2
level)
Tariff Overhang
Democracy
Democracy2
Overhang × Democracy
Overhang × Democracy2
Applied Tariff
Tariff Volatility
Tariff Dispersion
Imports (Ln)
Number of Exporters (Ln)
GDP (Ln)
GDPpc (Ln)
GDP Growth (annual %)
PTAs (Ln)
Veto Players
Country FE
Year FE
Observations
Countries
R2
Exporters’ Entry Rate
(1)
(2)
0.000
0.000
(0.001)
(0.001)
-0.263∗
-0.253
(0.158)
(0.158)
0.246∗
0.238∗
(0.127)
(0.128)
-0.003
-0.003
(0.002)
(0.002)
0.003
0.003
(0.002)
(0.002)
0.001∗∗∗
0.001∗∗∗
(0.000)
(0.000)
-0.001
-0.001
(0.000)
(0.000)
-0.000∗∗∗
-0.000∗∗∗
(0.000)
(0.000)
-0.009∗∗∗
-0.005∗∗∗
(0.001)
(0.001)
-0.009∗∗∗
(0.003)
-0.031
-0.027
(0.031)
(0.031)
0.026
0.024
(0.033)
(0.033)
0.000
0.000
(0.000)
(0.000)
-0.007
-0.007
(0.005)
(0.005)
-0.025∗
-0.024
(0.015)
(0.015)
X
X
X
X
56153
56153
101
101
0.09
0.09
Standard errors clustered at the country level in parentheses.
∗
p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01
16
-.0015
Effects on Linear Prediction
-.001
-.0005
0
.0005
.001
Figure 3: Effects of Tariff Overhang on Exporters’ Entry Rate by Regime type (HS-2
level)
0
.1
.2
.3
.4
.5
.6
Polity2 Score
.7
.8
.9
1
Note: Lines represent 95% C.I
thinking about policies in (potential) markets when overhang for their products is large, conditional on the political arena of such market (i.e., export channel). Two, (potential) importing
firms of intermediate goods should perceive higher economic policy uncertainty when the tariff overhang on their (potential) imported products is large, conditional on the political environment where they are based (i.e., import channel). The export channel has been the focus of
the trade policy uncertainty literature, as it is arguably the relatively more important channel. Here, however, I can only aim to test the import channel by relying on firm-level survey
data. Below I discuss the data and its limitations and then present the empirical strategy and
results.
4.4.1
Data & Limitations
As firms are the key economic agents here, I analyze firm-level perceptions by drawing
upon the World Bank’s World Business Environment Survey (WBES).14 The explicit goal of
WBES is to better understand conditions in the local investment climate and how they affect
14
Researchers have relied on the WBES data to answer political economy question; see Broz, Frieden, and
Weymouth (2008); Broz and Plouffe (2010); Kenyon and Naoi (2010).
17
firm-level productivity. To do so, the survey retrieves views about the business operations
and policy environment relying on a common common survey instrument administered to a
representative sample of firms in each country.
For the purposes of testing the underlying mechanisms behind the arguments of this paper, the WBES data presents two types of limitations. The first type of limitation is with
respect to the characteristics of the sample. The key question related to policy uncertainty
was asked in the 2002-2006 period. Consequently, some countries must be excluded from the
analysis either because they were not WTO members at the time of the survey (e.g., Russia)
or because they were members of the European Union (e.g., Estonia, Poland or Slovenia in
2004).15 This has implications for power as well as for the type of tests that can be conducted.
More specifically, the number of observations covering autocratic regimes is relatively low,
and given the sample of countries and years covered, the ideology hypothesis can not be properly tested. In addition, the sample size is further limited by the fact that the WBES focuses
on both manufacturing and service sectors, while tariff and trade data is only available for
the former.16 In addition, WBES relies on ISIC 2-digit level codes to identify such sectors,
which given the level of aggregation, is relatively less precise in measurement than the HS-4
level used up until this point. The second type of limitation I face here relates to the type
of channel that could be tested. WBES’s focus on the business experiences in the country
where the survey is being asked precludes a systemic test of the ‘export channel,’ which is
arguably the case where overhang has the biggest influence for firms’ decisions.17 Hence, I
can only attempt to test the ‘import channel.’ However, WBES does not systematically collect
information about the specific products that are (potentially) imported. Because of that, it is
hard to quantify the ‘amount of overhang’ that any given firm actually faces. An imperfect
proxy for the level of uncertainty faced by a given industry (used below) is to rely on the average overhang of other industries in a given country-year. Overall, these substantive and
methodological caveats ought to be kept in mind when interpreting the findings.
Just as in Kenyon and Naoi (2010), I operationalize perceptions of policy uncertainty with
answers from the following question: How problematic is economic policy uncertainty for the
operation and growth of your business?18 Respondents were asked to reply on a scale from 1
to 4, with 1 representing ‘no obstacle’ and 4 representing ‘a major obstacle.’
15
As done previously, given the common tariff schedule for EU members, the most principled decision is to
exclude them from the final sample.
16
WBES focuses on firms classified with ISIC (Rev. 3.1) codes 15-37 for the manufacturing sectors, and 45,
50-52, 55, 60-64, and 72 for the service sectors.
17
The necessary information, such as data on (i) the specific products that are (potentially) exported and the (ii)
(potential) export markets, is not retrieved.
18
Note that in some years, the wording of the question was “uncertainty about regulatory policies” instead of
economic policy. By including year fixed effects this should be accounted for.
18
4.4.2
Empirical Strategy & Results
To examine the extent to which firms’ perceptions of policy uncertainty are linked to the
tariff overhang they face as (potential) importers, I analyze nearly 10,000 respondents to the
WBES located in the 32 WTO member countries in the 2003–2006 period. The econometric
specification is estimated via OLS as follows:
Policy Uncertaintyijct =β1 Overhang−jct + β2 Democracyct + β3 Democracy2ct
+β4 (Overhang−jct × Democracyct ) + β5 (Overhang−jct × Democracy2ct )
+γXijct + ξZct + φc + δt + ijct
(4)
where the dependent variable is the perception of policy uncertainty of firm i in industry
j (ISIC Rev 3.1, 2-digit), country c and year t. Then, the first key variable of interest is the
average overhang for industries that are not j (I also control for the average applied tariff
for industries that are not j.) As before, the second key variables is the regime type. Here,
Democracy is the polity2 variable, scaled to range between 0 and 1.
The estimation includes other firm-level and country-level covariates as controls. The
vector Xijct includes key variables regarding firm size, namely the number of employees (in
logged terms) and sales in USD (in logged terms). It also controls for the age of the firm in
years, for the percentage of imported inputs and the percentage of national sales as well as
an indicator for whether the business is foreign own or not. Then, the vector Zct includes
standard economic variables, namely the natural logarithm of both GDP and GDP per capita,
annual GDP growth (measured in % GDP change) and the natural logarithm of PTAs. Finally,
I also control for domestic veto players. Summary statistics are shown in the Appendix on
Table A2.
As before, the specification includes country-fixed effects (φc ), year-fixed effects (δt ) and
errors are clustered at the country-level.
Since the main interest is exploring the conditional effects of other industries’ overhang,
the specification interacts the Democracy and Overhang variables. As such, the coefficient of
interest are β1 , β4 and β5 . Here, I expect β1 = 0, β4 > 0 and β5 < 0, with the coefficients β4
and β5 being similar in absolute value.
Results are shown in Table 4. The first column shows the estimation without year but
with country fixed effects while the second column –the preferred specification– incorporates
both. As expected, the interactions between Overhang with Democracy and Democracy2 are
positive and negative, respectively, with similar absolute values,
To better visualize the results, I estimate marginal effects of other industries’ tariff overhang on policy uncertainty perceptions, conditional on the level of democracy. These results
are shown in Figure 4.
While the lack of power precludes a more precise estimation the evidence suggests the
19
Table 4: Effect of Other Industries’ Overhang on Policy Uncertainty
Overhang (Others)
Others’ Overhang × Democracy
Others’ Overhang × Democracy2
Applied Tariff (Others)
Firm age
Import Inputs
National Sales
Employees (Ln)
Sales (Ln)
Foreign
Controls
Country FE
Year FE
Observations
Countries
R2
Perceptions of
Policy Uncertainty
(1)
(2)
-0.041
-0.041
(0.050)
(0.050)
0.275
0.275
(0.191)
(0.191)
-0.259
-0.259
(0.164)
(0.164)
0.057
0.057
(0.066)
(0.066)
-0.000
-0.000
(0.000)
(0.000)
0.067∗∗
0.067∗∗
(0.032)
(0.032)
0.013
0.013
(0.051)
(0.051)
-0.005
-0.005
(0.007)
(0.007)
0.016∗∗∗
0.016∗∗∗
(0.006)
(0.006)
∗∗∗
-0.099
-0.099∗∗∗
(0.029)
(0.029)
X
X
X
X
X
9817
9817
32
32
0.58
0.58
Standard errors clustered at the country level in parentheses.
Controls: GDPpc, GDP, Growth, Democracy, Democracy2 , PTAs, Veto Players.
∗
p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01
20
-.15
Effects on Linear Prediction
-.1
-.05
0
.05
Figure 4: Effects of Others’ Tariff Overhang on Policy Uncertainty (Firm level)
0
.1
.2
.3
.4
.5
.6
Polity2 Score
.7
.8
.9
1
Note: Lines represent 95% C.I
predicted non-monotonic relation with respect to regime type size. There is no systematic
associated between policy uncertainty and tariff overhang in both autocratic and democratic
regimes. However, as the mechanism behind Hypothesis 1 would predict, overhang is weakly
associated with higher perceptions of policy uncertainty in hybrid regimes (p-value = 0.06).
While only suggestive, these results are in line with theoretical claims.
5
Concluding Remarks
If policy uncertainty has real economic consequences, then it is key to understand the roots
of such uncertainty. By examining the case of tariff and trade policy uncertainty, this paper
argues that the uncertainty cost induced by tariff overhang is rooted in domestic politics. Both
established democratic and full autocratic regimes are posited to have very predictable trade
policies. This is a result of the identity of the supporters in their winning coalition and their
capacity to successfully demand for (extra) protection. Instead, countries that are neither
democratic nor autocratic, namely hybrid regimes, are the ones expected to suffer the cost of
uncertainty. While this is they main argument of the paper, I also explore one implication
of this argument with respect to the political ideology of democratic leaders. Analogously to
21
the difference between democracies and autocracies vis-à-vis hybrid regimes, centrists governments are the ones expected to face uncertainty costs, if at all, whereas left and right wing
governments are not.
Using industry-level data from WTO members from 1995–2013, I find support for such
claims by analyzing both import flows as well as exporters rate of entry. Moreover, analyses
of firm-level survey data reveals evidence in line with the purported uncertainty mechanism.
While theoretically relevant, these results also speak to policy debates. The issue of tariff
uncertainty is linked to the multilateral negotiations in the WTO. It is not only clear that
policy agreements should emphasize reducing bound rates even when applied rates are low,
but it is also crucial to understand how even the same bound and applied rates can have substantially different economic consequences as a function of domestic political environments.
22
References
Alesina, Alberto, and Roberto Perotti. 1996. “Income distribution, political instability, and
investment.” European Economic Review 40: 1203–1228.
Baccini, Leonardo, Andreas Dür, and Manfred Elsig. 2015. “The Politics of Trade Agreement
Design: Revisiting the Depth-Flexibility Nexus.” International Studies Quarterly 59: 765–
775.
Bagwell, Kyle, and Robert Staiger. 2005. “Enforcement, Private Political Pressure, and the
General Agreement on Tariffs and Trade/World Trade Organization Escape Clause.” Journal of Legal Studies 34 (2): 471–513.
Baker, Scott R., Nicholas Bloom, and Steven J. Davis. 2015. “Measuring Economic Policy
Uncertainty.” NBER Working Paper 21633.
Barro, Robert J. 1991. “Economic growth in a cross section of countries.” Quarterly Journal of
Economics 106: 407–443.
Bearce, David H., Cody D. Eldregde, and Brandy J. Jolliff. 2016. “Does Institutional Design
Matter? A Study of Trade Effectiveness and PTA Flexibility/Rigidity.” International Studies
Quarterly .
Bernanke, Ben S. 1983. “Irreversibility, Uncertainty, and Cyclical Investment.” The Quarterly
Journal of Economics 98 (1): 85–106.
Bittlingmayer, George. 1998. “Output, Stock Volatility and Political Uncertainty in a Natural
Experiment: Germany 1890-1940.” Journal of Finance 53 (6): 2243–2257.
Bloom, Nicholas. 2009. “The Impact of Uncertainty Shocks.” Econometrica 77 (3): 623–685.
Bloom, Nicholas, Stephen Bond, and John Van Reenen. 2007. “Uncertainty and Investment
Dynamics.” Review of Economic Studies 74: 391–415.
Broz, J. Lawrence, Jeffry Frieden, and Stephen Weymouth. 2008. “Exchange-Rate Policy
Attitudes: Direct Evidence from Survey Data.” IMF Staff Papers 55 (3): 417–444.
Broz, J. Lawrence, and Michael Plouffe. 2010. “The Effectiveness of Monetary Policy Anchors:
Firm-Level Evidence.” International Organization 64: 695–717.
Bueno de Mesquita, Bruce, Alastair Smith, Randolph Siverson, and James Morrow. 2003. The
Logic of Political Survival. MIT Press.
Canes-Wrone, Brandice, and Christian Ponce de Leon. 2015. “Electoral Cycles and Democratic
Development.” Working Paper.
23
Canes-Wrone, Brandice, and Jee-Kwang Park. 2012. “Electoral Business Cycles in OECD
Countries.” American Political Science Review 106 (1): 103–122.
Canes-Wrone, Brandice, and Jee-Kwang Park. 2014. “Elections, Uncertainty, and Irreversible
Investment.” British Journal of Political Science 44 (1): 83–106.
Carroll, Christopher D., and Wendy E. Dunn. 1997. NBER Macroeconomics Annual. MIT
Press chapter Unemployment Expectations, Jumping (S,s) Triggers, and Household Balance Sheets, pp. 165–229.
Cruz, Ceci, Philip Keefer, and Carlos Scartascini. 2016. “Database of Political Institutions
Codebook, 2015 Update (DPI2015).”.
Cukierman, Alex. 1980. “The Effects of Uncertainty on Investment under Risk Neutrality
with Endogenous Information.” Journal of Political Economy 88: 462–475.
Diamond, Larry. 2002. “Thinking About Hybrid Regimes.” Journal of Democracy 13 (2): 21–
35.
Dixit, Avinash K. 1989. “Entry and Exit Decisions under Uncertainty.” The Journal of Political
Economy 97 (3): 620–638.
Dixit, Avinash K., and Robert S. Pindyck. 1994. Investment Under Uncertainty. Princeton
University Press.
Dür, Andreas, Leonardo Baccini, and Manfred Elsig. 2014. “The Design of International Trade
Agreements: Introducing a New Database.” Review of International Organizations 9 (3):
353–375.
Dutt, Pushan, and Devashish Mitra. 2005. “Political Ideology and Endogenous Trade Policy:
An Empirical Investigation.” The Review of Economic and Statistics 87 (1): 59–72.
Fernandes, Ana M., Caroline Freund, and Martha Denisse Pierola. 2016. “Exporter behavior,
country size and stage of development: Evidence from the exporter dynamics database.”
Journal of Development Economics 119: 121–137.
Friedman, Milton. 1968. “The Role of Monetary Policy.” American Economic Review 58 (1):
1–17.
Frye, Timothy. 2002. “Perils of polarization: Economic performance in the post-communist
world.” World Politics 54 (4): 308–337.
Handley, Kyle. 2014. “Exporting under trade policy uncertainty: Theory and evidence.” Journal of International Economics 94: 50–66.
Handley, Kyle, and Nuno Limão. 2014. “Policy Uncertainty, Trade and Welfare: Theory and
Evidence for China and the U.S.” Working Paper.
24
Handley, Kyle, and Nuno Limão. 2015. “Trade and Investment under Policy Uncertainty:
Theory and Firm Evidence.” American Economic Journal: Economic Policy 7 (4): 189–222.
Hasset, Kevin A., and Gilbert E. Metcalf. 1999. “Investment with Uncertain Tax Policy: Does
Random Tax Policy Discourage Investment?” The Economic Journal 109: 372–393.
Hassler, John. 2001. “Uncertainty and the Timing of Automobile Purchases.” The Scandinavian Journal of Economics 103 (2): 351–366.
Henisz, Witold L. 2002. “The Political Constraint Index (POLCON) Dataset.”.
URL: http://www-management.wharton.upenn.edu/henisz/
Ingco, Merlinda, and John Croome. 2004. In Agriculture and the WTO: Creating a Trading
System for Development, ed. Merlinda Ingco, and John D. Nash. The World Bank chapter
Trade Agreements: Achievements and Issues Ahead.
Jensen, Nathan. 2003. “Democratic Governance and Multinational Corporations: Political
Regimes and Inflows of Foreign Direct Investment.” International Organization 57 (3): 587–
616.
Johns, Leslie. 2014. “Depth versus rigidity in the design of international trade agreements.”
Journal of Theoretical Politics 26 (3): 468–495.
Julio, Brandon, and Youngsuk Yook. 2012. “Political Uncertainty and Corporate Investment
Cycles.” The Journal of Finance 67: 45–83.
Kenyon, Thomas, and Megumi Naoi. 2010. “Policy Uncertainty in Hybrid Regimes: Evidence
From Firm-Level Surveys.” Comparative Political Studies 43 (4): 486–510.
Koremenos, Barbara. 2001. “Loosening the Ties that Bind: A Learning Model of Agreement
Flexibility.” International Organization 55 (2): 289–325.
Koremenos, Barbara. 2005. “Contracting around International Uncertainty.” American Political Science Review 99 (4): 549–565.
Levitsky, Steven, and Lucan Way. 2002. “The Rise of Competitive Authoritarianism.” Journal
of Democracy 13 (2): 51–65.
Limão, Nuno, and Giovanni Maggi. 2015. “Uncertainty and Trade Agreements.” American
Economic Journal: Microeconomics 7 (4): 1–42.
Maggi, Giovanni, and Andres Rodriguez-Clare. 1998. “The Value of Trade Agreements in the
Presence of Political Pressures.” Journal of Political Economy 106 (3): 574–601.
Milner, Helen V., and Benjamin Judkins. 2004. “Partisanship, Trade Policy, and Globalization:
Is There a Left–Right Divide on Trade Policy?” International Studies Quarterly 48 (1): 95–
120.
25
Pelc, Krzysztof. 2009. “Seeking Escape: The Use of Escape Clauses in International Trade
Agreements.” International Studies Quarterly 53: 349–368.
Pelc, Krzysztof. 2011a. “How States Ration Flexibility: Tariffs, Remedies, and Exchange Rates
as Policy Substitutes.” World Politics 63 (4): 618–646.
Pelc, Krzysztof. 2011b. “Why Do Some Countries Get Better WTO Accession Terms Than
Others?” International Organization 65: 639–672.
Pelc, Krzysztof. 2013. “The Cost of Wiggle-Room: Looking at the Welfate Effects of lexibility
in tariff Rates at the WTO.” International Studies Quarterly 57: 91–102.
Petrova, Maria, and Robert H. Bates. 2012. “Evolution of Risk and Political Regimes.” Economics & Politics 24 (2): 200–225.
Pierce, Justin, and Peter Schott. Forthcoming. “The Surprisingly Swift Decline of U.S. Manufacturing Employment.” American Economic Review .
Pinto, Pablo M. 2013. Partisan Investment in the Global Economy: Why the Left Loves Foreign
Direct Investment and FDI Loves the Left? Cambridge University Press.
Reinhardt, Eric, and Jeffrey Kucik. 2008. “Does Flexibility Promote Cooperation? An Application to the Global Trade Regime.” International Organization 62 (3): 477–505.
Rodrik, Dani. 1991. “Policy uncertainty and private investment in developing countries.”
Journal of Development Economics 36: 229–242.
Romer, Chirstina D. 1990. “The Great Crash and the Onset of the Great Depression.” Quarterly Journal of Economics 105: 597–624.
Rosendorff, B. Peter. 2005. “Stability and Rigidity: Politics and Design of the WTO’s Dispute
Settlement Procedure.” American Political Science Review 99 (3): 389–400.
Rosendorff, B. Peter, and Helen Milner. 2001. “The Optimal Design of International Trade
Institutions: Uncertainty and Escape.” International Organization 55: 829–857.
Stasavage, David. 2003. Public Debt and the Birth of the Democratic State: France and Great
Britain, 1688-1789. Cambridge University Press.
Walhenhorst, Peter, and Nora Dihel. 2003. “Tariff Bindings, Unused Protection and Agricultural Trade Liberalization.” OECD Economic Studies 36 (1): 231–249.
Weymouth, S., and P. M. Pinto. 2015. “Partisan Cycles in Offshore Outsourcing: Evidence
from U.S. Imports.” Working Paper.
Weymouth, Stephen, and J. Lawrence Broz. 2013. “Government Partisanship and Property
Rights: Cross-Country Firm-Level Evidence.” Economics & Politics 25 (2): 229–256.
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