Sticks and stones: the impact of economic sanctions
and threats upon international trade
By
Chiel Willem Gerie Klein Reesink
University of Groningen
Faculty of Economics and Business
Master’s Thesis for IE&B (EBM868A20)
1 ABSTRACT
This paper investigates the impact that threats and impositions of economic sanctions have on bilateral trade flows. A new dataset that includes threats besides impositions, and the recent advances in gravity model methodology bring about unprecedented research as the difference in impact between threats and impositions of economic sanctions has never been studied before. From the results follows that extensive threats increase trade, constituting an “anticipation effect” in which firms that operate in the quarreling states anticipate the sanction episode by increasing economic activity while it is still possible. Impositions decrease trade, consistent with earlier literature, but the magnitude of the decrease is not as extreme as earlier studies found. Ultimately, the goal of an economic sanction is not to decrease bilateral trade; the decrease is a means to an end. The goal is to attain a different behavior from the target state. This study quantifies the means as it is important to know that attaining these goals comes at a cost.
Keywords: Threats • Impositions • Economic sanctions • Impact • International trade • Gravity model
ACKNOWLEDGEMENT
2 1. INTRODUCTION
Economic sanctions1 have been used throughout most of modern history and usually accompanied or preceded war (Elliot et al., 2008). It wasn’t until the horrors of World War I shocked the world, that Woodrow Wilson put sanctions on the agenda as a substitute for military aggression and that they were seriously considered. He claimed that sanctions could be a very effective policy tool and subsequently they were incorporated as a tool of enforcement in the League of Nations, which later became the United Nations (Elliot et al., 2008).
From this moment on history witnessed an increase in the use of economic sanctions by nation states and supranational institutions like the United Nations in order to attain foreign policy goals; according to recent research the number of sanctions has doubled every decade between 1971 and 2000 (Kobayashi, 2013). The state that, by far, has utilized the sanction policy tool the most is the United States. “In most instances in the post-WWII period where economic pressure was brought to bear against the exercise of military power, the United States played the role of international policeman (Hufbauer et al., 1990, p.5).”
Given the increasing frequency with which nations use this policy instrument (see figure 1), the need for studying the effects of sanctions increases as well. Interestingly, the effect (impact) that sanctions have on bilateral trade flows has received relatively little attention in recent years, despite great recent progress in the methodology that usually applies to this type of research.
Another aspect that is receiving increasingly more attention are sanction threats. As Lacy & Niou (2004) and Kleinberg (2014) point out, most economic sanction studies focused on imposed sanctions, automatically ignoring a large number of cases that is decided at the
1 I use “economic sanctions” and “sanctions” interchangeably, however, the definition taken from Morgan et al.
3 threat stage. Moreover, it has never been empirically tested whether threats have an impact on bilateral trade flows.
This study is the first to empirically measure the impact of sanction threats, besides sanction impositions, on bilateral trade flows. Therefore, I formulate the following research questions:
RQ1a: What is the impact of economic sanction impositions upon international trade? RQ1b: What is the impact of economic sanction threats upon international trade?
RQ2: Does the imposition of economic sanctions have a greater impact upon international trade than do sanction threats?
The added value of this study is twofold. Due to the recent progress in the gravity model methodology the present paper should yield more reliable results than what has been found so far in the field of economic sanctions. Furthermore, it includes threat cases, which has never been done before and can create valuable attributions to the current literature on economic sanctions. Also, this report might function as a stepping stone for future research, especially when it comes to the aspect of sanction threats.
This study will show that impositions and threats each have a different impact on bilateral trade. The first has a clear negative impact on trade, while the latter increases trade. The negative impact that impositions have on trade can be reconciled with earlier studies on the subject, which also pointed in the same direction. The positive impact of sanction threats, however, is a new finding that hopefully inspires new future research.
4 estimation and results, after which the robustness checks follow in section 5. Finally, section 6 concludes with a discussion, conclusion and pointers for future research.
2. LITERATURE 2.1 Economic sanctions
Morgan et al. (2014, p.2) define economic sanctions as “actions that one or more countries take to limit or end their economic relations with a target country in an effort to persuade that country to change its policies.” Therefore, a sanction must (a) involve one or more sender2 states and a target state and (b) be implemented by the sender(s) in order to change the behavior of the target state. Actions taken by states that restrict economic relations with other countries for solely domestic economic policy reasons do not qualify as sanctions (Example: if Vietnam decides to place an import tariff on foreign computers to protect its domestic industry, this is not considered an economic sanction).
Economic sanctions are built upon two basic premises that belong to the core of the economic science (Van Bergeijk, 2009). First, sanctions are meant to deprive the target country of (part of) the gains that this country experiences from international trade and investment. Second, this (threat of) disutility will affect the target’s behavior. This means economic sanctions reduce welfare in the target country in order to force it to change its behavior. Sanctions can take many forms, including tariffs, export controls, embargoes, import bans, travel bans, freezing assets, cutting aid, and blockades. All except blockades are considered to be legal barriers to trade. Blockades on the other hand, are often considered an act of war.
Within aforementioned sanctions roughly three categories can be discerned: boycotts, embargoes and financial sanctions (Barber, 1979; Caruso, 2003). A boycott is a restriction of
2 Following convention, the sanctioning state will be referred to as “sender” and the sanctioned state will be
5 imports of one or more goods from the target country. It is meant to lower demand for the product from the target country. Furthermore, the import restriction attempts to reduce the foreign exchange earnings of the target country and thus its ability to purchase goods in international markets. These measures are usually deemed ineffective because of the ease with which target countries can circumvent the import restrictions by finding other trading partners or setting up triangular schemes to sell their products. A sender country can also restrict its own exports to a target country, this is called an embargo. Often these exports comprise goods that are of strategic importance to the target country. An example is the 2014 situation in Ukraine, that led the European Union among others to restrict the export of arms and related materials, technology for military use, energy-related equipment and technology, and oil exploration services to Russia3. Financial sanctions are meant to cut off lending and investment in the target country through the international credit markets, but also to freeze the target country’s foreign assets.
An economic sanction is an action by state A against state B in order to bring state B’s ‘behavior’ in line with state A’s view of what that behavior should be. Therefore economic sanctions usually have a desired outcome, namely the change in behavior/policies of the target state. Whether real outcomes meet desired outcomes is questioned and thus, economic sanctions are a constant subject of debate (Kaempfer & Lowenberg, 1988). Academics are not unanimously in favor of this policy tool, as research in the past has produced mixed results. It is questioned whether the sanctions led to the desired outcomes. For this reason much research conducted on economic sanctions has had the purpose to ascertain the effectiveness of economic sanctions (Bergeijk, 1989; Hufbauer et al., 1990; Pape, 1997; Drezner, 2000).
3
6 2.2 Military conflicts
It is often the case that an economic sanction accompanies or precedes war (Kerr & Gaisford,
2007; Elliot & Hufbauer, 2008), something which Caruso (2003) brings to attention as well.
An example he gives is the reign of terror by Mengistu in Ethiopia during the latter half of the seventies. The US imposed sanctions on Ethiopia in 1976, right before it actively played a role in the war between Ethiopia and Somalia in 1977. Another example is the conflict in former Yugoslavia, where the US imposed a range of sanctions before it started actively participating in the war in 1996. In these cases economic sanctions become intertwined with military disputes. This may lead to false inclinations when a certain impact is attributed to a sanction episode, yet it is actually brought on by a conflict.
When we assume firms are rational economic actors, chances are trade is affected without government sanctions as well. Firms with their goods or lives in mortal danger because of military hostilities in their country “will seek a greater margin of profit or more complete insurance coverage to compensate for the risk; but these actions raise costs and lower demand, reducing commerce” (Caruso, 2003; p.11). From this line of reasoning it becomes clear that a decrease in trade with state A is not necessarily caused by a sanction episode when military hostilities are present in state A. To separate the effects of military disputes from the effect of sanctions (threats), I have to control for military conflicts.
7 2.3 Datasets
Since Hufbauer, Schott, and Elliot published their research and corresponding dataset (hereafter called HSE) on economic sanctions in 1990, research on economic sanctions has progressed significantly. Prior to their research, most works focused on a single sanction case and tried to explain why sanctions did and could not work (Baer, 1973; Galtung, 1967;
Hoffman, 1967; Olson, 1979; Schreiber, 1973; von Amerongen, 1980; Wallensteen, 1983).
More recently (since the release of HSE), research has tried to determine the effects of economic sanctions on FDI (Biglaiser & Lektzian, 2011), on human rights (Peksen, 2009), on the level of democracy (Peksen et al., 2010), on jobs and wages (Hufbauer et al., 1997), along with the impact that sanctions have upon international trade (Caruso, 2003). From this last line of research sprouted dissertations on third-country effects; which is also known as sanctions-busting (Early, 2009; Yang et al., 2009). This is a situation in which a sanctioned state reroutes its trade to other trading partners that are not taking part in the sanctioning of aforementioned state, thus eliminating the effect of the sanction.
In 2009 Morgan et al. released their newly developed dataset on economic sanctions. Concurrently with the release of the dataset, the authors published a paper describing the dataset. In this paper they state that “in order to continue to advance our understanding of economic sanctions by testing hypotheses derived from recent theory, new data are needed”
(Morgan et al., 2009, p.93). This new dataset is called TIES, which stands for the Threat and
8 multilateral sanctions are more effective than unilateral sanctions. Theory suggested this outcome long before the TIES dataset was created, however it could not be proved with the HSE dataset. I believe that similar new insights are possible when investigating the impact of economic sanctions with the TIES dataset.
A table with a comparison (Morgan et al., 2009; 2014) of the two different datasets (HSE and TIES) is taken up in the appendix (see table 1). The newest version of each dataset is included in the comparison. From the table it becomes clear that the TIES dataset has several advantages over the HSE dataset. As said, TIES contains far more cases than HSE and it includes threats as well. Also, the US is the primary sender in 60% of the cases in HSE, compared to 48% in TIES. This means that HSE is more biased towards the US than TIES. Moreover, the mean duration of the cases in TIES is far shorter (2.43 years) than those in HSE (6.6 years), which tells us that HSE severely underestimates the number of relatively short sanction episodes.
2.4 Impact
9 The literature employing the gravity model to explain bilateral trade reduction is dominated by work from Hufbauer et al, especially the book Economic Sanctions Reconsidered: History and current policy written in 1990 has been influential. Their work can be seen as a milestone in the literature on economic sanctions, and is often an inspiration to new sanction research. Hufbauer et al. (1997) and Caruso (2003) delved into the impact that economic sanctions have upon international trade by applying a gravity model approach. Hufberger et al. (1997) contemporaneously searched for effects on jobs and wages. Yang et al. (2004) applied a gravity model to investigate the impact of U.S. sanctions on U.S. trade with target countries, and on third countries.
Hufbauer et al. (1997) is one of the first studies that aims to empirically measure the impact of U.S. economic sanctions on bilateral trade flows. The authors put three years under analysis (namely 1985, 1990 and 1995), and look at 88 countries. In their research they categorize sanctions into three types: limited, moderate and extensive. Minor financial, export, cultural, or travel sanctions are labeled as “limited” sanctions. Broader trade or financial sanctions are classified as “moderate”. The authors consider comprehensive trade and financial sanctions or a combination of moderate sanctions to be “extensive”. This categorization of sanctions has been used by other scholars as well (Elliott et al., 1999; Caruso, 2003; Wood, 2008; Peksen, 2009). Hufbauer et al. (1997) are able to show that extensive U.S. sanctions can reduce bilateral trade up to 90%. The results on limited and moderate sanctions are not as robust as the results on extensive sanctions, but they show an average reduction of about 30%.
10 finds a small positive coefficient for limited and moderate sanctions, however, this result is insignificant. Furthermore, he looks into the impact that economic sanctions have on trade of the targeted country with other G7-nations. Here Caruso shows that extensive sanctions induce a disruption of trade for other countries than the United States as well, which he calls negative network effects.
Yang et al. (2004) also employs a gravity model to investigate the impact of U.S. economic sanctions on U.S. trade with target countries, and on third countries. Their results are similar to the previously mentioned works. Extensive and comprehensive sanctions have large negative impacts through bilateral trade reductions while the impact of limited and moderate sanctions seems weak or absent.
11 Previous research on the impact of economic sanctions seems to suggest a couple of things, namely that extensive and comprehensive sanctions have a larger negative impact than limited and moderate sanctions, and that this impact is very high (up to 90% reductions in bilateral trade flows). See table 2 for an overview. Therefore, I propose the following hypotheses:
Hypothesis 1: The imposition of an economic sanction has a negative impact upon bilateral trade flows.
Hypothesis 2a: The imposition of a limited/moderate economic sanction has a negative impact upon bilateral trade flows.
Hypothesis 2b: The imposition of an extensive economic sanction has a negative impact upon bilateral trade flows.
Hypothesis 3: The imposition of an extensive economic sanction has a greater impact upon bilateral trade flows than does the imposition of a limited/moderate economic sanction.
2.5 Threats
A phenomenon that scholars are only able to research relatively recently is economic sanction threats. The imposition of economic sanctions has been widely researched for decades, however, only since the inception of the TIES dataset there is data available on sanction threats. Because of data restrictions, authors chose to focus on a single case such as the U.S. threatening to sanction China because of the Tiananmen Square massacre in 1989 (Li &
Drury, 2004; Drury & Li, 2006). Sanction threat cases are situations in which a sender state
12 sender’s demands. Usually these demands, much like an imposition case, entail policy changes or a different standpoint in a political matter.
The big question when it comes to sanction threats so far has been whether threats are as effective as sanction impositions. Most recent research focused on this question and looked solely at effectiveness of threats (Lacy & Niou, 2004; Drury & Li, 2006; Kleinberg, 2014). A threat is effective when the outcome of the threat case is equal to the desired outcome. For example, when a sender threatens a target with economic sanctions because the target does not allow gay couples to marry and in the wake of this threat gay marriage is suddenly allowed in the target state the threat is deemed effective. Therefore, effectiveness is more concerned with success rates and not with impact (reduction in trade flows).
To my understanding the impact that sanction threats might have on trade flows has never been researched before. Therefore, I want to mention research concerning the effectiveness of sanction threats in this paper in order to highlight the value that sanction threat research adds to the existing literature. Lacy & Niou (2004) set up their model as a multistage game of two-sided incomplete information between a sender and a target. The authors state that the threat stage of a case is critical to understanding the outcome of the sanction. “The model reveals that the threat of sanctions can be as potent a policy tool as the imposition of sanctions” (Lacy & Niou, 2004, p. 38). This paper suggests that threats are just as capable of changing a target’s behavior as impositions are.
13 is also considered limited/moderate if the sanction type that is threatened with is unspecific, here the sender threatens to sanction the target but the sender does not communicate clearly what the sanction entails exactly. Extensive threats, following the same logic, concern total economic embargoes, blockades, asset freezes, and suspension of economic agreement/protocol.
Regarding the impact of sanction threats, two scenarios are possible. Let us assume that the government of state A has become unhappy with the policies adopted by state B’s government. State A decides to act against it and threatens to impose economic sanctions on state B. Firms in these states witness the struggle their governments are in and act according to what they deem fit. In both scenarios firms expect an economic sanction to follow the sanction threat. However, it is the reaction to the threat that differs in each scenario. In the first scenario, firms expect a sanction episode and anticipate this by pulling out of deals with firms in state B. This line of events would impact negatively on trade flows between a given state and state B. The other scenario expects firms to increase business deals with firms in state B in order to reap the benefits of trade ‘while they still can’. In this situation a rise in trade between a given state and state B can be witnessed, constituting a positive impact on trade flows with state B.
Looking at it this way, it becomes clear that a threat case is significantly different from a case in which a sender actually imposes a sanction on a target, since there is no definite harm done yet. Until now, this aspect of sanction threats has not been tested. With the inclusion of threats in the TIES dataset it became possible to put this to the test. In this paper I try to answer whether sanction threats have an impact on bilateral trade flows. Therefore, I put forward the following hypotheses:
14 Hypothesis 4b: The threat of an economic sanction has a positive impact upon bilateral trade flows.
Hypothesis 5: The impact of limited/moderate sanction threats differs in magnitude from that of extensive sanction threats.
Hypothesis 6: The imposition of an economic sanction has a greater impact upon bilateral trade flows than does an economic sanction threat.
3. METHODOLOGY 3.1 Gravity Model
As mentioned before, scholars have been using the gravity model for a long time to study bilateral trade flows. The technique has evolved over time, especially in recent years, to increase its explanatory power. The basic gravity model, used by well-known scholars such as Andrew Rose (2000), explains bilateral trade flows by using a single log-linear equation:
ln TRADEijt = β0 + β1 ln GDPit + β2 ln GDPjt + β3 ln DISTANCEij + β4 THREATijt
+ β5 IMPOSITIONijt + β6 ɀijt + eijt (1)
In this equation TRADEijt denotes bilateral trade between state i and state j at time t, GDPit
and GDPjt represent their gross domestic products respectively, and DISTANCEij the physical
distance between states. THREATijt is a binary variable that takes the value 1 when a sanction
threat is active between countries i and j in year t, and 0 if not. The same goes for IMPOSITIONijt, however in this case it involves a sanction imposition. Finally, ɀijt stands for
15 border, etc., and eijt is the random error term. The physical distance between states is usually
proxied by the distance between their capitals in kilometers (Kohl, 2014), it is assumed that the capital of a country is its economic center (Caruso, 2003).
In 2003 “multilateral resistance terms” (MRT) entered the gravity model picture
(Anderson & van Wincoop, 2003). According to Anderson and van Wincoop MRT need to be
included in gravity equations “to take into account that trade between two trading countries is also affected by their bilateral trade barrier relative to their average trade barriers vis-à-vis all of their other trade partners” (Kohl, 2014, p.7).
For example, take bilateral trade between Germany and the Netherlands. This trade, as mentioned earlier, depends on “how costly it is for each to trade with each other relative to the costs involved for each of them in trading with other countries” (Adam & Cobham, 2007; p.1). A reduction in the bilateral trade barrier between Germany and a third country, such as Belgium, would reduce the multilateral trade resistance of Germany. Even though this reduction does not affect the bilateral trade barrier between Germany and the Netherlands, the fall in Germany’s multilateral trade resistance (caused by the decline in the Germany – Belgium bilateral trade barrier) does redirect bilateral trade from Germany – Netherlands trade to Germany – Belgium trade (Adam & Cobham, 2007). Therefore, trade barriers with countries outside the dyad may indirectly influence trade inside the dyad.
16 Feenstra (2004) demonstrates a way of incorporating these unobserved price indices by adding importer and exporter fixed effects to the equation (1), adjusting it to our situation yields:
ln TRADEijt = β0 + β1 ln GDPit + β2 ln GDPjt + β3 ln DISTANCEij + β4 THREATijt
+ β5 IMPOSITIONijt + β6 ɀijt + γiFi + δjFj + νijFt + eijt (2)
Here Fi represents fixed effects for the importing country and Fj for the exporting country.
Year effects (Ft) deal with common trends and shocks. Changes in trade costs on one bilateral
route can influence trade flows on all other routes because of relative price effects, this is picked up in the model (2). Because the multilateral resistance terms, which are correlated with trade costs, are not included in the basic model this model suffers from omitted variables bias (Shepherd, 2013). Most of the empirical analyses (Hufbauer et al. 1990; Caruso, 2003;
Yang et al., 2004) on economic sanctions make use of this basic model.
The model as shown in equation (2) only offers a partial solution to the problem of modeling multilateral trade resistance in panel-data. From work by Baier & Bergstrand (2007) follows that in a panel-setting, “Anderson and van Wincoop’s multilateral resistance terms are actually time-varying, which means that the importer and exporter effects also need to be time-varying to fully capture the MRT” (Kohl, 2014;p.8). When country fixed effects are not interacted with time, country fixed effects control for average trade resistance over time. However, key elements of trade resistance may be time-varying (Adam & Cobham, 2007).
17 importer, and time dummies since the first two would capture differences in the absolute levels of multilateral resistance while the third would capture their common time trend” (p. 21). They find that coefficient estimates of time-changing variables specific to a country pair (such as sanctions) are quite sensitive to the inclusion of time-varying fixed effects; not including time-varying country-fixed effects even reverses sign in some occasions.
Without accounting for time-varying MRT, the results are likely to suffer from an omitted variables bias (Baier & Bergstrand, 2007). If we adjust our model to this, the result is a time-varying fixed-effects version of the gravity equation:
ln TRADEijt = β0 + β1 ln GDPit + β2 ln GDPjt + β3 ln DISTANCEij + β4 THREATijt
+ β5 IMPOSITIONijt + β6 ɀijt + γitFit + δjtFjt + νijFij + eijt (3)
Fit and Fjt denote importer-year and export-year effects, respectively. This equation comprises
a time-varying form of equation (2). Fij controls for unobserved circumstances that may be
correlated with both a country-pair’s level of trade and factors related to the dyad experiencing a sanction episode (Kohl, 2014).
These “evolutions” in the gravity model greatly improve the explanatory power of the model. Previous sanction literature employing a gravity model did not make use of these new techniques and therefore it is interesting to see what this new approach will yield.
3.2 Data
18 World Development Indicators, the deflators were used to obtain real GDP, real imports and real exports. Following Baldwin & Taglioni (2006), I combine the dependent variables real exports and real imports to form one dependent variable real trade (by taking the log of the average of both). The real imports and real exports variables are taken up as dependent variable in the robustness check. Table 3 is added to the appendix, listing all countries (223) included in the dataset.
Conflict data is available from the Correlates of War (2010) project. Sanction data comes from the Threat and Imposition of Economic Sanctions (2006) project. This dataset contains data for both economic sanctions and sanction threats. Should a sender state convert a sanction threat into an actual sanction, then the case is split into a threat case and a sanction case. The TIES dataset contains 1412 cases of which 845 (60%) are cases in which sanctions were imposed and 567 (40%) cases involved threats (Morgan et al., 2014), and the period covered is 1948-2005. I have broken down the impositions and threats into limited/moderate and extensive impositions and threats, this breakdown is shown in figure 2.
4. ESTIMATION & RESULTS
Following Baier & Bergstrand (2007) and Kohl (2014) I estimate a fixed-effects version of the gravity equation:
ln TRADEijt = β0 + β1 ln GDPit + β2 ln GDPjt + β3 ln DISTANCEij + β4 THREATijt
+ β5 IMPOSITIONijt + β6 INTERSTATEWAR + β7 INTRASTATEWAR
+ γitFit + δjtFjt + νijFij + eijt (4)
As discussed earlier in this paper, importer-year (Fit) and exporter-year (Fjt) fixed-effects are
19 Wincoop’s (2003) MRT. Fij controls for unobserved phenomena that could be correlated to
the level of trade (LHS) or the active sanction episode (RHS) of the dyad4. As expected, the GDP and distance variables are not estimated because of their collinearity with the country-year and dyad fixed effects. However, the interstate war and intra-state war variables are also not estimated because they are perfectly collinear with the fixed-effects. Table 4 lists the results. The impact due to sanctions (threats) can be calculated as a percentage by taking the exponent5 of the coefficient value for the sanction or threat dummy and subtracting 1 (Yang et al., 2004). The threat coefficient is 0.045, indicating a small increase (+4.6%) in trade during a threat episode, and the result is significant at the 10% level. The imposition variable has a coefficient of -0.199, signifying a significant decrease (-18%). The estimate for the imposition variable is significant at the 1% level. Based on these results we can accept hypothesis 1, impositions have a negative impact upon bilateral trade flows. Hypothesis 4b is accepted, while 4a is rejected, as threats cause a small increase in bilateral trade flows. The impact of impositions on trade is greater than the impact of threats, therefore we accept hypothesis 6.
I also estimate a fixed-effects version of the gravity estimation with a breakdown of the threat and imposition variables into limited/moderate and extensive variables:
ln TRADEijt = β0 + β1 ln GDPit + β2 ln GDPjt + β3 ln DISTANCEij + β4 LMTHREATijt
+ β5 XTHREATijt + β6 LMIMPOSITIONijt + β7 XIMPOSITIONijt +
β8 INTERSTATEWAR + β9 INTRASTATEWAR + γitFit + δjtFjt + νijFij + eijt (5)
4 The user-written Stata package reg3hdfe was used to estimate the linear regression models with
high-dimensional fixed-effects.
5
20 Again, the GDP, distance, and COW variables are not estimated because of perfect collinearity with the fixed-effects. The results are listed in table 4. The coefficient for limited/moderate threats is 0.008, which means that limited/moderate threats increase bilateral trade flows by 0.8%. However, this estimate is insignificant. Extensive threats increase trade by 17% as the coefficient is 0.157 and significant at the 1% level. Both limited/moderate and extensive impositions decrease bilateral trade by 16% (-0.174) and 21.5% (-0.242), respectively. The estimates are both significant at the 1% level. Therefore, we accept hypothesis 2a, 2b and 3, for both types of impositions have a significantly negative impact on bilateral trade flows and the impact of extensive impositions is greater than that of limited/moderate impositions. Hypothesis 4b is accepted and 4a is rejected, as economic sanction threats increase bilateral trade flows. Hypothesis 5 cannot be accepted as the result for limited/moderate threats is insignificant. Table 5 lists the coefficients and corresponding impact on trade in a clear manner.
I also estimated models similar to those in equations (4) and (5), but with Anderson & Van Wincoop fixed-effects (as in equation (2) ) instead of the more advanced fixed-effects in equations (3), (4), and (5), to allow for a comparison. The results of this estimation can be found in table 6. The results are clearly different from the main results reported in table 4. Here, GDPi, GDPj, distance, and the COW variables are estimated because they are not
collinear with the fixed effects. The GDP and distance variables have the expected signs and are significant at the 1% level. However, the COW variable that is significant (intra-state wari) at the 1% level seems to suggest that war inside a country has a positive impact on
21 the main results, and go in against our theory and previous literature on the subject. The reversal of the signs seems similar to what Ruiz & Vilarubia (2007) describe in their paper.
5. ROBUSTNESS CHECKS
Now we turn to the robustness of the main results. For the sensitivity analysis I first want to take a closer look at my dependent variable because this variable is constructed from two other variables. Then it is tested whether the results hold for different time periods, within the period under investigation (1948 – 2005). Subsequently, I check whether the results remain robust when looking at different setups of top five sender states, and to see how much of the variation is explained by these states. Finally, I perform a Poisson6 estimation following Silva & Tenreyro (2006) that deals with zero trade flows.
As discussed in the data section, I combined the real exports and real imports variables into a trade variable. I estimate both equation (4) and (5) twice, first with real exports as the dependent variable, and then with real imports as the dependent variable. The aim here is to see whether the creation of the dependent variable does not lead to distortions in the results. Table 7 lists the results. The estimates for impositions are robust in both setups (same signs and significant at 1% level), however, the threat estimates are less robust in the export setup (same signs, but not all significant).
Next, I estimate both equations (4) and (5) for different time periods. Elliot et al. (2014) report a large increase in the use of sanctions since 1990. When we look at figure 1 and 2 we see this rapid increase in my data as well. That is why I decide to split the data at the year 1990, creating two periods, namely 1948-1990 and 1990-2005. The results are reported in table 8. Overall, the results seem robust, especially for the 1948-1990 period in which all variables remain the same signs and are significant. For the 1990-2005 period, the estimates
6 I use a Stata command created by Silva & Tenreyro (2006) called PPML, which stands for Poisson
22 for the imposition variables remain the same sign and are significant. The threat estimates do remain the same signs, except for limited/moderate threats that turns slightly negative.
Then I want to see whether the results remain robust when looking at the top five sender states. Three different compositions are reported, one based on cases (1,412 total), one based on threats active (5,105 total), and one based on impositions active (7,685 total). The number of active threats and active impositions are greater than the number of cases because multiple cases can be active during a single year. Table 9 lists the three different compositions of the top five sender states. The results are reported in table 10. Again, overall results seem to remain robust for all three compositions, especially the results from the estimation of equation (4) for all three compositions. All the signs remain the same and the coefficients are significant at the 5% level. The same cannot be said about the results from the estimation of equation (5), here only the limited/moderate threat and extensive imposition variables remain significant (and with the right signs). The other variables do have the same signs, but are insignificant, except for the extensive threat variable in the cases composition; this variable is significant.
23 variable has a positive sign, which is inconsistent with the literature. However, the coefficient is insignificant. The inter-state war variable has a very large (-9.380) coefficient and is significant at the 1% level. The other two COW estimates are far smaller in number, but positive and insignificant. The threat and imposition coefficients are all very large in number (ranging from -16.729 for limited/moderate threats to -19.702 for extensive threats), and they are all highly significant (at the 1% level). The imposition coefficient signs can be reconciled with the main results, however, the threat estimates are very different from those in the main results. According to the Poisson estimation not only impositions impact trade negatively, but so do threats.
6. DISCUSSION & CONCLUSION
I set out to answer whether results from earlier economic sanction research stood the test of time, since developments in the field have been great in recent years. By including threats and making use of the newest econometric techniques the results differ from earlier research, mainly in magnitude. Where Hufbauer et al. (1997), Caruso (2003), and Yang et al. (2004) found impressive results of up to 90% decreases in bilateral trade, my research shows less extreme results but these are in line with earlier research. I am now able to answer the questions I posed at the beginning of my investigation. Imposing economic sanctions has a negative impact on trade, while threatening to impose a sanction has a positive impact. Furthermore, impositions have a greater impact on bilateral trade flows than do threats.
24 probable due to the fact that notably threatening with extensive sanctions evokes the effect of increased trade. Especially during extensive sanction episodes economic activity between sender and target is weighed down heavily, therefore reaping the benefits of trade while it is still possible seems like something a rational economic actor would do.
Concerning economic sanction impositions, the coefficients have the expected signs. However, the magnitude of the coefficients differs from earlier research in the field. Impositions (both limited/moderate and extensive) decrease bilateral trade flows, and they do so by about 20%. Most likely, earlier research overestimated the impact that economic sanctions have on trade. What we know for certain, is that the methodology used in this paper is far more advanced than what is used in aforementioned research. And when subjected to various robustness checks, the results generally remain the same.
Future research could take a closer look at sanction episodes, especially the ones that start out as a threat and evolve into a full-blown sanction. The current setup of this research made it difficult to distinguish an evolving episode (threat becomes an imposition over the course of a certain period) from an imposition- or threat-only episode (in this case an episode contains either a threat or an imposition, but not both). Progress can be made in disentangling ‘evolving cases’ from ‘simple cases’ to see whether the increase in trade for extensive threats remains when the episode undergoes a transformation from threat to imposition, or whether it yields different results due to the transformation.
25 The field of economic sanctions provides more room for future research. A topic that has fallen outside the scope of this paper but generated mixed results in the past is unilateral versus multilateral sanctions. Research by Elliot et al. (2007) stating that multilateral sanctions were less effective than unilateral sanctions goes in against policy makers’ intuition completely, and contrasts sharply with findings by Bapat & Morgan (2009). Caruso (2003) finds no significant difference in impact between unilateral and multilateral sanctions. However, the more advanced econometric techniques displayed in this paper yielded results that were different from Caruso’s results on limited/moderate and extensive sanctions as well. The same might be true for unilateral versus multilateral sanctions.
Finally, I want to point attention towards cases in which multiple sanctions were either threatened with or imposed. In this paper these cases were labeled extensive sanctions, and regarded as such. But perhaps, they ought to make up a category of their own, and researched as such. A cluster of different types of sanctions supposedly has more impact than a single type of sanction. Creating a setup where these cases are treated separately could yield interesting new results.
26
APPENDICES
Figure 1. Active threats/impositions per year
Table 1. HSE – TIES comparison
HSE TIES Period covered 1945-2005 1914-2006 Number of cases 204 1412 Number of impositions 204 845 Number of threats - 567 US as primary sender 60% 48%
Mean duration cases 6.6 years 2.43 years
27 Table 2. Overview empirical analyses
Hufbauer et al. (1997) Caruso (2003) Yang et al. (2004)
Years studied 1985 1990 1995 1960 – 2000 1980 1985 1990 1995 1998 Number of countries studied 88 50 225
Focus Impact on US trade Impact on US trade Impact on US trade
Dataset used HSE HSE HSE
Threats/impositions Impositions Impositions Impositions Gravity specification Equation (1) Equation (1) Equation (1)
Effect found LM: -30% * LM: +13% LM: +34%
X: -90% * X: -89% * X: -78% *
Note: Effects have been averaged when multiple effects were found. LM stands for limited/moderate sanctions, X stands for extensive/comprehensive sanctions. “*” means that result was significant. Authors use a specification of the gravity model that is similar to equation 1 in the methodology of this paper.
Table 3. List of countries
Afghanistan, Albania, Algeria, American Samoa, Andorra, Angola, Anguilla, Antigua & Barbuda, Argentina, Armenia, Aruba, Australia, Austria, Azerbaijan, Bahamas, Bahrain, Bangladesh, Barbados, Belarus, Belgium, Belize, Benin, Bermuda, Bhutan, Bolivia, Bosnia & Herzegovina, Botswana, Brazil, British Indian Ocean Territory, Brunei, Bulgaria, Burkina Faso, Burundi, Cambodia, Cameroon, Canada, Cape Verde, Cayman Islands, Central African Republic, Chad, Chile, China, Colombia, Comoros, Cook Islands, Costa Rica, Croatia, Cuba, Cyprus, Czech Republic, D.R. Congo, Denmark, Djibouti, Dominica, Dominican Republic, Ecuador, Egypt, El Salvador, Equatorial Guinea, Eritrea, Estonia, Ethiopia, Faeroe Islands, Falkland Islands, Fiji, Finland, France, French Polynesia, French Southern Territories, Gabon, Gambia, Georgia, Germany, Ghana, Gibraltar, Greece, Greenland, Grenada, Guam,
Guatemala, Guinea, Guinea-Bissau, Guyana, Haiti, Honduras, Hong Kong, Hungary, Iceland, India, Indonesia, Iran, Iraq, Ireland, Israel, Italy, Ivory Coast, Jamaica, Japan, Jordan,
28 Tunisia, Turkey, Turkmenistan, Tuvalu, Uganda, Ukraine, United Arab Emirates, United Kingdom, United States, Uruguay, Uzbekistan, Vanuatu, Venezuela, Vietnam, Virgin Islands, Wallis & Futuna, Yemen, Zambia, Zimbabwe
Figure 2. Breakdown of active threats/impositions per year
Table 4. Estimates with country-year and dyad fixed-effects Variable Equation (4) Equation (5)
Threat 0.045* (0.027) - Imposition -0.199*** (0.035) - Lmthreat - -0.008 (0.031) Xthreat - 0.157*** (0.047) Lmimposition - -0.174*** (0.038) Ximposition - -0.242*** (0.058) N 463,568 463,568
29 Table 5. Summary of results
Variable Coefficient Impact on trade
Threat 0.045* +4.6% Imposition -0.199*** -18% Lmthreat 0.008 +0.8% (n.s.) Xthreat 0.157*** +17% Lmimposition -0.174*** -16% Ximposition -0.242*** -21.5%
Note: Estimates marked *** are significant at the 1% level. Estimates marked ** are significant at the 5% level. Estimates marked * are significant at the 10% level.
(n.s.) indicates that the corresponding coefficient is not significant.
Table 6. Estimates with Anderson & Van Wincoop (2003) fixed-effects
Variable Equation (4) Equation (5)
GDPi 3.473*** (0.121) 3.473*** (0.121) GDPj 1.040*** (0.081) 1.041*** (0.081) Distance -1.566*** (0.020) -1.566*** (0.020) Inter-state war -0.276 (1.274) -0.284 (1.277) Intra-state wari 0.551*** (0.053) 0.552*** (0.526) Intra-state warj 0.040 (0.046) 0.041 (0.046) Threat 0.289*** (0.882) - Imposition 0.279*** (0.094) - Lmthreat - 0.575*** (0.079) Xthreat - 0.231* (0.123) Lmimposition - 0.058 (0.136) Ximposition - 0.464*** (0.128) N 463,568 463,568
30 Table 7. Estimates with country-year and dyad fixed-effects with imports/exports as DV
DV: real exports DV: real imports Variable Equation (4) Equation (5) Equation (4) Equation (5)
Threat 0.031 (0.030) - 0.094*** (0.033) - Imposition -0.197*** (0.037) - -0.207*** (0.041) - Lmthreat - 0.021 (0.036) - 0.045 (0.039) Xthreat - 0.083 (0.052) - 0.221*** (0.057) Lmimposition - -0.164*** (0.038) - -0.172*** (0.423) Ximposition - -0.246*** (0.062) - -0.269*** (0.069) N 387,668 387,668 411,578 411,578
Note: Estimates marked *** are significant at the 1% level. Robust standard errors (clustered by dyad) are reported in parentheses. Collinear results are not reported.
Table 8. Estimates with country-year and dyad fixed-effects for different time periods
1948 - 1990 1990 - 2005
Variable Equation (4) Equation (5) Equation (4) Equation (5)
Threat 0.107** (0.042) - 0.014 (0.026) - Imposition -0.266*** (0.068) - -0.113*** (0.029) - Lmthreat - 0.080** (0.041) - -0.016 (0.032) Xthreat - 0.224*** (0.084) - 0.057 (0.043) Lmimposition - -0.197** (0.080) - -0.078** (0.033) Ximposition - -0.337*** (0.095) - -0.165*** (0.048) N 247,251 247,251 308,643 308,643
31 Table 9. Top 5 senders
Number of cases Number of active threats Number of active impositions United States 678 (48%) United States 1273 (24.9%) United States 1600 (20.8%) Canada 112 (7.9%) Canada 266 (5.2%) Canada 586 (7.6%) Russia 38 (2.7%) United Kingdom 217 (4.3%) United Kingdom 485 (6.3%) United Kingdom 38 (2.7%) Russia 211 (4.1%) France 379 (4.9%) India 34 (2.4%) France 168 (3.3%) Italy 244 (3.2%)
Total 900 (63.7%) Total 2135 (41.8%) Total 3294 (42.9%)
Note: Total number of cases: 1412. Total number of active threats: 5,105. Total number of active impositions: 7,685. Percentages correspond to these numbers.
Table 10. Estimates with country-year and dyad fixed-effects for top five sender states
Number of cases Number of active threats Number of active impositions Variable Eq. (4) Eq. (5) Eq. (4) Eq. (5) Eq. (4) Eq. (5) Threat 0.112** (0.045) - 0.080** (0.037) - 0.076** (0.038) - Imposition -0.146** (0.059) - -0.125** (0.049) - -0.011** (0.052) - Lmthreat - 0.132*** (0.048) - 0.103*** (0.040) - 0.092** (0.037) Xthreat - 0.108* (0.065) - 0.079 (0.060) - 0.088 (0.065) Lmimposition - -0.093 (0.061) - -0.025 (0.048) - -0.015 (0.048) Ximposition - -0.200** (0.085) - -0.239*** (0.075) - -0.215*** (0.079) N 30,121 30,121 30,951 30,951 36,169 36,169
32 Table 11. PPML Estimation with Anderson & Van Wincoop fixed-effects
Variables Equation (4) Equation (5)
GDPi 0.535*** (0.129) 0.535*** (0.129) GDPj 0.859*** (0.141) 0.859*** (0.141) Distance 0.216 (0.163) 0.216 (0.163) Inter-state war -9.380*** (1.243) -9.380*** (1.243) Intra-state wari 0.437 (0.324) 0.436 (0.324) Intra-state warj 0.040 (0.517) 0.040 (0.517) Threat -17.413*** (0.811) - Imposition -17.362*** (1.148) - Lmthreat - -16.729*** (0.842) Xthreat - -19.702*** (0.805) Lmimposition - -17.609*** (0.350) Ximposition - -17.337*** (1.258) N 978,630 978,630
33 Table 12. Do-file
* Create variable lntrade . gen imports = exp(lrm) . gen exports = exp(lrx)
. egen impexp = rmean(imports exports) . gen Trade = ln(impexp)
. rename Trade lntrade
* Create variables lmthreat xthreat lmimposition ximposition . gen lmthreat = 0
. gen xthreat = 0 . gen lmimposition = 0 . gen ximposition = 0
. replace lmthreat = . if missing(threattype) . replace xthreat = . if missing(threattype)
. replace lmimposition = . if missing(impositiontype) . replace ximposition = . if missing(impositiontype)
. replace lmthreat = 1 if threattype == 1 & 3 & 4 & 5 & 8 & 9 . replace xthreat = 1 if threattype == 2 & 6 & 7 & 10 & 11
. replace lmimposition = 1 if impositiontype == 2 & 3 & 4 & 7 & 8 & 10 . replace ximposition = 1 if impositiontype == 1 & 5 & 6 & 9 & 11 * Main results: Estimation (4)
. reg3hdfe lntrade lry1 lry2 ld interstatewar intrastatewar1 intrastatewar2 threat imposition lmthreat xthreat lmimposition ximposition, id1(fit) id2(fjt) id3(fij) cluster(fij) outdata(data1) accel2
. reg3hdfe lntrade lry1 lry2 ld interstatewar intrastatewar1 intrastatewar2 threat imposition, id1(fit) id2(fjt) id3(fij) cluster(fij) indata(data1) accel2
* Main results: Estimation (5)
. reg3hdfe lntrade lry1 lry2 ld interstatewar intrastatewar1 intrastatewar2 lmthreat xthreat lmimposition ximposition, id1(fit) id2(fjt) id3(fij) cluster(fij) indata(data1) accel2
* Results: Estimation with Anderson & Van Wincoop fixed-effects
34 . reg3hdfe lntrade lry1 lry2 ld interstatewar intrastatewar1 intrastatewar2 threat imposition, id1(fi) id2(fj) id3(ft), cluster(fij) indata(data2) accel2
. reg3hdfe lntrade lry1 lry2 ld interstatewar intrastatewar1 intrastatewar2 lmthreat xthreat lmimposition ximposition, id1(fi) id2(fj) id3(ft), cluster(fij) indata(data2) accel2
* Sensitivity analysis Exports/imports: Estimation (4)
. reg3hdfe lrx lrm lry1 lry2 ld interstatewar intrastatewar1 instrastatewar2 threat imposition lmthreat xthreat lmimposition ximposition, id1(fit) id2(fjt) id3(fij) cluster(fij)
outdata(robustness1) accel2
. reg3hdfe lrx lry1 lry2 ld interstatewar intrastatewar1 instrastatewar2 threat imposition, id1(fit) id2(fjt) id3(fij) cluster(fij) indata(robustness1) accel2
* Sensitivity analysis Exports/imports: Estimation (5)
. reg3hdfe lrm lry1 lry2 ld interstatewar intrastatewar1 instrastatewar2 lmthreat xthreat lmimposition ximposition, id1(fit) id2(fjt) id3(fij) cluster(fij) indata(robustness1) accel2 * Sensitivity analysis different time periods 1948-1990: Estimation (4)
. drop if year>1990
. reg3hdfe lntrade lry1 lry2 ld interstatewar intrastatewar1 intrastatewar2 threat imposition lmthreat xthreat lmimposition ximposition, id1(fit) id2(fjt) id3(fij) cluster(fij),
outdata(robustness2) accel2
. reg3hdfe lntrade lry1 lry2 ld interstatewar intrastatewar1 intrastatewar2 threat imposition, id1(fit) id2(fjt) id3(fij) cluster(fij), indata(robustness2) accel2
* Sensitivity analysis different time periods 1948-1990: Estimation (5)
. reg3hdfe lntrade lry1 lry2 ld interstatewar intrastatewar1 intrastatewar2 lmthreat xthreat lmimposition ximposition, id1(fit) id2(fjt) id3(fij) cluster(fij), indata(robustness2) accel2 * Sensitivity analysis different time periods 1990-2005: Estimation (4)
. drop if year<1990
. reg3hdfe lntrade lry1 lry2 ld interstatewar intrastatewar1 intrastatewar2 threat imposition lmthreat xthreat lmimposition ximposition, id1(fit) id2(fjt) id3(fij) cluster(fij),
outdata(robustness3) accel2
35 * Sensitivity analysis different time periods 1948-1990: Estimation (5)
. reg3hdfe lntrade lry1 lry2 ld interstatewar intrastatewar1 intrastatewar2 lmthreat xthreat lmimposition ximposition, id1(fit) id2(fjt) id3(fij) cluster(fij), indata(robustness3) accel2
* Sensitivity analysis top 5 senders US Canada UK Russia India: Estimation (4) . keep if country1name =="United States" | country1name=="Canada" |
country1name=="Russia" | country1name=="United Kingdom" | country1name=="India" . reg3hdfe lntrade lry1 lry2 ld interstatewar intrastatewar1 intrastatewar2 threat imposition lmthreat xthreat lmimposition ximposition, id1(fit) id2(fjt) id3(fij) cluster(fij),
outdata(robustness4) accel2
. reg3hdfe lntrade lry1 lry2 ld interstatewar intrastatewar1 intrastatewar2 threat imposition, id1(fit) id2(fjt) id3(fij) cluster(fij), indata(robustness4) accel2
* Sensitivity analysis top 5 senders US Canada UK Russia India: Estimation (5)
. reg3hdfe lntrade lry1 lry2 ld interstatewar intrastatewar1 intrastatewar2 lmthreat xthreat lmimposition ximposition, id1(fit) id2(fjt) id3(fij) cluster(fij), indata(robustness4) accel2 * Sensitivity analysis top 5 senders US Canada UK Russia France: Estimation (4)
. keep if country1name =="United States" | country1name=="Canada" |
country1name=="Russia" | country1name=="United Kingdom" | country1name=="France" . reg3hdfe lntrade lry1 lry2 ld interstatewar intrastatewar1 intrastatewar2 threat imposition lmthreat xthreat lmimposition ximposition, id1(fit) id2(fjt) id3(fij) cluster(fij),
outdata(robustness5) accel2
. reg3hdfe lntrade lry1 lry2 ld interstatewar intrastatewar1 intrastatewar2 threat imposition, id1(fit) id2(fjt) id3(fij) cluster(fij), indata(robustness5) accel2
* Sensitivity analysis top 5 senders US Canada UK Russia France: Estimation (5)
. reg3hdfe lntrade lry1 lry2 ld interstatewar intrastatewar1 intrastatewar2 lmthreat xthreat lmimposition ximposition, id1(fit) id2(fjt) id3(fij) cluster(fij), indata(robustness5) accel2 * Sensitivity analysis top 5 senders US Canada UK Italy France: Estimation (4)
. keep if country1name =="United States" | country1name=="Canada" |
36 . reg3hdfe lntrade lry1 lry2 ld interstatewar intrastatewar1 intrastatewar2 threat imposition lmthreat xthreat lmimposition ximposition, id1(fit) id2(fjt) id3(fij) cluster(fij),
outdata(robustness6) accel2
. reg3hdfe lntrade lry1 lry2 ld interstatewar intrastatewar1 intrastatewar2 threat imposition, id1(fit) id2(fjt) id3(fij) cluster(fij), indata(robustness6) accel2
* Sensitivity analysis top 5 senders US Canada UK Italy France: Estimation (5)
. reg3hdfe lntrade lry1 lry2 ld interstatewar intrastatewar1 intrastatewar2 lmthreat xthreat lmimposition ximposition, id1(fit) id2(fjt) id3(fij) cluster(fij), indata(robustness6) accel2 * Sensitivity analysis PPML estimation with Anderson & Van Wincoop fixed-effects . gen imports = exp(lrm)
. gen exports = exp(lrx)
. egen trade = rmean(imports exports) . replace trade = 0 if trade == .
. ppml trade lry1 lry2 interstatewar intrastatewar1 intrastatewar2 threat imposition fi fj ft, cluster(fij)
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