The Effect of Economic Sanctions: Do Managers Need to Worry?

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Master Thesis

The Effect of Economic Sanctions:

Do Managers Need to Worry?

by

Rafaël Giorgio Cornelus Todorovici

Student number: S2168383 E-mail: r.g.c.todorovici@student.rug.nl

June 18, 2018

Supervisor: Dr. D.H.M. Akkermans Co-assessor: Prof. Dr. S. Beugelsdijk

Word count: 11.075

MSc International Business & Management 2017-2018 University of Groningen

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ABSTRACT

This thesis investigates the influence of economic sanctions on export performance. Particularly, the effect of imposed import restrictions on the export performance to the sanctioning country is investigated. Using panel data from 60 cases between 1990 and 2004 concludes that import restrictions do not significantly influence export performance. Moreover, directly imposed sanctions do not tend to be more effective than imposed sanctions which first made use of a threat. Additionally, a small case study is done to get a more detailed understanding of the effect of economic sanctions. The focus of this case study is on economic sanction against Iran in 2008 and 2012. The case investigates the national and firm level effect of economic sanctions and concludes that managers should not worry if sanctions are imposed as there are several ways to bypass the restrictions. Also, managers can more easily find new export partners in our current globalized world.

Key Words:

Economic Sanction, Import Restrictions, Export performance, Iran, Directly Imposed Sanctions, Globalization, Panel Data, Case Study, TIES dataset

ACKNOWLEDGMENT

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List of figures and tables

Figure 1: Conceptual model

Figure 2: Total export of Iran (in USD billions)

Figure 3: Export of Iran to sanctioning countries and the rest of the world (in USD billions) Figure 4: Export of Iran to China (in USD billions)

Figure 5: Operating revenue of Iranian banks (in USD millions)

Figure 6: Operating revenue of Iranian automotive firms (in USD millions) Figure 7: Operating revenue of Iranian mining firms (in USD millions) Figure 8: Average operating revenue of Iranian firms (in USD millions) Figure 9: Export of Iran to the USA (in USD millions)

Table 1: Descriptive statistics Table 2: Correlation matrix

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INTRODUCTION

On 8 May 2018, Donald Trump announced that the United States would exit the nuclear agreement with Iran and reinstate the imposed economic sanctions against the country. From the mid-1980s Iran was sanctioned with economic restrictions to reduce Iranian nuclear and missile programs. Despite international opposition, Iran has nonetheless developed its nuclear and missile programs with help from the Russian government (Ataev, 2013). The economic sanctions imposed have taken a serious toll on Iran’s economy and citizens (Katzman, 2013). Therefore, the E3+3 countries (China, France, Germany, The Russian Federation, the United Kingdom and the United States) and Iran agreed on a framework to abolish these sanctions in 2015. According to this framework, Iran would redesign, convert and reduce its nuclear-related facilities (European Union, Joint Statement, 2016). Trump put the fate of the deal in doubt, and this has led the US to withdraw from the agreement made in 2015 and impose economic sanctions again. These sanctions prohibit almost all trade with Iran, with some exceptions only for products intended to benefit the Iranian people. The exceptions include the export of humanitarian assistance, medical and agricultural equipment and trade in informational materials. The key focus of these economic sanctions is on the Iranian export of petroleum, natural gas, oil and chemicals (Gharibnavaz & Waschik, 2017).

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agreement of what a successful case is, which makes results difficult to compare. Therefore, instead of focusing on the success of economic sanctions, this thesis will focus solely on the economic effect. In other words, this thesis will focus on the means and not on the end of political coercion. Research about the usefulness of economic sanction is primarily conducted in the 20th century. These results may be outdated and not generalizable as most studies did not use large-N samples, but only prominent cases of economic sanctions. Morgan, Bapat, and Kobayashi have been developing a database of Threat and Imposition of Sanctions (TIES) for the past two decades. Their dataset includes 1412 cases in which one or more states threatened and imposed economic sanctions during 1945-2005. Economic sanctions are defined as actions that one or more countries take to limit or end their economic relations with a target country to persuade that country to change its policies (Morgan, Bapat and Kobayashi, 2014). The most commonly used sanction type is an import restriction, which implies that the sanctioning country refuses to allow or places a restriction on a certain good or set of goods to be imported from the sanctioned country. This thesis will focus on import restrictions, which will be measured by the export performance of the sanctioned country.

As mentioned before, diverging views exist in current literature about the usefulness of economic sanctions to politically coerce other states. Instead of focusing on the political success of economic sanctions, this thesis aims to determine whether import sanctions affect the export performance of the sanctioned country. The research question of this thesis is, therefore:

“Do import restrictions influence the export performance of the sanctioned country?”

Moreover, directly imposed sanctions will be investigated to show whether these sanctions are more effective than sanctions involving threats. Additionally, this thesis will deepen into what managers of firms can do if economic sanctions are imposed. Do they need to worry or are there ways to bypass the restrictions?

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LITERATURE REVIEW

In this section, literature about economic sanctions will be discussed. Most extant literature focuses on situations when economic sanctions are more likely to be successful to attain political coercion. Therefore, a general overview of economic sanctions and the effect will be given. Next, export performance will be shortly elaborated, to give a picture of factors which determine export performance. Finally, firms from both countries will be looked upon, and hypotheses will be formed by using insights from current literature.

Economic sanctions

In general, economic sanctions seek to lower the economic welfare of a target state by setting restrictions on its international trade to force the target state to change its political behavior (Morgan, Bapat & Kobayashi, 2013). For example, the United States ended imports of Cuban sugar in 1960, which was primarily a punitive act of retaliation coming after months of deteriorating relations with Cuba (Schreiber, 1973). The main reasons for this import ban where growing contacts with the Soviet Union, expressions of anti-Americanism and the rise of Fidel Castro.

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question is then how senders can maximize their gains on both the political and economic dimensions. Bapat and Kwon (2015) demonstrate that sender face enforcement problems when sanctioning a target country economically. The decision to impose sanctions, and what the optimal level of enforcement should be, is a function of the export share of the target country’s market. If the sanctioning country has a high market share in the sanctioned country, sanctioning country firms will try to evade the economic sanctions imposed to the target country, due to the economic dependency on the target country.

Effect of economic sanctions

Literature about the usefulness of economic sanctions as a political instrument varies considerably. On the one hand, economic sanctions lead to concessions. The focus here is on factors that determine the success of these sanctions (Drezner 1999; Bapat and Morgan 2009; Early 2009; Martin 1992; Allen 2005; Hart 2000; Lektizian and Souva, 2003; Cortright and Lopez 2002). On the other hand, economic sanctions do not affect the success of political coercion (Pape, 1997, 1998; Marinov, 2005). Sanctions may lead to more costs than benefits for the sanctioning country or may even not affect the target country economy. Scholars have tried to tackle this empirical puzzle by examining the reason for the frequent use of sanctions despite its impressive fail record.

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Previous research investigated conditions under which sanctions did work. These studies suggest that sanction effectiveness to attain political goals is shaped by the dyadic relations between the sender and target (Drezner, 1999; Whang, 2010), domestic political institutions (Allen 2005, 2008; Lektizian and Souva 2003, 2007; Hart, 2000), issue salience (Ang and Peksen, 2007), the informational role of sanctions (Hovi, Huseby, and Sprinz, 2005), interest group politics (Kaempfer and Lowenberg, 1992), the sender’s ability to reduce exchange between national firms and the target (Morgan and Bapat, 2003) and the type of sanctions imposed (Cortright and Lopez, 2002; Dashti-Gibson, Davis, and Radcliff, 1997). Interestingly, sanctions do often serve as a symbolic function, and they can often be useful in support of other policy initiatives (Daoudi and Dajani, 1983; Baldwin, 1985; Linsday, 1986; Nossal, 1989).

The problem with current research is that it focuses on the political success of economic sanctions and not on the economic effect. Most literature uses the database of Hufbauer and Schott (1990) to study the effect of economic sanction, which scores the success of sanctions between 1 (outright failure) and 16 (significant success). This specification of ‘success’ is extremely arbitrary (Lam, 1990). For that reason, instead of focusing on the political success of a sanctioned case, the focus of this thesis is on whether economic sanctions influence the economy of the target state, without looking if the sanctions were perceived successful by the sanctioning country.

Different economic sanctions

There are several types of economic sanctions which countries can impose. Following the Threat and Imposition of Sanctions (TIES) Data 4.0 of Morgan, Bapat, and Kobayashi (2013), these are a total economic embargo, partial economic embargo, import restriction, export restriction, blockade, asset freeze, termination of foreign aid, travel ban and suspension of economic agreement. For the full description of these sanctions, see Appendix A. The most used types of economic sanctions in this dataset are import restrictions (43%), termination of foreign aid (18%) and partial economic embargo (11%).

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was primarily on a selection of prominent cases where economic sanctions were implemented for a long period without any success. Due to this selection bias of cases, the general conclusion in literature is that economic sanctions do not work. The latest dataset of TIES was released in late 2006 that covers 1412 individual cases in which one or more states threatened and/or imposed economic sanctions on a single target during 1945-2005. A sanction must involve one or more sender states and a target state and be implemented by the sender 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, therefore, do not qualify as sanctions (Morgan, Bapat & Kobayashi, 2014).

Export performance determinants

There have been various studies published in the past fifty years on the determinants of export performance (Bilkey, 1978; Chetty and Hamilton, 1993; Aaby and slater, 1989; Madsen, 1987; Zou and Stan, 1998). The amount of studies reflects the increasing importance and interests in export marketing. However, despite widespread research on export determinants of firms, no integrative review of the empirical work about export development models exists in the extant literature (Leonidou and Katsikeas, 1996). Literature about export performance is still characterized by highly fragmented and confusing findings.

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This thesis will focus on the external-uncontrollable factors, namely sanctioned import restrictions. This is due to data availability on export performance and the number of import restrictions sanctioned in the past fifty years. The use of import restrictions means that the sender refuses to allow or places a restriction on a certain good or set of goods to be imported from the target state. This import restriction will, therefore, influence the export performance of the target country to the sanctioning country.

Firms in both states

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To hypothesize, there is little research about the effect of economic sanctions on the economy of the target state. Literature focuses on the end and not on the means of political coercion. Export barriers do affect the export performance of a country; however, firms can bypass these restrictions. Also, in the current globalized world firms can fill the export void by exporting to other countries.

Based on current literature and the gap between research, I hypothesize that:

Hypothesis 1: Import restriction will not significantly affect the export performance of the

target country to the sender country

Threats and direct imposition

Most theories on how economic sanctions are implemented use a decision tree. This decision tree involves several steps. First, the sender state threatens to interrupt the status quo and block a stream of economic exchange with the target, unless the target country acquiesces to a specific political demand made by the sender (Drezner, 2003). Second, if the target country complies, sanctions will not be imposed. Finally, if the target country stands firm, the sender faces a choice between carrying out its threat and impose its sanctions or backing down. Drezner (2003) states that most successful cases of economic sanctions are likely to end before sanctions are imposed. Their preliminary test of 195 cases supports their argument since most of these cases ended without sanctions being imposed. However, 99% of the TIES database cases on import restriction imposed the sanctions threatened. Therefore, this thesis will only look at imposed import restrictions and will take out any case with only a threat.

Literature solely about threats and/or direct imposition of sanctions is minimal. Using common understanding, one could argue that if the sanctioning country first threatens the target country, the effect on the economy would be less. Namely, firms from the target country can anticipate on the future sanctions and look for alternative solutions to bypass the effect of these sanctions. The more time a country has to react to future restrictions, the better solutions firms can find to evade the potential economic losses.

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Hypothesis 2: The direct imposition of import restriction will strengthen the intended negative

effect of import restrictions on the export performance of the target country. Specifically, if a sanction is imposed directly (without a threat), the intended negative effect import restrictions have on the target export performance will be higher.

Conceptual model

Figure 1 presents the visualization of the conceptual model.

Figure 1: Conceptual model

Case study Iran

In addition to the empirical analysis of the conceptual model, a small case study will be conducted to have a deeper insight into the consequences of economic sanctions on the firm level. The focus of this case study will be on the effect of economic sanctions on Iran measured at the country level, followed by the effect of economic sanctions on Iranian companies measured at the firm level. The case will look at 14 Iranian firms in three different sectors. Based on current literature and hypothesis 1, I hypothesize that:

Hypothesis 3: Iranian firms will find alternative ways to export products when economic

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METHODOLOGY

In the section before, an extensive overview of the literature has been provided. Based on current research, hypotheses have been formulated and visually presented in a conceptual model. To test the mentioned hypothesis, empirical research will be conducted. In this part, the selection of the sample and data will be discussed. Next, the measurement of the different variables used will be discussed.

Sample selection and data collection

The sample used is part of the Threat and Imposition of Sanctions Database (TIES), which is created by Morgan, Bapat, and Kobayashi. The original dataset contains 1412 individual cases where economic sanctions were threatened and/or imposed between 1945 and 2005. The data collection for this database involved three stages. First, potential cases were identified through keywords which included sanctions, tariffs, export controls, embargoes, travel bans, import bans, asset freezes, aid cuts and blockades. Second, each of these potential cases was researched extensively and written case summaries of each of which the researchers believed to meet his or her requirements for inclusion in the dataset. Finally, coders used the written case summaries to determine the values of the variables to include in the dataset. Additionally, if it was determined that additional investigation was required, this was conducted by the researchers (Morgan, Bapat & Kobayashi, 2014). The mean duration of economic sanctions in the database is 2.43 years, and the USA is the primary sender of economic sanctions in almost 50% of the cases, followed by Canada with 7.9% of the cases.

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these sanctions are still ongoing or the end date is not available. Within this sample, only a few cases contained no end-date, and these sanctions were imposed before 1990. Due to lack of data availability, these cases are taken out. Finally, Bolks and Al-Sowayel (2000) investigated why some sanctions last longer than others and why some target states concede faster than others do. Their research concludes that it depends on the target country its political structure, regime stability and political changes within the target state. Due to the fact that the duration depends on the target state, data availability was the key factor determining which cases to include and how long to measure each case. For that reason, three years before and three years after the sanction are used to compare the effect of import restrictions. This means that the panel data is unbalanced since the length of sanctions differs.

Taken the criteria together, the final panel data sample consists of 60 cases of import restrictions between 1990-2004, which can be found in Appendix B. In total, 530 observations are used in this research. The USA is the primary sender of import restriction with 58% of the cases, followed by Canada with 15% of the cases.

In the case study, the sample used for the Iranian national level based analysis consists of 3.471 export numbers for Iran between 1988 and 2017. This data is retrieved from UN Comtrade, which is a repository of official international trade statistics and relevant analytical tables (UN Comtrade). The sample used for the firm level based analysis consists of 14 Iranian firms; seven firms from the financial industry, four firms from the automotive industry and three firms from the mining industry. These firms were chosen based on the availability of operating revenue data via Orbis. Unfortunately, little information on Iranian firm level data is available. However, these firms are very large and can, therefore, give an acceptable overview of the reaction of Iranian firms after sanctions were imposed.

Independent variable

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years, which is slightly lower than the mean duration of all economic sanction in the TIES database.

Dependent variable

The dependent variable in this research is export performance. The export performance is measured by the trade value in US$ from the sanctioned state to the sanctioning state. For example, if the USA sanctioned Romania with an import restriction, the trade value in US$ from Romania to the USA is taken as the dependent variable. The data is retrieved from UN Comtrade. For this research, the total of all harmonized system commodity codes is taken. Distinguishing between commodities is not feasible for this research, as there is no precise information available which products are restricted within each case of import restrictions and data from UN Comtrade on product group export is not extensive for many cases.

Moderator variable

The moderating variable is directly imposed import restriction. The TIES database states whether a sanction is first threatened or directly imposed. The moderating variable is a dummy variable and takes the value one if the sanction is directly imposed and takes the value zero if there as first a threat before the sanction is imposed. In this sample, 47% of the sanctions were directly imposed, and 53% of the sanctions first had a threat before being imposed. The variable directly imposed sanctions will be multiplied with the variable sanction to estimate the interaction effect.

Control variables

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RESULTS

Table 1 provides the descriptive statistics results of all variables. An interesting outlier is the max value of inflation, which is 85%. This value is of Russia in 1999, which can be explained by hyperinflation which resulted from the removal of Soviet price controls and the Russian financial crisis of 1998 (Ferguson and Granville, 2000)

Variable Obs. Mean Std. Dev. Min. Max. Inflation 530 6.765 10.188 -1.359 85.742

Total export (dropped due to multicollinearity) 530 2.63e+11 2.12e+11 2.02e+08 1.46e+12

GDP 530 2.61e+12 3.64e+12 1.44e+09 1.50e+13 GDP per capita 530 18314.56 14657.57 320.59 49984.15 Distance 530 6907.683 4451.336 944 18976

Sanction 530 0.321 0.467 0 1

Direct imposed sanction 530 0.481 0.500 0 1 Table 1: Descriptive statistics

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ten may indicate trouble, and we can see that no variable is higher than this threshold after removing the variable total export. Moreover, the mean VIF is 1.85 which is very low. All in all, multicollinearity may not be a problem in this research. Additionally, the scattergram of the variables can be found in Appendix E.

Table 2: Correlations Matrix

*. Correlation is significant at the 0.05 level (2-tailed) **. Correlation is significant at the 0.01 level (2-tailed)

To test for heteroscedasticity, the Breusch-Pagan/Cook-Weisberg test is used. The null hypothesis of this test assumes constant variance for the error term. The null hypothesis states that there is constant variance, which is also known as homoscedasticity. In our sample, the p-value is below 0.01 which means that the null hypothesis is rejected at a 1% level. This means that there is a problem of heteroscedasticity and this needs to be corrected for when regressions are executed. Robustness test will be added to the regressions. GDP and GDP per capita are transformed to normal distributions by natural logarithm. Results of the Breasch/Pagan/Cook-Weisberg test can be found in Appendix E.

Next, autocorrelation is examined in the panel data. Autocorrelation arises when the error terms are correlated. Autocorrelation is the error that can arise from an autocorrelated

Export to sender Inflation export Total GDP GDP per capita Distance Sanction Direct imposed sanction Export to sender 1 Inflation -0.2564** 1 Total export 0.3875** -0.3663** 1 GDP 0.2281** -0.2938** 0.9335** 1 GDP per capita 0.5284** -0.5246** 0.8102** 0.7222** 1 Distance -0.3622** -0.1547** -0.2253** -0.1603** -0.1609* 1 Sanction 0.0217 -0.0655 -0.0315 -0.0139 -0.0222 -0.0008 1

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explained by the independent variables (Hill et al., 2007). To test for autocorrelation, the Wooldridge test for panel data is executed. The null hypothesis states that there is no first-order autocorrelation. In our sample, the p-value is below 0.01 which means that the null hypothesis is rejected at a 1% level. In other words, there is an autocorrelation problem in the sample. To correct this problem, cluster robustness will be applied. The results of the Wooldridge test can be found in Appendix G.

This research uses panel data, which has the advantage that you can control for variables you cannot observe and/or control for variables that change over time but not across entities. When using panel data regression methods, there are generally two statistical regression estimators; the fixed effect estimator and the random effect estimator (Hill et al., 2007). Fixed effects are used when the impact of variables that vary over time is analyzed. Fixed effect explores the relationship between predictor and outcome variables within an entity. In short, fixed effect models are designed to study the causes of change within a person or entity. The random effect model, unlike the fixed effects model, assumes that variation across entities is to be random and uncorrelated with the independent variables in the model. If there is a reason to suppose that differences across entities have some influence on the dependent variable, then the random effect model should be used. To decide between fixed or random effects, the Hausman test is used. The Hausman test tests whether the unique errors are correlated with the regressor, where the null hypothesis is that the differences in coefficients are not systematic. The p-value from the Hausman test is 0.2873, which means that the results are not significant and the null hypothesis is not rejected. This means that the random effect model is appropriate for this study. The results of the Hausman test can be found in Appendix H.

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two lines reflecting the confidence interval are both below and above the horizontal zero line, the interaction between directly imposed sanction and imposed sanction is significant (Meyer, Witteloostuijn, and Beugelsijk, 2017). In Appendix I we can see that the results are not significant.

Additionally, the fixed effects model is used, as the impact of sanctions that vary over time is investigated. Table 4 provides the results of this model. What is noticeable is the limitation of the fixed effects model. This model cannot deal with time-invariant variables. The variable direct imposed sanction is (almost) time-invariant per case. This explains the omission of this variable. The rest of the results do not vary significantly, however only the R² values differ. The R² value is much lower in the fixed effects model, but this does not mean this is inherently bad.The outcomes of the Wald Chi² test for the random effects model and F test for the fixed effects model show a good model fit since the random effects model is significant at over 99% confidence level and the fixed effects model at over 95% confidence level.

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Table 3

Results of random effects model

Variable (1) (2) (3)

Inflation 2.822e+08 2.713e+08 2.738e+08

[1.95] [1.91] [1.93]

LN GDP 1.074e+10*** 1.051e+10*** 1.041e+10**

[3.35] [3.31] [3.28]

LN GDP per capita 1.092e+10** 1.101e+10** 1.117e+10**

[1.49] [1.51] [1.52]

Distance -2.565e+10** -2.572e+10** -2.572e+10**

[-2.69] [-2.69] [-2.68]

Direct imposed sanction -4.699e+09 -4.727e+09 -4.322e+09

[-0.31] [-0.31] [-0.28]

Sanction -9.429e+08 -2.472e+08

[-1.28] [-0.19]

Direct imposed sanction interaction -1.411e+09

[0.80]

Constant -1.302e+11 -1.239e+11 -1.227e+11

[-1.05] [-1.01] [-1.00] Number of obs. 530 530 530 R2_within _0.1230 _0.1235 _0.1238 R2_between _0.2754 _0.2762 _0.2768 R2_overall _0.2793 _0.2800 _0.2807 Wald Chi2 _29.72*** _29.84*** _31.36*** t statistics in parentheses

*: P-value<0.10, **: P-value<0.05, ***: P-value<0.01 Table 4

Results of fixed effects model

Variable (1) (2) (3)

Inflation 2.524e+08 2.451e+08 2.453e+08

[1.71] [1.67] [1.68]

LN GDP 1.506e+11** 1.051e+11** 1.501e+11**

[2.85] [2.85] [2.82]

LN GDP per capita -1.336e+11** -1.332e+11** -1.332e+11*

[-2.70] [-2.69] [-2.66]

Distance -5.572e+09 -4.980e+09 -4.947e+09

[-1.94] [-1.80] [-1.59]

Direct imposed sanction Omitted Omitted Omitted

Sanction -6.625e+08 -6.272e+08

[-0.84] [-0.42]

Direct imposed sanction interaction -7.196e+08

[0.04]

Constant -2.799e+12** -2.795e+12** -2.794e+12**

[-2.88] [-2.88] [-2.86] Number of obs. 530 530 530 R2_within _0.1849 _0.1853 _0.1853 R2_between _0.0079 _0.0078 _0.0078 R2_overall _0.0081 _0.0080 _0.0079 F 3.20** 2.80** 3.04** t statistics in parentheses

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CASE STUDY: ECONOMIC SANCTIONS IRAN

To have a more detailed insight of the effect of economic sanctions, this section will provide a small case study on economic sanctions against Iran in March 2008 and October 2012. The reason for choosing Iran is the recent development regarding the sanctions that will come back into place now that president Donald Trump has pulled out of the Iran nuclear deal. The goal of this case study is to give a more in-depth insight into an import restriction case. The key insight of this case is how managers of sanctioned country firms behave when faced with export restrictions. In the first part, the effect of economic sanctions imposed on Iran in 2008 will be discussed on the national level. The second part will discuss the firm level effect of the economic sanctions imposed at the end of 2012.

In 2006, the United Nations nuclear watchdog has voted to report Iran to the Security Council (BBC News, 2006). Out of 35 members of the International Atomic Energy Agency, 27 states voted in favor of this decision, three against and five abstentions. Iran denied that is has been developing nuclear weapons, but maintaining its program only for producing energy and not for military purposes. Two years later, the first non-oil export sanctions against Iran was imposed. The USA, Canada, Australia, United Kingdom and the European Union were part of this restriction in 2008. The reaction of Iranians was different than expected. The Iranian government claimed that the economic sanctions would not impact Iran at all. President Mahmoud Ahmadinejad even said that the member states of the Security Council were “political retards” for still believing that economic sanctions are effective (The Daily Star, 2010). The Iranian president claims that neither the USA nor Europe plays a major role in their economy and therefore these sanctions would not impact them.

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imposition of export sanctions in 2008, export to sanctioning countries declines sharply and increased significantly during the same period to the rest of the world. Data suggests that Iranian firms found new exporting partners. Iran exported more to the rest of the world in response to imposed export restrictions. For example, during this period trade with China increased significantly by nearly 35% after the imposition of export sanctions (Financial Times, 2010). Figure 4 shows the graph of Iranian export to China, which supports the previous statement. A possible explanation of this increased export relation could be the geopolitics. Another example of an increased export relationships is that with the United Arab Emirates which has been a back door for Iranian exporters, due to 400.000 Iranians living there as well as to 8000 Iranian firms operating in the United Arab Emirates (Bloomberg, 2010). This illustrates the fact that firms are bypassing the imposed export restrictions. Haidar (2016) found that small exporters are more affected by sanctions than large exporters, due to the significant more experience of large exporters, which leads to a higher probability to redirect their export via other channels. Next, a firm level analysis of economic sanction on Iranian firms will be conducted. The sample consists of 14 Iranian firms which operate in three different industries; financial industry, mining industry and automotive industry. The selection of these firms is based on the available information of the operating revenue retrieved via Orbis. Due to the scarce availability of data, the imposed sanctions of 2008 cannot be used to review the consequences. Interestingly, in 2011 and at the end of 2012 new sanctions were imposed against Iran. These sanctions were imposed by the same countries as in 2008 and included sanctions against Iranian banks, trade and –energy firms. The goal of these sanctions was aimed at banning the import of Iranian crude oil and petroleum products. Unfortunately, no data is available of Iranian oil companies. Therefore, the effect of the economic sanctions is investigated in three other sectors. Moreover, operating revenue data of 2011 and before is not available at the firm level and therefore the impact of the sanctions in 2012 is used. In figure 2 the national level effect of the sanctions in 2011 and 2012 are visible, as there is a steep decline in the total export of Iran after the imposition of the sanctions in both years.

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the decline decreased, and for some banks, the operating revenue even increased. Overall, the economic sanctions on Iranian banks did have an impact. Bank Mellat is one of the largest commercial banks in Iran and brought suit against the Council of the European Union, which froze the assets of the bank due to alleged involvement in nuclear proliferation. On February 18, 2016, the Court of Justice of the European Union handed down its final judgment and affirmed that the there was no evidence that proved the bank’s involvement in Iran’s nuclear program (Peihani, 2017). In figure 5 can be seen that the operating revenue increased after the lawsuit.

Figure 6 and 7 show the operating revenue of respectively Iranian automotive and mining firms. The Obama administration escalated sanctions pressure against Iran as it continued to fail to meet its international obligations (New York Times, 2013). This pressure was aimed at the automotive industry, as this industry is one of the biggest employers in Iran. Moreover, this industry is a major procurement network that import materials and technologies used to build uranium centrifuges instead of cars. Looking at the mining industry, Iran has become a significant steel exporter in recent years. Since the sanctions against Iran were lifted in 2015, the country has set a target to expand its steel capacity to 55 million metric ton per year by 2025, from 32 million metric ton per year currently (Platts, 2018). The newly imposed sanctions of the USA could, therefore, be threatening for this industry. However, Iranian steel firms mainly export to Asia, the Middle East and, Europe. Due to data availability, observations of firms in these sectors start from 2013. Based on available data, the operating revenue of the firms in the automotive and mining industry declined after the imposition of sanctions and in some cases increased hereafter. The same trend as with Iranian banks can be observed from these firms, where the operating revenue first decreases after sanctions were imposed and hereafter increased. It is hard to say whether the trend can be explained by economic sanctions, as data before these sanctions were imposed, is not available.

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To summarize, the goal of this small case study was to give a more in-depth insight into how firms deal with export sanctions. The case focuses on the imposed import sanctions on Iran in 2008 and 2012. Export declined after the imposition of the sanction in 2008 but increased significantly after. The main cause for the increase in export is the fact that Iranian firms exported more to the rest of the world and bypassed the restrictions via other countries. A key driver for the ease with which Iranian firms could find alternative buyers is the increased globalization. On the firm level, three industries were investigated. In all three industries, the effect of the imposed sanctions at the end of 2012 was noticeable. The operating revenue decreased during this period but increased hereafter. It is difficult to draw conclusions about the impact of these sanctions on Iranian firms, as data is very limited. However, based on the data used in this case study we can accept hypothesis 3 and conclude that Iranian firms found alternative ways to export products when economic sanctions were imposed.

It is interesting to speculate what will happen now, as sanctions will be imposed again by the USA. After the USA pulled out of the nuclear deal on May 8, 2018, different companies outside Iran reacted to the consequences of these sanctions for their business. In the aviation sector, Airbus and Boeing Co. will lose licenses to sell passenger jets to Iran. IranAir recently ordered 200 passenger aircraft, but this deal is canceled. Airbus reacted that they need time to study the impact of the sanctions and that they will consult with the US government on next steps (Bloomberg, 2018). In the automotive industry, Volkswagen reacted. Volkswagen just started selling cars in Iran after a 17-year break. In a statement, Volkswagen said it was “tracking and examining the development of the political and economic environment in Iran.” “In principle, Volkswagen adheres to al international and national laws and export regulations” (Reuters, 2018). British tobacco company Imperial Brands stated that it did not see any harmful impact on its business in the Middle East (Reuters, 2018). All in all, reactions of firms outside Iran varied.

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Figure 2: Total export of Iran (in USD billions)

Figure 3: Export of Iran to sanctioning countries and the rest of the world (in USD billions) 0 1 2 3 4 5 6 7 8 9 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Export of Iran to sanctioning countries and the rest of the world (in

USD billions)

Export to sanctioning countries (in USD billions) Export to rest of the world (in USD billions) 0 2 4 6 8 10 12 14 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

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Figure 4: Export of Iran to China (in USD billions)

Figure 5: Operating revenue of Iranian banks (in USD millions) 0 1000 2000 3000 4000 5000 6000 7000 2012 2013 2014 2015 2016 2017

Operating revenue of Iranian banks (in USD millions)

Bank Saderat Iran Parsian Bank Bank Mellat Bank Sepah

Bank Tejarat Bank Keshavarzi Bank Pasargad

0,00 0,50 1,00 1,50 2,00 2,50 3,00 3,50

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Figure 6: Operating revenue of Iranian automotive firms (in USD millions) 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 2013 2014 2015 2016 2017

Operating revenue of Iranian automotive firms (in USD millions)

Iran Khodro Saipa Automotive Manufacturing Iran Khodro Diesel Pars Khodro Iran

0 1000 2000 3000 4000 5000 6000 7000 2013 2014 2015 2016 2017

Operating revenue of Iranian mining firms (in USD millions)

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Figure 8: Average operating revenue of Iranian firms (in USD millions)

Figure 9: Export of Iran to the USA (in USD millions) 0 500 1000 1500 2000 2500 3000 3500 4000 2013 2014 2015 2016 2017

Average operating revenue of Iranian firms (in USD millions)

0 20 40 60 80 100 120 140 160 180 200 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

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CONCLUSION

Using a panel data study from 1990 to 2004, this paper investigates the relationship between economic sanctions and the economy of the sanctioned state. Specifically, import restrictions were investigated. In total, 60 cases were used to empirically test this relationship. Results show that import restrictions do not significantly influence the export of the sanctioned country to the sanctioning country. A possible explanation for this result is that economic sanctions are often used symbolically to pressure the target country into political concessions. It must not be forgotten that if states impose import restrictions on a specific country, the home-country firms’ competitiveness also declines as other firms will take over this market. Moreover, sanctioning states face a tradeoff between the costs of enforcing these restrictions and the potential benefits. Firms in both states may choose to export illegally as the economic dependency between the countries increases. A more legal way of bypassing restrictions is to export via other countries. This method is used in many cases. Contrary as expected, there is no significant difference between directly imposed sanctions and sanctions which first involved a threat. Firms may not be more affected by directly imposed restrictions because it takes time for the sanctioning country to enforce their directly imposed import restrictions. Finally, globalization helps firms from the sanctioned state to find new export markets more easily and faster. Therefore, the question arises whether economic sanctions are effective in our current society.

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Theoretical contribution

By focusing on the economy of the target state and not on whether the sanction was politically successful, I hope to contribute to the economic sanctions literature and highlight that empirically economic sanctions do not influence the target state economy. In particular import restrictions, which is aimed at influencing the export of the target state to the sanctioning state. In current literature, the success of an economic sanction is arbitrary as there are different definitions of ‘success.’ Therefore, this research will contribute to the economic side of literature about the effectiveness of sanctions. By using the TIES database and focusing on recent cases, this study challenges current literature which is mainly based on cases before the 90s.

Managerial implication

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Limitations and future research

Despite the contributions, there are several limitations of this study. First, the magnitude of all imposed import restrictions is considered equal and therefore the results cannot be generalized. Each economic sanction has its own impact on the sanctioned case, which depends on several factors. Future research should investigate each individual case and determine if the sanctions affect the economy. Second, due to data availability, import restrictions on all products was used. This research assumes that the import restrictions are imposed on all export products. In reality, import restrictions can be on specific products. For example, an import restriction on chemical products may influence the export performance of this product, however in our research we could not investigate this effects since the total of all export is taken. Future research should investigate per case which products are prohibited. These results will be more generable and accurate. Third, the effect of economic sanctions is based on yearly data. To have a more accurate overview about the direct effect of sanctions, future research should investigate this subject by retrieving export data on a monthly basis. By doing this, the effect of directly imposed sanctions can also be better investigated, as the imposition of the sanction is on a specific date. Finally, the firm level analysis of Iranian firms does not include the operating revenue before sanctions were imposed. This makes it difficult to draw reliable conclusions. Additionally, future research should focus on the Iranian oil export market, as this is the most important product group sanctions are focused on.

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APPENDICES

Appendix A: Sanction type description (Morgan, Bapat & Kobayashi, 2013)

Sanction type Description

Total Economic Embargo The sender(s) stop the flow of all economic exchange to and from the target state

Partial Economic Embargo The sender(s) stop the flow of certain commodities or services to and from the target state. For a case to qualify as a partial embargo, some exchange must still be allowed while a sector’s trade must be frozen

Import Restriction The sender(s) refuse to allow or places a restriction on a certain good or set of goods to be imported from the target state. Import restrictions differ from partial embargoes in that import restriction only restrict the flow of goods into the sender(s). While the sender does not restrict the flow of goods to the target, the sender may prevent target commodities from being traded in its home markets or impose tariffs or duties on target commodities.

Export Restriction The sender(s) refuse to allow certain goods or services to be exported to the target state. Export restrictions differ from partial embargoes in that export restrictions only restrict the flow of goods to the target from the sender(s). Although the sender places no restriction on goods from the target for import, the sender does not allow a certain good or set of goods to flow out of the sender(s) firms to the target.

Blockade The sender(s) attempts to physically prevent all

states from engaging in an economic transaction with the target state. Such actions may be enforced physically by the sender(s) military. An alternative is for the sender to threaten any state that engages in transactions with the target with similar economic sanctions.

Asset Freeze The sender(s) partially or completely seize all assets of the target state under the sender(s)’ jurisdiction.

Termination of Foreign Aid The sender(s) reduce or ends foreign aid or loans if the target state does not comply with the sender(s) demands.

Travel Ban The sender(s) ceases allowing an individual, group, or citizenry of the target country to enter the territory of the sender(s).

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Appendix B: Sample of import restrictions used

Case number Sender Target Start year End

year

1 United States of America Canada 2004 2005

2 Canada India 2004 2009

3 Japan United States of America 2003 2005

4 Japan United States of America 2003 2004

5 Japan Canada 2003 2005

7 Ukraine Russia 2002 2004

8 Russia Ukraine 2002 2004

9 Colombia Chile 2002 2006

10 Russia Ukraine 2002 2002

11 Mexico United States of America 2002 2006

12 Hungary Russia 2001 2004

13 China Japan 2001 2001

15 United States of America Ukraine 2001 2005

16 Afghanistan Russia 2000 2001

17 Afghanistan United States of America 2000 2001

18 Ukraine Russia 2000 2002

19 South Korea Japan 2000 2003

20 Canada India 2000 2005

21 Indonesia South Korea 2000 2000

22 Indonesia Japan 2000 2000

23 Indonesia China 2000 2000

24 United States of America New Zealand 1999 2001

25 United States of America Australia 1999 2001

26 Colombia United States of America 1998 1998

27 Colombia Malaysia 1998 1998

28 Colombia Japan 1998 1998

29 United States of America Taiwan 1997 2000

30 Germany Iran 1997 1997

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33 Indonesia Australia 1997 1997

34 United States of America India 1996 1998

35 Germany United Kingdom 1996 1999

36 United States of America Mexico 1996 1996

39 Canada Italy 1995 1995

40 Canada United States of America 1995 1997

41 United States of America Germany 1994 1998

43 United States of America Ecuador 1994 1995

44 United States of America Colombia 1994 1995

45 Canada New Zealand 1993 1995

46 Canada Australia 1993 1995

47 United States of America South Korea 1993 1993

48 Canada United States of America 1993 1993

49 Canada New Zealand 1993 1993

50 Canada Germany 1993 1993

52 Mexico Ireland 1992 1994

53 Brazil United States of America 1992 1993

54 United States of America Japan 1992 1993

55 United States of America Nicaragua 1992 1997

56 United States of America Spain 1992 1997

57 United States of America Colombia 1992 1997

58 United States of America Indonesia 1992 1992

59 United States of America Japan 1992 1997

60 United States of America South Korea 1990 1992

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Appendix C: Graphical representation of the correlation matrix

Appendix D: VIF values

Variable VIF 1/VIF

GDP per capita 2.82 0.354006

GDP 2.16 0.453050

Direct imposed sanction 1.74 0.573464

Sanction 1.73 0.577800

Inflation 1.55 0.646874

Distance 1.13 0.887976

Mean VIF 1.85

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Appendix F: Breusch-Pagan/Cook-Weisberg test

Breusch-Pagan/Cook-Weisberg test for heteroscedasticity

H0: Constant variance

Chi2(6) 404.12

Prob > Chi2 0.0000

Appendix G: Wooldridge test

Wooldridge test for autocorrelation in panel data

H0: No first-order autocorrelation

F(1,59) 259.408

Prob > F 0.0000

Appendix H: Hausman test Hausman test

H0: Difference in coefficients not systematic

Chi2(4) 5.00

Prob > Chi2 0.2873

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Appendix J: Kernel density estimate

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The Effect of Economic Sanctions: Do Managers Need to Worry?

Master Thesis