External Sector: Between Congestion and Sanctions—‘Syrian Economy Case,1987

Abstract

Since the outbreak of the Syrian crisis in March 2011, the USA, European Union, Arab League and several other regulatory entities imposed negative economic sanctions on Syria—some of the most comprehensive ever implemented. This article first provides an assessment of Syrian foreign trade sector during the reform period of the 2000s and its impact on economic growth. Second, it estimates the impact of sanctions and conflict on the trade sector of the Syrian economy. The analysis is conducted using a panel-gravity model between Syria and 78 trading partners (1987–2017). Multilateral sanctions and conflict-related disruptions demonstrate a large significant negative impact on Syria-bilateral trade flow by 65 per cent. We attempt to find out whether the Syrian economy was able to divert trade away from Europe and/or conduct de-Europeanisation. Findings confirm that the Syrian economy was unable to divert trade flow to Asian and other countries due to the conflict-related congestion and distance factor.

JEL: C33, F10

Keywords

Sanctions trade policy gravity model conflict Syria

Introduction

Syria’s access to a long-distance trade network has played a vital role in its economic development path since early times. Over the decade of 2000–2010, a government reform agenda coupled with International Monetary Fund (IMF) technical assessment placed an emphasis on trade and trade-related reforms in anticipation of Syria’s post-oil outlook. In this stage, the Syrian government had understood completely that the flow of international trade is determined-beside geography and political factors—by economic factors.¹ A breakthrough had been achieved in September 2004 with the implementation of Syria/European Union (EU) Association Agreement; ‘EU–Syria MEDA Co-operations’. Yet, a few unresolved issues imbedded the Syrian government to be an integral part of the WTO. These issues related to the competition policy and regulations which are not yet to come into the WTO regime (Gogia, 2004).

However, the assessment of external sector developments shows that trade was significantly liberalised and that free trade agreements (FTAs), generally, proved successful; despite declining oil production, total foreign trade accounted for 60 per cent of GDP in 2010 (Official data: 2010, Central Bureau of Statistics [Syria]; IMF, 2009; The World Bank, 2011).

Later on, by Spring-2011, with the outbreak of conflict in Syria and, on the account of it, the USA, EU and Arab League (AL) imposed negative economic sanctions on the Syrian government, individuals and companies (2011: human rights, democracy). The sanctions campaign imposed by multilateral organisations—OFAC, HM Treasury, the EU, the UN and several other regulatory entities—was unprecedented and one of the most comprehensives ever implemented (Portela, 2012).

In practical terms, international economic negative sanctions have become a common and recurring instrument used in order to coerce the target to change its political behaviour (Pape, 1997, pp. 93–94). Yet, empirical studies on its efficiency in bringing about a specific change in government behaviour or war outcomes (as in the case of Syria) are mixed and have not produced clear-cut results. The answer to this question remains a purely political and relative to country-specific conditions.

The aim of this article is not to assess the political outcomes of sanctions, since the Syrian regime neither collapsed nor changed its political behaviour. Our aim is to prove that economic sanctions brought a threat of another social collapse in addition to the enormous human and economic disasters caused by the ongoing armed conflict, setting the country decades back in terms of development and social welfare.

To accomplish this, our study reports panel gravity estimates of the bilateral trade between Syria and 78 countries over the period 1987–2017. The results show that multilateral sanctions, imposed by multilateral international institutions and bodies have contributed significantly to what can be called the ‘collapse of foreign trade’.

The article proceeds as follows. First, we summarise the literature concerning economic negative sanctions in different countries. Next, we deal with the effects of economic sanctions and conflict on the GDP in Syria, paying attention to the mechanism that links sanctions to GDP. In the following sections, we present data and methods employed and discuss the empirical results. The last section is devoted to the main findings and recommendations.

Literature Review

The literature related to the phenomenon of negative economic sanctions can be viewed from different analytical points: the political economy of sanctions, the effectiveness of sanctions and the response of target country. We concentrate on empirical studies that tried to capture the effects of economic negative sanctions on trade flow on the target country.

Challenging the optimism about the effectiveness of economic sanctions emerged with the most encyclopaedic taxonomy of sanctions; Schott et al. (2007) examined the universe of sanctions from 1914 to 1990, 115 identified cases in all. The study reported the success of sanctions in 40 cases or 34 per cent of the total. Pape (1997) re-examined the HSE database, concluding that economic sanctions have little independent usefulness for the pursuit of noneconomic goals. A quarter-century after Schott et al. (2007) re-examined the cost of sanctions (in the pre- and post-cold world) on targets and senders using 174 case studies encompassing 204 observations. Overall, the authors stated the following: ‘Sanctions never work’ is demonstrably wrong.

Further studies by Drezner (1998), Ang and Peksen (2007) and Dashti-Gibson et al. (1997) have provided different cases in which sanctions could be more effective; they found conflict anticipations, the initial stability of the target and the expected cost to the target country, respectively. Drezner (1999) brought a support in his analysis that unilateral sanctions tend to be more effective than the opposite. Another strand of literature has linked external intervention in the case of sanctions to the war duration within the target economy. Escribà-Folch (2010) used a sample of 87 wars with different sanction types and found sanctions and their durations statistically associated with shorter intrastate conflicts, while the economic embargo is the most effective type of coercive measure in the case using data set of Morgan et al. (2009).

Using a gravity model, the study of Yang et al. (2004) reported no significant impact of the US economic sanctions on trade for countries are subject to selective sanctions, while the opposite is true when sanctions were more comprehensive.

The same results were found by Caruso (2003) in a panel gravity estimation of bilateral trade between the USA and 49 target countries over the period 1960–2000. Furthermore, Yang et al. (2004) results indicate that the US comprehensive economic sanctions have some positive spillover effects on the USA’s main competitors—the EU and Japan.

Anderson and van Wincoop (2003) defined multilateral resistance factors (MRFs), such as language, culture and border, to calculate the comparative statics of trade frictions. They found that national borders reduce trade between the USA and Canada by about 44 per cent while reducing trade among other industrialised countries by about 30 per cent.

Other researches have concentrated on target countries that have a long history of political conflict with the USA and the EU. Vast studies considered the effects of sanctions on the Iranian economy in different time periods such as Aghazadeh (2014), Bazoobandi (2015) and Borszik (2016). The main results of these three papers concluded and confirmed that western sanctions on Iran have a significant impact of on Iran’s trade flow, diverting the trade to China, and the Iranian system has gradually become immune against the economic sanctions. Further, Popova and Rasoulinezhad (2016) reveal that Iran’s trade direction has shifted away from Europe (de-Europeanisation) towards Asia (Asianisation).

Lastly, the research that considered the effects of sanctions on the Syrian economy are relatively few. Seeberg (2014) discussed the EU policies toward and sanctions against Syria. He concludes that EU sanctions against Syria represented a deviation from the traditional pragmatic EU policies vis-a₹-vis the Middle East. Another paper by Mehchy et al. (2015) reported Heckman’s two-step approach in order to identify the export determinants for Syria between 1995 and 2010 only.

Therefore, our main contribution is to be the first empirical study on the impact of economic sanctions and the ongoing conflict on the potential trade flow in Syria and to study the Syrian trade policy of de-Europeanisation during the period of sanction the gravity model.

First, a brief assessment of the external sector at the down of crisis is reviewed.

Syria at the Dawn of Crisis: External Sector Analysis

With a special reference to the external sector, Syria’s economy is affected through two major channels—conflict-related disruptions and international sanctions—together they literally led to the collapse of Syrian foreign trade.

Conflict and GDP: Production Congestion

Pre-2011

The first decade of the new millennium,² key measures of reforms introduced in different areas strongly reflected in economic performance, particularly since 2004. Growth volatility has reduced significantly, translated into sustained increase in per capita incomes (see Figure 1). The economic growth rate during 10th FYPs (2006–2010) jumped to 5.07 per cent compared to an average of 3.2 per cent achieved from 2000 to 2004. This growth has been aided by high demand for Syrian non-oil exports from the Gulf region (IMF, 2009) and the investment boom in telecommunications and banking sectors (The World Bank, 2011).

Syria promoted its integration into the world economy and made substantial progress in the context of bilateral, regional and international trade agreements (see Table A1). Furthermore, a harmonised system of tariffs were introduced and import duties and tariffs lowered significantly from 255 to 65 per cent (official data), and non-tariff trade barriers and foreign exchange restrictions were removed.

Figure 1.

Source: Syrian National Bureau.

The accelerated pace of economic liberalisation process since 2004 had contributed to a ‘historic’ revival of the private sector in Syria. Its share of national income increased from 60 to 70 per cent in foreign trade (excluding oil). Investment approvals reached US$9.2 billion (26% of GDP) in 2006, while the share of foreign direct investment (FDI) in total has remained stable at about 20 per cent (IMF, 2007). Financial sector grew by 200 per cent in 2006; a spectacular improvement developed via the boom in private banks which contributed to 45 per cent of the growth in loans to the private sector. The proportion of manufacturing exports to the total doubled from 21.2 in 2005 to 48.3 in 2009 (see Table 1). And the index of Syria’s ranking in international competitiveness has improved as a response to trade and other related reforms from 90.7 in 2008 to 82.5 in 2010 (The World Bank, 2010). However, the overall success of this decade has completely crumbled. Figure 2 exposes the general losses in GDP for the period 2011–2018 as we will see next.

The Period of 2011–2018

In real term, Syria’s GDP has contracted by 63 per cent between 2011 and 2016 and by an additional 1.4 per cent in 2017—according to the estimated data, 67.4 declines compared to 2010 GDP.

Table 1.

Export Diversifications: Oil and Non-oil Exports

Index/Year	2003	2007
Total exports (million US$)	7,472	15,599
In which
Oil-exports (million US$)	4,369	4,392
Non-Oil exports (million US$)	3,102	11,206
Share of non-oil exports in GDP (%)	12.4	25.68
Number of products exported	150	162

Source: Syrian National Bureau.

Figure 2.

Source: Syrian National Bureau.

Conflict-driven factors have significantly affected Syrian citizens’ welfare through multidimensional channels:

Sustained damage to transport infrastructure: airports, railways and ports.

Public services-related infrastructures: schools, hospitals, water resources and dams.

The Syrian government had lost control of a large part of the oil and gas fields which led to a sharp drop in the public power supply.³

Agriculture GDP contracted by 55 per cent between 2011 and 2016.

Industrial zones suffered major damages, most notably in Aleppo and Homs which have main industries including pharmaceuticals, garments, chemicals and agro-processing industry. Industrial GDP (including oil) contracted by 108 per cent between 2011 and 2016, and some manufactures have shifted to other countries in the region, mainly Egypt, Jordan and Turkey.

Among all consequences of the conflict, the export capabilities particularly eroded. Figure 3 exhibits the Syrian trade sector pre and post conflict. Just before the global financial crisis, Syria’s trade amounted to 75.3 per cent of GDP for the period of 2003–2008, which is relatively high compared with the average for the Middle East and North Africa (The World Bank, 2011). In contrast, the period of 2011–2017 witnessed a collapse in foreign trade. According to the official data, in real terms, international trade, especially exports, declined severely by 164 per cent while imports declined by 100.3 per cent for the same period. While the trade deficit was estimated to have reached 16 per cent of GDP in 2015, 16 per cent in 2017 up from 5.66 per cent in 2010.

In dollar terms, total exports fell from US$10.5 billion in 2011 to US$783 million in 2015, a decline of 92 per cent in just four years. The additional decline by 10 per cent in 2017—102 total declines compared with 2010 export. Imports declined from US$19.9 million in 2011 to US$5.545 million in 2015—72 per cent fall, additional decline by 12 per cent in 2016, while 2017 witnessed a rise of imports by 22 per cent. The rise in imports was reflected through the rise of fuel and lubricants imports, which constituted half of the total import bill in 2017. The decline in manufactured goods imports (from US$11.5 billion in 2011 to US$2.8 billion in 2015) explained two-thirds of the total reduction in imports (see Figure 4).

Figure 3.

Source: Syrian National Bureau.

Figure 4.

Source: Syrian National Bureau.

Sanctions and Their Implications: USA, EU and AL Sanctions

Sanctions have had a significant impact on the trade sector; it literally collapsed. The transmission mechanism of the cost of sanctions can be illustrated through different channels: (a) suspension of banking transactions against goods and services traded with Syria, (b) erosion of international commercial credit facilities which led to a significant reduction in working capital–commercial and industrial, (c) rising cost of transfer as international payments are increasingly being transmitted via informal payment channels, (d) diversion of trade and shifting procurement to regional dealers or alternative sources; in both cases, quality was reduced and cost increased and (e) sanctions have contributed to the revival of the black economy and have led increase the prices of main essential goods and medicines

The Biggest Impact?

A first glance over FTAs, pre-2011 appeared very successful, especially with the EU in 2004, Turkey in 2007 and the Great Arab Free Trade Agreement (GAFTA). Relations with the EU had been improved significantly after signing of Syria/EU Association Agreement; EU became the second destination of Syria’s exports after Middle East region by 36 per cent for the former and 54 per cent for the later of total exports compared to 1 per cent to former USSR (it is worth noting here that more than 36 per cent of total Syrian exports pre-1989 was to Soviet Union (UNCTAD Secretariat, 1988). On the other hand, trade between Syria and Turkey, subject to FTA, rose from $795 million in 2007 to $1.8 billion in 2009, and Syrian exports to Turkey doubled from 2007 to 2010.

Yet, sanctions on Syrian exports have effectively blocked economic activities. The EU’s oil import ban is likely to hit Syria’s economy hardest. Oil revenues account for around 20 per cent of Syrian GDP. Before the EU ban, in 2010 in particular, 90 per cent of oil exports went to the EU, mainly to Germany (23%), Italy (31%) and France (11%). However, the European sanctions halt the export of 150,000 barrels per day to the EU’s market, equivalent to about 5 billion dollars a year. Refining capacity of oil also declined by 50 per cent (see Figure 5 and 6).

Figure 5.

Source: Syrian National Bureau.

Figure 6.

Source: Syrian National Bureau.

According to the official data, in 2006, the EU was Syria’s biggest trading partner, accounting for 40 per cent of Syrian trade, followed by Arab countries of 37 per cent mainly, Iraq (13.3%), Saudi Arabia (9%) and China (6.9%.) Turkey was in fifth place with 6.6 per cent and Russia accounted only to 3 per cent of the total. When the ban came into force, the EU’s export share dropped to 8.88 per cent in 2016, a decline of 77 per cent.

The effect of overall sanctions will be tested in the next section of this article.

Gravity Model

International economic negative sanctions are a common and recurring tool used in order to influence another state’s behaviour without resorting to a military conflict. The study of sanctions tends to focus on sanctions’ effectiveness. This study reports panel gravity estimates of bilateral trade between Syria and 78 countries over the period 1987–2017. A drawback of this approach is that the indirect effects of sanctions on trade between Syria and a third country, which did not impose a sanction, cannot be captured properly (Anderson & van Wincoop, 2003).

We use the standard gravity model with additional dummy variables in an attempt to capture the effects of political and military conflicts in Syria. Using a single log-linear equation, the estimated gravity equation takes the form:

\ln T R A D E_{i j t} = β_{0} + β_{1} \ln G D P_{i t} + β_{2} \ln G D P_{j t} + β_{3} \ln D I S T_{i j} + β_{4} D_{t} + μ_{i j t}

Where the trade flow between country i (here, Syria only) and j (here, Syria’s trading partners) at time t (in log form) has a relationship with the GDP in countries i and j, meanwhile the distance between both countries as a proxy for transportation cost. Dt is a variety of other factors (commonly dummy variables), as an accurate projection requires that the estimated parameters be consistent and efficient. Anderson and van Wincoop (2003) included dummies multilateral resistance term to the gravity model. And μ is the error term.

The gravity equation affirms that bilateral trade flows are supposed to be positively related to the size of countries and negatively related to the distance between them. More recently, different works used the augmented gravity model by adding trade policy index (Cheng & Wall, 2005), and bilateral exchange rate/regional trade preference (Nguyen, 2010). Population size, per capita income and FDI are also added to the basic model (see Linnemann, 1966; Frankel, 1992; Pfaffermayr, 1994, respectively).

Data Description

This study covers bilateral trade between Syria and her trade partners of total 78 countries, consists of 27 EU member states (including the UK), the 22 Arab states and other Asian and Latin American countries over the period 1987–2017 (list of countries provided in Table A2). The study made a distinction between two groups of countries. The first group imposed different types of sanctions on Syria, includes all EU’s countries, the USA, Turkey and Arab countries (except Algeria, Lebanon and Iraq). These three Arab countries have been excluded since they represent kind of resistance term for being reject to impose negative sanctions due to culture, historical relations and border sharing in the case of Lebanon and Iraq. The second group encompasses the main Syrian trading partners that did not impose any type of sanction for the same period (list of countries provided in Table A2).

The variables used in this study are shown in Table 2. The dependent variable is the trade volume (sum of exports and imports) between Syria and its trade partners in billions US dollar.⁴ The bilateral trade data are taken from the United Nations Commodity Trade Statistics Database and International Financial Statistics, IMF. GDP of the target country has been taken from the official Syrian data source.⁵ While GDP for trading partners is extracted from the World Bank database (The World Bank, 2019). Because of the lack of data, some countries are not considered in the sample.⁶ Also, all data that does not regard Syria being the exporter and importer are dropped from the data and not included in the observation. For the measurement of distance, the study used the most common approach in the literature, the straight-line distances (expressed in kilometres) between capitals of countries gathered from the web.⁷

Table 2.

The Variables of Model

Variables	Definition	Unit
Trade	Aggregated trade volume (sum of export and import) between Syria and trade partners	Billions US$
GDP-i	GDP in Syria at market price	Billions US$
GDP-j	GDP in Syria’s trade partners at market price	Billions US$
Distance	Distance between capitals of Syria and trade partners	Kilometres
Sanctions	Dummy variable: Bi-nominal	Dummy (1/0)

All the time-variant series level are transformed into natural logarithms. Further, ‘following the existing literature, I drop the observations where recorded bilateral trade is zero’ (Caruso, 2003).

D_t is time-variant dummies which take the value of 1 if a country-imposed sanctions on Syria at time t, and 0 otherwise. The information on sanctions has been taken from the US Department of State,⁸ and Case 2011–2 (PDF): EU, US v. Syrian Arab Republic.⁹

Model Specifications

In the Syrian case, sanctions had been imposed since 2011 after the outbreak of conflict and on the account of it (EU, US v. Syrian Arab Republic [2011: human rights, democracy]). Accordingly, sanctions could not be disentangled from military conflict that also affects trade linkages. However, an attempt has been made in this study in order to isolate the effects of conflict from that of sanctions through which the countries have been divided into two groups; we isolate countries that have imposed sanctions on Syria from those that have not, and run the following models.

Model (1)

Model (1) in this study includes all 78 of Syria’s trading partners together without making any distinctions. Model (1) will be used to depict the situation in which both conflict and sanctions had affected trade linkages captured by the same dummy variable SANC&CONF.

\begin{matrix} \ln T R A D E_{i j t} = β_{0} + β_{1} \ln G D P_{i t} + β_{2} \ln G D P_{j t} \\ + β_{3} \ln D I S T_{i j} + β_{4} S A N C & C O N F + μ_{i j t} \end{matrix}

In the second step, we break the gravity model into two additional various models based on Syria’s trader partners as we mentioned above. The first one includes all countries that imposed sanctions on Syria. While the second includes other countries that did not impose sanctions on Syria. This method will allow us to track whether Syria was able to divert its trade directions/de-Europeanisation regardless of conflict effects or not.

Syria–EU and AL Bilateral Trade: Model (2)

\begin{matrix} \ln T R A D E_{i j t} = β_{0} + β_{1} \ln G D P_{i t} + β_{2} \ln G D P_{j t} \\ + β_{3} \ln D I S T_{i j} + β_{4} S A N C + μ_{i j t} \end{matrix}

Further, the countries that imposed sanctions are not all equal; they vary between trade restrictions on import, exports or both, while other sanctions took the form of financial restrictions. Caruso (2003) had made a distinction in evaluating sanctions between export embargo, boycott and financial sanctions regarding the magnitude and the severity of restrictions. He grouped them into two categories: moderate and extensive. Following this approach, a distinction of the magnitude and severity of restrictions has been made. I distinguish two categories: extensive and moderate. In the first dummy, financial and trade embargo are considered and grouped under extensive sanctions xtnSANC, which are imposed by USA, EU and Turkey since these sanctions are consider the most comprehensive ever implemented on Syria.

In opposite to the first dummy, the second one grouped under modSANC. It considers the sanctions imposed by the AL, which are mainly political and involved restrictions on the Syrian government’s entity and not on the private sector causing some loose earn.

We can re-write Model (2) as:

\begin{matrix} \ln T R A D E_{i j t} = β_{0} + β_{1} \ln G D P_{i t} + β_{2} \ln G D P_{j t} + β_{3} \ln D I S T_{i j} \\ + β_{4} x t n S A N C + β_{5} \mod S A N C + μ_{i j t} \end{matrix}

Syria–Asian and Latin Bilateral Trade: Model (3)

\begin{matrix} \ln T R A D E_{i j t} = β_{0} + β_{1} \ln G D P_{i t} + β_{2} \ln G D P_{j t} \\ + β_{3} \ln D I S T_{i j} + β_{4} A S L A S A N C + μ_{i j t} \end{matrix}

The above three gravity models comprise time-variant variables and one time-invariant variable (distance). The expected signs of coefficients in our three models can be explained as shown in Table 3.

Table 3.

Expected Signs of Variables

Variable		Type	Expected Sign
Trade		Time variant	Positive
GDP-i		Time variant	Positive
GDP-j		Time variant	Positive
Distance		Time-invariant	Negative
Model (1)	SANC&CONF	Time variant, dummy	Negative
Model (2)	Xtn-SANC Mod-SANC	Time variant, dummy Time variant, dummy	Negative Negative
Model (3)	ASLA-SANC	Time variant, dummy	Positive/negative (Trade with Asia and Latin America)

Source: The authors.

According to the theoretical framework of the gravity model, it is expected that economy size expressed through GDP at market price would have positive impacts on trade volume and encourage trade between Syria and her trading partners in a normal situation. The time-invariant variables, the coefficient of distance is expected to be a negative sign as distance reflects the transportation cost between trade partners. In Models (1) and (2), we expect to get a negative coefficient as evidence that sanctions reduced trade volume. While in Model (3), due to the abnormal situation in Syria, we are sceptical about Syria’s trade policy and ability to divert the trade to Asia and Latin America, and the sign of dummy sanction is expected to be positive if Syria was able to do so. Otherwise, it will be negative.

Results and Conclusion

Findings of the three regression models are shown in the following table:

Model (1): All countries
Periods included: 31
Cross-sections included: 78
Total panel (unbalanced) observations: 1,894
	Fixed Effects		Random Effects
	Coeff.	P-value	Coeff.	P-value
Ln_GDP_sy	0.312651	0	0.324572	0
Ln_GDP_j	1.147029	0	1.046022	0
ln_Distance	dropped		–1.16169	0
SANC	–1.05665	0	–0.95319	0
Constant	–4.58598	0	5.033098	0
Model (2): Sanctioned countries
Periods included: 31
Cross-sections included: 42
Total panel (unbalanced) observations: 1,136
	Fixed Effects		Random Effects
	Coeff.	P-value	Coeff.	P-value
Ln_GDP_sy	0.317209	0	0.32417	0
Ln_GDP_j	1.077498	0	1.003399	0
ln_Distance	dropped		–1.51872	0
X-SANC	–1.48995	0	–1.48293	0
M-SANC	–0.68857	0.0013	–0.52042	0.0075
Constant	–3.9297	–3.9297	7.911285	0
Model (3): Non-sanctioned countries
Periods included: 31
Cross-sections included: 36
Total panel (unbalanced) observations: 758
	Fixed Effects		Random Effects
	Coeff.	P-value	Coeff.	P-value
Ln_GDP_sy	0.329307	0	0.338157	0
Ln_GDP_j	1.111523	0	1.028522	0
ln_Distance	dropped		–1.05759	0
SANC	–0.67679	0	–0.5863	0.0012
Constant	–5.1601	0.0004	4.138241	0.0348

Source: Authors’ compilation from Eviews 9.0.

Analysing panel data imposes to make a choice between the random effects (RE) and the fixed effects. The decision between the two models can be based on the Hausman test. The fixed-effects model is more useful since it can depict some country-specific factors. Hausman test shows that the fixed effect model for Models (1) and (2) is more appropriate, while in Model (3), the Chi-square (X²) is statistically insignificant meaning there is no correlation between the intercept and the explanatory variables; thus, the RE in Model (3) are more preferable.

Model (1)

Model (1) for the trade of Syria–78 countries performed quite well. Most of the results are as expected. Also significant and seem quite reasonable. Since the regression in logarithm form, variables will be interpreted as elasticity. The estimation results of Model (1) confirm that GDP has a significant and positive impact on Syria-bilateral trade. The results reveal that 1 per cent increase in the GDP of Syria’s trade partners raises the bilateral trade volume by 1.07 per cent. GDP of Syria has a less positive influence on Syria–78 countries’ bilateral of 0.31 per cent. While distance negatively and significantly influences the trade volume.

Moreover, as we predicted, sanctions against Syria decrease the trade volume of this country. Yet, in Model (1), dummy includes both conflict effects and sanctions effects on trade volume. The coefficients of SANC is estimated as a percentage shift in the dependent variable when the dummy equals 1 versus when it equals 0.

For SANC by 65.23 per cent (= Exp (–1.05665) –1). This indicates that both conflict and sanctions imposed in Syria with the outbreak of the crisis in 2011 had decreased the total trade volume by 65.23 per cent.

Models (2) and (3)

Breaking the model into two sections as we mentioned before. In Model (2), the case of Syria’s trade with 27 EU Countries, 13 Arab Countries, Turkey and USA. We distinguished between extensive and moderate sanctions. The results show that multilateral sanctions surely disrupt bilateral trade of Syria. For both X-SANC and M-SANC, coefficients are always statistically significant. It is worth noting here that the extensive sanctions imposed by the EU and the USA have fostered the decrease in bilateral trade of Syria by 77.5 per cent compared to moderate sanctions imposed by AL of 49 per cent. This result quite restorable since more than 40 per cent of Syria’s trade directed to EU and 37 per cent to the Arab countries.

Finally, in regards to Model (3), we expect a positive effect of sanctions on Syria’s trade with other countries that never impose sanctions and some of them have geographical, historical and cultural relations with Syria such as Iraq, Lebanon, Iran and Russia the coefficient of SANC is negative and significant. The trade volume of Syria with 34 other countries has decreased by 49.1 per cent. Meaning that Syria was unable to modify and divert her foreign trade policy to increase ties and relations with Asian and Latin American countries. This is because of different factors mainly, the conflict effects on trade linkages in Syria and geographical distance as a proxy of transportation cost. We found here that distance has negative sign of its coefficient, estimated by RE; 1 per cent increase in the distance decreases the trade volume between Syria and other countries by 1.17 per cent, 1.5 per cent and 1.05 per cent, respectively, in the three models.

Conclusion and Recommendations

Conclusion

This study, by means of a gravity equation, estimated the impact of multilateral negative economic sanctions on Syria’s international trade flow. Since the sanctions cannot be disentangled from the conflict effects, the study first reports panel gravity estimates of bilateral trade flow between Syria and its total 78 trading partner countries with one dummy variable for both conflict and sanctions.

The study tried to empirically determine whether the imposition of sanctions upon Syria has pushed its policy toward de-Europeanisation by making a distinction between two groups of countries. The first group imposed different types of sanctions on Syria (42 countries), while the second group encompasses the main Syrian trader partners that did not impose any type of sanction for the same period (36 countries). The study differentiated between two types of sanction; those imposed by EU as extensive and moderate sanctions imposed by Arab countries. The empirical results showed that both conflict and sanctions imposed in Syria since 2011 had decreased the total trade volume by 65.23 per cent.

The results also showed that extensive and comprehensive sanctions imposed by EU and USA have a large negative impact on bilateral trade of Syria by 77.5 per cent, while this is not the case for limited and moderate sanctions imposed by AL which decreased the trade volume by 49 per cent. Furthermore, Syria was unable to modify and divert her foreign trade policy to increase ties and relations with Asian and Latin American countries. This is because of different factors, mainly the conflict effects on trade linkages in Syria and geographical distance as a proxy of transportation cost.

Finally, for more than eight years, Syria has been facing numerous challenges due to the brutal war on its land. In the course of fighting against terrorism and for sovereignty, the country deliberately de-developed, losing decades of progress. The Syrian state has been suffering from wide-ranging sanctions have imposed on it by the international community.

However, sanctions, of course, did no damage to the ‘regime’ in Syria. In fact, they strengthened it, as is normally the case, because the population became more dependent on the government for its survival and less connected to the rest of the world.

Western sanctions are a manifestation of a morally bankrupt strategy and the refusal to recognise reality. These economic measures did not put pressure on Syrian elites. They had the same effect on millions of Syrians. Poor and middle-class Syrians lost their electricity supplies, healthcare and other goods and services. Their quality of life deteriorated, their days occupied with basic survival.

Recommendations

An urgent review of sanctions against its initial objectives and within practical context.

Imports of food, medicine and fuel for the essential means of living for ordinary citizens must be assured to be excluded from the sanction framework.

Beyond the supply of food, medicine and fuel, it is becoming increasingly important to address the immediate civilian need to repair electricity infrastructure, healthcare facilities, the transport network and wider services.

Identification of a list of particular commodities and materials that may go under the embargo umbrella (requires a special license) and lifting, in return, restrictions on all other goods and services (permitted by general license). This shall reduce the ‘compliance buffer zone’ established by suppliers and banking institutions.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

Appendix

Table A2.

List of Countries

Group 1: 42 Countries Imposed Sanctions on Syria
European Union Countries		23	Romania
1	Austria	24	Slovenia
2	Belgium	25	Spain
3	Bulgaria	26	Sweden
4	Croatia	27	United Kingdom
5	Cyprus	28	Turkey
6	Czech and Slovakia		Arab Countries
7	Denmark	29	Kingdom of Bahrain
8	Estonia	30	Egypt
9	Finland	31	Jordan
10	France	32	Kuwait
11	Germany	33	Libya
12	Greece	34	Mauritania
13	Hungary	35	Morocco
14	Ireland	36	Oman
15	Italy	37	Qatar
16	Latvia	38	Saudi Arabia
17	Lithuania	39	Sudan
18	Luxembourg	40	Tunisia
19	Malta	41	United Arab Emirates
20	Netherlands	42	United States
21	Poland
22	Portugal
Group 2: 36 Countries Do Not Impose Sanctions on Syria
	Other Arab Countries	59	Brazil
43	Algeria	60	Chile
44	Iraq	61	Colombia
45	Lebanon	62	Cuba
Others in Europe and Central Asia		63	Ecuador
46	Belarus	65	Mexico
47	Russian Federation	66	Costa Rica
48	Norway	67	Paraguay
49	Republic of Serbia	68	Republica Bolivariana de Venezuela
50	Switzerland	East Asia and Pacific
51	Ukraine	69	China, P.R.: Mainland including Taiwan
52	Bosnia and Herzegovina	70	China, P.R.: Hong Kong
53	Azerbaijan, Republic of	71	Japan
54	Kazakhstan	72	Indonesia
55	Iran, Islamic Republic of	73	Malaysia
56	Moldova	74	Korea, Republic of
Others		75	Thailand
57	Canada	76	Singapore
Latin America and Caribbean		77	Vietnam
58	Argentina	78	Australia

Source: Authors’ compilation from Ministry of Economy and Foreign Trade of Syria, and Ministry of Foreign Affairs and Expatriates of Syria.

Notes

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External Sector: Between Congestion and Sanctions—‘Syrian Economy Case,1987–2018’

Abstract

Keywords

Introduction

Literature Review

Syria at the Dawn of Crisis: External Sector Analysis

Conflict and GDP: Production Congestion

Pre-2011

The Period of 2011–2018

Sanctions and Their Implications: USA, EU and AL Sanctions

The Biggest Impact?

Gravity Model

Data Description

Model Specifications

Model (1)

Syria–EU and AL Bilateral Trade: Model (2)

Syria–Asian and Latin Bilateral Trade: Model (3)

Results and Conclusion

Model (1)

Models (2) and (3)

Conclusion and Recommendations

Conclusion

Recommendations

Footnotes

Declaration of Conflicting Interests

Funding

Appendix

Notes

References