Economic Effects Estimation of Iranian Potential Accession to the Eurasian Economic Union: Application of Autoregressive Distributed Lag

Abstract

Despite the blow dealt by the re-imposition of US sanctions, the interim agreement signed between Iran and the

Eurasian Economic Union (EAEU) to establish a free trade area highlights the prospect of Iran’s integration into the

global market. By taking a qualitative-based approach and applying an Autoregressive Distributed Lag Model (ARDL)

this paper assesses whether a potential EAEU membership could provide any economic advantages for Iran. Our

analysis concludes that the dominance of socio-political actors in the domestic market, sanctions regime, dependency

on oil price and fundamental differences in institutional structures, all limit the economic gains that come with

integration.

Keywords: Iran; Russia; Policy; Integration; EAEU; Sanctions; ARDL

JEL Classifications: F15, F51,

1. Introduction

Considering the current geopolitical situation, a free trade agreement can potentially provide a degree of relief for an

economically isolated country such as Iran. The article applies both a qualitative-based and a quantitative-based

approach. Prior to the analysis, we outline an in-depth overview of the structure of the economy. In our overview we

highlight the dominance of oil in terms of exports and revenue for the state budget as well as the monopoly of political

and religious institutions in the domestic market. Moreover, we find that the level of FDI inflow that is badly needed to

develop national industries, particularly for oil and gas, is undermined not only due to the sanctions imposition, but also

due to internal factors, such as the market monopoly by social-political institutions. To evaluate the potential impact of

integration into the Eurasian Economic Union, we have chosen to use trade turnover and FDI flow as our main

indicators for the qualitative-based analysis. For the quantitative-based analysis we will apply an econometric model

using the Autoregressive Distributed Lag (ARDL) model. The econometric model uses Kazakhstan and Belarus as the

baseline examples to calculate the accession impact on each respective country’s trade turnover. Due to the above

mentioned internal factors, as well as the impact of sanctions and oil price fluctuations, a potential EAEU membership

will most probably not vitalize the Iranian economy. Moreover, the results from the econometric model indicates that so

far, the Customs Union has not had any significant impact on trade turnover among the current member states. In

conclusion, the likelihood that Iran will gain any significant benefits in terms of greater trade with and investment from

the current members after joining the Eurasian Economic Union seems highly unlikely.

2. Literature Review

There are several stages of economic integration but for the sake of this paper, we will overview only the most relevant

ones and highlight the main differences between them. The initial stage of economic integration is the creation of a

preferential trade area (PTA), which abolishes price controls such as tariffs and/or quotas on some, but not all goods,

between two or more signatory countries. The second phase of economic integration comes with the establishment of a

Free Trade Area. A Free Trade Area is a deeper and more comprehensive version of the former, with a higher reduction

of tariffs and/or quotas on goods. The third phase, creating a Customs Union, entails the establishment of a common

external tariff among all the parties involved, meaning that once goods have cleared customs in one member country,

they can then be shipped to another without being subject to additional tariffs. The fourth phase, the Common Market,

adds the free movement of goods capital, labour and services as well as the harmonization of regulations on goods and

industries. An Economic Union, the fifth stage, is basically the combination of the former and the latter.

Any trade agreement, be it in its infant stage (PTAs) or the more advanced stage (Economic Union), must be viewed

through the lens of effects on trade, namely; trade creation and trade diversion. The concepts of trade creation and

trade diversion were first developed by Jacob Viner in his book “The Customs Union Issue” and even though these were

applied to the Customs Union model, the expanded literature on economic integration has come to include these

concepts in all the above mentioned phases. In short, trade creation is an increase in trade between two nations due to

the formation of a trade agreement/area as tariffs and quotas are removed. Trade diversion, on the other hand, is the

decrease in trade between a member country and a third-country, not part of the agreement/area, as cost of goods

become cheaper within a free trade area that has reduced or eliminated tariffs.

The literature on the pros and cons of economic integration is vast with a variety of methodologies to analyze the

economic effects produced by trade agreements. By using synthetic control methods in his analysis, Hannan finds that

trade agreements ‘generate substantial gains, on average an increase of exports by 80 percentage points over ten years’

(2016). However, he points out that the export gains are substantially more when ‘emerging markets have trade

agreements with advanced markets’ (Hannan, 2016). On the other hand, Deme & Ndrianasy by applying a gravity

model, argue that free trade agreements between small and low-income countries has ‘a particularly robust trade-

creation effect’ (see Deme & Ndrianasy, 2017). Moreover, Rekiso argues that there is a positive correlation between

economic integration and industrialization, based on his analysis on Sub-Saharan African countries (2017). Using the

example of economic effects of European integration, Campos et al. point out that ‘per capita European incomes in the

absence of the institutional integration would have been about 10% lower on average in the first ten years after joining

the EU’ (2018). On a lower scale, Harvie et al. argue that developing the competitiveness and capability of SMEs can

actually act as an effective counter measure to the negative effects of economic integration (2015). Olaru, supports this

argument when analyzing the potential impact of Moldova joining to the DCFTA, highlighting that ‘only (by) fostering

the SMEs, it will be possible for Moldova to really profit of DCFTA’ (2014). However, it is likewise important here to

underscore that it is difficult to quantify exactly the degree of positive or negative outcomes in terms of economic effects

in free trade agreements, such as trade creation or trade diversion, as it very much depends on the exact content of the

trade agreement (Mattoo et al., 2017).

A prime example where the researchers disagree about the benefits and drawbacks of economic integration is the

Eurasian Economic Union (EAEU). On November of 2011, Belarus, Kazakhstan and Russia agreed to form the union,

which later was enlarged with the accession of Armenia and Kyrgyzstan. The creation of the EAEU had generated a

number of positive impacts to a certain degree, namely promotion of competition, increase in trade and average salary,

facilitation of the payback of new technologies, higher productivity etc. (Khussainova et al., 2016).

The competencies of the EAEU such as the implementation of common external tariffs, antidumping measures etc. have

contributed to the union’s development (Moldashev, & Hassan, 2015). On the other hand, Knobel notes that the union

still has a long way to go, particularly with regards to existing tariff barriers, no common decision making for export

tariffs and no supervisory agencies (2017). The trade policy is not coordinated and unified, demonstrated by the

imposition of protective measures by Russia on Ukrainian goods (Knobel, 2017). Even the common external tariffs are

simply a different version of Russia’s external tariffs, effectively safeguarding Russian industry inside the union and

potentially hurting the customers of other member states (Tarr, 2016). Still, there are several factors that may make the

EAEU more successful than the previous failed integration attempts in the region, since Russia is now a member of

WTO, which increases the likelihood of lowering its trade barriers (Tarr, 2016).

By looking at different trade integration indices, Gurova et al. concludes that trade among current members of EAEU is

mostly symmetric, except for trade with Russia (2017). All three other countries, particularly Belarus, have

unsymmetrical trade with the Russian Federation, even when fuels are excluded from the calculations, the asymmetry

still persist, although to a lesser degree (Gurova et al., 2017). These indicators essentially highlight the dependence of

other member states on Russia (Gurova et al., 2017). This dependence is understandable considering the history of the

member states. Indeed, as Khitakhunov et al. mention, ‘The EAEU is not a group of unrelated countries willing to form

a customs union, but due to its Soviet history all members were part of single economic entity’ (2016). These countries

share common past, language and have similar institutions (Khitakhunov, et al., 2016). In a similar vein, regional

institutional projects, such as the EAEU, contain a set or norms, which produce spatial imaginaries for societies

(Akchurina & Della Sala, 2018). Russia has been very successful in exerting its normative power, which resonance

particularly well with local elites, through the EAEU (Akchurina & Della Sala, 2018). Moreover, “Russia understands

and respects the negative feelings associated with the Soviet Union” (Bayramov, 2013). In this sense, establishing and

promoting regional integration in the Post-Soviet space through the EAEU is seen by Russia as a more reasonable path

(Bayramov, 2013). Kaczmarski argues that the “ultimate end goal (of the Eurasian Economic Union) is a civilizational

entity, performing the function of a pole in the contemporary international order (Kaczmarski, 2017).

The similar history and institutions of the members may increase the chance of success of the EAEU, but a number of

articles suggest that its establishment and participation by member states is not necessarily based on economics. As

mentioned above for the Russian Federation, the motive behind creating the EAEU is more about politics than economic

benefit (Dobbs, 2015). On the other hand, Dobbs states that in the case of Armenia, remittances and security assurances

are the main drivers of its willingness to join the EAEU. The potential advantages of the accession are constrained for

the country by the fact that it shares no land or sea border with the rest of the union (Dobbs, 2015). Also, Armenia might

have to expand its protectionist measures due to the common external tariff policy (Dobbs, 2015). This drawback of the

EAEU might be compensated by several other factors, such as higher remittances from freedom of labor, receiving

Russian gas without export duties and military/political assurances (Volchkova, et al., 2016). Still, excluding the

remittances and other minor economic benefits, Armenia’s decision to join the EAEU has little justification on economic

grounds (Ter-Matevosyan et al., 2017). Instead of boosting the economic growth, the accession has in fact ‘significantly

slowed economic performance’ (Ter-Matevosyan et al., 2017). Considering all the above, the authors conclude that

Armenia’s decision was mainly based on political motives (Ter-Matevosyan et al., 2017). However, this is not a unique

case, as similar arguments can also be made for the rest of the existing member states.

3. Overview of the economy of Iran

In this section, we provide an overview of the structure of Iran’s economy. To better assess the impact accession would

have on the Iranian economy it is vital to analyze recent developments up to the present period. Our choice as

benchmark year is 2011 (which is 1389/1390 in the Iranian calendar). The reasons for choosing this year are because

2011 preludes the most important events up to 2018, namely: the biggest round of sanctions imposition by the West on

the Iranian economy in 2012, the election of President Rouhani and his “liberal” faction in 2013, the signing of the Joint

Comprehensive Plan Action in 2015 that resulted in some sanctions relief, and finally, the USA’s exit from the JCPOA

in 2018.

Considering Iran is the sixth biggest producer of oil, and third in terms of natural gas, according to the US Energy

Information Administration, it is necessary to evaluate the overall impact oil and gas has on the economy. Thus, we have

decided to separate GDP growth and exports/imports into two categories: oil and non-oil, to facilitate this analysis. We

will then assess the degree of public/private ownership and participation in the economy.

For most of the last two decades, GDP growth in Iran has been in relative constant fluctuation but remained positive,

with the exception of the year 1994. According to data from the World Bank, Iran’s GDP growth started to decline in

2011 and fell sharply in 2012, down to -7.44%, as a result of tightening of sanctions by the US and EU 1. This was the

sharpest drop in growth not seen since the 1980s, during the Iran-Iraq war. It is important to highlight here that the aim

of the sanctions was not only to target enterprises dealing with arms trade or financial institutions but also oil exports

and oil and natural gas industries. The impact of the sanctions is equally evident when one looks at total GDP of Iran.

Iran’s total GDP peaked at USD598.85 billion in 2012 and by 2015 it had almost halved to USD385.87 billion, only to

recover following the implementation of the JCPOA the next year, reaching USD439.51 billion as of 2017, according to

data from the World Bank Database2. It is important to highlight here the fact that the extreme fluctuations of the rial are

the main cause of volatility in terms of GDP measured in USD. Similarly, looking at GDP growth, following the

sanctions relief Iran’s GDP growth rose to 13.39% in 2016, the highest growth in 26 years. However, it bounced back

down to 4.3% the following year. This initial growth spike was attributed to a hike in exports of already-produced and

stored oil (Wald, 2018; Rascouet, 2018). This is due to the impact of the sanctions which almost halved Iranian exports

of crude oil, from 2.10 million barrels per day in 2011 to 1.08 million barrels per day in 2015, based on figures from

OPEC’s database3.

Looking at the oil sector’s share of total GDP we find that it only constitutes about 14.9%, as of 1396 (2017/2018),

having gradually decreased since 1390 (2011/2012) when it totaled 26.8% of the economy, according to figures from the

Statistical Center of Iran. In fact, non-oil GDP is much larger than oil-GDP. The biggest sector, in terms of non-oil GDP,

by far, is services, which come to 47.9%, followed by the manufacturing sector at 17.4% for 1396 (2017/2018). In other

words, the oil sector is only the third largest contributor to GDP.

[Figure 1 Here]

Even though the oil sector’s share of GDP is low, Iran’s economy is very much dependent on oil. If we look at oil

exports, based on data from the Central Bank of the Islamic Republic of Iran, we find that they represent 67% of Iran’s

total exports, for the fiscal year 1396 (2017/2018) 4. This number is significant as oil revenues’ contribution to Iran’s

1 GDP Growth (annual %), Islamic Republic of Iran, World Bank Database, available at: https://data.worldbank.org/indicator/NY.GDP.MKTP.KD.ZG?locations=IR accessed on: 28 August 20182 GDP Growth (current US$), Islamic Republic of Iran, World Bank Database, available at: https://data.worldbank.org/indicator/NY.GDP.MKTP.CD?locations=IR accessed on: 27 August 2018 3 Oil and Gas Data, OPEC Database, available at: https://www.opec.org/library/Annual%20Statistical%20Bulletin/interactive/current/FileZ/Main-Dateien/oilgasdata.html accessed on: 28 August 20184 Economic Time Series Database, Central Bank of the Islamic Republic of Iran, available on: https://tsd.cbi.ir/DisplayEn/Content.aspx accessed: 4 September 2018

budget constituted USD26.656 billion or 35.41% of total budget revenue for that same year according to the Ministry of

Economic Affairs and Finance of Iran5.

If we look at Iran’s current account balance starting from 2010/2011 up to 2017/2018, based on data from the Ministry

of Economic Affairs and Finance, we see that in the year 2012/2013 oil exports dropped from USD119.14 billion

(2011/2012) to USD68.08 billion, or with an almost 12% year-on-year decrease as the sanctions kicked in. This drop in

oil exports continued up until 2015/2016, only to recover the following year, as the sanctions relief began. Moreover,

according to Iran’s budget revenues from the same period, oil revenue did indeed drop for the year 2012/2013, from

USD47.7 billion to USD35.1 billion, a year-on-year decrease of 26.4%. This obviously is the result of the sanctions

impact on Iran’s oil exports. Furthermore, despite, oil revenues bounced back up in the period 2013/2014 to USD37.9

billion, it decreased further to USD23.3 billion the following year. This year-on-year drop of 37.65% can be attributed to

the slump in the price of global crude oil that occurred during the second half of 2013. Looking at the oil’s share of total

budget revenues from the period 2010/2011 to 2017/2018 we see a downward trend, from 53% to 35.41%. It is

important to point out that while the decrease in share has been significant; oil revenue contribution to the budget is still

much lower than many other Middle-Eastern oil-producing countries.

[Figure 2 Here]

[Figure 3 Here]

Like many other oil-producing countries, Iran has established an oil fund. In 2011 the Oil Stabilization Fund of Iran was

replaced with the National Development Fund. According to Iran’s socioeconomic development plan, 20% of oil income

is transferred to the NDF, which then allocates about half of its funds to the private sector, 30% is invested in overseas

markets and the remaining 20% is used to promote foreign investment. Based on limited data, the Fund amounted to

USD34.4 billion as of the third-quarter of the Iranian fiscal year 2017/20186. The state-owned National Iranian Oil

Company (NIOC) has the exclusive right over Iran’s oil and gas development, from drilling to production and

distribution to export. In addition, NIOC is the third biggest oil company in the Middle-East after Saudi Arabia’s

Aramco and Kuwait Petroleum Corporation.

5 Ministry of Economic Affairs and Finance, Economic Modeling and Information Management Office: Economic and Financial Databank of Iran, available at: https://databank.mefa.ir/data?lang=en accessed on: 4 September 20186 Private Sector NDFI’s main Beneficiary, Financial Tribune, 22 January 2018, available at:https://financialtribune.com/articles/economy-business-and-markets/80518/private-sector-ndfi-s-main-beneficiary accessed on: 16 September 2018

The difficulty in accessing information on the distribution of the revenue from oil exports leaves one to pure speculation

and estimation. If we take the example of the fiscal year 2017/2018, we see, as pointed out above, that USD26.656

billon, or 35.41% of total budget revenues, came from oil. However, oil exports for that same year amounted to

USD65.81 billion and if we assume that 20% of income from the oil exports gets allocated to the National Development

Fund that leaves USD26 billion left in oil income to be distributed. Considering that some amount has to be allocated to

cover production expenses incurred by NIOC, we can presume that a certain amount of the remaining revenue will be

allocated to the state-oil company. Yet, even if NIOC receives half of the remaining oil income, in other words USD13

billion, there is still a significant amount left. Even though there is clearly a lack of transparency in terms of oil income

redistribution, we can assess that oil income, and the price distortions in the oil markets that come with, clearly exerts a

strong influence on the overall well-being of the economy, as is the case with Russia and Kazakhstan, albeit to a

different degree.

In terms of exports, the biggest importers of Iranian crude oil, as of the first half of 2018, was the EU (31%), China

(29%), India (24%) and South Korean & Japan (16%) (Ghaddar & Gloystein, 2018). On the other hand, even though

Iran’s non-oil exports are relatively diversified, the main non-oil exports are plastic and rubber products (oil-derived

products) at 11.6% of total non-oil exports, followed by chemical products and metals7. The top destinations for non-oil

exports are China at 37%, India at 14% and Turkey with 10%2. Overall, Iran has maintained a trade surplus for the

whole period 2011-2018. According to the Customs Administration, for the fiscal year 2017/2018 Iran exported

USD46.93 billion of non-oil commodities (out of USD98.13 billion total exports), while imports amounted to USD54.3

billion, of which auto parts and capital goods formed the majority8. Iran’s main import destinations are as follows:

China, United Arab Emirates, South Korea and Turkey3.

[Figure 4 Here]

Even though the government takes a pretty active role in the economy, looking at the composition of the market share of

Iranian companies shows a different picture. The two main categories of companies that dominate the Iranian market are

banks and petrochemical and oil product companies. The biggest petrochemical company for the fiscal year 2016/2017

7 IMF Country Report No. 18/94: Islamic Republic of Iran, International Monetary Fund, March 2018, available at:https://www.imf.org/en/Publications/CR/Issues/2018/03/29/Islamic-Republic-of-Iran-Selected-Issues-45768 accessed: 18 September 20188 Iran non-oil exports rise to $47bn, The Iran Project, April 10 2018, available at:https://theiranproject.com/blog/2018/04/10/iran-non-oil-exports-rise-to-47bn/ accessed on: 20 September 2018

was Persian Gulf Petrochemical Industries Company, ranked 2nd biggest company in Iran9. Other big companies that

topped the list of 10 biggest firms were Isfahan Refinery, Bandar Abbas Refinery, Parisan Oil & Gas Development

Group Co, and Tehran Oil Refinery, for the fiscal year 2013/201410. If we count the top 15, there are also three non-

petrochemical companies that top the list; Supplying Automative Parts Co, Mobarake Steel Company and

Telecommunication Company of Iran5. It is important to mention that all of the above mentioned companies, with the

exception of Telecommunication Company of Iran, are privately-owned. The other principal category is banks, who

account for 60% of total assets owned by Iran’s top 500 firms, making them the richest entities in the country, and also

accounted for 26% of total job creation for the fiscal year 2016/20174. Out of the top 30 largest companies 14 are banks,

with Bank Melli Iran being the third largest company in Iran as well as being the largest commercial bank in the Middle-

East, with 18 international branches and services in 11 countries4. However, what is vital to point out here is that, the

dominance of the banking sector in Iran’s economy inherently creates a risky dependency on banks for companies

outside this sector. Add to that the existing sanctions on Iran’s banking sector and the fragility of the economy becomes

apparent. Turning our attention back to ownership structure, out of the 14 major banks mentioned above, only three are

officially state-owned. However, it is estimated that the vast majority of the biggest companies are owned directly or

through shareholding by institutions directly related to the government and religious institutions.

The two most influential players in the private market are the Islamic Revolutionary Guard Corps (IRCG) and the

Bonyads (note that these are in no way the only ones). Both institutions own a vast portion of the private market and in a

variety of sectors, ranging from the oil and banking sector to car manufacturing and even post services and major hotels.

It is estimated that between 20-40% of Iranian GDP is owned by the IRCG while 10-20% is in the hands of the Bonyads

(Ottolenghi et al., 2016; Thaler, et al., 2010). A report published by the U.S. office of the National Council of Resistance

of Iran (NCRI) put estimates that Khamenei’s office and the IRCG control “at least” 50% of Iran’s GDP (Basiri, 2017).

Looking at FDI inflows from the World Bank Database, Iran is the third biggest destination among the Gulf Countries,

after the UAE and Saudi Arabia. According to the same source, FDI inflow went from USD1.98 billion in 2008 to

USD4.66 in 2012. The sanctions impact, however, affected the level of FDI inflow, reaching a low of USD2.05 billion

in 2015. The flow of FDI reverted following the sanctions relief, jumping to USD3.37 as of 2016 and reached USD5

9 Banks steal the limelight on Biggest Firms Rankings, Financial Tribune, 2017, January 26, available at:https://financialtribune.com/articles/economy-business-and-markets/58255/banks-steal-the-limelight-on-biggest-firms-rankings accessed: 21 September 201810 IMI-100: Ranking of the Top 500 Iranian Firms, Industrial Management Institute, 24 July 2017, available at:http://www.imi.ir/en/SitePages/Insight.aspx?p_id=12 accessed: 20 September 2018

billion in 2017 according to estimates11. The sixth 5 year plan envisions Iran attracting USD50 billion for the period

2017-2022, which entails a minimum annual FDI inflow of USD10 billion per year. Considering the existing uncertainty

of the survival of the nuclear deal and the pullout of several major global companies, such as Total, Siemens, Boeing

and even Russian Lukoil, it is highly likely that the post-sanctions trend of higher FDI flows will either stagnate or

decrease in the coming years. On the other hand, if one disregards the effect of sanctions, the centralization of the

economy in the hands of a few regime connected institutions has been a major obstacle for incentivizing FDI to Iran.

The IRCG and the Bonyads are part of the faction of hardliners that are against President Rouhani’s desire to open up

the Iranian economy to the outside world and attract foreign investment for fear of losing their monopolies and to protect

their interests. Thus, even without sanctions, only a policy of enforced liberalization of the domestic market can yield

the desired results, something that will not go down well with the current regime. According to the Global

Competitiveness Report for 2018-2019, published by the World Economic Forum, Iran ranks 89 th out of 140 countries

(Schwab, 2018). Looking at the indicators, Iran ranks worst of all countries listed (140/140) in terms of trade tariffs, but

also 136 out of 140 in Shareholder Governance, 134 out of 140 in Budget Transparency, and 131 out of 140 in

Soundness of Banks, to name a few (Schwab, 2018). The fact that Iran has the worst tariff regime is not surprising

considering its international isolation and the centralized nature of its economy. However, it is also worth mentioning

that Iran is one of few countries that is not a member of the WTO and is one of only a handful of countries that has not

signed and implemented a free trade agreement of any sort. In fact, should the envisioned Iran-EAEU free trade area

materialize it would be Iran’s first. Additionally, according to Transparency International’s 2017 Corruption Index, both

Iran and the current members of the EAEU rank pretty low on the scale, between 107 and 135 out of 180 (1 being least

corrupt), with Iran claiming position 130 (2018). A final point that needs to be highlighted is FDI in the oil and gas

sectors. As stated in the sixth 5 year development plan, the sector requires an investment to the tune of USD100 billion

by 2021, to increase production and reach the designated targets (Mahnaz, 2016). The mining sector is another vital

sector for the Iranian economy which, according to the same plan, requires USD50 billion in investments (Zhanaltay,

2017). Considering the current geopolitical situation, the FDI targets outlined in the sixth 5 year development plan will

be difficult to attain.

4. Trade Turnover & FDI

11 World Investment Report 2018, United Nations Conference on Trade and Development (UNCTAD), 6 June 2018 available at:https://unctad.org/en/pages/PublicationWebflyer.aspx?publicationid=2130 accessed on: 25 September 2018

If we turn our attention to assess, from a qualitative perspective, what factors in the economy are most likely to be

affected by a potential integration of Iran within the Eurasian Economic Union, the main focus should be on imports and

exports (trade turnover) and flow of FDI. Trade between Iran and Kazakhstan, Belarus, Armenia and Kyrgyzstan is

relatively small. According to data from the Islamic Republic of Iran’s Customs Administration, for the Iranian fiscal

year 2017/2018 Iran’s non-oil trade with 9 CIS countries amounted to USD2.3 billion (this includes the above

mentioned countries)12. Considering that the trade turnover between Russia and Iran in 2016 (2016/2017 Iranian fiscal

year) amounted to USD2.2 billion, it is clear that Russia is by far and large Iran’s biggest trade partner among the CIS

countries. Moreover, when looking at data from the Federal Customs Service of the Russian Federation, we find that the

development of trade between Russia and Iran has a recurring pattern. Figures from the World Bank Database show that

Iran’s overall total trade turnover reached an all-time high in 2011 of USD250 billion. A similar high was recorded in

terms of trade turnover between Russia and Iran for that year, amounting to USD3.75 billion. While trade turnover stood

at USD3.75 billion in 2011, following the sanctions imposition, this figure dropped significantly, down to USD2.3

billion, which amounts to a 39% year on year decrease. Total trade turnover kept a steady decline the following three

years, reaching an all-time low of USD1.26 billion in 2015. However, the sanctions relief in 2016 resulted in a rebound

in trade between the two countries of about 75%, year on year, jumping to USD2.2 billion. In terms of percentage, trade

with Russia accounted for 4.2% of Iran’s total trade turnover in 2016, but Iran represents only 0.4% of Russia’s total

trade turnover (Edovina & Kriuchkova, 2017). Additionally, data from the Federal Customs Service indicates that Iran

has been running a massive trade deficit with Russia. In terms of trade composition; cars, machinery and metals made up

the vast majority of Russian exports to Iran, while most of Iran’s exports to Russia was foodstuff (Edovina &

Kriuchkova, 2017). If we compare the exports and imports; in 2011 Iran imported USD3.4 billion from Russia and

exported only USD0.35 billion but by 2016 Russian exports to Iran had decreased massively, totaling just USD1.9

billion, while imports from Iran had stayed rather stagnant, at USD0.3 billion. This clearly highlights the rippling effect

of the sanctions imposition on trade between Iran and Russia, a country that has not slapped sanctions against Iran.

As mentioned above, attracting FDI is essential to develop Iran’s economy. Investment flows between Russia and Iran

are characteristically one-sided, with the former providing for the latter. However, while there have been many top-level

meetings between representatives from both countries, few binding agreements have been materialized. This is

highlighted in the fact that ‘since 2012, Russia has signed 10 oil exploration and production agreements with Iran—more

12 Iran non-oil trade with CIS registers sharp decline, Financial Tribune, 2 July 2018, available at:https://financialtribune.com/articles/domestic-economy/89101/iran-non-oil-trade-with-cis-registers-sharp-decline accessed on: 27 September 2018

than any other country… but only one of the agreements have been binding’ (Vatanka & Mammadov, 2018). Investment

in non-oil/gas sector has been limited to a few smaller joint-projects such as the construction of the Bushehr and Sirik

power plants, as well as some railway developments (Kozhanov, 2018). In fact, if one looks at all the outlined

investment plans (such as the latest USD50 billion investment plan by Russia), there is a clear preference by the Russian

government and state-owned enterprises to invest in the oil and gas sector above others (Paraskova, 2018). Moreover,

the current oil for goods barter agreement that began in 2017, whereby Iran sells oil to Russia in exchange for goods

(mostly steel, wheat and oil products), sustains the above argument (Cagatay & Gurkan, 2017). However, Russia, like

other countries investing in Iran, has faced several obstacles mainly due to secondary sanctions. The close economic

relations Russian companies have with the West is highlighted by the fact that, following the announcement about the

sanctions re-imposition, Lukoil, Russia’s second-biggest company, declared that any future projects in Iran will be

placed on hold, while at the same time, the Russian-Middle Eastern Export Center decided to pull out of a USD180

million infrastructure project (Kozhanov, 2018)13. In other words, economic relations with the West for Russian

companies still outweigh any potential new projects with Iran. Finding banking channels for dollar transactions with

Iran has been another big impediment experienced not only by Russian companies, but by Chinese and Indian

companies, which has led to delayed project implementations (Faucon, 2018). While there has been talk about using

non-dollar denominated currencies, in the case of oil-producing countries such as Russia and Iran, the underlying

problem is that both currencies are susceptible to oil price shocks. In particular in the case of the rial, the combination of

oil shock and sanctions makes it a very weak currency.

Thus, it is highly unlikely that a membership in the EAEU for Iran will provide sufficient FDI nor increase in trade

turnover that Iran’s economy badly needs. This is not to say that there will be no benefits at all. The elimination of

internal tariffs could potentially boost trade turnover and Iranian companies could gain access to smaller lucrative

sectors, such as agriculture. However, the underlying issue is that benefits due to integration will be limited and

minimal. It is highly improbable that the biggest domestic companies in the EAEU countries will risk being sanctioned

for doing business with Iran. In addition, the market monopoly by, and, favorable terms, for both state-owned and

private companies affiliated with government and religious institutions makes competition on the Iranian market

difficult. As such, it is more likely that economic cooperation will continue through smaller case-by-case bilateral

agreements on joint-projects, particularly between Russia and Iran.

13 Russian Company Pulls out of Major Iran infrastructure Project after U.S Sanctions, Russia Business Today, 11 May 2018, available at:https://russiabusinesstoday.com/economy/russian-company-pulls-out-of-major-iran-infrastructure-project-after-u-s-sanctions/ accessed: 28 September 2018

5. Econometric Analysis

In order to have a better understanding of the potential effects of EAEU accession to the economy of Iran, we first need

to analyze how it has affected the current members of the EAEU. Ideally, being a member of the union would hike the

trade turnover with rest of the member states. Considering the fact that majority of trade in EAEU either starts or ends

with Russia, as well as the unavailability of reliable quarterly data on external trade of all current member states, we will

use the trade turnover between the member states and the Russian Federation only. On the other hand, constructing an

econometric model for each of the 4 other countries (naturally excluding Russian Federation) would be time consuming

and ineffective, thus, we have decided to go with Belarus and Kazakhstan because (a) their economies are more

significant than Kyrgyzstan or Armenia and (b) Kazakhstan’s economy is particularly more comparable to the economy

of Iran.

Apart from the dummy variable accounting for EAEU membership, we also included variables such as exchange rate of

the Russian Ruble against the appropriate country’s currency, Gross Domestic Product (representing purchasing power),

and price of oil into our regression in order to get more reliable estimates.

5.1. Data

On the table below, you may see the descriptive statistics of variables used in the models for Belarus and Kazakhstan

and their respective plots. The variables are collected quarterly, ranging from 2007 to 2016 (in the case of Kazakhstan,

the variables range from 2007 to 2015 due to the unavailability of up to date data). Sources of are also given below the

tables.

[Table 1 Here]

[Figure 5 Here]

[Table 2 Here]

[Figure 6 Here]

5.2. Unit root tests

Stationarity in time series is a basic assumption which indicates that mean and variance of the data does not vary over

time. If the set is non-stationary, then the series will move to a new “path” after a shock, which makes it much harder to

develop a theory for the long run using the same variables. A series can also be trend stationary or difference stationary.

For the purpose of checking stationary, we have used Augmented Dickey-Fuller (ADF) test where the null hypothesis is

that time series has unit root, which means stationary is not present. The maximum lag length is set to 9 with automatic

selection using Schwarz Info Criterion.

[Table 3 Here]

[Table 4 Here]

The results indicate that in the case of Kazakhstan all variables are stationary in first difference without trend. Variables

of exchange and turnover are also stationary at I(0). When it comes to Belarus, only gdpbelarus is not stationary at first

difference without trend. For that reason, using OLS regression is not appropriate, since these variables do not behave

like constants and vary over time. Bearing this in mind, we will use the Autoregressive Distributed Lag (ARDL) model,

allowing us to use our current data set with different orders of integration.

5.3. Autoregressive Distributed Lag model

The ARDL model will be useful in this case because it will allow us to see the long-term relationships regardless of

variables being I(0) or I(1). It is also more desirable than Johansen cointegration techniques in this case because it can

estimate relationship with a smaller sample size.

Below you can see the Model Selection Table for both countries. In each of the tables, the best 5 models are given with

their appropriate Akaike, Schwarz and Hannan-Quinn information criterions.

[Table 5 Here]

[Table 6 Here]

Before proceeding to the regression, we need to run several tests on our data and the model in order to ensure that the

estimations are reliable.

[Table 7 Here]

[Table 8 here]

The null hypothesis of Breusch-Godfrey LM test is that there is no serial correlation. When the p value is above 0.10 in

both of the cases with 1 and 4 lags, it indicates that we cannot reject the null hypothesis. Thus, we conclude that the

residuals in both of the models are not serially correlated.

For checking normality, we have used Jarque – Bera test, which decides if a sample data is normally distributed by

looking at its skewness and kurtosis. Null hypothesis of the test is “normally distributed”. With p values of 0.6006 and

0.5997, residuals of Belarus and Kazakhstan regression models are normally distributed.

We have checked the Heteroskedacity assumption with 4 tests: Breusch – Pagan – Godfrey, Harvey, Glejser and ARCH

tests. These tests have the null hypothesis of homoskedasity. All tests gave us p values of above .10, indicating that

heteroskedacity is not present.

Finally, we checked for functional form of misspecification using Ramsey RESET test, which adds polynomials (or

higher degree) and interaction terms and checks if they are statistically significant when combined. If the results indicate

a significant relationship, then a functional misspecification has occurred, and the model should be re-generated.

However, in our cases the p values obtained from the tests are 0.8365 and 0.4610, respectively, showing that there is no

form of misspecification in the models.

Results of the ARDL models are given in the tables below:

[Table 9 Here]

[Table 10 Here]

There is only one step left before moving on to the interpretation, and that is the test for cointegration, in other words,

presence of long run relationship.

5.4. Bound Testing

The results of the bound tests are given on the table below along with I0 and I1 bound values according to each

significance level. The f value of Kazakhstan is larger than the I1 bound of 1%, which indicates presence of long run

relationship. On the other hand, the f value of Belarus falls within the boundaries of I0 and I1 at 5% level of

significance, meaning that we cannot conclude whether there is or there is not a long run relationship.

[Table 11 Here]

[Table 12 Here]

[Table 13 Here]

[Table 14 Here]

The results of the bound tests show cointegration for the Kazakhstan model while the model of Belarus falls within the

inconclusive zone. In both of the cases, the coefficient of variable of interest, namely the dummy variable of eaeu is

insignificant with p values well over the acceptable levels. These results indicate that so far, the accession into the

Eurasian Economic Union did not lead to increasing trade between Russian Federation and Kazakhstan/Belarus.

6. Conclusion

In this paper, we have analyzed the potential scenario of Iran integrating into the Eurasian Economic Union. From the

examples of other regional integrations, we know that similar levels of macroeconomic development, common culture

and institutions, high levels of mutual trade are the basic factors that facilitate such a process. Most of these factors can

be observed for the current members of the EAEU, who, after all, used to be part of one single economic and political

entity. In the case of Iran, whose economy is highly centralized and with institutions that vastly differ to the former, the

process of integration becomes much more challenging.

Years of US sanctions, a policy of self-reliance and dependency on the oil/gas sector has made Iran one of the most

isolated economies in the world. For those reasons, financial relations with the other member states is almost impossible

for Iran, as its banks are not connected to global banking networks. We prove that there is a positive correlation between

the sanctions application and volume of trade turnover, in our case between Russia and Iran. Thus, even if Iran joins the

EAEU, the indirect effects of the US sanctions would limit the potential benefits of accession, particularly in trade

turnover and FDI inflows.

The Autoregressive Distributed Lag model estimations showed us in terms of trade turnover with the Russian

Federation, that there has been no significant benefit from joining the EAEU for Kazakhstan and Belarus. Considering

the marginal size of the other member states’ economies, it is unlikely that Iran’s total trade turnover will see any

considerable boost either. The elimination of trade tariffs that is required for member states could in theory expand trade

among the countries. However, based on our above mentioned arguments, a significant expansion in trade turnover

remains highly unlikely.

The necessity of FDI to expand the deteriorating industries of Iran cannot be provided by the current member states due

to the fact that their economies are relatively small and do not possess the financial resources. In theory, the largest

economy in the union, the Russian Federation could be the sole provider of investment for Iran. However, according to

our qualitative analysis, Russian investment during the last few years has been meager and mostly focused on the oil and

gas sector. We also find that the U.S. sanctions have in fact curbed investment by Russian companies. Thus, it is clear

that the cycle of investment inflows is very much correlated with the presence and degree of sanctions.

Looking again at trade, Iran has been ranked last in terms of trade tariffs among 140 evaluated countries. Should the

country join the EAEU it would have to significantly adjust its trade tariffs and reduce barriers in accordance with the

common external tariff. Moreover, there are several other institutional obstacles that makes the Iranian market

unattractive and demotivates potential investors. The dominance of political and religious institutions who wish to

protect their local monopolies by countering liberalization policies is another factor hampering the integration potential

of the country. In other words, a process of trade liberalization would not be in the interest of these influential actors, as

it would undermine their dominance in the economy. As such, even if we disregard the sanctions, these internal

obstacles make it difficult for foreign companies to enter the domestic market.

We conclude that the process of integration for Iran into the EAEU will be impossible without large-scale reform in the

economic, political and social sphere. In a scenario where such reforms are fully implemented one can expect a greater

degree of benefits, yet even so, this integration would only yield marginal gains from an economic perspective.

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Table 1: Descriptive Statistics of Variables of Belarus

Variable Mean Median Std. Dev Observations

exchange 27.65229 34.60843 14.83428 40gdpbelarus 4475.343 4582.511 621.8753 40gdprussia 13083144 13131672 1844964 40oilprice 83.89473 82.74205 26.82979 40turnover 7506.755 7381.150 1605.782 40“Dynamics of the official exchange rates” - Central Bank of Russia: http://www .cbr.ru/eng/currency_base/dynamics/ , “Gross Domestic Product and Components” – IMF Data: http://data .imf.org/regular.aspx?key=61545852 , “World bank Commodity Price Data (The Pink Sheet)” – World Bank: http://www .worldbank.org/en/research/commodity- markets , “Внешняя торговля Российской Федерации по основным странам” - Federal Customs Service of Russia: http://customs .ru/index.php?option=com_newsfts&view=category&id=125&Itemid=1976

Table 2: Descriptive Statistics of Variables of KazakhstanVariable Mean Median Std. Dev Observations

exchange 21.73705 20.64257 3.106315 36gdpkazakhstan 5671425 5094554 1735869 36gdprussia 13079235 13131672 1918622 36oilprice 88.32221 92.80844 24.42467 36turnover 4652.499 4478.850 1050.033 36“Dynamics of the official exchange rates” - Central Bank of Russia: http://www .cbr.ru/eng/currency_base/dynamics/ , “Gross Domestic Product and Components” – IMF Data: http://data .imf.org/regular.aspx?key=61545852 , “World bank Commodity Price Data (The Pink Sheet)” – World Bank: http://www .worldbank.org/en/research/commodity- markets , “Внешняя торговля Российской Федерации по основным странам” - Federal Customs Service of Russia: http://customs .ru/index.php?option=com_newsfts&view=category&id=125&Itemid=1976

Table 3: Unit Root Test results for BelarusVariables Intercept Intercept with Trend

I (0) I (1) I (0) I(1)eaeu - - - -exchange -1.8979 -6.776*** -2.332 -6.74***gdpbelarus -1.654 -2.457 -2.207 -2.611gdprussia -1.927 -3.078** -2.851 -1.812oilprice -1.588 -4.639*** -1.819 -4.647***turnover -2.485 -5.901*** -2.555 -2.701

Note: * means statistically significant at 10%, ** statistically significant at 5% and *** statistically significant at 1%. The variable of eaeu is not subject to the test due to the fact that dummy variables are stationary by nature.

Table 4: Unit Root Test results for KazakhstanVariables Intercept Intercept with Trend

I (0) I (1) I (0) I(1)eaeu - - - -exchange -3.27** -4.71*** -3.63 -4.72***gdpkazakh 2.29 -3.07** -0.24 -14.73***gdprussia -1.86 -2.79* -3.12 -1.96oilprice -2.31 -4.30*** -1.35 -4.42***turnover -2.943* -6.407*** -2.775 -5.528***

Note: * means statistically significant at 10%, ** statistically significant at 5% and *** statistically significant at 1%. The variable of eaeu is not subject to the test due to the fact that dummy variables are stationary by nature. The reason for different ADF test statistic in two cases is the difference in observations, as the data set for Kazakhstan stops at 2015Q4.

Table 5: Model selection Criteria for BelarusModel AIC* BIC HQ Specification

1 -3.682275 -2.494636 -3.267757 ARDL (4, 4, 4, 4, 4)

26 -3.651154 -2.507502 -3.251989 ARDL (4, 4, 3, 4, 4)

11 -3.610569 -2.510903 -3.226756 ARDL (4, 4, 4, 2, 4)

636 -3.596558 -2.540879 -3.228098 ARDL (3, 4, 4, 2, 4)

6 -3.560629 -2.416976 -3.161463 ARDL (4, 4, 4, 3, 4)

Table 6: Model selection Criteria for KazakhstanModel AIC* BIC HQ Specification

2132 -1.875878 -0.959793 -1.572222 ARDL (1, 2, 4, 3, 3)

2127 -1.855236 -0.893347 -1.536397 ARDL (1, 2, 4, 4, 3)

2252 -1.849968 -0.933883 -1.546311 ARDL (1, 1, 4, 4, 3)

1882 -1.837125 -0.829431 -1.503103 ARDL (1, 4, 4, 3, 3)

2016 -1.827649 -0.911564 -1.523993 ARDL (1, 3, 4, 1, 4)

Table 7: Econometric Tests for Belarus ModelTests: Belarus F-Statistic P-ValueSerial Correlation: Breusch-Godfrey LM Test (1 lag) 1.784694 0.2183Serial Correlation: Breusch-Godfrey LM Test (4 lags) 1.295947 0.3839Normality: Jarque-Bera Test 1.686293 0.4304Heteroskedacity: Breusch – Pagan – Godfrey Test 0.408055 0.9642Heteroskedacity: Harvey Test 3.026560 0.0432Heteroskedacity: Glejser Test 0.610031 0.8441Heteroskedacity: ARCH Test (1 lag) 0.006285 0.9373Functional Misspecification: Ramsey RESET Test (fitted values = 1)

1.364893 0.2094

Table 8: Econometric Tests for Kazakhstan ModelTests: Kazakhstan F-Statistic P-ValueSerial Correlation: Breusch-Godfrey LM Test (1 lag) 1.793993 0.2075Serial Correlation: Breusch-Godfrey LM Test (4 lags) 0.775254 0.5711Normality: Jarque-Bera Test 0.961929 0.6182Heteroskedacity: Breusch – Pagan – Godfrey Test 0.490425 0.9201Heteroskedacity: Harvey Test 2.128676 0.0913Heteroskedacity: Glejser Test 0.794869 0.6829Heteroskedacity: ARCH Test (1 lag) 0.355185 0.5558Functional Misspecification: Ramsey RESET Test (fitted values = 1)

0.581235 0.4619

Table 9: Model for turnover of Belarus with Russian FederationVariable Coefficient Std. Error t-Statistic Probability

log(turnover(-1)) 0.054075 0.268982 0.201037 0.8451log(turnover(-2)) 0.041148 0.182732 0.225183 0.8269log(turnover(-3)) -0.590749 0.212958 -2.774018 0.0216log(turnover(-4)) 0.439416 0.307869 1.427283 0.1873log(exchange) 0.200123 0.066705 3.000133 0.0150log(exchange(-1)) -0.206915 0.083992 -2.463509 0.0360log(exchange(-2)) 0.186583 0.136064 1.371286 0.2035log(exchange(-3)) -0.038895 0.095129 -0.408860 0.6922log(exchange(-4)) -0.134643 0.080436 -1.673918 0.1285log(gdpbelarus) 0.162232 0.672856 0.241109 0.8149log(gdpbelarus(-1)) -0.004777 0.251526 -0.018991 0.9853log(gdpbelarus(-2)) -1.084351 0.261424 -4.147872 0.0025log(gdpbelarus(-3)) 0.213374 0.326770 0.652980 0.5301log(gdpbelarus(-4)) 0.372681 0.412845 0.902714 0.3902log(gdprussia) 0.414562 0.395911 1.047108 0.3224log(gdprussia(-1)) 0.529916 0.357501 1.482277 0.1724log(gdprussia(-2)) 1.257549 0.285419 4.405968 0.0017log(gdprussia(-3)) -0.087485 0.404533 -0.216261 0.8336log(gdprussia(-4)) -0.389737 0.295048 -1.320926 0.2191log(oilprice) 0.485813 0.072779 6.675178 0.0001log(oilprice(-1)) -0.154282 0.169559 -0.909897 0.3866log(oilprice(-2)) -0.202309 0.133026 -1.520821 0.1626log(oilprice(-3)) 0.331066 0.165109 2.005139 0.0759log(oilprice(-4)) -0.365749 0.194009 -1.885220 0.0920eaeu 0.063913 0.049931 1.280017 0.2325c -16.16518 10.01552 -1.614013 0.1410@trend -0.012480 0.007090 -1.760253 0.1122

Adj. R-squared 0.972551Note: * means statistically significant at 10%, ** statistically significant at 5% and *** statistically significant at 1%.

Table 10: Model for turnover of Kazakhstan with Russian FederationVariable Coefficient Std. Error t-Statistic Probabilitylog(turnover(-1)) -0.271726 0.227549 -1.194142 0.2555log(exchange) -0.587831 0.421527 -1.394528 0.1884log(exchange(-1)) 1.915681 0.691103 2.771918 0.0169log(exchange(-2)) -0.608348 0.499810 -1.217159 0.2469log(gdpkazakh) 0.131994 0.365448 0.361183 0.7242log(gdpkazakh(-1)) -0.688778 0.341277 -2.018235 0.0665log(gdpkazakh(-2)) -1.037126 0.358261 -2.894886 0.0135log(gdpkazakh(-3)) -0.343874 0.219285 -1.568155 0.1428log(gdpkazakh(-4)) 0.493491 0.252308 1.955906 0.0742log(gdprussia) -1.765923 0.917675 -1.924346 0.0783log(gdprussia(-1)) -0.226252 0.485284 -0.466227 0.6494log(gdprussia(-2)) 1.107876 0.519815 2.131290 0.0544log(gdprussia(-3)) 1.165078 0.472898 2.463695 0.0298log(oilprice) 0.117305 0.169965 0.690175 0.5032log(oilprice(-1)) 1.757099 0.451144 3.894765 0.0021log(oilprice(-2)) -0.638218 0.375497 -1.699664 0.1149log(oilprice(-3)) -0.763670 0.264378 -2.888556 0.0136eaeu -0.269103 0.298487 -0.901557 0.3850c 23.41743 21.05256 1.112331 0.2878@trend 0.043822 0.023290 1.881582 0.0844

Adj. R-squared 0.882596Note: * means statistically significant at 10%, ** statistically significant at 5% and *** statistically significant at 1%.

Table 11: ARDL Bounds Test resultsEquation F statistic OutcomeBelarus 3.479068 InconclusiveKazakhstan 8.530109 Cointegrated

Table 12: Critical Value BoundsSignificance Level (%) I0 Bound I1 Bound

10 3.03 4.065 3.47 4.571 4.4 5.72

Table 13: Long Run Coefficients of BelarusVariable Coefficient Std. Error t-Statistic Probability

log(exchange) 0.340977 0.798844 0.426838 0.6795

log(gdpbelarus) 21.576676 32.319134 0.667613 0.5211log(gdprussia) 0.095459 3.263551 0.029250 0.9773log(oilprice) 0.095459 0.700964 0.136182 0.8947eaeu 0.019092 0.191787 0.099547 0.9229c 0.019092 37.066502 0.000515 0.9996@trend 0.019092 0.032769 0.582613 0.5745

Table 14: Long Run Coefficients of KazakhstanVariable Coefficient Std. Error t-Statistic Probability

log(exchange) 0.565768 0.782362 0.723154 0.4834

log(gdpkazakh) -1.135695 0.795308 -1.427993 0.1788

log(gdprussia) 0.220785 0.701314 0.314816 0.7583

log(oilprice) 0.371555 0.345865 1.074278 0.3038eaeu -0.211605 0.241216 -0.877243 0.3976c 18.413891 17.080780 1.078047 0.3022@trend 0.034459 0.020105 1.713944 0.1122

Figure 1: Composition of Iranian GDP over years

2011 Q2

2011 Q4

2012 Q2

2012 Q4

2013 Q2

2013 Q4

2014 Q2

2014 Q4

2015 Q2

2015 Q4

2016 Q2

2016 Q4

2017 Q2

2017 Q4

0%10%20%30%40%50%60%70%80%90%

100%

Agriculture Oil and Gas ManufacturingServices Construction Other

Source: Statistical Center of Iran

Figure 2: Oil and Non-oil exports of Iran (million dollars)

2010/2011

2011/2012

2012/2013

2013/2014

2014/2015

2015/2016

2016/2017

2017/2018

0 10,000 20,000 30,000 40,000 50,000 60,000 70,000 80,000 90,000100,000

Non-oil Exports Oil Exports

Source: Central Bank of Iran

Figure 3: Oil and Non-oil GDP of Iran

2010/2011 2011/2012 2012/2013 2013/2014 2014/2015 2015/2016 2016/2017 2017/20180.0

10.0

20.0

30.0

40.0

50.0

60.0

Oil sector Non-oil

Source: Central Bank of Iran

Figure 4: Largest trade partners of Iran

19.50%

16.80%

16.30%

8.10%

7.50%

7.40%

ChinaEU28IndiaSouth KoreaTurkeyJapan

Source: European Commission, Directorate-General for Trade

Figure 5: Time series of variables of Belarus

Figure 6: Time series of variables of Kazakhstan