This policy brief addresses risks tied to Russian business ownership in Georgia. The concentration of this ownership in critical sectors such as electricity and communications makes Georgia vulnerable to risks of political influence, corruption, economic manipulation, espionage, sabotage, and sanctions evasion. To minimize these risks, it is recommended to establish a Foreign Direct Investment (FDI) screening mechanism for Russia-originating investments, acknowledge the risks in national security documents, and implement a critical infrastructure reform.
Russia exerts substantial influence over Georgia. First and foremost, Russia has annexed 20 percent of Georgia’s internationally recognized territories of Abkhazia and South Ossetia. Further, it employs a variety of hybrid methods to disrupt the Georgian society including disinformation, support for pro-Russian parties and media, trade restrictions, transportation blockades, sabotage incidents, and countless more. These tactics aim to hinder Georgia’s development, weaken the country’s statehood, and negatively affect pro-Western public sentiments (Seskuria, 2021 and Kavtaradze, 2023).
Factors that may also increase Georgia’s economic dependency on Russia concern trade relationships, remittances, increased economic activity driven by the most recent influx of Russian migrants, and private business ownership by Russian entities or citizens (Babych, 2023 and Transparency International Georgia, 2023). This policy brief assesses and systematizes the risks associated with Russian private business ownership in Georgia.
Sectoral Overview of Russian Business Ovnership
Russian business ownership is significant in Georgia. Recent research from the Institute for Development of Freedom of Information (IDFI) has addressed Russian capital accumulation across eight sectors of the Georgian economy: electricity, oil and gas, communications, banking, mining and mineral waters, construction, tourism, and transportation. Of the eight sectors considered by IDFI, Russian business ownership is most visible in Georgia’s electricity sector, followed by oil and natural gas, communications, and mining and mineral waters industries. In the remaining four sectors considered by IDFI, a low to non-existent level of influence was observed (IDFI, 2023).
Figure 1. Overview of Russian Ownership in the Georgian Economy as of June 2023.
There are several reasons for concern regarding the concentration and distribution of Russian business ownership in the Georgian economy.
First, it is crucial to keep Russia’s history as a hostile state actor in mind. Foreign business ownership is not a threat in itself; However, it may pose a threat if businesses are under control or influence of a state that is hostile to the country in question (see Larson and Marchik, 2006). Business ownership has been a powerful tool for the Kremlin, allowing Russia to influence various countries and raising concerns that such type of foreign ownership might negatively affect national security of the host country (Conley et al., 2016). Similar concerns have become imperative amidst Russia’s full-scale war in Ukraine (as, for instance, reflected in Guidance of the European Commission to member states concerning Russian foreign acquisitions).
Further, Russian business ownership in Georgia is particularly threatening due to the ownership concentration within sectors of critical significance for the overall security and economic resilience of the country. While there is no definition of critical infrastructure or related sectors in Georgia, at least two sectors (energy and communications) correspond to critical sectors, according to international standards (see for instance the list of critical infrastructure sectors for the European Union, Germany, Canada and Australia). Such sectors are inherently susceptible to a range of internal and external threats (a description of threats related to critical infrastructure can be found here). Intentional disruptions to critical infrastructure operations might initiate a chain reaction and paralyze the supply of essential services. This can, in turn, trigger major threats to the social, economic, and ecological security and the defense capacity of a state.
Georgia’s Exposure to Risks
Identifying and assessing the specific dimensions of Georgia’s exposure to risks related to Russian business ownership provides a useful foundation for designing policy responses. This brief identifies six distinct threats in this regard.
Russia’s business and political interests are closely intertwined, making it challenging to differentiate their respective motives. This interconnectedness can act as a channel for exerting political influence in Georgia. Russians that have ownership stakes in Georgian industries (e.g. within electricity, communications, oil and gas, mining and mineral waters) have political ties with the Russian ruling elite facing Western sanctions, or are facing sanctions themselves. For instance, Mikhail Fridman, who owns up to 50 percent of the mineral water company IDS Borjomi, is sanctioned for supporting Russia’s war in Ukraine. Such interlacing raises concerns about indirect Russian influence in Georgia, potentially undermining Georgia’s Western aspirations.
Export of Corrupt Practices
The presence of notable Russian businesses in Georgia poses a significant threat in terms of it nurturing corrupt practices. Concerns include “revolving door” incidents (movement of upper-level public officials into high-level private-sector jobs, or vice versa), tax evasion, and exploitation of the public procurement system. For instance, Transparency International Georgia (2023) identified a “revolving door” incident concerning the Russian company Inter RAO Georgia LLC, involved in electricity trading, and its regulator, the Georgian state-owned Electricity Market Operator JSC (ESCO). One day after Inter RAO Georgia LLC was registered, the director of ESCO took a managerial position within Inter RAO Georgia LLC. Furthermore, tax evasion inquiries involving Russian-owned companies have been documented in the region, particularly in Armenia, further highlighting corruption risks. We argue that such corrupt practices might harm the business environment and deter future international investments.
A heavy concentration of foreign ownership in critical sectors like energy and telecommunications, also poses a risk of manipulation of economic instruments such as prices. The significant Russian ownership in Armenia’s gas distribution network exemplifies this threat. In fact, Russia utilized a price manipulation strategy for gas prices when Armenia declared its EU aspirations. Prices were then reduced after Armenia joined the Eurasian Economic Union (Terzyan, 2018).
Russian-owned businesses within Georgia’s critical sectors also pose espionage risks, including economic and cyber espionage. Owners of such businesses may transfer sensitive information to Russian intelligence agencies, potentially undermining critical infrastructure operations. As an example, in 2022, a Swedish business owner in electronic trading and former Russian resident, was indicted with transferring secret economic information to Russia. Russian cyber-espionage is also known to be used for worldwide disinformation campaigns impacting public opinion and election results, compromising democratic processes.
The presence of Russian-owned businesses in Georgia raises the risk of sabotage and incapacitation of critical assets. Russia has a history of using sabotage to harm other countries, such as when they disrupted Georgia’s energy supply in 2006 and the recent Kakhovka Dam destruction in Ukraine (which had far-reaching consequences, incurring environmental damages, and posing a threat to nuclear plants). These incidents demonstrate the risk of cascading effects, potentially affecting power supply, businesses, and locations strategically important to Georgia’s security.
Sanctions and Sanction Evasion
Russian-owned businesses in Georgia face risks due to Western sanctions as they could be targeted by sanctions or used to evade them. Recent cases, like with IDS Borjomi (as previously outlined) and VTB Bank Georgia – companies affected by Western sanctions given their Russian connections – highlight Georgia’s economic vulnerability in this regard. Industries where these businesses operate play a significant role in Georgia’s economy and job market, and instabilities within such sectors could entail social and political concerns. There’s also a risk that these businesses could help Russia bypass sanctions and gain access to sensitive goods and technologies, going against Georgia’s support for international sanctions against Russia. It is crucial to prevent such sanctions-associated risks for the Georgian economy.
Assessing the Risks
To operationalize the above detailed risks, we conducted interviews with Georgian field experts within security, economics, and energy. The risk assessment highlights political influence through Russian ownership in Georgian businesses as the foremost concern, followed by risks of corruption, risks related to sanctions, espionage, economic manipulation, and sabotage. We asked the experts to assess the severity level for each identified risk and notably, all identified risks carry a high severity level.
Considering the concerns detailed in the previous sections, we argue that Russia poses a threat in the Georgian context. Given the scale and concentration of Russian ownership within critical sectors and infrastructure, a dedicated policy regime might be required to improve regulation and minimize the associated risks. Three recommendations could be efficient in this regard, as outlined below.
Study the Impact of Adopting a Foreign Direct Investment Screening Mechanism
To effectively address ownership-related threats, it’s essential to modify existing investment policies. One approach is to introduce a FDI screening mechanism with specific functionalities. Several jurisdictions implement mechanisms with similar features (see a recent report by UNCTAD for further details). Usually, such mechanisms target FDI’s that have security implications. A dedicated screening authority overviews investment that might be of concern for national security and after assessment, an investment might be approved or suspended. In Georgia, a key consideration for designing such tool includes whether it should selectively target investments from countries like Russia or apply to all incoming FDI. Additionally, there’s a choice between screening all investments or focusing on those concerning critical sectors and infrastructure. Evaluating the investment volume, possibly screening only FDI’s exceeding a predefined monetary value, is also a vital aspect to consider. However, it’s important to acknowledge that FDI screening mechanisms are costly. Therefore, this brief suggests a thorough cost and benefit analysis prior to implementing a FDI screening regime in Georgia.
Consider Russian Ownership-related Threats in the National Security Documents
Several national-level documents address security policy in Georgia, with the National Security Concept – outlining security directions – being a foundational one. Currently, these concepts do not specifically address Russian business ownership-related threats. When designing an FDI screening mechanism, however, acknowledging various risks related to Russian business ownership must be aligned with fundamental national security documents.
Foster the Adoption of a Critical Infrastructural Reform
To successfully implement a FDI screening mechanism unified, nationwide agreement on the legal foundations for identifying and safeguarding critical infrastructure is needed. The current concept for critical infrastructure reform in Georgia envisages a definition of critical infrastructure and an implementation of an FDI screening mechanism. We therefore recommend implementing this reform in the country.
This policy brief has identified six distinct risks related to Russian business ownership in several sectors of the Georgian economy, such as energy, communications, oil and natural gas, and mining and mineral waters. Even though Georgia does not have a unified definition of critical infrastructure, assets concentrated in these sectors are regarded as critical according to international standards. Considering Russia’s track record of hostility and bearing in mind threats related to foreign business ownership by malign states, this brief suggests regulating Russian business ownership in Georgia by introducing a FDI screening instrument. To operationalize this recommendation, it is further recommended to consider Russian business ownership-related threats in Georgia’s fundamental security documents and to foster critical infrastructural reform in the country.
- Babych, Y. (2023). The Georgian Economy after One Year of Russia’s War in Ukraine: Trends and Risks. ISET Policy Institute. https://iset-pi.ge/storage/media/other/2023-03-13/6982ed30-c1ad-11ed-896a-efa0ef78cee7.pdff
- Conley, H. A., Mina, J., Stefanov, R., & Vladimirov, M. (2016). The Kremlin Playbook: Understanding Russian Influence in Central and Eastern Europe. Center for Strategic and International Studies. https://csis-website-prod.s3.amazonaws.com/s3fs-public/publication/1601017_Conley_KremlinPlaybook_Web.pdf
- Institute for Development of Freedom of Information (IDFI). (2023, June). Russian Capital and Russian Connections in Georgian Business. https://idfi.ge/public/upload/Analysis/Russian%20capital%20and%20Russian%
- Kavtaradze, N. (2023). Hybrid Warfare and Russia’s Modern Warfare. Georgian Foundation for Strategic and International Studies (GFSIS). https://gfsis.org.ge/files/library/opinion-papers/201-expert-opinion-eng.pdf
- Larson, A. P., & Marchik, D. M. (2006). Foreign Investment and National Security. ETH Zurich. https://www.files.ethz.ch/isn/20513/2006-07_ForeignInvestmentCSR.pdf
- Seskuria, N. (2021). Russia’s “Hybrid Agression” against Georgia: The Use of Local and External Tools. Center for Strategic and International Studies. https://csis-website-prod.s3.amazonaws.com/s3fs-public/publication/210921_Seskuria_Russia_Georgia.pdf?VersionId=__d9rw2TtaDba9xaHASf6lCEmJ.oqhA7
- Terzyan, A. (2018). The anatomy of Russia’s grip on Armenia: Bound to Persist? https://www.econstor.eu/bitstream/10419/198543/1/ceswp-v10-i2-p234-250.pdf
- Transparency International Georgia. (2023). Georgia’s Economic Dependence on Russia: Impact of the Russia-Ukraine War. Transparency International Georgia. https://transparency.ge/en/post/georgias-economic-dependence-russia-impact-russia-ukraine-war-1
Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.
In economic literature the effect of minimum wage on the labour market and its relevance as an anti-poverty, equality-enhancing policy tool, is a matter of vigorous debate. The focus of this policy brief is a hypothetical effect on poverty rates, particularly among women, following an increase in the minimum wage in Georgia. A simulation exercise (Babych et al., 2022) by the ISET-PI research team shows that, in Georgia, a potential increase in the minimum wage is likely to result in an overall positive albeit small reduction in poverty rates in general. At the same time, women are likely to gain more from such minimum wage policy than men. The findings are consistent with the literature claiming that a minimum wage increase alone may not result in meaningful poverty reduction. Any minimum wage increase should thus be enhanced by other policies such as training programs increasing labor force participation among women.
Many countries around the world have enacted minimum wage laws. According to the International Labour Organization (ILO) “Minimum wages can be one element of a policy to overcome poverty and reduce inequality, including those between men and women” (ILO, 2023). In economic literature, the minimum wage debate has been particularly acute, with pros and cons of the minimum wage increases, their effect on the labor market, and their relevance as an anti-poverty and equality-enhancing policy tool fiercely contested in empirical studies and simulation studies. In this policy brief, we focus on the effect of a minimum wage increase in Georgia on poverty rates, and in particular poverty rates among women.
Minimum Wage Effects
According to the European Commission (2020) a number of benefits is associated with the introduction of minimum wage. These benefits include a reduction in in-work poverty, wage inequality and the gender pay gap, among others.
International evidence, however, cautions against considering an increase in minimum wage as the silver bullet to end poverty. A 2019 report by the International Labour Organization (ILO, 2019) shows that the incidence of poverty among the working poor is comparable to the incidence of poverty among individuals outside of the labor market. Therefore, even if an increase in minimum wages would lift all working poor out of poverty, a substantial number of poor would remain.
Moreover, minimum wage can have a potential adverse effect on employment of the most vulnerable by deterring firms from hiring low-wage, low-skilled labor (Neumark, 2018). The adverse employment effect will be stronger if current wages correspond more closely to the real productivity of labor. In such scenario companies would lose by retaining low-productivity workers and, likely respond to the increase in minimum wage by laying off workers, resulting in the loss of wages, rather than in their increase. On the other hand, if salaries are lower than the real productivity of the less productive workers, companies might still be able to profit from employing them and will not be forced to lay them off, resulting in a wage increase for low-wage workers.
Whether – and to what extent – the introduction of a minimum wage reduces poverty and/or assists low-income households then depends on how many individuals are going to lose their jobs, how many workers will maintain their jobs and receive a higher wage, and where these winners and losers are positioned along the distribution of family incomes.
With regard to employment effects, the results are not perfectly homogeneous. On the one hand, a large body of evidence suggests that minimum wages do lower the number of jobs accessible to low-skill employees (Sabia, Burkhauser and Hansen, 2012; Sotomayor, 2021; Neumark, 2018) On the other hand, some scholars argue that once the study design is changed to take into account the non-random distribution of minimum wage policies in different parts of the country in question, the “disemployment effect” of minimum wage policies (considering the example of United States) largely disappear (Allegretto et al., 2013; Dube et al., 2010).
With regards to poverty, a number of studies look at minimum wage as an anti-poverty policy tool for developing countries and consider its effectiveness in reducing poverty and/or inequality. For example, a study by Sotomayor (2021) suggests that poverty and income inequality in Brazil decreased by 2.8 and 2.4 percent respectively within three months of a minimum wage increase. Effects diminished with time, particularly for bottom-sensitive distribution measures, a process that is consistent with resulting job losses being more frequent among poorer households. The fact that the subsequent yearly increase in the minimum wage in Brazil resulted in a renewed drop in poverty and inequality shows that possible unemployment costs might be outweighed by benefits in the form of higher pay among working persons and – potentially – by positive spillover effects such as increased overall consumption.
Minimum Wage and Female Poverty
As in the case of poverty in general, there is some discrepancy in the literature on whether a minimum wage increase would help reduce poverty among women. Single mothers have been the focus of research in this regard since they are typically the most vulnerable low-wage workers, likely to be hurt by the loss of employment following an increase/ introduction of a minimum wage. Burkhauser and Sabia (2007) argue that the minimum wage increases in the U.S. (1988-2003) did not have any effect on the overall poverty rates, on the poverty rates among the working poor, or on poverty among single mothers. They argue that an increase in Earned Income Tax Credit (EITC), which provides a wage subsidy to workers depending on income level, tax filing status, and the number of children, would have a higher impact on poverty, in particular among single mothers.
In the meantime, Neumark and Wascher (2011) find that EITC and minimum wage reinforce each other’s positive effect for single women with children (boosting both employment and earnings), but negatively affects childless single women and minority men. Another study on the U.S. (Sabia, 2008) looked at the effect of minimum wage increases on the welfare of single mothers, finding that most of them were unaffected as they earned above-minimum wage. Single mothers with low-education levels did not see an increase in net incomes due to the negative effect on employment and hours worked: for low-skilled individuals, a 10 percent increase in minimum wage resulted in an 8.8 percent decline in employment and an 11.8 percent reduction in hours worked.
Yet another study (DeFina, 2008) focus on child poverty rates and show that minimum wage increases have a positive (reducing) impact on child poverty in female-headed families. The effect is small but significant (a 10 percent increase in the minimum wage decreases child poverty rates by 1.8 percentage points), controlling for other factors.
Ultimately, the effect of minimum wage on poverty among women or female-headed households is somewhat ambiguous. It depends on the poverty threshold used, other policy instruments (such as the EITC), existing incentives to enter employment and how, in the specific country of interest, labor laws may affect the employer’s cost of hiring (e.g. for France, see Laroque and Salanie, 2002).
The discussion is however relevant for countries like Georgia, where the wage gap between men and women is quite large, and where more women than men tend to work in low-wage and vulnerable jobs. While the overall poverty gap between men and women in Georgia is insignificant (mainly because poverty is measured at the household level), the gap becomes apparent when comparing female-headed households to male-headed ones. The poverty rates in the former case are nearly 2 percentage points higher in Georgia (20 percent vs. 18.3 percent in 2021). The poverty rates are the highest among households with only adult women (39.3 percent for all-female households vs. 20.1 percent overall in 2018).
A Simulation of a Minimum Wage Raise in Georgia
The Georgian minimum wage legislation dates back to 1999. The presidential decree N 351 from June 4, 1999 states that the minimum (monthly) wage that is to be set in Georgia is equal to 20 GEL (with some specific exceptions in the public sector). This is a non-binding threshold. Therefore, one has to think carefully what consequences might arise from raising the minimum wage to a much higher level. In addition to previously discussed aspects, one issue to keep in mind is the different average wages across different regions in Georgia. For example, a national minimum wage increase might have more of an impact in poorer regions, where both wages and incomes are lower, while it may still be non-binding in Tbilisi.
The ISET-PI research team (Babych et al., 2022) use Georgian micro data from the Labor Force Survey (LFS) and the Household Integrated Expenditure Survey (HIES), to simulate the effect of instituting a nation-wide minimum wage on both employment and poverty rates in different regions of Georgia. One focus area of the study was to analyze the effects of a minimum wage increase on female poverty. As with any exercise using a simulation approach, this study is subject to limitations imposed by the assumptions used, e.g. how much labor demand would respond to changes in the minimum wage, etc. The study considered two hypothetical thresholds of the minimum wage; 250 and 350 GEL respectively.
Figure 1. Share of private sector employees earning below certain thresholds, by gender, 2021.
The expected household income after the minimum wage increase was calculated and then compared to the poverty threshold (for each household in a standard way, using the “adult equivalence” scale). According to this methodology, any person who lives in a household which falls below the poverty threshold is considered to be poor. A “working poor” household is defined as a household below the poverty threshold where at least one adult is working.
Figure 1 shows that there is a substantial share of both men and women whose monthly wage income falls below the hypothetical minimum wage thresholds. In addition, women are more than two times as likely to be earning below these thresholds. However, the possible impact from an increased minimum wage on female vs. male poverty is not clear-cut. Since many women are part of larger households which include adult males, their possible income losses/gains may be counterbalanced by income gains/losses of male family members, leaving the overall effect on household income ambiguous.
In addition, poverty rates are not likely to be much affected by a minimum wage increase if most poor households are “non-working poor” (where adult family members are either unemployed or outside of the labor force), a consideration particularly relevant for Georgia. The share of poor individuals who live in “working poor” households (with at least one household member employed) is just 41 percent nationally (and 35 percent in rural areas), meaning that close to 60 percent of poor individuals nationwide (and 65 percent in rural areas) are not likely to be directly affected by minimum wage increases.
Female vs. Male Poverty: Scenarios Following a Minimum Wage Increase
As one can see in Figure 2, increased minimum wages tend to reduce poverty, but the impact is not larger than one percentage point. Not surprisingly, females benefit more than males (0.3 and 0.8 percentage points vs. 0.2 and 0.9 percentage points poverty reduction for men and women respectively, under different threshold scenarios). The maximum positive impact on poverty reduction is observed under a higher minimum wage threshold.
Figure 2. Estimated impact on poverty rates, based on the national subsistence minimum.
The impact of an increased minimum wage on the expected median consumption of households doesn’t exceed a few percentage points either, as illustrated in Figure 3.
The impact is greatest in urban areas other than Tbilisi (between a 2.5 percent and a 4.2 percent increase in median consumption relative to the status quo). The lower impact in Tbilisi is most likely driven by relatively higher wages, while the low impact in rural areas is likely driven by lower participation in wage employment.
In the hypothetical case of Georgia, an impact of a minimum wage increase on poverty rates is expected to be limited, in line with the literature. In our study this finding is mostly driven by the fact that only a relatively small share of poor individuals live in “working poor” households (about 40 percent, nationally). The remaining 60 percent of poor individuals will be unaffected by the reform.
The quantitative impact on female and male poverty is estimated to be low, although the female poverty rate reduction is somewhat larger than among males.
It is important to note that the analysis doesn’t consider possible differential impacts on different groups of vulnerable families, such as families with small children and single mothers with small children. Some reasons to why groups of households may or may not be affected by the hypothetical minimum wage increase, based on their employment status and other factors, have been discussed above.
Another important point is that our exercise should not be seen as an argument against an increase of the minimum wage in Georgia. Instead, it suggests that such a reform would not have much of an impact if done in isolation. Indeed, the existing literature on minimum wage seems to be in consensus on the fact that minimum wage policies would be more impactful if supplemented by the following measures:
- Maintain and expand targeted social assistance to groups that do not benefit or that are losing jobs/incomes as a result of the minimum wage changes
- Have job re-training programs in place to help laid-off workers
- Have human capital investment programs in place to increase workers’ productivity, in particular for low-productivity sectors
- Consider other support instruments targeted toward the most affected groups of the population such as single working mothers etc.
These recommendations should be incorporated in the policy making regarding minimum wages in Georgia.
We are grateful to Expertise France for financially supporting the original report (Babych et al., 2022), which features some of the results and points raised in this policy brief.
- Allegretto, S., Dube, A., Reich, M., & Zipperer, B. (2017). Credible Research Designs for Minimum Wage Studies: A Response to Neumark, Salas, and Wascher. ILR Review, 70(3), 559–592. https://doi.org/10.1177/0019793917692788
- Babych, Y., Pignatti, N., Chapichadze, A., Lobzhanidze, G. and Shubitidze, E. (2022). Report on Minimum Wage in Georgia. ISET Policy Institute. Unpublished manuscript.
- Belman, D. and Wolfson, Paul J. (2014). What Does the Minimum Wage Do? Kalamazoo, MI: W.E. Upjohn Institute for Employment Research. https://doi.org/10.17848/9780880994583
- Burkhauser, R. V. and Sabia, J. J. (2007). The effectiveness of minimum‐wage increases in reducing poverty: Past, present, and future. Contemporary Economic Policy, 25(2), 262-281. https://doi.org/10.1111/j.1465-7287.2006.00045.x
- DeFina, R. H. (2008). The impact of state minimum wages on child poverty in female-headed families. Journal of Poverty, 12(2), 155-174. https://doi.org/10.1080/10875540801973542
- Dube, A., T.W. Lester, and M. Reich. 2010. Minimum Wage Effects Across State Borders: Estimates Using Contiguous Counties. The Review of Economics and Statistics, 92(4), 945–964. https://doi.org/10.1162/REST_a_00039
- European Commission. (2020). Proposal for a directive of the European parliament and of the council on adequate minimum wages in the European Union. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A52020PC0682GEOSTAT
- International Labour Organization (ILO). (2023). https://www.ilo.org/global/topics/wages/minimum-wages/definition/lang–en/index.htm
- International Labour Organization (ILO). (2019). The working poor or how a job is no guarantee of decent living conditions chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://www.ilo.org/wcmsp5/groups/public/—dgreports/—stat/documents/publication/wcms_696387.pdf
- Geostat. (2021). https://www.geostat.ge/en
- Laroque, G. & Salanié, B. (2002). Labour market institutions and employment in France. Journal of Applied Econometrics, 17(1), 25-48. https://doi.org/10.1002/jae.656
- Neumark, D. & Wascher, W. (2011). Does a higher minimum wage enhance the effectiveness of the Earned Income Tax Credit? ILR Review, 64(4), 712-746. https://doi.org/10.1177/001979391106400405
- Neumark, D. (2018). Employment effects of minimum wages. IZA World of Labor 2018: 6. https://wol.iza.org/articles/employment-effects-of-minimum-wages/long
- Sabia, J. J., Burkhauser, R. V. & Hansen, B. (2012). Are The Effects Of Minimum Wage Increases Always Small? New Evidence From A Case Study Of New York State. Sage Publications, 350-376. https://doi.org/10.1177/001979391206500207
- Sabia, J. J. (2008). Minimum wages and the economic wellbeing of single mothers. Journal of Policy Analysis and Management, 27(4), 848-866. https://doi.org/10.1002/pam.20379
- Sotomayor, O. J. (2021). Can the minimum wage reduce poverty and inequality in the developing world? Evidence from Brazil. World Development 138. https://doi.org/10.1016/j.worlddev.2020.105182.
Disclaimer: Opinions expressed during events and conferences are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.
Belarus has faced unprecedented sanctions during the last year and the new economic conditions have led to a GDP decline and inflation growth. At the same time, the situation on the currency market has been stable since April 2022. The Belarusian Ruble demonstrated a gradual appreciation to the US Dollar and the Euro and a decline to the Russian Ruble. The appreciation of the Belarusian Ruble against the US Dollar has given households the illusion that the economic situation is not that bad. This brief analyses the main factors of the current situation on the currency market as well as describes the challenges which might destabilise the market. The importance of changing selected currencies in the currency basket and the start of a reorientation of the Belarusian economy from Western to Eastern partnerships, are also described.
The National Bank of the Republic of Belarus’ Policy on the Currency Market
In Belarus, currency has always played an important role as an indicator of economic stability. Household’s reactions to sharp fluctuations of the Belarusian Ruble have been expressed in an immediate demand growth for foreign currency (US Dollar and Euro mostly). After the war in Ukraine started and the exchange rate of the Belarusian Ruble began declining, people tried to make currency deposits from banks and buy foreign currency. In contrast to the Central Bank of Russia, the National Bank of the Republic of Belarus (NBRB) introduced no restrictions on the currency market. However, Belarusian financial institutions imposed their own limits on carrying out non-cash exchange operations, cash withdrawals from ATMs and from bank accounts. Financial institutions also limited the availability of currencies in exchange offices and imposed limits on payment transactions by credit card outside of Belarus. All these processes took place under the condition of a sharp devaluation of the Russian Ruble.
The dynamics in the Russian Ruble have affected the Belarusian Ruble fluctuation (see Figure 1). The correlation between the currencies was strong even before the war, given that the Russian Federation is a dominant economic partner for Belarus, and has since become stronger.
The share of Russian Ruble in the Belarusian currency basket is at 50 percent. Moreover, in Q1-Q3 2022 the Belarusian dependency on the Russian economy increased in the aftermath of losing the Ukrainian market and facing European export shortages. Between January and August 2022, the share of export of goods to CIS countries (where the main share of exports goes to Russia) was 65,7 percent, as compared to 58,4 percent for the corresponding months in 2021. The same tendencies are apparent when considering the import of goods. The share of import from CIS countries reached 64,7 percent between January and August in 2022, as compared to 61,3 percent for January-August in 2021 (BSCBR, 2022).
Figure 1. The weighted average exchange rate of the Belarusian Ruble, in Belarusian Rubles.
Sanctions and the Russian Central Bank’s policy have led to a stabilisation on the Russian currency market. The Central Bank of Russia has introduced restrictions on capital outflow from the country, limited cash withdrawals from bank accounts and foreign currency purchases in exchange offices (Tinkoff, 2022). The cancelation of budget rule has further supported the Russian Ruble exchange rate. But the main reason for the Russian currency exchange rate reversal post March 2022, relates to the situation regarding foreign trade. Due to sanctions, imports had significantly decreased. At the same time, high energy prices allowed for export growth. Between January and June 2022 Russia displayed a high positive trade balance (169,62 billion USD), the largest in the last 7 years (CBR, 2022). As a result of sanctions, the Central Bank of Russia started to prepare the market to work with currencies of friendly countries.
Similar tendencies can be seen in Belarus. NBRB has changed the composition of the foreign currency trade to turn the Belarusian economy from a Western to an Eastern direction regarding economic cooperation. In July 2022 the Chinese Yen was included in the currency basket. At the same time the share of Russian Ruble was at 50 percent, the US Dollar at 30 percent, the Euro at 10 percent and the Chinese Yen at 10 percent. In August 2022, the NBRB began to define daily exchange rates for the Vietnamese Dong, Brazilian Real, Indian Rupee and UAE Dirham. Finally, since October 2022, the exchange rate for the Qatari Riyal has been defined on a monthly basis (The National Bank of Belarus, 2022). These changes are indicators of ongoing and planned structural changes to the economy to accommodate increased cooperation with the Eastern economies.
Currency Market Stabilisation and Current Risks
The Belarusian Ruble has not repeated the fluctuation of the Russian currency. It did however copy its tendency to appreciate to the US Dollar and the Euro, as of April 2022. Besides the appreciation of the Russian Ruble and personal bank’s restrictions on national currency markets, the stabilisation of the Belarusian Ruble can be explained by the positive trade balance. In contrast to Russia, the growth of net export in Belarus was due to a faster decline of imports than exports. There are several reasons why this can be a problem for currency market stabilisation in the future.
First, Belarus’ foreign trade has become more and more oriented toward the Russian market. If the main trade partner experiences difficulties (for example, oil price caps) this could lead to a devaluation of the Russian Ruble and, as a result, declining competitiveness of Belarusian goods on the Russian market.
Second, reorientation of Belarusian exports from Western to Eastern countries require time and additional financial resources and exports are not always profitable due to high logistical costs. Any additional sanctions may further limit such opportunities.
Third, main export-oriented services, such as the Transport and ICT sectors, are affected by sanctions and their consequences. In Q3 2022, the transport turnover was equal to 68,3 percent, as compared to the same period 2021. The ICT sector is still having a positive impact on GDP growth. However, in January-September 2021 the positive contribution from this sector to the Belarusian GDP was 0,9 percent, while it between January and September 2022 was only 0,2 percent.
Recent success in foreign trade is mostly due to the continuation of selling potash, nitrogen fertilisers and other products on the global market, a strong Russian Ruble and Russian market openness towards Belarusian companies, low levels of Belarusian imports, and cheap Russian gas (the special price for Belarus is 128 US Dollars for 1000 cubic meters). If the terms of trade with Russia worsen and key export-oriented industries suffer from sanctions and reputational risks, the currency market could however be destabilised.
Another problem for the Belarusian Ruble stability in the middle and long term is related to household behaviour. In January-August 2022 Belarusians sold more foreign currency than they bought. Despite the Ruble fluctuation, the high levels of net sales in March was due to bank restrictions. In June, the net purchase was related to seasonal factors (see Figure 2). For the other months of the period the net selling can be explained by a stable situation on the currency market and real incomes declining. People sold currency in an attempt to maintain their previous standards of living.
Figure 2. Balance of purchase and sale of foreign currency by households (+ “net purchase”, – “net sale”), mln. USD.
In September-October 2022 Belarusian households bought more than (an equivalent of) 300 mln. USD on net basis, primarily in USD or Euro, which is very unusual for the Belarusian market situation. There are several possible explanations for such behaviour:
- Despite difficulties with obtaining visas Belarusians are going to Poland and other European countries to shop. Because of sanctions, retaliatory sanctions as well as a high price control on the domestic market, the range of goods has shrunk, and prices have risen. In European countries Belarusians can purchase much cheaper goods both for personal use and for resale.
- Partial mobilisation in Russia has increased the uncertainty of further political steps in Belarus. Households thus purchase foreign currency to establish an extra safety cushion.
- In Q3 2022 there was a net cash outflow on international remittances, for the first time since 2017. Traditionally, Belarus has seen a net inflow of foreign remittances. In 2022 Belarusian banks were switched off from the SWIFT system which incurred problems with operations in foreign currencies for banks under sanctions. As a result, cash inflow has declined (see Figure 3). Cash outflows however remained on the same level as in previous years. This can be explained by high-level specialists and people employed within ICT leaving the country. During relocation people have sold apartments and cars and exchanged accumulated incomes from Belarusian Rubles to US Dollars or Euros and sent to foreign bank accounts (even under the conditions of facing difficulties with conducting money transfers).
Figure 3. Net cash inflow (+)/ outflow (-) for international remittances, USD mln.
Maintaining the trend of net currency purchase together with possible trade balance deterioration may exacerbate the situation on the domestic currency market. Another risk to the currency market stability is posed by the insufficient size of FX reserves (in the amount of less than 3 months of import). Moreover, the 900 mln. US Dollars in reserves, given by the IMF in 2021 as support to fight Covid-19, can’t be used as this financial support is given in the form of SDR (Special Drawing Rights), and the exchange of SDR to US Dollars or other currencies is challenging due to sanctions (Congress, 2022).
At the same time, the Government’s decision to make external debt payments in Belarusian Rubles supports the FX reserves level. It has also been decided that payments on Eurobonds to the Nordic Investment Bank, the European Bank of Reconstruction and Development and the International Bank of Reconstruction and Development are to be paid in Rubles. These decisions have decreased the country’s long-term rating on foreign liabilities to the Restricted Default level. In that sense, short-term gains can lead to significant financial losses in the long term. In the future it will be necessary not only to pay outstanding debts but also to improve Belarus’ reputation on the international financial market. Today, the Russian Federation is the main investor in the Belarusian economy. But since its support is limited, it is likely to be insufficient for the safe functioning of the Belarusian economy.
The stability of the Belarusian currency market is not the result of economic success, but rather a reflection of the tightening of the economy. The appreciation of the Belarusian Ruble to the US Dollar and Euro has taken place during an accelerated reduction in Belarusian imports. At the same time the weakness of the Belarusian currency to the Russian Ruble entails competitiveness of Belarusian products on the Russian market. Foreign exchange reserves, although insufficient, have maintained in size due to the low demand for foreign currency and foreign debt payments in Belarusian Rubles. Disruptions to economic and political relations with Western countries stimulates the Belarusian authorities to reorient the economy towards Eastern partners, which has led to a modification of the currency basket composition. In the long run, the current stability of the Belarusian currency can quickly disappear in case one or several risks are realised. If the Russian Ruble devaluates or trade balance deteriorates and demand for foreign currency increases, the stability of the Belarusian Ruble exchange rate can be ruined.
- Belarusian State Committee of the Republic of Belarus (BSCRB). (2022). Socio- Economic Situation of the Republic of Belarus in January- September 2022. https://www.belstat.gov.by/ofitsialnaya-statistika/publications/izdania/public_bulletin/index_58794/
- The National Bank of the Republic of Belarus. (2022). Statistical Bulletin #9 (279) 2022.
- The National Bank of the Republic of Belarus. (2022). https:// www.nbrb.by
- Tinkoff. (2022). The Central Bank has extended the currency restrictions for six months. https://secrets.tinkoff.ru/novosti/czentrobank-prodlil-valyutnye-ogranicheniya-na-polgoda/
- CBR. (2022). Balance of payments, international investment position and external debt of the Russian Federation in the first half of 2022. http://www.cbr.ru/statistics/macro_itm/svs/p_balance/
- Congress. (2022). H.R. 6899- Russia and Belarus SDR Exchange Prohibition Act of 2022. Public Law No: 117-185 (10/04/2022). https://www.congress.gov/bill/117th-congress/house-bill/6899.
Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.
Georgia has an 8000-year-old winemaking tradition, making the country the first known location of grape winemaking in the world. In this policy brief we analyze and discuss major characteristics of the wine sector in Georgia, government policies regarding the sector and major outcomes of such policies. The brief provides recommendations on how to ensure sustainable development of the sector in a competitive, dynamic environment.
The Georgian winemaking tradition is 8000 years old, making Georgia the world’s first known location of grape winemaking. There are many traditions associated with Georgian winemaking. One of them is ‘Rtveli’ – the grape harvest that usually starts in September and continues throughout the autumn season, accompanied with feasts and celebrations. According to data from the National Wine Agency, the annual production of grapes in Georgia is on average 223.6 thousand tones (for the last ten-years), with most grapes being processed into wine (see Figure 1).
Figure 1. Grape Processing (2013-2021)
Wine is one of the top export commodities for Georgia. It constituted 21 percent of the total Georgian agricultural export value in 2021 (Geostat, 2022). Since 2012 wine exports have, on average, grown 21 percent in quantitative terms, and by 22 percent in value (Figure 2). The average price per ton varies from 3 thousand USD to 3.9 thousand USD (Figure 2). Exports of still wine in containers holding 2 liters or less constitute, on average, 96 percent of the total export value.
Figure 2. Georgian Wine Exports (2012-2021)
The main destination market for exporting Georgian wine is the Commonwealth of Independent States (CIS) countries which account for, on average, 78 percent of the export value (2012-2021). The corresponding share for EU countries is 10 percent. As of 2021, the top export destinations are Russia (55 percent), Ukraine (11 percent), China (7 percent), Belarus (5 percent), Poland (6 percent), and Kazakhstan (4 percent). While Russia is still a top market for Georgian wine, Russia’s share of Georgian wine exports declined after Russia imposed an embargo on Georgian wines in 2006. The embargo forced market diversification and even after the reopening of the Russian market and Georgian wine exports shifting back towards Russia, its share declined from 87 percent in 2005 to 55 percent in 2021.
While there are more than 400 indigenous grape varieties in Georgia, only a few grape varieties are well commercialized as most of the exported wines are made of Rkatsiteli, Mtsvane, Kisi, and Saperavi grape varieties (Granik, 2019).
Government Policy in the Wine Sector
The Government of Georgia (GoG) actively supports the wine sector through the National Wine Agency, established in 2012 under the Ministry of Environmental Protection and Agriculture (MEPA). The National Wine Agency implements Georgia’s viticulture support programs through: i) control of wine production quality and certification procedures; ii) promotion and spread of knowledge of Georgian wine; iii) promotion of export potential growth; iv) research and development of Georgian wine and wine culture; v) creation of a national registry of vineyards; and vi) promotion of organized vintage (Rtveli) conduction (National Wine Agency, 2022).
During 2014-2016, the GoG’s spending on the wine sector (including grape subsidies, promotion of Georgian wine, and awareness increasing campaigns) amounted to 63 million GEL, or 22.8 million USD (As of November 1, 2022, 1 USD = 2.76 GEL according to the National Bank of Georgia). Out of the spending, illustrated in Figure 3, around 40-50 percent was allocated to grape subsidies implemented under the activities of iv) (as mentioned above).
There are two types of subsidies used by the GoG– direct and indirect. Direct subsidies imply cash payments to producers per kilogram of grapes. As for indirect subsidies, they entail state owned companies purchase grapes from farmers.
Starting from 2017, the GoG decided to abandon the subsidiary scheme and decrease its spending on of the wine sector. The corresponding figure reached a minimum of 9.2 million GEL (3.3 million USD) in 2018. Meanwhile, the grape production has been increasing, reaching its highest level in 2020 (317 thousand tons). In 2020, the GoG resumed subsidizing grape harvests to support the wine sector as part of the crisis plan aimed at tackling economic challenges following the Covid-19 pandemic. The corresponding spending in the wine sector increased from 16.7 million GEL (around 6 million USD) in 2019 to 113.4 million GEL (41 million USD) in 2020, out of which the largest share (91 percent) went to grape subsidies. In 2021, the GoG continued its extensive support to the wine sector and the corresponding spending increased by 44 percent, compared to 2020. The largest share again went to grape subsidies (90 percent).
Figure 3. Grape Production and Government Spending on the Wine Sector (2014-2021)
In 2022, the GoG have continued subsidizing the grape harvest to help farmers and wine producers sell their products. During Rtveli 2022, wine companies are receiving a subsidy if they purchase and process at least 100 tons of green Rkatsiteli or Kakhuri grape varieties grown in the Kakheti region, and if the company pays at least 0.90 GEL per kilogram for the fruit. If these two conditions are satisfied, 0.35 GEL is subsidized from a total of 0.9 GEL per kilogram of grapes purchased (ISET Policy Institute, 2022). Moreover, the GoG provides a subsidy of 4 GEL per kilogram for Alksandrouli and Mujuretuli grapes (unique grape varieties from the Khvanchkara “micro-zone” of the north-western Racha-Lechkhumi and Kvemo Svaneti regions), if the buying company pays at least 7 GEL per kilogram for those varieties (Administration of the Government of Georgia, 2022). Overall, about 150 million GEL (54.2 million USD), has been allocated to grape subsidies in 2022.
Although the National Wine Agency is supposed to implement support programs in various areas like quality control, market diversification, promotion and R&D, these areas lack funding, as most of the Agency’s funds are spent on subsidies. Given that the production and processing of grapes have increased over the years, subsidies have been playing a significant role in reviving the wine sector after the collapse of the Soviet Union (Mamardashvili et al., 2020). However, since the sector is subsidized as of 2008, the grape market in Georgia is heavily distorted. Prices are formed, not on the bases of supply and demand but on subsidies, which help industries survive in critical moments, but overall prevent increases in quality and fair competition. They further lead to overproduction, inefficient distribution of state support and preferential treatment of industries (Desadze, Gelashvili, and Katsia, 2020). After years of subsidizing the sector, it is hard to remove the subsidy and face the social and political consequences of such action.
Nonetheless, in order to support the sustainable development of the sector, it is recommended to:
- Replace the direct state subsidy with a different type of support (if any), directed towards overcoming systemic challenges in the sector related to the research and development of indigenous grape varieties and their commercialization level.
- Further promote Georgian wine on international markets to diversify export destination markets and ensure low dependence on unstable markets like the Russian market. Although wine exporters have in recent years entered new markets, to further strengthen their positions at those markets, it is vital to:
- ensure high quality production through producers’ adherence to food safety standards.
- promote digitalization – e-certification for trade and distribution, block chain technology for easier traceability and contracting, e-labels providing extensive information about wine etc. – enabling producers to competitively operate in the dynamic environment (Tach, 2021)
- identify niche markets (e.g. biodynamic wine) and support innovation within these sectors to ensure competitiveness of the wine sector in the long-term (Deisadze and Livny, 2016).
- Administration of the Government of Georgia. (2022). “Gov’t releases updated conditions for vineries in grape harvest subsidies”
- Deisadze, S., Gelashvili, S. and Katsia, I. (2020). ”To Subsidize or Not to Subsidize Georgia’s Wine Sector?”, ISET Economist Blog.
- Deisadze, S. and Livny, E.(2016). “Back to the Future: Will an Old Farming Practice Provide a Market Niche for Georgian Farmers?”, ISET Economist Blog.
- GeoStat. (2020). Statistics of food balance sheets, retrieved from: https://www.geostat.ge/en/modules/categories/297/food-security
- Mamardashvili, P., Gelashvili, S., Katsia, I., Deisadze, S., Ghvanidze, S., Bitsch, L., Hanf, J. H., Svanidze, M. and Götz, L. (2020). “The Cradle of Wine Civilization”—Current Developments in the Wine Industry of the Caucasus”. Caucasus Analytical Digest (CAD), Vol 117.
- Granik, L. (2019). “Understanding the Georgian Wine Boom”. SevenFiftyDaily.
- ISET Policy Institute, 2022. “Agri Review October 2022“
- Ministry of Finance of Georgia. (2022). Statistics of State Budget, retrieved from: https://www.mof.ge/en/4537
- National Wine Agency (NWA). (2022). Main activities the agency, retrieved from: https://wine.gov.ge/En/Page/mainactivities
- Tach, L. (2021). “What Are The Future Digital Technology Trends In Wine? New OIV Study Reveals Answers”. Forbes.
Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.
On June 27, 2014, Georgia and the EU signed an Association Agreement (AA) and its integral part – the Deep and Comprehensive Free Trade Area (DCFTA). In this policy brief, we discuss the changes and analyze the agricultural exports statistics of Georgia since 2014. Furthermore, we will provide the recommendations to capitalize on the opportunities that the DCFTA offers to Georgia.
Georgia is a traditional agrarian country, where agriculture constitutes an important part of the economy. 36.6% of the country’s territory are agricultural lands and 48.2% of the Georgian population live in villages. Although 55% of population are employed in agriculture, Georgia’s agriculture accounts for only 15.8% of its GDP (Geostat, 2019). Agricultural exports constitute an important part of Georgia’s economy, accounting for about 25-30% of total exports.
On June 27, 2014, Georgia and the EU signed an Association Agreement (AA) and its integral part, the Deep and Comprehensive Free Trade Area (DCFTA). On July 1st, 2016, the DCFTA fully entered into force. The DCFTA aims to create a stable and growth-oriented policy framework that will enhance competitiveness and facilitate new opportunities for trade. The DCFTA widens the list of products covered by the Generalized System of Preferences+ (GSP+) and sets zero tariffs on all food categories (only garlic is under quota), including potentially interesting products for Georgian exports – wine, cheese, berries, hazelnuts, etc. (Economic Policy Research Center, 2014).
As July 2018 marked only two years since the implementation of the DCFTA between Georgia and EU, valuable conclusions on its impact cannot be formulated yet. In this policy brief, we will give an overview of Georgia’s agricultural trade statistics, particularly, we will focus on agricultural exports and provide recommendations for capitalizing on opportunities offered by the DCFTA.
Georgia’s agricultural trade
Despite its potential and natural resources, Georgia is a net importer of agricultural products. In 2018, Georgia’s agricultural exports increased by 23.2% (181 million USD), while the respective imports grew by only 15.5% (179 million USD) compared to 2017. Therefore, the trade balance (the difference between exports and imports) remained almost unchanged at (-394) million USD (Figure 1).
Figure 1: Georgia’s Agricultural Trade (2014-2018)
Source: Geostat, 2019
Out of the sharp increase in agricultural exports, 100 million USD are attributed to tobacco and cigars. Since Georgia cultivates very little tobacco, the growth was instigated mostly from the import, slight processing and re-export of tobacco products. Consequently, the export of tobacco and cigars increased by 240% in 2018, and it currently holds second place (after wine) in Georgia’s total food and agricultural exports. It should be mentioned that wine exports contributed to 26 million USD in export growth.
Over the last five-year period, the top export countries for Georgia were mainly neighboring counties (Azerbaijan, Russia, Armenia, Turkey); for imports, we see the same neighboring countries as well as China and Ukraine. Observing the trade statistics over the years, 45% of Georgia’s agricultural exports were destined for markets in countries of the former Soviet Union, so-called Commonwealth of Independent States (CIS), while the EU’s share in Georgia’s total agricultural exports was 24%.
Trade relationships between Georgia and the EU
The EU is one of Georgia’s largest trade partners. The EU’s share of total Georgian imports was 28% in 2018, and for exports, 24%. Total exports have been more or less stable since 2014, except for 2016, when an 11% decrease was observed (Figure 2). Specifically, for agriculture, in 2017, the EU’s share of Georgian imports was 22%, and its share of exports was 19%. During the same period, the top export products were hazelnuts (shelled), spirits obtained by distilling grape wine or grape marc, wine, mineral and aerated waters and jams, jellies, marmalades, purées or pastes of fruit.
Figure 2: Total and Agricultural Exports to the EU (2014-2018)
Source: Geostat, MoF, 2019
In 2015 (before the full enforcement of the DCFTA), Georgia’s agricultural exports to EU countries (including the United Kingdom) increased by 20% compared to the previous year. This positive trend remained in 2016, when the same indicator increased by 5%. In 2017, which was quite a bad year in terms of harvest in Georgia, we observed a 38% decrease in the country’s agricultural export to the EU (Figure 2). This decrease was mainly caused by a significant decrease (64%) in hazelnut exports during the same period. The reason for such a large decrease is that hazelnut production suffered from various fungal diseases due to unfavorable weather conditions in 2017. The Asian Stink Bug invasion worsened the situation, and in the end, hazelnut exports dropped dramatically in both value and quantity. In 2018, Georgia’s agricultural export in EU slightly increased by 6% compared to 2017.
Trade relationships between Georgia and CIS countries
It is interesting to observe agricultural trade within the same time period with CIS countries. In 2018, the CIS’ share of Georgian imports was 51%, and its share of exports was 60%. The top export products to CIS countries were wine, mineral and aerated waters, spirits obtained by distilling grape wine or grape marc, hazelnuts (shelled), and waters, including mineral and aerated, with added sugar, sweetener or flavor, for direct consumption as a beverage. As we can see in both EU and CIS countries, the top export products are more or less the same. However, the main export destination market for Georgian hazelnuts are EU countries, but wine is mostly exported to the CIS countries.
Figure 3: Agricultural Exports to CIS Countries (2014-2018)
Source: Geostat, MoF, 2019
Due to the worsened economic situation in CIS countries, Georgia’s agricultural exports to these countries decreased by 37% in 2015. Such a sharp decrease was mainly driven by a significant decrease in the export of alcoholic and non-alcoholic beverages, hazelnut, and live cattle. However, since 2015, Georgia’s agricultural exports to CIS countries have been increasing; we observed a slight 2% increase in the value of agricultural exports in 2016, while the same indicator was 37% in 2017 (Figure 3). That was mainly caused by the increased exports of alcoholic and non-alcoholic beverages (wine by 61%, spirits by 28%, mineral and aerated waters by 22%). In 2018, Georgia’s agricultural export in CIS countries increased by 12% compared to 2017.
Despite its potential and comparative advantage in agriculture, Georgia is still a net importer of agricultural products and has negative trade balance (-394 mn USD). Two years after the DCFTA came into force, it is challenging to know its impact on Georgia’s agricultural trade due to the insufficient passage of time since. Notwithstanding, we can formulate some conclusions from trade statistics. The diversity of the destinations for Georgia’s agricultural exports has not changed through the years. Georgia’s agricultural exports has increased to the EU, but at a quicker pace to CIS too. Furthermore, Georgia’s share of agricultural exports to CIS countries is still significant (60%).
While it is obvious that Georgia needs to diversify its agricultural export destination markets, there are several challenges facing small and medium size farmers and agricultural cooperatives in Georgia that are not specific to implementation of the DCFTA. As the previous regime (GSP+) with the EU already covered most products, the DCFTA did not represent a significant breakthrough. On the path to European integration, the biggest challenge for Georgia is to comply to non-tariff requirements such as food safety standards and SPS measures. The attention should be paid on providing consultations to farmers regarding certification processes and standards and better information sharing (e.g. developing online platforms).
In Georgia, agri-food value chains are not well-developed and lack coordination among different actors. In order to capitalize on opportunities offered by the DCFTA, government and private sector should work together to improve logistics infrastructure. There is a need for upgrading at every stage of export logistics: warehousing, processing, labeling, regional consolidation, final customer services. In this regard, there are high approximation costs for business that should be considered as long-term investment to modernize agriculture and improve food the safety system in the country. This would boost the export potential not only to the EU, but to other countries with similar requirements as well.
- ISET Policy Institute, 2016. “DCFTA Risks and Opportunities for Georgia”
- Economic Policy Research Center, 2014. “Agreement on the Deep and Comprehensive Free Trade Area and Georgia”. Available only in Georgian
In this brief we provide a quantitative assessment of the consequences of countersanctions introduced by the Russian government in 2014 in response to sectoral restrictive measures initiated by a number of developed countries. Commodity groups that fell under countersanctions included meat, fish, dairy products, fruit and vegetables. By applying a basic partial equilibrium analysis to data from several sources, including Rosstat, Euromonitor, UN Comtrade, industry reviews etc., we obtain that total consumers’ loss due to countersanctions amounts to 288 bn Rub or 2000 rubles per year for each Russian citizen. Producers capture 63% of this amount, importers 26%, while deadweight loss amounts to 10%. 30% of the transfer from Russian consumers toward importers was acquired by Belarus. The gain of Belarusian importers of cheese is especially impressive – 83% of total importer’s gains on the cheese market.
In August 2014, in response to sectoral sanctions initiated by some countries against Russia, the national government issued resolution No. 778, which prohibited import of processed and raw agricultural products from the United States, the EU, Ukraine and a number of other countries (Norway, Canada, Australia, etc.).
Russian countersanctions were, in particular, imposed on meat, fish, dairy products, fruit and vegetables. Later the list of counter sanctioned goods was edited: inputs for the production of baby food and medicines have been deleted from the ban list, while new items were added. Salt was added to the list in November 2016 and animal fats in October 2017.
The popular idea behind the countersanctions was to limit market access for countries, which supported sectoral sanctions. The other rhetoric of the countersanctions was to support domestic producers via trade restrictions, or by other words – import substitution.
We apply a basic partial equilibrium analysis in order to evaluate the effect of countersanctions on the welfare of main stakeholders – consumers, producers and importers. The overall results are in line with general microeconomic consequences of trade restrictions in a small open economy, that is, we observe a decline in consumer surplus, increase in producer surplus and redistribution across importers. Perhaps, even more interestingly, we are able to provide a numerical assessment of redistribution effects between Russian consumers and producers, on the one hand, and among importers from different countries, on the other.
Partial equilibrium welfare analysis
We apply a framework of the classical analysis of import tariff increases to Russian countersanctions. Countersanctions resulted in increased domestic prices, declining consumption and increased domestic production. Given the increase in prices and declined volumes of consumption, we evaluate the losses by consumers as a decline in consumer surplus. Respectively, given the increase in prices and increase in domestic output we identify the producers gains as an increase in producer surplus. The only difference with a classical analysis is the lack of increase in government revenues. In this case increases in domestic prices were driven by restrictions on trade with historical partners which were substituted by more costly producers. Given the changes in the composition of importers after sanctions, we identify countries which lost and gained access to the Russian market. We use changes in volumes of trade as a measure of respective gains and losses. Figure 1 presents all relevant concepts.
In order to measure all relevant welfare changes, we rely on consumption, production and price data from Rosstat and Euromonitor, trade data from the UN Comtrade database. We use data for 2013 as a benchmark before countersanctions and compare it to 2016. The measures of own price elasticities of Russian demand and supply were taken from the literature. We use real price (in terms of 2013 prices) and volume information for consumption and supply in 2016 as the resulting points on the supply (point C) and demand (point A) curves as shown on Figure 1. Then we restore the consumption and production points on these curves (points F and B) as they would have been in 2013 given the own price elasticities of demand and supply and price level as of 2013.
Figure 1. Visualization of deadweight losses, consumer and producer surplus changes
We consider 12 commodity groups that were included in 2014 in the countersanctions list: pork, cheese, poultry, apples, beef, tomatoes, processed meat, fromage frais, butter, oranges, condensed milk, grapes, cream, sour milk products, milk, and bananas.
Prices and volumes information are taken from Rosstat official statistics, which in a few cases were adjusted by data from Euromonitor. Import values were obtained from the UN Comtrade database. The summary of the original data and results of welfare analyses are reported in table 1. Below we discuss in details the situation in three markets – beef, apples and cheese.
Table 1. Summary table of the welfare effects of countersanctions
|Group||Price (RUR per kg, 2013)||Production (thous. tons)||Consumption (thous. tons)||Elasticity||Consumer losses, RUR mn||Producer surplus, RUR mn||Deadweight loss, RUR mn||Importer gains, RUR mn|
Data sources: Rosstat, Euromonitor, UN COMTRADE
Bold figures were used to mark the commodity groups with a noticeable consumption growth in 2013-2016, italic figures – for those with consumption decrease, and underlined – for groups where consumption changed insignificantly during the period.
The Russian beef market experienced a drastic decrease in consumption during two years under countersanctions. In 2013 constant prices, the average real of 1 kg of beef increased by 5.3% from 357 Rub/kg in 2013 up to 376 Rub/kg in 2016. Domestic output decreased by 0.8% and to 238 thousand tons in 2016 from 240 in 2013. Domestic consumption decreased by 33.1% to 600 thousand tons in 2016 from 897 in 2013. Our estimations indicate that consumer losses amount to 11.3 bn Rub or 3.5% of beef consumption in 2013; producers’ gains are 4.4 bn Rub or 1.4%; deadweight losses are estimated at 0.2 bn Rub or 0.07%; and importers’ gains equal 6.7 bn Rub or 2.1%.
Out of total 6.7 bn Rub of importers’ gains, importers from Belarus acquire the major share (88%) – 5.9 bn Rub. Importers of beef from India and Colombia gained 0.4 bn Rub (6% of total) and 0.3 bn Rub (5%) respectively. Beef importers from Mongolia gained 0.03 bn Rub, from Kazakhstan – 0.01 bn Rub. Importers of beef from Brazil, Paraguay, Australia, Uruguay, Ukraine, Lithuania, Poland, and Argentina lost market shares in over the period 2013-2016.
Average real price for 1 kg of cheese increased by 26.5% up to 358 Rub/kg in 2016 from 283 Rub/kg in 2013, both in constant 2013 prices. Domestic output increased by 39.1% to 605 thousand tons in 2016 from 435 thous. tons in 2013. Domestic consumption decreased by 2.1% to 748 thous. tons in 2016 from 764 thous. tons in 2013. Our results indicate the following effects of countersanctions on cheese market: consumers’ losses amounted to 63.5 bn Rub or 29.4% of cheese consumption in 2013; producer’s gain is 44.3 bn Rub or 20.5%; deadweight loss is estimated at 8.4 bn Rub or 3.9%; importers’ gains equal 10.8 bn Rub or 5.0%.
Out of a total 10.8 bn Rub of importer’s gains on the cheese market, importers of cheese from Belarus acquired the major share (82.9%) – 9.0 bln Rub, importers of cheese from Argentina gained 0.5 bn Rub (4.8% of total importers’ gain), importers from Uruguay gained 0.4 bn Rub (3.9%), Swiss cheese importers gained 0.2 bn Rub, importers from Armenia – 0.2 bn Rub (1.8%). While importers of cheese from Ukraine, the Netherlands, Germany, Finland, Poland, Lithuania, France, Denmark, Italy, and Estonia lost market access over 2013-2016.
In 2013 constant prices, average real price for 1 kg of apples increased by 20.0% up to 84 Rub/kg in 2016 from 70 Rub/kg in 2013. Domestic output increased by 3.5% to 324 thous. tons in 2016 from 313 thous. tons in 2013. Domestic consumption decreased by 40.8% to 986 thous. tons in 2016 from 1665 thous. tons in 2013. According to our analysis, the effects of countersanctions on the apple market are the following: consumers’ losses amounted to 15.2 bn Rub or 13.1 of apple consumption in 2013; producer’s gain is 4.6 bn Rub or 3.0%; deadweight loss is estimated at 1.2 bn Rub or 1.1%; importers’ gains equal 9.4 bln Rub or 8.1%.
Out of a total 9.4 bn Rub of importer’s gains, importers from Serbia acquired the major share (49.7%) – 4,7 bn Rub, importers of apples from China gained 1.6 bn Rub (16.7% of total importers’ gains), those importing from Macedonia gained 0.8 bn Rub (8.4%), from Azerbaijan 0.6 bn Rub (6.0%), and from South Africa 0.4 bn Rub (4.5% of total importers’ gains). While importers of apples from Poland, Italy, Belgium, and France lost market access.
Overall effects for 12 commodity groups
We calculated the welfare effects for 12 commodity groups: beef, poultry, milk, cheese, cottage cheese, ton butter, dairy products, apples, bananas, oranges, grapes and tomatoes.
Total consumers’ loss due to countersanctions amounts to 288 bn Rub, producers gain 63% out of this amount (182 bn Rub), 26% of total consumers’ loss is redistributed to importers (75 bn Rub), deadweight losses amount to 10% (31 bn Rub).
Distribution of importers’ gains
Belarus is the major beneficiary of Russians countersanctions: its exporters gain 29.4 bn Rub (38%), Ecuador’s exporters are in the second place with 16.4 bln Rub (21). Exporters from Serbia gained 5.1 bn Rub (7%).
There is no doubt that countersanctions were paid out of the pockets of Russian consumers: our estimation of total consumer losses amounts to 288 billion rubles, i.e. each Russian citizen paid 2000 rubles per year. Out of this sum, Russian producers received 144 billion rubles, i.e. transfer from Russian consumers to producers equals 1260 rubles per person per year. Among Russian sectors, major gains and associated increases in production happened in pork industries (50%), poultry (20%), dairy products (10-30%), fruit and vegetables (10-50%).
The transfer from Russian consumers toward importers from non-sanctioned countries equals 75 billion rubles a year (520 rubles per person per year), out of which 30% was acquired by Belarusian importers. Countersanctions lead to deadweight losses in the efficiency of Russian economy equal to 31 billion rubles or 215 rubles per person per year.
Do ethnic networks facilitate international trade when formal institutions are weak? Using data collected by ethnologists on the share of ethnic groups across countries, this study assesses the effect of ethnic networks on bilateral trade across the sphere of the former Soviet Union. This region provides a perfect setting to test for this effect as both forced re-settlement of entire ethnic groups during the Stalin era and artificially drawn borders in Central Asia led to an exogenous ethnic composition within countries. While ethnic networks do not seem to have played a role in inter-republic trade during the Soviet Union, they did facilitate trade in the years following the collapse of the Soviet Union, a transitional period when formal institutions were weak. This effect, however, eroded steadily from the early 2000s.
Economists and historians alike study the role of ethnic networks in international trade. Some prominent examples are the Greek commercial diaspora of the Black Sea in the 19th century (Loannides and Minoglou, 2005), the Maghribi traders in 11th-century North Africa (Greif, 1993), or the overseas Chinese all around the world in the last decades (Rauch and Trindade, 2002). Such networks facilitate trade by building trust relationships, enforcing contractual agreements in weak legal environments, matching buyers with faraway sellers that speak different languages, and by exchanging information on arbitrage opportunities.
In “Ethnic Minorities and Trade: The Soviet Union as a Natural Experiment”, forthcoming in The World Economy, we study the Soviet Union (USSR) to assess the role of ethnic networks in international trade. We argue that ex-USSR countries are particularly well suited for such a study. Indeed, the ethnic diversity of ex-USSR countries is exogenous, partly due to the creation of artificial borders cutting through ethnic homelands, and partly due to forced relocations (deportations) during the Stalin era, which brought ethnic groups to various remote regions of the USSR. This exogeneity adds power to our empirical strategy.
Ethnic Networks in the USSR
We first build a measure of ethnic networks based on the size of common ethnic groups using ethnologists’ data from the Ethnic Power Relations Dataset on the resulting ethnic groups across ex-USSR countries (Vogt et al., 2015; Bormann et al., Forthcoming). It covers all ethnic groups in every country of the world from 1946 to 2013. While there is some yearly variation in the data, we focus on the cross-section average for the pre-1991 period as per our identification strategy based on exogenous distributions.
Figure 1 gives an overview of the spatial distribution of ethnic groups, such as Russian, Kazakh, or Uzbek.
Figure 1. Ethnic Groups in the USSR
Russians are ubiquitous across the Soviet sphere. Countries with the largest ethnic Russian populations are Kazakhstan, Estonia, Latvia and Moldova. At the same time, Russia is very diverse. Almost all of the 60 ex-USSR ethnic groups are present in Russia, and ethnic Russians account for only 62% of the population. Most countries are ethnically diverse. Kazakhstan for example is home to Russians as well as Germans, Tatars, Ukrainians, Uzbeks and Uighurs.
From the information on ethnic populations within each country, we create an ethnic network index as the sum of products of common ethnic groups as a share of the country’s population. Figure 2 presents a matrix overview of the ethnic network index among country pairs with darker shades corresponding to higher scores. Some high scoring country pairs are Russia—Kazakhstan, Ukraine—Russia, Uzbekistan—Tajikistan, Kyrgyzstan—Uzbekistan, Latvia—Kazakhstan, and Ukraine—Kazakhstan.
Figure 2. Ethnic Networks Index
Effect of Ethnic Networks on Bilateral Trade in the USSR
Next, we evaluate the impact of ethnic networks on aggregate trade between the countries of the former Soviet sphere. We use trade data from two sources. First, the data on internal trade between Soviet republics from 1987 to 1991 are from the input-output tables of each Soviet Union republic as compiled by the World Bank mission to the Commonwealth of Independent States (Belkindas and Ivanova, 1995). Second, the Post-1991 to 2009 trade data are from the Correlates of War Project (Barbieri et al., 2009, 2016), which offers the best coverage of the trade in the region.
We follow the migrant network and trade literature and estimate a standard log-linear gravity equation controlling for importer-year and exporter-year fixed effects (Anderson and van Wincoop, 2003).
Figure 3 presents the results on the effect of ethnic networks on trade over time. We observe that there is no effect in the period before the end of the USSR, a positive effect after the breakup of the Soviet Union, and an erosion of this effect from 2000s on (omitting Russia from the sample does not alter the results).
These results can be explained with the fact that in the Soviet Union ethnic ties did not matter as official production and trade were centrally planned by the State Planning Committee, Gosplan, and by State Supplies of the USSR, or Gossnab, which was in charge of allocating producer goods to enterprises. Free trade was forbidden. However, once the Soviet system collapsed and before countries could establish more formal trade ties, the first reaction and fallback option for many people was to reach out to their co-ethnics (in the 1990s) to substitute for the broken chains of the centrally planned trade (Gokmen, 2017). The other reason is that the institutional framework was at its weakest in this transitional period, and hence, reliance on informal institutions such as ethnic networks may have been especially strong (Greif, 1993). Once systematic and formal trade ties could be established, more and more traders no longer had to rely on their ethnic networks and this could explain the decline in the effect in the 2000s.
Figure 3. The Effect of Ethnic Networks on Trade over Time
This study shows that ethnic minorities played a role in shaping trade patterns across ex-USSR countries, but only in the early years following the collapse of the Soviet Union. Thus, we argue that reliance on informal institutions, such as ethnic networks, in forming trade relations is especially strong when the institutional framework is at its weakest in the transition period. This message may hold, not only for transition countries, but also for other developing countries with poor institutions.
- Anderson, J. E. and E. van Wincoop, 2003. “Gravity with Gravitas: A Solution to the Border Puzzle,” American Economic Review, 93, 170-192.
- Barbieri, K., M. G. Omar, and O. Keshk, 2016. “Correlates of War Project Trade Data Set Codebook, Version 4.0.”
- Barbieri, K., M. G. Omar, O. Keshk, and B. Pollins, 2009. “TRADING DATA: Evaluating our Assumptions and Coding Rules,” Conflict Management and Peace Science, 26, 471-491.
- Belkindas, M. and O. Ivanova, 1995. “Foreign Trade Statistics in the USSR and Successor States,” Tech. rep., The World Bank, Washington, DC.
- Bormann, N. C., L. E. Cederman, and M. Vogt, Forthcoming. “Language, Religion, and Ethnic Civil War,” Journal of Conflict Resolution.
- Gokmen, G., 2017. “Clash of civilizations and the impact of cultural differences on trade,” Journal of Development Economics, 127, 449-458.
- Gokmen, Gunes; Elena Nickishina; and Pierre-Louis Vezina, forthcoming. “Ethnic Minorities and Trade: The Soviet Union as a Natural Experiment”, The World Economy.
- Greif, A., 1993. “Contract enforceability and economic institutions in early trade: The Maghribi traders’ coalition”, The American Economic Review, 525-548.
- Loannides, S.; and I. P. Minoglou, 2005. “Diaspora Entrepreneurship between History and Theory”, London: Palgrave Macmillan UK, 163-189.
- Rauch, J. E. and V. Trindade, 2002. “Ethnic Chinese networks in international trade”, Review of Economics and Statistics, 84, 116-130.
- Vogt, M., N. C. Bormann, S. Regger, L. E. Cederman, P. Hunziker, and L. Girardin, 2015. “Integrating Data on Ethnicity, Geography, and Conflict: The Ethnic Power Relations Dataset Family,” Journal of Conflict Resolution, 1327-1342.
This year marks the 25-year anniversary of the breakup of the Soviet Union and the beginning of a transition period, which for some countries remains far from completed. While several Central and Eastern European countries (CEEC) made substantial progress early on and have managed to maintain that momentum until today, the countries in the Commonwealth of Independent States (CIS) remain far from the ideal of a market economy, and also lag behind on most indicators of political, judicial and social progress. This policy brief reports on a discussion on the unfinished business of transition held during a full day conference at the Stockholm School of Economics on May 27, 2016. The event was organized jointly by the Stockholm Institute of Transition Economics (SITE) and the Swedish Ministry for Foreign Affairs, and was the sixth installment of SITE Development Day – a yearly development policy conference.
A region at a crossroads?
25 years have passed since the countries of the former Soviet Union embarked on a historic transition from communism to market economy and democracy. While all transition countries went through a turbulent initial period of high inflation and large output declines, the depth and length of these recessions varied widely across the region and have resulted in income differences that remain until today. Some explanations behind these varied results include initial conditions, external factors and geographic location, but also the speed and extent to which reforms were implemented early on were critical to outcomes. Countries that took on a rapid and bold reform process were rewarded with a faster recovery and income convergence, whereas countries that postponed reforms ended up with a much longer and deeper initial recession and have seen very little income convergence with Western Europe.
The prospect of EU membership is another factor that proved to be a powerful catalyst for reform and upgrading of institutional frameworks. The 10 countries that joined the EU are today, on average, performing better than the non-EU transition countries in basically any indicator of development including GDP per capita, life expectancy, political rights and civil liberties. Even if some of the non-EU countries initially had the political will to reform and started off on an ambitious transition path, the momentum was eventually lost. In Russia, the increasing oil prices of the 2000s brought enormous government revenues that enabled the country to grow without implementing further market reforms, and have effectively led to a situation of no political competition. Ukraine, on the other hand, has changed government 17 times in the past 25 years, and even if the parliament appears to be functioning, very few of the passed laws and suggested reforms have actually been implemented.
Evidently, economic transition takes time and was harder than many initially expected. In some areas of reform, such as liberalization of prices, trade and the exchange rate, progress could be achieved relatively fast. However, in other crucial areas of reform and institution building progress has been slower and more diverse. Private sector development is perhaps the area where the transition countries differ the most. Large-scale privatization remains to be completed in many countries in the CIS. In Belarus, even small-scale privatization has been slow. For the transition countries that were early with large-scale privatization, the current challenges of private sector development are different: As production moves closer to the world technology frontier, competition intensifies and innovation and human capital development become key to survival. These transformational pressures require strong institutions, and a business environment that rewards education and risk taking. It becomes even more important that financial sectors are functioning, that the education system delivers, property rights are protected, regulations are predictable and moderated, and that corruption and crime are under control. While the scale of these challenges differ widely across the region, the need for institutional reforms that reduce inefficiencies and increase returns on private investments and savings, are shared by many.
To increase economic growth and to converge towards Western Europe, the key challenges are to both increase productivity and factor input into production. This involves raising the employment rate, achieving higher labor productivity, and increasing the capital stock per capita. The region’s changing demography, due to lower fertility rates and rebounding life expectancy rates, will increase already high pressures on pension systems, healthcare spending and social assistance. Moreover, the capital stock per capita in a typical transition country is only about a third of that in Western Europe, with particularly wide gaps in terms of investment in infrastructure.
Unlocking human potential: gender in the region
Regardless of how well a country does on average, it also matters how these achievements are distributed among the population. A relatively underexplored aspect of transition is to which extent it has affected men and women differentially. Given the socialist system’s provision of universal access to education and healthcare, and great emphasis on labor market participation for both women and men, these countries rank fairly well in gender inequality indices compared to countries at similar levels of GDP outside the region when the transition process started. Nonetheless, these societies were and have remained predominantly patriarchal. During the last 25 years, most of these countries have only seen a small reduction in the gender wage gap, some even an increase. Several countries have seen increased gender segregation on the labor market, and have implemented “protective” laws that in reality are discriminatory as they for example prohibit women from working in certain occupations, or indirectly lock out mothers from the labor market.
Furthermore, many of the obstacles experienced by small and medium-sized enterprises (SMEs) are more severe for women than for men. Female entrepreneurs in the Eastern Partnership (EaP) countries have less access to external financing, business training and affordable and qualified business support than their male counterparts. While the free trade agreements, DCFTAs, between the EU and Ukraine, Georgia, and Moldova, respectively, have the potential to bring long-term benefits especially for women, these will only be realized if the DCFTAs are fully implemented and gender inequalities are simultaneously addressed. Women constitute a large percentage of the employees in the areas that are the most likely to benefit from the DCFTAs, but stand the risk of being held back by societal attitudes and gender stereotypes. In order to better evaluate and study how these issues develop, gendered-segregated data need to be made available to academics, professionals and the general public.
Looking back 25 years, given the stakes involved, things could have gotten much worse. Even so, for the CIS countries progress has been uneven and disappointing and many of the countries are still struggling with the same challenges they faced in the 1990’s: weak institutions, slow productivity growth, corruption and state capture. Meanwhile, the current migration situation in Europe has revealed that even the institutional development towards democracy, free press and judicial independence in several of the CEEC countries cannot be taken for granted. The transition process is thus far from complete, and the lessons from the economics of transition literature are still highly relevant.
Participants at the conference
- Irina Alkhovka, Gender Perspectives.
- Bas Bakker, IMF.
- Torbjörn Becker, SITE.
- Erik Berglöf, Institute of Global Affairs, LSE.
- Kateryna Bornukova, Belarusian Research and Outreach Center.
- Anne Boschini, Stockholm University.
- Irina Denisova, New Economic School.
- Stefan Gullgren, Ministry for Foreign Affairs.
- Elsa Håstad, Sida.
- Eric Livny, International School of Economics.
- Michal Myck, Centre for Economic Analysis.
- Tymofiy Mylovanov, Kyiv School of Economics.
- Olena Nizalova, University of Kent.
- Heinz Sjögren, Swedish Chamber of Commerce for Russia and CIS.
- Andrea Spear, Independent consultant.
- Oscar Stenström, Ministry for Foreign Affairs.
- Natalya Volchkova, Centre for Economic and Financial Research.
‘Diversification’ is a constant concern of policy-makers in resource rich economies, but measurement of diversification can be hard. The recently formulated Economic Complexity Index (ECI) is a promising predictor of economic development characterizing the overall complexity and diversity of the economy as a system. The ECI is based on the diversity and ubiquity of a country’s exports. This brief uses ECI to discuss the economic diversity of transition economies in the post-Soviet decades, and the relationship between economic diversification and per capita income.
The search for and construction of appropriate predictors of economic development are among the main goals of economists and policy-makers. Education, infrastructure, rule of law, and quality of governance are all among the commonly used indicators based on inputs. The recently formulated Economic Complexity Index (Hidalgo and Hausmann, 2009) is a new promising predictor of economic development characterizing the overall complexity and diversity of the economy as a system.
Indeed, the importance of production and trade diversification for economic development has been highlighted by the economic literature. Numerous studies have found a positive relationship between diversified and complex export structure, income per capita and growth (Cadot et al., 2011; Hesse, 2006; Hausmann et al., 2007). In line with this, Hausmann et al. (2014) demonstrate the predictive properties of the ECI for economic development and GDP per capita, which implies that the ECI can serve as a useful complement to the input-based measures for policy analysis by reasoning from current outputs to future outputs.
This brief uses the ECI to discuss the evolution of economic diversification, its relationship to per capita income in transition economies in the post-Soviet decades, and its policy implications.
How is economic complexity measured?
The economic complexity index (ECI) is a novel measure that reflects the diversity and ubiquity of a country’s exports. The index considers the number of products a country exports with revealed comparative advantage and how many other countries in the world export such goods. If a country exports a high number of goods and few other countries export these products, then its economy is diversified (a wide range of exports products) and sophisticated (only a few other countries are able to export these goods). Thus, the measure tries to capture not a specific aspect of the economy, but rather its overall sophistication.
For example, Japan, Switzerland, Germany and Sweden have been in a varying order at the top of the ranking of the Economic Complexity Index from 2008 until 2013. This means that these countries export a large number of highly sophisticated products.
In contrast, Tajikistan is among the countries at the bottom of the world ranking by the ECI with raw aluminum, raw cotton and ores making up 85% of all Tajikistan’s exports in 2013. However, not only are Tajikistan’s exports concentrated among very few narrow products, these products are also ubiquitous and the ability to export them does not require knowledge and skills that can be used in the production and exports of many other products.
As the index for each country is constructed relative to other countries’ exports, it is comparable over time.
What can we learn from the economic complexity of transition economies?
The economic complexity index can serve as a useful indicator for understanding transition economies in the post-Soviet period. A strong relationship between GDP per capita and economic complexity is found in the sample of transition economies in Figure 1. This figure presents the relationship for the last year for which data is available for the sample of 13 post-Soviet states and Poland. As can be seen in Figure 1, the economic complexity is positively related to income per capita. This is especially true for Poland, Estonia, Lithuania, Latvia and Russia, who all have higher than average economic complexity and high levels of per capita income. While Belarus and Ukraine also have diverse and complex economies, they have somewhat lower income per capita than the first group.
Figure 1. Economic Complexity and GDP per capita
Source: Data on GDP per capita is from the World Bank, and the data on the Economic Complexity Index is from the Observatory of Economic Complexity.
Natural resource-rich, or rather, oil-rich countries are the exception from the abovementioned correlation. Most transition countries with below than average economic complexity are characterized by low income per capita levels, except for Kazakhstan and Azerbaijan, which are oil-rich countries. Still, the overall picture is straightforward: countries with a complex export structure have a higher level of income.
One of the advantages of a systemic measure like export complexity is its straightforward policy application. The overall diversity and sophistication of the economy can thus be a complementary measure for the assessment of economic progress and development to GDP and GDP per capita, which are more susceptible to the volatile factors such as commodity prices.
Figure 2 shows the development of economic complexity for 14 post-Soviet countries and Poland between 1994 and 2013 (due to data availability issues, only one year is available for Armenia).
First, we see that the economic complexity has diverged over time, although there is some similarity in the rankings among countries over time. The initial closeness is likely related to the planned nature of the Soviet economy that aimed to distribute production among Soviet Republics. In the post-Soviet context, however, the more complex economies (Estonia, Belarus, Lithuania, Ukraine, Latvia, Russia) kept or increased their sophistication and diversity of exports. Poland is the leading economy in terms of complexity, both in the beginning and towards the end of the sample period. Belarus, the second most complex economy in 2013 and the most complex economy in several years prior, shows an increasing trend in its sophistication of exports. Although its GDP per capita is noticeably lower than what would be expected from such a sophisticated economy, the complex production structure may explain its ability to withstand a permanent high inflation and external macroeconomic shocks. Some others, e.g., Tajikistan and Azerbaijan, saw a decreasing trend in economic complexity; Georgia and Kazakhstan, notably, lost in economic complexity but also in their ranking among their peers.
Figure 2. Economic Complexity of Transition Economies
Source: Data on GDP per capita is from the World Bank, and the data on the Economic Complexity Index is from the Observatory of Economic Complexity.
This brief revisited the economic complexity of transition economies and its evolution since the 1990s. The post-Soviet and other transition countries have had diverging economic development paths: Some have managed to build complex production economies, while others’ comparative advantage remains in raw materials. These differences are also reflected in their income levels.
Across the world, economic diversification is associated with higher per-capita income. As the brief showed, this relationship also holds for the post-Soviet countries; policy-makers should take economic diversification seriously. Increasing economic complexity may well pave the path to higher income levels.
- Cadot, O., Carrère, C., & Strauss-Kahn, V. (2011). Export diversification: What’s behind the hump?. Review of Economics and Statistics, 93(2), 590-605.
- Hausmann, R., Hidalgo, C. A., Bustos, S., Coscia, M., Simoes, A., & Yildirim, M. A. (2014). The atlas of economic complexity: Mapping paths to prosperity. Mit Press.
- Hausmann, R., Hwang, J., & Rodrik, D. (2007). What you export matters. Journal of economic growth, 12(1), 1-25.
- Hesse, H. (2006). Export diversification and economic growth. World Bank, Washington, DC.
- Hidalgo, C. A., & Hausmann, R. (2009). The building blocks of economic complexity. proceedings of the national academy of sciences, 106(26), 10570-10575.
It is a commonly held view that the Eurasian Economic Union (EAEU) is a political enterprise (Popescu, 2014) that has little economic meaning other than redistribution of oil rents (Knobel, 2015). With a new reality of low oil prices and reduced rents, a legitimate question is how stable this Union is, or whether there is any hope for mutual economic benefits that can provide incentives to all the participants to maintain their membership in the Union? Our answer is yes, there is hope, but only if countries, especially Russia, make progress on deep integration such as services liberalization, trade facilitation, free movement of labor and especially in the reduction of the substantial non-tariff barriers (NTBs). NTBs are hampering trade both within the Union (World Bank, 2012; Vinokurov, 2015), as well as against third country imports. Our research shows (see Knobel et al., 2016) that all the EAEU members will reap benefits from a reduction of NTBs against each other, but they will obtain considerably more substantial gains from a reduction in NTBs against imports from the EU and China.
Since the early stages of creation of the Customs Union (CU) between Belarus, Kazakhstan, and Russia back in 2010, the economic benefits of the CU have been questionable. The main reason for this in Kazakhstan was the increase in its import tariffs in order to implement the common external tariff of the CU, which initially was Russia’s external tariff (Tarr, 2015). Kazakhstan almost doubled its average tariff from 5.3% to 9.5% (Shepoltylo, 2011; Jondosov and Sabyrova, 2011) in the first year of its CU accession. Belarus did not increase its average tariff, but the structure of its tariffs shifted toward a protection of Russian industry.
In 2015, the CU was transformed into the EAEU, and Armenia and Kyrgyz Republic have joined the EAEU. These two countries are WTO members; Kyrgyzstan entered the WTO in 1998, and Armenia in 2001. In 2014, the simple average most-favored nation (MFN) applied tariff rate in Armenia was 3.7%, and 4.6% in the Kyrgyz Republic. Due to differences between Armenia and Kyrgyzstan’s WTO commitments and the EAEU tariff schedule, the new members of the EAEU are not implementing the full EAEU tariff schedule. That is, they have numerous exemptions. However, they have started a WTO commitments modification procedure.
Despite adverse impacts from the higher import prices from implementing the common external tariff of the EAEU in Armenia and the Kyrgyz Republic, there are potentially offsetting gains. Given the importance of remittances to the Kyrgyz Republic, the benefits coming from the right of workers to freely move and legally work inside EAEU likely dominate the tariff issues. Armenia also benefits from the free movement of labor, receives Russian gas free of export duties, and wants to preserve the military guarantee granted by Russia through the six-country Collective Security Treaty Organization.
In the case of Belarus, it receives Russian oil and natural gas free of export-duties, which, when oil prices were high, tended to dominate their calculus. Kazakhstan hopes for more FDI as a platform for selling to the EAEU market; but President Nazarbaev has expressed concerns that the EAEU is not providing net benefits to his country.
To date, the members have judged participation to be in their interest, but with the plunge in the price of oil and gas, the calculus could swing against participation in the EAEU. That is why it is so important to achieve progress with deep integration in the EAEU. One of the most important areas of deep integration for the EAEU is the substantial reduction of non-tariff barriers in goods trade, both between the EAEU members and against third countries. Estimates by the Eurasian Development Bank (Vinokurov et al., 2015) reveal that NTBs account are 15% of the value of intra-union trade flows.
In our paper, Knobel et al (2016), we estimate substantial gains to all the EAEU members from a reduction of NTBs. We employ a global computable general equilibrium model with monopolistic competition in the Helpman-Krugman style based on Balisteri, Yonezawa, Tarr (2014). Estimates of the ad-valorem equivalents of NTBs were based on Vinokurov et al (2015) for the EAEU member countries and Kee, Nicita, Olarreaga (2009) for non-members.
We find that the effects of deep integration are positive for all countries of the EAEU. Armenia’s accession to the EAEU will have a strong positive effect only if coupled with a decrease of non-tariff barriers. Armenian accession is associated with an increase in external tariffs, which causes a negative economic impact and decrease in output.
The effect of deep integration in the EAEU will be even greater if any spillovers effect reducing NTBs for EAEU’s major trading partners are present. Knobel et al. (2016) simulate a 50% decrease in “technical” NTBs inside the EAEU and a 20% spillover effect of reduction NTBs toward either the EU and USA or China. Reduction of NTBs in trade with the EU and the USA dominates the comparable reduction of NTBs with China for all countries of the EAEU in terms of the welfare gain. Armenia’s welfare gain with a spillover effect towards the EU is 1.1% of real consumption compared to 1.02% with a spillover effect towards China. Growth in welfare in Belarus will be 2.7% with a EU spillover versus 2.5% with a spillover effect towards China. Kazakhstan’s gain in real consumption is also greater in the first (EU+USA) case: 0.86% versus 0.66% (with spillover towards China). Russia’s gain in real consumption in the case of a spillover effect with the EU is 2.01% versus 0.63% in the case of China.
Summing up, our findings suggest an answer to the recent concern about stability of the EAEU. We think that eliminating NTB, hampering mutual trade, and decreasing NTBs in either European or Chinese direction could provide mutual economic benefits that could tie countries of the EAEU together, thereby giving a much needed solid economic ground for the Union.
- Balistreri, Edward J., Tarr, David G. and Hidemichi Yonezawa (2014). Reducing trade costs in east Africa : deep regional integration and multilateral action (No. 7049).
- EEC (2015) Eurasian economic integration: facts and figures, (in Russian).
- Kee, Hiau Looi, Nicita, Alessandro, and Marcelo Olarreag (2009) Estimating Trade Restrictiveness Indices, Economic Journal, 119, 172–199.
- Knobel, Alexander (2015) Eurasian Economic Union: Prospects and Challenges for Development, Voprosy Ekonomiki, 2015, No. 3, pp. 87—108. (in Russian).
- Knobel, Alexander, Andrey Lipin, Andrey Malokostov, David G. Tarr, and Natalia Turdyeva (2016) Non-tariff barriers and trade integration in the EAEU, mimeo
- Plekhanov, Alexander and Asel Isakova (2012) Customs Union and Kazakhstan’s Imports (July 1, 2012). CASE Network Studies and Analyses No. 422.
- Popescu, Nicu (2014), “Eurasian Union: the real, the imaginary and the likely,” Chaillot Paper – No. 132, European Union Institute for Security Studies, September 9.
- Shepotylo, Oleksandr (2011), “Calculation of the tariff rates of Kazakhstan before and after the imposition of the customs union common external tariff in 2010.” Available as part of World Bank (2012), Assessment of Costs and Benefits of the Customs Union for Kazakhstan, Report Number 65977-KZ, Washington DC, January 3, 2012.
- Tarr, David G. (2015) The Eurasian Economic Union among Russia, Belarus, Kazakhstan and Armenia: Can it succeed where its predecessor failed? Paper prepared for the BOFIT conference of the TIGER project, Helsinki, Finland, September 16, 17, 2015
- Vinokurov, Evgeny, Mikhail Demidenko, Igor Pelipas, Irina Tochitskaya, Gleb Shymanovich, Andrey Lipin (2015) Measuring the Impact of Non-Tariff Barriers in the Eurasian Economic Union: Results of Enterprise Survey. EDB Centre for Integration Studies Report no. 30, EDB: Saint-Petersburg.
- World Bank (2012), Assessment of Costs and Benefits of the Customs Union for Kazakhstan, Report Number 65977-KZ, Washington DC, January 3, 2012