Location: Kazakhstan
Trade Diversification, Export Complexity, and Structural Transformation in the South Caucasus and Central Asia
This policy paper examines trade diversification, export sophistication, and economic complexity in the South Caucasus and Central Asia during 2019–2024. Using detailed product-level trade data, it assesses how concentrated or diversified countries’ exports and imports are, as well as changes in the sophistication of the products they export. Evidence from the Atlas of Economic Complexity is also used to evaluate diversification opportunities based on countries’ productive capabilities.
The results reveal substantial heterogeneity across the region. Georgia and Kazakhstan maintain relatively diversified export structures, while Armenia and Azerbaijan exhibit increasing export concentration. Export sophistication improves modestly in several countries, particularly Armenia and Uzbekistan. Overall, the findings suggest gradual but uneven structural transformation across the region, with diversification into more complex export sectors remaining limited.
Introduction
International trade plays a central role in shaping economic growth and macroeconomic stability in the South Caucasus and Central Asia (CCA). The economies of the region are highly open, with trade flows accounting for a large share of GDP in most countries. This strong integration into global markets creates important opportunities for growth, but it also exposes these economies to fluctuations in global demand, commodity prices, and international supply chains, potentially with drastic consequences.
This exposure has become particularly concerning in the context of the global economic environment since 2019. A series of major shocks—including the COVID-19 pandemic, Russia’s invasion of Ukraine, and ongoing conflicts in the Middle East—have disrupted global supply chains, energy markets, and transport corridors. At the same time, geopolitical tensions and shifting industrial and trade policies have increased global policy uncertainty. Frequent changes in tariff policies and strategic trade measures by major economies, including the United States, have further contributed to an increasingly uncertain global trading environment.
In this context, the resilience of national economies depends not only on the scale of trade but also on its structure. Countries with concentrated export baskets or strong dependence on a small number of trading partners are typically more vulnerable to external shocks. By contrast, economies with diversified exports and greater participation in higher-value production tend to be more resilient and better positioned for long-term growth. Three related concepts—trade diversification, product sophistication, and economic complexity—provide useful tools for evaluating these structural characteristics. Diversification captures the breadth of the export basket; product sophistication reflects the income and knowledge intensity of exported goods; and economic complexity reflects the broader productive capabilities that underpin them.
This research brief examines the trade structures of the South Caucasus and Central Asian countries through these three dimensions. Using detailed product-level international trade data, the analysis evaluates export and import diversification, the income and technological content of export baskets, and the broader productive capabilities reflected in economic complexity indicators. By comparing patterns across countries and over time, the brief provides new insights into how the region’s economies are positioned to navigate an increasingly uncertain global trading environment.
Stylized Facts: External Balances and Trade Structure
Recent data highlight two closely related characteristics of South Caucasus and Central Asian economies: substantial variation in external balances and strong exposure to international trade. Current account positions differ significantly across the region and fluctuate over time, reflecting differences in export structures, commodity dependence, and import demand (Figure 1). Resource-rich economies such as Azerbaijan, Kazakhstan, and Turkmenistan periodically record sizable surpluses driven largely by oil and natural gas exports, while several other economies experience persistent deficits associated with narrower export bases and higher reliance on imports. Particularly large deficits were observed in the Kyrgyz Republic during 2022–2023, illustrating the sensitivity of smaller economies to shifts in trade flows and external demand. These dynamics are closely linked to the high degree of trade openness observed across the region: smaller economies such as Georgia, Armenia, and the Kyrgyz Republic exhibit particularly high trade-to-GDP ratios, while larger economies such as Kazakhstan and Uzbekistan show somewhat lower—but still substantial—levels of trade exposure.
Figure 1. Current Account as a percentage share of GDP (2019-2025)

Source: Authors’ calculations, IMF
Figure 2 summarizes the geographic composition of trade across the region and highlights the continued importance of a relatively small number of external partners. The European Union, Russia, China, and other CIS economies dominate both export destinations and import sources. Comparing 2019 and 2024, several broad regional shifts emerge. On the export side, the European Union increased its relative importance as a destination for many exports—particularly energy and resource-based products; exports directed to Russia and other CIS markets also grew in importance after 2022. In turn, the share going to the residual category of other countries fell substantially. On the import side, Russia and China strengthened their positions as key suppliers across much of the region, reflecting geographic proximity, established transport corridors, and China’s growing role in regional trade. Imports from the European Union remained important, especially for machinery, equipment, and higher-value manufactured goods.
Overall, despite some adjustments between 2019 and 2024, the region’s trade patterns remain concentrated among a relatively small group of partners. This concentration increases exposure to destination-specific shocks and may weaken trade resilience. It is therefore important to assess not only how diversified exports are, but also how sophisticated and capability-intensive they are, since these characteristics affect an economy’s ability to adapt and redirect trade over time.
Figure 2. Geographic Structure of Regional Exports and Imports for South Caucasus and Central Asian economies, 2019 vs. 2024

Source: Authors’ Calculation, UN Comtrade. Note: Shares of major partner groups (European Union, Russia, China, CIS, and other countries) in total exports and imports of South Caucasus and Central Asian economies.
Methodology
To analyse the structure and evolution of trade patterns, the study employs four complementary indicators: the Herfindahl–Hirschman Index (HHI), the Theil index, the export sophistication indicators PRODY and EXPY, and the Economic Complexity Index (ECI). These indicators capture different aspects of trade structures, including concentration, diversification, technological sophistication, and productive capabilities embedded in economies.
Trade concentration is first evaluated using the Herfindahl–Hirschman Index (HHI). The index measures the extent to which a country’s exports or imports are concentrated across products. In this study, the index is calculated at the HS-4 product level, allowing for a detailed assessment of trade structures.
The Herfindahl–Hirschman Index is defined as:
\[
HHI_i^X = \sum_{k=1}^{N} \left(s_{ik}^X\right)^2
\]
\[
HHI_i^M = \sum_{k=1}^{N} \left(s_{ik}^M\right)^2
\]
where
\[
s_{ik}^X = \frac{X_{ik}}{X_i}
\]
\[
s_{ik}^M = \frac{M_{ik}}{M_i}
\]
Here
- $s_{ik}^X$ = the share of the product in country ’s total exports
- $s_{ik}^M$ = the share of the product in country ’s total imports
- $X_{ik}$ denotes exports of the product by country
- $M_{ik}$ denotes imports of the product by country
- $X_i$ denotes the total exports of country
- $M_i$ denotes the total imports of country
The index ranges between 0 and 1. Values close to zero indicate highly diversified trade structures, while values approaching one suggest strong concentration in a limited number of products.
The HHI provides a simple summary of whether trade is concentrated in a small number of products or partners. To complement this, the analysis also uses the Theil index, which captures how unevenly trade is distributed across all destinations or product categories. This distinction matters because similar levels of overall concentration can mask different underlying structures. In plain terms, the Theil index compares the observed trade distribution with a benchmark of equal shares across all categories: a value of zero indicates perfect equality, and the more uneven the distribution, the higher the index. As a result, comparing the two indicators allows a more nuanced assessment of whether concentration changes reflect dominance by a few categories or broader structural shifts in trade patterns.
Unlike the HHI, the Theil index can also be decomposed into within-group and between-group components, which helps identify the sources of concentration. The Theil index for exports and imports is defined as:
\[
T_i^X = \sum_{k=1}^{N} s_{ik}^X \ln\left(\frac{s_{ik}^X}{\bar{s}}\right)
\]
\[
T_i^M = \sum_{k=1}^{N} s_{ik}^M \ln\left(\frac{s_{ik}^M}{\bar{s}}\right)
\]
where
\[\quad \bar{s} = \frac{1}{N}
\]
and N represents the total number of HS-4 products, so that the terms in brackets measure how far the actual share allocation is from the equal share one. Higher values of the Theil index indicate greater concentration of trade across products, while lower values indicate higher diversification. Compared with the HHI, the Theil index has the advantage of being decomposable into within-sector and between-sector components. That is, the HHI can be broken down into group-specific contributions, but it does not provide the same standard additive decomposition with equally clear interpretation. allowing a more detailed examination of diversification patterns. The policy paper assesses export and import concentration using the HHI and Theil indices not only by product categories but also by trading partner countries (in the formulas above, products are replaced by countries).
While the HHI and Theil indices measure concentration and diversification, they do not say much about the type of goods a country exports. To capture this dimension, the analysis uses the PRODY and EXPY indicators introduced by Hausmann, Hwang, and Rodrik (2007). These indicators assess the sophistication of a country’s export bundle, inferring it from the characteristics of the countries that export the respective products. In particular, a product receives a higher PRODY value when it is exported more intensively by higher-income economies. A country’s EXPY then summarizes the sophistication of its overall export basket by taking a weighted average of the PRODY values of the goods it exports.
More specifically, the PRODY index measures the income content of a product and is calculated as the weighted average of the GDP per capita of countries exporting that product:
\[
PRODY_k = \sum_{c} \theta_{ck} \, Y_c
\]
where
\[
\theta_{ck} = \frac{\frac{X_{ck}}{X_c}}{\sum_{c’} \left(\frac{X_{c’k}}{X_{c’}}\right)}
\]
Here
- $Y_c$ denotes GDP per capita of country $c$,
- $X_{ck}$ denotes exports of product $k$ by country $c$,
- $X_c$ denotes total exports of country $c$,
In plain terms, PRODY asks whether a product is typically associated with richer or poorer exporters.
The PRODY calculation gives more weight to countries for which a product is relatively important in the export basket. This prevents the measure from being driven by very small or incidental exports. As a result, a product receives a high PRODY score when it is a meaningful export to richer economies, not merely when it appears in their trade data.
The weights $\theta_{ck}$ capture the relative importance of the product k in each country’s export basket. Products exported primarily by high-income economies, therefore, receive higher PRODY values.
Using the PRODY values of individual products, the sophistication of a country’s export basket is measured using the EXPY index:
\[
EXPY_i = \sum_{k} \left(\frac{X_{ik}}{X_i}\right) PRODY_k
\]
EXPY applies the same logic at the country level: it shows whether a country’s export basket is tilted toward products that are more commonly exported by higher-income economies. Higher EXPY values, therefore, suggest a more sophisticated export structure – they indicate that the country exports goods that are typically produced by higher-income economies.
Unlike the HHI, PRODY and EXPY do not lie between 0 and 1. Their values are expressed on the scale of the underlying income measure used in the data, so they are most informative in comparative terms across countries and over time. Also, empirical applications frequently use the natural logarithm of EXPY. This transformation reduces skewness and facilitates interpretation in regression analysis.
Finally, to capture the deeper productive capabilities embedded in economies, the analysis incorporates the Economic Complexity Index (ECI) developed by the Harvard Growth Lab and published in the Atlas of Economic Complexity. The ECI measures the knowledge intensity of an economy by combining information on the diversity of products a country exports and the ubiquity of those products across countries.
The calculation begins with the revealed comparative advantage (RCA) indicator:
\[
RCA_{ck} = \frac{\dfrac{X_{ck}}{X_c}}{\dfrac{\sum_{c} X_{ck}}{\sum_{c} X_c}}
\]
where
- $X_{ck}$ denotes exports of product $k$ by country $c$,
- $X_c$ denotes total exports of country $c$.
Countries are considered competitive exporters of product k if their revealed comparative advantage in that product exceeds 1. Based on this country–product matrix relationship, economic complexity is inferred from two simple ideas: diversity and ubiquity. Diversity refers to the number of different products a country can export competitively. Ubiquity refers to how many countries can export a given product competitively. Economies tend to be ranked as more complex (have a higher value of ECI) when they export a broad range of products that relatively few other countries can produce, because this indicates a deeper and more versatile set of productive capabilities.
Taken together, these indicators provide a comprehensive framework for analysing trade structures. The HHI and Theil indices measure trade concentration and diversification at the HS-4 product level for both exports and imports (as well as concentration by countries), the PRODY and EXPY indicators capture the income sophistication of export baskets, and the Economic Complexity Index reflects the underlying productive capabilities of national economies.
Results
The HHI results reveal significant cross-country differences in export diversification. Georgia and Kazakhstan consistently exhibit the lowest export concentration by destination country across the period, with HHI values remaining below 0.15. Although both countries experience a gradual increase in concentration over time, their exports remain comparatively diversified across destination countries relative to the rest of the region.
In contrast, Armenia and Azerbaijan show a noticeable increase in export concentration by destination country after 2021. Armenia’s export HHI rises sharply and remains close to the upper benchmark threshold by 2024, suggesting that exports became increasingly reliant on a smaller set of countries. Azerbaijan also shows a temporary increase in export concentration around 2022, followed by a modest decline by 2024, indicating partial normalization after the peak of external shocks.
The Kyrgyz Republic and Uzbekistan exhibit persistently higher average export concentration by destination country than other countries in the region. Kyrgyzstan reaches particularly high levels during the early pandemic years and remains relatively concentrated thereafter. Uzbekistan also maintains a relatively high concentration, although its export structure shows some signs of gradual diversification toward the end of the period. Tajikistan remains in the intermediate range, with export concentration by country relatively stable across years.
Import concentration patterns differ from those of exports. Several countries maintain relatively diversified import structures by source country throughout the period. Georgia and Azerbaijan show consistently low import HHI values, indicating broad import structures. However, for some other countries in the region, import concentration increases sharply during the shock period. Kazakhstan experiences a substantial increase during 2020–2022, followed by a return to lower levels in subsequent years. Armenia also records a sharp increase in import concentration in 2024, suggesting increased reliance on a narrower set of partner countries. Kyrgyzstan shows a gradual increase toward the end of the sample period.
Figure 3. HHI of Export by Country, 2019–2024

Source: Author’s Calculation, UN Comtrade
Figure 4. HHI of Import by Country, 2019–2024

Source: Author’s Calculation, UN Comtrade
Examining concentration at the HS4 product level provides additional insight into the structure of trade baskets. Changes in overall HHI may arise either because the product distribution becomes more uneven or because a small number of product categories become temporarily dominant.
The HS4-product level export results (Figure 5) reveal substantial cross-country variation in product concentration. Azerbaijan remains the most concentrated exporter throughout the period. Although the figure shows some decline in concentration in the earlier years, this change is not sustained, and the country’s export basket remains heavily concentrated in a narrow set of products. Kazakhstan also shows a relatively high concentration, although the decline after 2022 suggests some gradual diversification. In contrast, Georgia and Tajikistan maintain consistently low HHI values, indicating relatively diversified export baskets across HS4 product categories. Armenia and Uzbekistan remain in the intermediate range, although Armenia shows an increase in concentration in 2024.
Import concentration at the HS4 product level (Figure 6) remains generally lower than export concentration but exhibits greater volatility across countries. Most economies maintain relatively diversified import baskets, with HHI values typically below 0.05–0.06. However, several temporary spikes are visible. Armenia records a sharp increase in import concentration in 2024, suggesting growing reliance on a narrower set of imported goods. Kyrgyzstan experiences a pronounced spike in 2023, while Georgia shows a moderate increase during 2022–2023, then stabilizes. These fluctuations likely reflect temporary supply disruptions, shifts in trade routes, or changes in import demand during periods of economic and geopolitical shocks.
Figure 5. HHI of Export by HS4 Product Categories, 2019–2024

Source: Author’s Calculation, UN Comtrade
Figure 6. HHI of Import by HS4 Product Categories, 2019–2024

Source: Author’s Calculation, UN Comtrade
The country-level Theil index largely reinforces the message from the HHI analysis: across the region, recent changes in export concentration have been driven mainly by shifts in the distribution of exports across destination markets rather than by a restructuring of export baskets.
Armenia shows the clearest increase in geographic concentration, while Azerbaijan also remains relatively concentrated despite some normalization after the 2022 spike. Georgia remains the most geographically diversified case, and Kazakhstan, Kyrgyzstan, and Uzbekistan show only moderate changes over time. Overall, the Theil results add nuance rather than overturning the HHI findings: they suggest that the main source of recent concentration has been unevenness across partner countries, not a uniform narrowing of export structures across all economies.
At the product level, the Theil index points to a more nuanced picture. In several countries, product-level inequality declines or remains moderate even when destination-country concentration rises, suggesting that geographic concentration and product concentration do not always move together. This is especially important for interpretation: an economy may become more dependent on a smaller set of trading partners while still maintaining or even broadening the composition of its export basket. Azerbaijan remains the clearest case of persistently high product concentration, whereas Georgia continues to display a relatively diversified product structure.
In cases where the Theil and HHI measures differ somewhat, the gap likely reflects that the HHI is more sensitive to dominant categories, whereas the Theil index captures unevenness across the full distribution of trade shares.
The product-level Theil index (Figure 7) also provides additional insights into the composition of export baskets. Armenia, Kazakhstan, and Uzbekistan show noticeable declines in product-level inequality between 2019 and 2024, suggesting some diversification across product categories despite rising geographic concentration. This pattern indicates that while exports may increasingly rely on fewer destination countries, the underlying product composition has broadened.
In contrast, Azerbaijan maintains a relatively high product concentration, which is fully consistent with the HS4-product level HHI results showing the highest export concentration across product categories in the region. Georgia shows a slight increase in product-level inequality, although overall concentration remains relatively low compared to most other countries, confirming the diversified structure observed in the HHI product-level analysis.
Overall, the Theil index results reinforce the conclusions drawn from the HHI analysis while providing additional insight into the drivers of concentration. The evidence suggests that recent changes in trade structures across the South Caucasus and Central Asia are driven primarily by shifts in geographic export patterns rather than by widespread narrowing of product specialization. In several countries, product diversification appears to be improving even as exports become more concentrated across trading partners.
Figure 7. Theil Index by Country and Product Categories, 2019 and 2024

Source: Author’s Calculation, UN Comtrade. Note: Higher values indicate greater concentration in the distribution of products.
Export sophistication is measured by the EXPY index, with higher values indicating that a country exports products typically produced by higher-income economies.
Between 2019 and 2024, export sophistication increases for most countries in the region, although the magnitude of change varies (Figure 8). Armenia shows the largest improvement in EXPY, suggesting a shift toward higher-value exports. Uzbekistan and the Kyrgyz Republic also show moderate increases in export sophistication. In contrast, Azerbaijan, Georgia, and Kazakhstan experience only modest changes, indicating relatively stable export structures over the period.
Importantly, increases in export sophistication should be interpreted alongside changes in concentration indicators. When EXPY increases while export concentration remains low or declines, the improvement reflects broader structural upgrading. However, when increases in EXPY coincide with rising concentration, the shift may reflect specialization in a smaller number of higher-value products rather than broad-based diversification.
Figure 8. Export Sophistication (EXPY), 2019 and 2024

Source: Author’s Calculation, UN Comtrade. Note: Higher values indicate a more sophisticated export basket.
The previous indicators evaluate diversification and sophistication based on observed trade patterns. An additional perspective on structural transformation can be obtained by examining future diversification opportunities, using the feasibility analysis derived from the Atlas of Economic Complexity Index (ECI) developed by the Growth Lab at Harvard University (see Figure 9). This framework maps potential export opportunities based on the relationship between product sophistication and proximity to existing productive capabilities.
Figure 9. Economic Complexity Index (ECI), 2012- 2024

Source: Growth Lab at Harvard University
Across the South Caucasus and Central Asia, the feasibility analysis reveals substantial heterogeneity in the pace and depth of structural transformation. Armenia, Kazakhstan, and Uzbekistan show the most pronounced improvements in economic complexity over time, suggesting that a growing number of technologically more sophisticated products are becoming feasible given existing productive capabilities. This pattern indicates a widening diversification frontier and reflects the accumulation of capabilities that can support expansion into more complex sectors.
These findings are broadly consistent with the earlier results on export sophistication (EXPY), which also show noticeable improvements in Armenia and moderate gains in Uzbekistan and Kazakhstan. At the same time, the concentration indicators provide an important qualification. While Armenia shows rising export sophistication, the HHI and Theil indices indicate increasing export concentration in recent years. This suggests that structural upgrading may be occurring alongside a narrowing export base, implying that diversification into complex products has not yet become broad-based. Uzbekistan and Kazakhstan present a more balanced picture, with modest improvements in sophistication accompanied by relatively stable or moderate concentration levels, which is more consistent with gradual structural diversification.
Georgia and Kyrgyzstan display more incremental dynamics in the ECI analysis. Their export structures have become somewhat more sophisticated, but diversification largely occurs within sectors that remain relatively close to their existing productive structures and only moderately more complex than current exports. This pattern aligns with the earlier results showing relatively stable concentration indicators and only modest increases in export sophistication, pointing to gradual capability accumulation rather than rapid structural upgrading.
In contrast, Azerbaijan and Tajikistan remain more constrained by relatively low levels of economic complexity. In these economies, the distribution of feasible products remains concentrated in lower-complexity segments of the product space, and many technologically more sophisticated activities remain distant from their current capability base. This result is partly consistent with the earlier findings from concentration indicators: Tajikistan’s export structure remains relatively stable but limited in diversification, while Azerbaijan’s export structure continues to be influenced by resource-based specialization. As a result, the set of feasible diversification opportunities remains narrower and concentrated in sectors with relatively limited technological sophistication.
Overall, the ECI analysis complements the empirical results obtained from HHI, Theil, and EXPY indicators. While some countries in the region demonstrate signs of capability accumulation and gradual upgrading, the results suggest that structural transformation remains uneven across the region. In several cases, improvements in export sophistication occur alongside persistent concentration in a limited number of products, indicating that diversification into more complex sectors has not yet translated into broad-based structural change.
Conclusion
This brief examined the evolution of trade diversification, export sophistication, and structural transformation in the South Caucasus and Central Asia between 2019 and 2024. The results show substantial cross-country differences. While some economies maintain relatively diversified export structures, others remain more dependent on a narrow set of products. Export sophistication has improved modestly in several countries, but in some cases, this has coincided with rising export concentration. This does not necessarily indicate a negative development: such a pattern may reflect successful specialization based on comparative advantage or upgrading into higher-value activities. However, when sophistication gains are concentrated in a small number of products or markets, the resulting export structure may remain vulnerable to external shocks and less supportive of broad-based structural transformation.
The analysis also points to uneven progress in productive capabilities across the region. Some countries are gradually expanding the range of products they can competitively produce, while others remain constrained by narrower capability bases.
These results highlight the nuanced relationship between diversification, sophistication, and economic complexity. Diversifying into more complex sectors can strengthen economic resilience by broadening the range of activities an economy can rely on, reducing dependence on a limited set of simple or commodity-based exports, and enhancing the capacity to adapt to changes in demand, prices, or trade routes. In this context, the key policy challenge is not diversification for its own sake, but fostering the development of productive capabilities that enable more sophisticated, adaptable, and resilient export structures over time.
References
- Balland, P.-A., Boschma, R., Crespo, J., & Rigby, D. (2019). Smart specialization policy in the European Union: relatedness, knowledge complexity and regional diversification. Regional Studies, 53(9), 1252–1268.
- Brummitt, C. D., Gómez-Lievano, A., Hausmann, R., & Bonds, M. H. (2018). Machine-learned patterns suggest that diversification drives economic development.
- Hausmann, R., Hwang, J., & Rodrik, D. (2007). What you export matters. Journal of Economic Growth, 12(1), 1–25.
- Hausmann, R., Hidalgo, C., Bustos, S., Coscia, M., Chung, S., Jimenez, J., Simoes, A., & Yıldırım, M. (2014). The Atlas of Economic Complexity: Mapping Paths to Prosperity. MIT Press.
- Hidalgo, C. A., & Hausmann, R. (2009). The building blocks of economic complexity. Proceedings of the National Academy of Sciences, 106(26), 10570–10575.
- International Monetary Fund. (various years). Regional Economic Outlook: Middle East and Central Asia.
- Neffke, F., Hartog, M., Boschma, R., & Henning, M. (2018). Agents of structural change: The role of firms and entrepreneurs in regional diversification.
- Rodrik, D. (2006). What’s so special about China’s exports? China & World Economy, 14(5), 1–19.
- World Bank. (2020). Central Asia Trade: Structural Transformation and Diversification.
- Harvard Growth Lab. (2024). Atlas of Economic Complexity – Feasibility Analysis.
- United Nations Comtrade Database. (2024). International Trade Statistics.
- World Bank. (2023). World Development Indicators.
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.
The Expectation Boom: Evidence from the Kazakh Oil Sector
This policy brief shows that an oil price boom may trigger dissatisfaction with one’s income and that this dissatisfaction is independent of the effect of the boom on real economic conditions. Unique data from Kazakhstan allows us to quantify the impact of the recent oil price boom on satisfaction with income. Compared to other households in the country, households related to the oil sector suffer a marked drop in their satisfaction with their income during the period of high oil prices. Based on our results, we argue that an oil price boom creates a gap between people’s expectations of the benefits from the boom and the observed economic conditions. Our results call for researchers, policy makers and companies to devote more attention to the dynamics of satisfaction, not only during resource busts but also during resource booms.
Local Impact of Natural Resources
Often, resource wealth is associated with a curse, slowing economic growth in resource-rich developing countries (Venables, 2016). While traditionally, this relationship has been explored across countries, more recently, the literature started exploiting plausibly exogenous spatial variation in resource wealth within countries (Cust and Polhekke, 2015). We now know that resources can generate local economic wealth (Aragon and Rud, 2013), while also attracting corrupted individuals to power (Asher and Novosad, 2018) and triggering local conflicts (Berman et al. 2017; Rigterink, 2018). But, up to now, we know very little about the impact of resource booms on individuals’ perceptions. Since perceptions and behavioral biases may also drive actions, understanding whether and how resources affect perceptions is key in understanding the local impact of natural resources (Collier, 2017).
In a new working paper (Girard, Kudebayeva and Toews, 2020) we use Kazakhstan as a case study to shed more light on the importance of such perceptions. We document the conditions that preceded and presumably contributed to the violent conflicts in the oil rich districts of Kazakhstan in 2011. We show that periods of high oil prices can actually lead to a drop in reported satisfaction with income. This implies that due to mere changes in perceptions, which are not reflected in economic conditions, a large number of people may experience a significant drop in satisfaction with income, creating a fertile ground for conflicts.
The Zhanaozen Conflict
Our attention to the case of Kazakhstan is driven by the extreme events that took place in 2011 in the city of Zhanaozen, a booming oil town in the west of the country’s desert. In May 2011, after several years of high oil prices, private sector workers in Zhanaozen demanded amendments to the pre-existing collective bargaining agreement asking in particular for a raise in wages. Difficulties in negotiating an agreement resulted in local oil companies dismissing more than 2000 employees in the summer of 2011 and oil production dropping by 7% in the first three quarters of 2011 relative to the same period in the previous year. At the conflict climax, the police tried to clear the central square of Zhanaozen for the upcoming preparations of the Independence Day, resulting in the killing of 17 and the injuring of over 100 people (Satpayev and Umbetaliyeva, 2015).
Oil Price Boom in Kazakhstan
Kazakhstan offers an ideal case study for our research question for two reasons. First, the government of Kazakhstan closely monitored citizens’ satisfaction with income throughout most of the 2000s using a representative household panel survey. Using this data allows us to link variation in the price of oil to within household variations in satisfaction with income – conditional on household income, thus, capturing the changing perceptions of household heads regarding their income. Secondly, Kazakhstan is a small open resource rich economy, with sparsely populated and remote districts, whose economic activity nearly exclusively depends on the extraction of oil and gas. The fact that Kazakhstan is a small open economy implies that changes in the oil price may be treated as exogenous to households located in Kazakhstan. The spatial isolation of the oil rich districts allows us to consider the group of household heads employed in the private sector in the oil rich districts as either directly or indirectly involved in the extraction of oil and gas.
Figure 1. Kazakhstan

Source: Resource rich districts are indicated as treated. The information on the spatial identification of oil and gas rich district is taken from the Petroleum Encyclopedia of Kazakhstan and captures more than 90% of total oil and gas production in Kazakhstan (Munayshy Public Foundation, 2005).
Satisfaction With Income
To identify the effect of oil price fluctuations on satisfaction with income, we exploit three sources of variation: location of the household, sectoral employment and time. The group affected by the price of oil is the group of oil-related households. Oil-related households consist of households whose head is employed in the private sector of the oil rich districts of Kazakhstan, and who are thus the closest to the oil sector (by nature of their activity and place of residence). The differential evolutions in satisfaction of household heads employed in other sectors and households located in other districts – in other words, households which are more remote from oil and gas extraction than the oil-related households provide a plausible counterfactual.
The main results are depicted in Figure 2 which represents the relationship between income and satisfaction for 8 groups based on the three sources of variation: oil price (which was low between the years 2001 and 2004, and high between 2005 and 2009), place of residence as indicated in Figure 1, and sector of activity.
First, we note that the relationship between income and satisfaction is upward sloping: reported satisfaction with household income increases with income. This is intuitive. Focusing on oil poor districts that appear in the bottom panel, we observe that the relation between satisfaction and income is virtually the same across sectors and time periods of low and high prices of oil. This is, however, not true for oil-rich districts, which are depicted in the top panel. Here, the relationship between income and satisfaction only remains unaffected across time for household heads who are not employed in the private sector. The picture changes if we turn to household heads employed in the private sector, who are the oil-related household heads. The satisfaction with the income of oil-related household heads shifts downwards, compared to other households, in the period of high oil prices (years 2005-2009). This downward shift is even more striking since oil-related household heads valued their income relatively higher than other households during the period of low oil prices (2001-2004).
Figure 2. Satisfaction with Income

Source: Authors’ calculations based on satisfaction with income and household income as reported in the Household Survey of Kazakhstan. The figures above depict the relationship between logged household income per family member and reported satisfaction with income by the head of the household on a scale from 1 to 5. The relationship is depicted conditional on household fixed effects and year fixed effects. The former account for time-invariant household specific characteristics such as individual biases. The latter account for Kazakhstan specific shocks affecting households in oil poor and oil rich districts simultaneously due to political and economic business cycles. As a result, the relationship between logged income per family member and satisfaction is normalized to zero in both dimensions.
Lastly, we document that the negative variation in satisfaction is related to the contemporaneous change in the price of oil. The satisfaction with income is not persistent, it is unrelated to past and future levels of the oil price.
Conclusion
Our results suggest that oil prices fluctuations can be linked to the individual’s perception of income. The fact that oil-related household heads express a strong dissatisfaction compared to other household heads may help to understand what made December 2011 possible, when 17 people were killed and over 100 people were wounded in Zhanaozen. If generalizable, such dynamics of perceived satisfaction with income should be kept in mind by both policy makers and extractive companies not only during resource busts but also during resource booms.
References
- Aragon, Fernando M. and Juan Pablo Rud (2013). “Natural resources and local communities: evidence from a Peruvian gold mine.” American Economic Journal: Economic Policy 5(2):1–25.
- Asher, Sam and Paul Novosad (2018). Rent-seeking and criminal politicians: Evidence from mining booms. Working Paper.
- Berman, Nicolas, Mathieu Couttenier, Dominic Rohner and Mathias Thoenig (2017). “This Mine Is Mine! How Minerals Fuel Conflicts in Africa.” American Economic Review 107(6):1564–1610.
- Collier, Paul (2017). “The institutional and psychological foundations of natural resource policies.” The Journal of Development Studies 53(2):217–228.
- Cust, James and Steven Poelhekke (2015). “The local economic impacts of natural resource extraction.” Annu. Rev. Resour. Econ., 7(1):251–268.
- Girard, Victoire, Alma Kudebayeva and Gerhard Toews (2020). “Inflated Expectations and Commodity Prices: Evidence from Kazakhstan.“ GLO Discussion Paper Series 469.
- Rigterink, Anouk S. (2020). “Diamonds, rebel’s and farmer’s best friend: Impact of variation in the price of a lootable, labour-intensive natural resource on the intensity of violent conflict.” Journal of Conflict Resolution 64(1):90–126.
- Munayshy Public Foundation (2005). “Petroleum Encyclopedia of Kazakhstan.”
- Girard, Victoire, Alma Kudebayeva and Gerhard Toews (2020). “Inflated Expectations and Commodity Prices: Evidence from Kazakhstan” Working Paper.
- Satpayev, Dossym and Umbetaliyeva, Òolganay (2015). “The protests in Zhanaozen and the Kazakh oil sector: Conflicting interests in a rentier state.” Journal of Eurasian Studies 6(2):122–129.
- Venables, Anthony J. (2016): “Using Natural Resources for Development: Why Has It Proven So Difficult?” Journal of Economic Perspectives, 30, 161 – 84.
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.
Liberal Democracy in Transition – The First 30 Years
This year marks 30 years since the first post-communist election in Poland and the fall of the Berlin Wall. Key events that started a dramatic transition process from totalitarian regimes towards liberal democracy in many countries. This brief presents stylized facts from this process together with some thoughts on how to get this process back on a positive track. In general, the transition countries that joined the EU are still far ahead of the other transition countries in terms of democratic development.
The recent decline in democratic indicators in some EU countries should be taken seriously as they involve reducing freedom of expression and removing constraints on the executive, but should also be discussed in light of the significant progress transition countries entering the EU have shown during the first 30 years of transition. The brief shows that changes in a democracy can happen fast and most often happen around elections, so getting voters engaged in the democratic process is crucially important. This requires politicians that engage the electorate and have an interest in preserving democratic institutions. An important question in the region is what the EU can do to promote this, given its overloaded political agenda. Perhaps it is time for a Greta for democracy to wake up the young and shake up the old.
This brief provides an overview of political developments in transition countries since the first post-communist elections in Poland and the fall of the Berlin Wall 30 years ago. It focuses on establishing stylized facts based on quantitative indices of democracy for a large set of transition countries rather than providing in-depth studies of a small number of countries. The aim of the brief is thus to find common patterns across countries that can inform today’s policy discussion on democracy in the region and inspire future studies of the forces driving democracy in individual transition countries.
The first issue to address is what data to use to establish stylized facts of democratic development in the region. By now, there are several interesting indicators that describe various aspects of democratic development, which are produced by different organizations, academic institutions and private data providers. In this brief, three commonly used and well-respected data providers will be compared in the initial section before we zoom in on more specific factors that make up one of these indices.
The big picture
The three indicators that we look at first are: political rights produced by Freedom House; polity 2 produced by the Polity IV project; and the liberal democracy index produced by the V-Dem project. Figures 1-3 show the unweighted average of these indicators for two groups of countries. The EU10 are the transition countries that became EU members in 2004 and 2007 and include Bulgaria, the Czech Republic, Estonia, Hungary, Lithuania, Latvia, Poland, Romania, Slovakia, and Slovenia. The second group, FSU12, are the 12 countries that came out of the Soviet Union minus the three Baltic countries in the EU10 group, so the FSU12 group consists of Armenia, Azerbaijan, Belarus, Georgia, Kazakhstan, Kyrgyzstan, Moldova, Russia, Tajikistan, Turkmenistan, Ukraine, and Uzbekistan.
Figure 1. Freedom House

Source: Freedom House and author’s calculations
Note: Scale inverted, 1 is best and 7 worst score
Figure 2. Polity IV project

Source: Polity IV project and author’s calculations
Note: Scale from -10 (fully autocratic) to 10 (fully democratic)
Figure 3. V-Dem

Source: V-Dem project and author’s calculations
Note: Scale from 0 to 1 where higher is more democratic
All three indicators convey the message that the democratic transformation in the EU10 group was very rapid in the early years of transition and the indicators have remained at high levels since the mid-90s only to show some decline in the most recent years for two of the three indicators. The FSU12 set of countries have made much less progress in terms of democratic development and remain far behind the EU10 countries in this regard. Overall, there is little evidence at the aggregate level that the democratic gap between the EU10 and FSU12 groups is closing. While the average EU10 country is more or less a full-fledged democracy, the average FSU12 country is at the lower end of the spectrum for all three democracy measures.
The average indicators in Figures 1-3 obviously hide some interesting developments in individual countries and in the following analysis, we will take a closer look at the liberal democracy index at the country level. We will then investigate what sub-indices contribute to changes in the aggregate index in the countries that have experienced significant declines in their liberal democracy scores.
For the first part of the analysis, it is useful to break down the democratic development in two phases. The first phase is from the onset of transition (1989, 1991 or 1993 depending on the specific country) to the time of the global financial crisis in 2009 and the second phase is from 2009 to 2018 (the last data point).
Figure 4. Liberal democracy, the first phase

Source: V-Dem project and author’s calculations
Figures 4 and 5 compare how the liberal democracy indicator changes from the first year of the period (measured on the horizontal axis) to the last year of the period (on the vertical axis). The smaller blue dots are the individual countries that make up the EU10 group while the red dots are the FSU12 countries. The 45-degree line indicates when there is no change between start and end years, while observations that lie below (above) the line indicate a deterioration (improvement) of the liberal democracy index in a specific country.
In the first phase of transition (Figure 4), all of the EU10 countries increased their liberal democracy scores and the average increase for the group was almost 0.5, going from 0.26 to 0.74. This was a result of many of the countries in the group making significant improvements without any countries deteriorating. The FSU12 group had a very different development with the average not changing at all since the few countries that improved (Georgia and Ukraine) were counterbalanced by a significant decline in Belarus and a more modest decline in Armenia.
Figure 5. Liberal democracy, the second phase

Source: V-Dem project and author’s calculations
The very rapid improvement in the liberal democracy index in the EU10 countries in the first phase of transition came to a halt and also reversed in several countries in the second phase of transition. Of course, as they had improved so much in the first period, there was less room for further positive developments, but the rapid decline in some of the countries was still negative news. However, it does point towards that reform momentum was very strong in the EU accession process, but once a country had entered the union, the pressure for liberal democratic reforms has faded.
Overall, the EU10 average fell by 0.1 from 2009 to 2018. This was a result of declining scores in several countries. The particularly large declines in this period have been seen in Hungary (-0.28), Poland (-0.27), Bulgaria (-0.14), the Czech Republic (-0.14), and Romania (-0.12). Again, the average FSU12 score did not change much, although Ukraine (-0.2) put its early success in reverse and lost as much in this period as it had gained earlier.
Country developments
Since much of the current discussion centers on how democracy is being under attack, the figures name the countries that have seen significant declines in the liberal democracy score in the first or second phase of transition. Figures 6 and 7 show the time-series of the liberal democracy index in the countries with significant drops at some stage of the transition process.
Figure 6. FSU12 decliners

Source: V-Dem project and author’s calculations
In many countries, the drop comes suddenly and sharply, with the first and most prominent example being Belarus. There, it only took three years to go from one of the highest ranked FSU12 countries to fall to one of the lowest liberal democracy scores. In Poland, Romania, Bulgaria and Armenia, the process was also very rapid and significant changes happened in 2-3 years.
Figure 7. EU10 decliners

Source: V-Dem project and author’s calculations
In the Czech Republic and Hungary, the period of decline was much longer and in the case of Hungary, the drop was the most significant in the EU10 group. Ukraine stands out as more of an exception with a roller-coaster development in its liberal democracy score that first took it up the list and then back down to where it started. For those familiar with politics in these countries, it is easy to identify the elections and change in government that have occurred at the times the index has started to fall in all of these countries. In other words, the democratic declines have not started with coups but followed election outcomes where in most cases the incumbent leaders have been replaced by a new person or party.
How democracy came under attack
We will now take a closer look at what has been behind the instances of decline in the aggregate index by investigating how the sub-indices have developed in these countries. The sub-indices that build up the liberal democracy index are: freedom of expression and alternative sources of information; freedom of association; share of population with suffrage; clean elections; elected officials; equality before the law and individual liberty; judicial constraints on the executive; and legislative constraints on the executive (the structure is a bit more complex with mid-level indices, see V-Dem 2019a).
Table 1 shows how these indicators have changed in the time period the liberal democracy indicator has fallen significantly (with shorter versions of the longer names listed above but in the same order). The heat map of decline indicated by the different colours is constructed such that positive changes are marked with green, smaller declines are without colour, declines greater that 0.1 but smaller than 0.2 are in yellow and larger declines in red. Note that the liberal democracy index is not an average of the sub-indices but based on a more sophisticated aggregation technique (see V-Dem 2019b). Therefore, the Czech Republic and Bulgaria can have a greater fall in top-level liberal democracy index that what is indicated by the sub-indices.
Table 1. Changes in liberal democracy indicators at times of democratic decline

Source: V-Dem project and author’s calculations
For the countries with the largest changes in the liberal democracy index, it is clear that both freedom of expression and alternative sources of information have come under attack together with reduced judicial and legislative constraints on the executive. Among the EU10 countries, Hungary and Poland stand out in terms of reducing freedom of expression, while Romania has seen most of the decline coming from reducing constraints on the executive. Not surprisingly, Belarus stands out in terms of the overall decline in liberal democracy coming from reducing both freedom of expression and constraints on the executive in the most significant way.
On a more general level, the attack on democracy does differ between the countries, but in the cases where serious declines can be seen, the attack has been particularly focused on information aspects and constraints on the executive. At the same time, all countries let all people vote (suffrage always at 1) and let the one with the most votes get the job (elected officials).
Policy conclusions
This brief has provided some stylized facts on the first 30 years of liberal democracy in transition and some details on how democracy has come under attack in individual countries. It leaves open many questions that require further studies and some of these are indeed ongoing in this project and will be presented in future briefs and policy papers here.
Some observations have already been made here that can inform policy discussions on liberal democratic developments in the region. The first is that changes can happen very rapidly, both in terms of improvements but also in terms of dismantling important democratic institutions, including those that provide constraints on the executive or media that provides unbiased coverage before and after elections. What is also noteworthy is that these changes have almost always happened after an election where a new person or party has come to power, so the democratic system is used to introduce less democracy in this sense.
It is also interesting that in all of the countries, the most easily observed indicators of democracy such as suffrage and having the chief executive or legislature being appointed by elections are given the highest possible scores. In other words, even the most autocratic regime wants to look like a democracy; but as the old saying goes, “it is not who votes that is important, it is who counts”.
The regime changes at election times that have led to declining liberal democracy scores have also in many cases come as a result of the incumbents not doing a great job or voters not turning up to vote. It was enough for Lukashenko in Belarus to promise to deal with corruption and rampant inflation that was a result of the old guard’s mismanagement to turn Belarus into an autocracy. In Hungary, the change of regime came after the Socialist leader was caught on tape saying he had been lying to voters. While in Romania, only 39% voted in the 2016 election. And in Bulgaria, around half of the voters stayed at home in the presidential election the same year.
In sum, both incompetent and corrupt past leaders and disengaged or disillusioned voters are part of the decline in a liberal democracy that we have seen in recent years. It is clearly time for policy makers that are interested in preserving liberal democracy in the region and elsewhere to think hard about how democracy can be saved from illiberal democrats. Part of the answer clearly will have to do with how voters can be engaged in the democratic process and take part in elections. It also involves defending free independent media and the thinkers and doers that contribute to the liberal democracy that we cherish. The question is if the young generation will find a Greta for democracy that can kick-start a new transition to liberal democracy in the region and around the world.
For those readers that want to participate more actively in this discussion and have a chance to be in Stockholm on November 12, SITE is organizing a conference on this theme which is open to the public. For more information on the conference, please visit SITE’s website (see here).
References
- Freedom house data downloaded on Oct 4, 2019, from https://freedomhouse.org/content/freedom-world-data-and-resources
- Freedom house methodological note available at https://freedomhouse.org/report/methodology-freedom-world-2018
- Polity IV project data downloaded on Oct 4, 2019, from http://www.systemicpeace.org/inscrdata.html
- Polity IV project manual available at http://www.systemicpeace.org/inscr/p4manualv2018.pdf
- V-Dem project data downloaded on Sept 24, 2019, from https://www.v-dem.net/en/data/data-version-9/
- Coppedge, Michael, John Gerring, Carl Henrik Knutsen, Staffan I. Lindberg, Jan Teorell, David Altman, Michael Bernhard, M. Steven Fish, Adam Glynn, Allen Hicken, Anna Lührmann, Kyle L. Marquardt, Kelly McMann, Pamela Paxton, Daniel Pemstein, Brigitte Seim, Rachel Sigman, Svend-Erik Skaaning, Jeffrey Staton, Steven Wilson, Agnes Cornell, Lisa Gastaldi, Haakon Gjerløw, Nina Ilchenko, Joshua Krusell, Laura Maxwell, Valeriya Mechkova, Juraj Medzihorsky, Josefine Pernes, Johannes von Römer, Natalia Stepanova, Aksel Sundström, Eitan Tzelgov, Yi-ting Wang, Tore Wig, and Daniel Ziblatt. 2019a. “V-Dem [Country-Year/Country-Date] Dataset v9”, Varieties of Democracy (V-Dem)
- Pemstein, Daniel, Kyle L. Marquardt, Eitan Tzelgov, Yi-ting Wang, Juraj Medzihorsky, Joshua Krusell, Farhad Miri, and Johannes von Römer. 2019b. “The V-Dem Measurement Model: Latent Variable Analysis for Cross-National and Cross-Temporal Expert-Coded Data”, V-Dem Working Paper No. 21. 4th edition. University of Gothenburg: Varieties of Democracy Institute.
Non-Tariff Barriers and Trade Integration in the EAEU
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.
References
- 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
The Application of Composite Leading Indicators on the Single Economic Space Economies
This brief is based on a CEFIR research project aimed at the short-term forecasting of socio-economic development of the member-countries of the Single Economic Space (SES), conducted for the Eurasian Economic Commission in 2013. This project focused on compiling composite leading indicators that could allow policymakers to identify phases of a business cycle and to forecast its turning points. We suggest a methodology for the selection of components of the Composite Leading Indicators (CLIs) for industrial production, and apply this methodology to predict industrial production in SES member states. Our methodology performs well for Russia and Kazakhstan, and slightly less so for Belarus.
Tajikistan Joining the Customs Union of Russia, Belarus and Kazakhstan: Pros and Cons
Authors: I.A. Densiova, A.M. Malokostov, and N.A. Turdyeva, CEFIR
In this brief we summarize the results obtained in a CEFIR research project on the economic impact of Tajikistan joining the Customs Union of Russia, Belarus and Kazakhstan conducted for the Eurasian Development Bank in 2013 (EBD, 2013). We argue that integration has to be comprehensive to be mutually beneficial: indeed, trade effects are marginal, and the highest stakes are at migration regulation in the CU member-countries and the investment opportunities in Tajikistan.
The Customs Union Between Russia, Belarus and Kazakhstan: Some Evidence from the New Tariff Rates and Trade Flows
Author: Arevik Mkrtchyan, European University Institute.
This brief addresses the Customs Union between Russia, Belarus and Kazakhstan that was established in 2010. It argues that the external tariff schedule reflects a compromise between the interests of its members rather than simple expansion of Russian influence on the CU partners, and that the reduction in trade costs due to elimination of internal borders, benefits both the members of the CU and their external trade partners. Moreover, the impact of alleviated non-tariff trade costs on trade flows is strong and significant, while the tariff impact is insignificant for all members.
The Eurasian Customs Union among Russia, Belarus and Kazakhstan: Can It Succeed Where Its Predecessor Failed?
In 2010, Russia, Belarus and Kazakhstan formed the Eurasian Customs Union and imposed the Russian tariff as the common external tariff of the Customs Union. This resulted in almost doubling the external average tariff of the more liberal Kazakhstan. Russia has benefited from additional exports to Kazakhstan under the protection of the higher tariffs in Kazakhstan. However, estimates reveal that the tariff changes have resulted in substantial transfers from Kazakhstan to Russia since importers in Kazakhstan now purchase lower quality or higher priced Russian imports which are protected under the tariff umbrella of the common external tariff. Transfers from the Central Asian countries to Russia were the reason the Eurasian Economic Community (known as EurAsEC) failed, so this bodes badly for the ultimate success of the Eurasian Customs Union. What is different, however, is that the Eurasian Customs Union and its associated Common Economic Space aim to reduce non-tariff barriers and improve trade facilitation, and also to allow the free movement of capital and labor, liberalize services, and harmonize some regulations. Estimates by my colleagues and I show that if substantial progress could be made in trade facilitation and reducing non-tariff barriers, this could make the Customs Union positive for Kazakhstan and other potential Central Asian members. Unfortunately, so far the Customs Union has made these matters worse. On the other hand, Russia’s accession to the World Trade Organization will eventually substantially reduce the transfers from Kazakhstan to Russia, but this will need a strong political commitment from Russia which we have not yet seen. If that Russian political leadership is forthcoming, the Eurasian Customs Union could nonetheless succeed where its predecessor has failed.
In January 2010, Russia, Belarus and Kazakhstan formed the Eurasian Customs Union. Two years later, the three countries agreed to even closer economic ties, by signing the agreement to form a “common economic space.” Regarding tariffs, the key change was that the three countries agreed to apply the tariff schedule of the Customs Union as their common external tariff for third countries. With few exceptions, the initial common external tariff schedule was the Russian tariff schedule. Kazakhstan negotiated exceptions from the common external tariffs for slightly more than 400 tariff lines, but was scheduled to phase out the exceptions over a period of five years (World Bank, 2012). In addition, the members agreed to have the Customs Union determine the rules regarding sanitary and phyto-sanitary standards (SPS) and standards on good. Fearing transshipment of goods from China through Kazakhstan and from the European Union through Belarus, Russia negotiated and achieved agreement on stricter controls on the origin of imports from countries outside of the Customs Union. The common economic space (CES) stipulates that, in principle, there will be free movement of labor and capital among the countries, there will be liberalization of services on the CES and coordination of some regulatory policies such as competition policy.
In February 2012, the Eurasian Economic Commission began functioning. It is intended to act as the regulatory authority for the Customs Union in a manner similar to the European Commission for the European Union.
The Economics of Tariff Changes — Gains for Russia and Losses for Kazakhstan
Some proponents of the Eurasian Customs Union have argued that as a result of the Customs Union firms in the three countries will have improved market access through having tariff free access to the markets in all three countries. Prior to 2010, however, along with other countries in the Commonwealth of Independent States (CIS), the three countries had agreements in place that stipulated free trade in goods among them. Thus, the Customs Union could not provide improved market access due to reducing tariffs on goods circulating among the three countries.
Since the common external tariff was essentially the Russian tariff, there was little change in incentives regarding tariffs in Russia. The big change occurred in Kazakhstan, who had a much lower tariff structure than Russia prior to implementing the Customs Union tariff. Despite the exemptions, Kazakhstan almost doubled its tariffs in the first year of the Customs Union (see World Bank, 2012). The increase in tariffs on many items which were not produced in Kazakhstan but produced in Russia, led to a substantial increase in imports from Russia and displacement of imports from Europe. Many of Russia’s manufacturing firms, which were not competitive in Kazakhstan prior to the Customs Union, were now able to expand sales to the Kazakhstani market. This represents gains for Russian industry. Given the deeper manufacturing base in Russia compared with most of the CIS countries and the resulting uneven benefits of the common external tariff in favor of Russia, acceptance of the common external tariff has been a fundamental negotiating position of Russia regarding acceptance of members in the Customs Union.
Some cite the expanded Russian exports in Kazakhstan as evidence of success of the Customs Union. But the displacement of European imports, to higher priced or lower quality imports from Russia, represents a substantial transfer of income from Kazakhstan to Russia and is an example of what economists call “trade diversion”. Moreover, it is the reason the World Bank (2012) has evaluated the tariff changes of the Customs Union as a loss of real income for Kazakhstan.
Furthermore, the three countries together (and even a broader collection of CIS countries) constitute too small a market to erect tariff walls against external competition. They would lose the benefits of importing technology from advanced countries and would rely on high priced production from within the Customs Union. Some would argue that there are political benefits of trade to be taken into account, but experience has shown that when a customs union is inefficient and the benefits and the costs of the customs union are very unequal, the customs union can inflame conflicts (see Schiff and Winters, 2003, 194-195).
Non-Tariff Barriers — Extremely Costly Methods of Regulating Standards Worsened by the Customs Union
Non-tariff barriers, in the form of sanitary and phyto-sanitary (SPS) conditions on food and agricultural products and technical barriers to trade (TBTs) on goods, are a very significant problem of the Customs Union. There are standards based trade disputes between Belarus and Russia on several products, including milk, meat, buses, pipes and beer (see Petrovskaya, 2012). Anecdotal evidence indicates that Kazakhstani exporters complain bitterly regarding the use by the Russian authorities of SPS and TBTs measures, either to extract payments or for protection.
If the Customs Union could make substantial progress on reducing these barriers, it would be a significant accomplishment. My colleagues and I have estimated that progress on the non-tariff barriers and trade facilitation could outweigh the negative impact of the tariff changes for Kazakhstan (see World Bank, 2012). Unfortunately, so far the Customs Union has taken a step backward on both non-tariff barriers and trade facilitation.
A big problem in reducing standards as a non-tariff barrier is that standards regulation, in all three countries, is still primarily based on the Soviet system. As a holdover from the Soviet era, mandatory technical regulations are employed where market economies allow voluntary standards to apply. This regulatory system makes innovation and adaption to the needs of the market very costly as firms must negotiate with regulators when they want to change a product or how it is produced. Legislation in both Russia and Kazakhstan calls for conversion to a system of voluntary standards, but this is happening too slowly in all three countries. The problem is that the Customs Union has worsened the situation. Technical regulations are now decided at the level of the Customs Union, so firms that previously negotiated with their national standards authority, have had to now get agreement from the Customs Union. This has reportedly caused further delays, impeding innovation and the ability of firms to meet the demands of the market.
A second problem with efforts to reduce the non-tariff barriers is that the Customs Union is trying to harmonize standards of the three countries by producing mandatory technical regulations. The alternative is to use Mutual Recognition Agreements (MRAs). Experience has shown that no customs union has been able to broadly harmonize standards based on mandatory technical regulations, with the exception of the European Union. In fact, even in the European Union, they have had to use MRAs and only harmonized technical regulations after decades of work. While each member of the Customs Union is expected to create a system of mutual recognition of certificates of conformity, these certificates are not presently recognized in the other countries of the Customs Union. There is little hope for a significant reduction in standards of non-tariff barriers unless the system of mutual recognition is more widely recognized and adopted.
Trade Facilitation —Participation in International Production Chains Made More Difficult by the Customs Union
Customs posts between the member countries have been removed and this has reduced trade costs for both exporters and importers in the three countries. Russia’s concerns regarding transshipment have, however, led to an opposite impact on trade with third countries, i.e., the costs of trading with countries outside the Customs Union have increased. Participation in international production chains has become a key feature of modern international production and trade. If goods cannot move easily in and out of the country, multinational firms will look to other countries to make their foreign direct investment and for international production sharing. Addressing this significant problem will take a change of emphasis on the part of Russia.
Russian WTO Accession —Liberalization That Will Significantly Reduce Transfers to Russia
It has apparently been agreed by the Customs Union members that the common external tariff of the Customs Union will change to accommodate Russia’s WTO commitments. As a result, the applied un-weighted average tariff will fall in stages from 10.9 percent in 2012 to 7.9 percent by the year 2020 (see Shepotylo and Tarr, forthcoming).[1] This will have the effect of lowering the trade diversion costs of Kazakhstan. In addition, the Customs Union will be expected to adapt its rules on standards to conform to commitments Russia made as part of its WTO accession commitments. In the case of Belarus, it remains to be seen if it will implement the changes, as this will increase competition for its industries.
Conclusion — the Need to Russia to Exercise Political Leadership for Standards and Trade Facilitation Reform for Success of the Customs Union
In 1996, the same three countries formed a customs union. Later the same year, they were joined by Kyrgyzstan, then by Tajikistan and in 2005 by Uzbekistan. As Michalopoulos and I (1997) anticipated, the earlier Customs Union failed because it imposed large costs on the Central Asian countries, which had to buy either lower quality (including lower tech goods) or higher priced Russian manufactured goods under the tariff umbrella. The present Customs Union also started with the Russian tariff, which protects Russian industry and suffers from the same problem that led to the failure of the earlier Customs Union. Nonetheless, the present Customs Union could succeed. Crucially, due to Russia’s accession to the WTO, the tariff of the Customs Union will fall by about 40 to 50 percent.[2] This will make the Customs Union a more open Customs Union, very significantly reduce the transfers from Kazakhstan to Russia, and thereby reduce the pressures from producers and consumers in Kazakhstan on their government to depart from enforcement of the tariffs of the Customs Union. Further, the present Customs Union aims to reduce non-tariff barriers and improve trade facilitation, as well as it has “deep integration” on its agenda, i.e., services liberalization, the free movement of labor and capital and some regulatory harmonization. Although, to date, the Customs Union has moved backwards on non-tariff barriers and trade facilitation, one could optimistically hope for substantial progress. In the important area of non-tariff barriers, given the common history of Soviet mandatory standards, Russia will have to take the lead in moving the Customs Union toward a system of voluntary standards where no health and safety issue are involved, and toward a system of mutual recognition agreements and away from commonly negotiated technical regulations. On trade facilitation, Russia will have to reverse its pressure and find a way to allow the freer movement of goods with third countries while addressing its transshipment concerns.
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References
- Michalopoulos, Constantine and David G. Tarr (1997), “The Economics of Customs Unions in the Commonwealth of Independent States,” Post-Soviet Geography and Economics, Vol. 38, No. 3, 125-143.
- Petrovskaya, Galina (2012), “Belarus, Rossia, Ukraina. Obrechennye na torgovye konflikty” (Belarus, Russia, Ukraine. Doomed for trade conflicts), Deutsche Welle, June 14. www.dw.de/dw/article/0,,16023176,00.html.
- Schiff, Maurice and L. Alan Winters (2003), Regional Integration and Development, Washington DC: World Bank and Oxford University Press.
- Shepotylo, Oleksandr, and David G. Tarr (2008), “Specific tariffs, tariff simplification and the structure of import tariffs in Russia: 2001–2005,” Eastern European Economics, 46(5):49–58.
- Shepotylo, Oleksandr, and David G. Tarr (forthcoming), “Impact of WTO Accession on the Bound and Applied Tariff Rates of Russia,” Eastern European Economics.
- Shymulo-Tapiola, Olga (2012), “The Eurasian Customs Union: Friend or Foe of the EU?” The Carnegie Papers, Carnegie Endowment for International Peace, October. Available at: www.CarnegieEurope.eu,
- World Bank (2012), Assessment of Costs and Benefits of the Customs Union for Kazakhstan, Report Number 65977-KZ, Washington DC, January 3, 2012. Available at: http://documents.worldbank.org/curated/en/2012/01/15647043/assessment-costs-benefits-customs-union-kazakhstan
[1] The final “bound rate” of Russia is higher at 8.6 percent on an un-weighted average basis; but there are about 1,500 tariff lines where the applied rate of Russia is below the bound rate. The applied weighted average tariff will fall from 9.3 percent in 2012 to 5.8 percent in 2020.
[2] Russian tariffs fall more on an un-weighted average basis than they do on a weighted average basis. See Shepotylo and Tarr (forthcoming).