Location: Ukraine
And the Lights Went Out – Measuring the Economic Situation in Eastern Ukraine
This policy brief evaluates the economic situation in war-affected Eastern Ukraine, focusing on how the conflict has influenced economic activity and recovery. Because official statistics are unavailable or unreliable, the study uses changes in nighttime light intensity (captured by satellites) to estimate the scale of economic destruction and potential post-war recovery since the Minsk II agreement.
Challenges in Measuring Economic Performance During War
Measuring economic performance is complex even under stable conditions when the data is reliable. During conflict, however, collecting accurate statistics becomes nearly impossible. In such cases, indirect economic indicators provide valuable insights into real economic activity.
The Ukrainian conflict exemplifies this challenge. For instance, Talavera and Gorodnichenko (2016) estimated economic conditions in the Luhansk and Donetsk People’s Republics (LNR/DNR) using price integration data. Meanwhile, reports such as the BBC (2015) cited the Ukrainian Ministry of Economy, which estimated that between 50% and 80% of jobs were lost in these regions by mid-2015 compared to pre-war levels.
Understanding the economic impact of the war in Eastern Ukraine is essential for evaluating both the viability of the separatist territories and the humanitarian situation in the region.
Using Nighttime Light Intensity as an Economic Indicator
An innovative and indirect method to assess economic activity during conflict is through satellite-based nighttime light intensity. This metric correlates closely with electricity consumption and, by extension, overall economic output.
Studies such as Henderson et al. (2012), Li and Li (2014), and Arora and Lieskovsky (2014) demonstrate that changes in light intensity reliably mirror economic trends. For example, a 1% increase in nighttime light intensity corresponds roughly to a 1% rise in income in low- and middle-income countries.
This approach has been successfully applied to analyze economic conditions in sub-Saharan Africa, the Syrian conflict, and global regional inequalities—making it a powerful tool for conflict-zone economic analysis.
Economic Activity in Eastern Ukraine Since 2014
In this note, we use nighttime light intensity to measure economic activity in Eastern Ukraine since the outbreak of the war in the East of Ukraine in April 2014.[2] As a reference point, we use the nighttime light intensity in March 2014, prior to the outbreak of violence in the East of Ukraine, and we focus on Ukraine’s capital Kyiv and a number of big and small cities in Eastern Ukraine, which we know have been heavily affected by the conflict. In Table 1, we compare the light intensity at several points in time (May 2014; August 2014; January 2015; March 2015; March 2016) to the light intensity in March 2014 in these selected cities.
Figure 1. Nighttime images of Kyiv (a), Donetsk (b), and Luhansk (c) in March 2014, 2015, and 2016
| (a) Kyiv | ||
| March 2014 | March 2015 | March 2016 |
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| (b) Donetsk | ||
| March 2014 | March 2015 | March 2016 |
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| (c) Luhansk | ||
| March 2014 | March 2015 | March 2016 |
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Notes: Radiance was linearly scaled from 0 to 10 nW/cm2/sr, where black pixels represent 0 and white represent 10 or more nW/cm2/sr. Administrative boundaries for cities: © OpenStreetMap contributors, CC BY-SA.
Figure 1 presents sample images of nighttime illumination for Kyiv, Donetsk and Luhansk in March 2014, 2015 and 2016. We can see that between March 2014 and 2015, in the case of Donetsk and Luhansk, both the surface area lit as well as the measured light intensity significantly decreased, while there is very little change in the case of Kyiv. A similar picture emerges in other cities that were not directly affected by the war, such as, for example Zaporizhia, Dnipropetrovsk and Kharkiv (see Table 1). While, as in Kyiv, there are ups and downs in terms of measured nighttime light intensity, by and large, the level of economic activity remains fairly similar over time.
Table 1. Change in nighttime light intensity across time for selected cities in Ukraine
Notes: The numbers in the table are ratios of light intensity, comparing a given point in time to March 15, 2014. Hence, number 1 suggests no change, numbers above 1 suggest improvements, and numbers below 1 suggest decreases in economic activity.
The situation is clearly different in Donetsk and Luhansk, the two major occupied towns. Nighttime light intensity in Donetsk is about half of the level it was before the outbreak of violence in the East of Ukraine. Luhansk fares even worse – light intensity as measured in March 2015 and 2016 is roughly a third of the initial level (Table 1).
Ilovaisk and Debaltseve, two cities where major battles took place and which are now under control of the so-called DNR/LNR, clearly have suffered a lot and are still far from recovering. Illovaisk is at about a third of its original level of light intensity, while Debaltseve is at less than a tenth (!) of the level in 2014. It is thus clear that economic recovery in these areas takes a long time, and that this is also true for the government-controlled areas. This is illustrated by the fact that cities such as Sloviansk and to a lesser extent, Kramatorsk are also still far away from their pre-conflict level of light intensity.
Conclusion
The above analysis of changes in nighttime light intensity data leads to two important conclusions. First, the impact of the war in Eastern Ukraine on the level of economic activity in the area is sizeable and varies considerably across towns. Levels of nighttime light intensity are at 30 to 50% of their pre-war level in the big cities and at only a tenth of their pre-war level in some smaller cities. Using the Henderson et al. (2012) one-to-one ratio of changes in nighttime light intensity and economic development, this suggests the economic activity in the Donbas region has similarly dropped in economic terms to 30 to 50% of the pre-war level for the big cities and to only a tenth of the pre-war level for some smaller cities. [3]
Second, there has been no sign of economic recovery in the region since the Minsk I and II agreements. Even though military activity in the Donbas region has decreased compared to the period April 2014-February 2015, the economy – at least as measured by the intensity of lights – has not been improving and the economic situation of the Donbas population remains very far from what it used to be before the war.
[1] ‘The elasticity of growth of lights emanating into space with respect to income growth is close to one (p. 1025)’
[2] We use version 1 nighttime monthly data from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) generated by the Earth Observation Group at NOAA National Geophysical Data Center and made publicly available for download.
[3] Given the specificity of light intensity measures, we focus on changes between periods rather than levels because light intensity is computed as the sum of radiance over a selected area, and hence the level of intensity depends on the scale of the area. For comparisons over time, we always use the same geographic area. It is important to remember that these changes are proxies only since changes in light intensity can be sensitive to weather conditions over time. Thus, to be able to make an informative judgment on the basis of these data, we focus on the broad picture that emerges from the data, rather than on specific values.
References
- Arora, Vipin and Jozef Lieskovsky (2014), “Electricity Use as an Indicator of U.S. Economic Activity”, U.S. Energy Information Administration Working Paper.
- BBC (2015) – Ukrainian Service, ‘ One year after the referendum DNR/LNR: Economic Losses’, May 12 2015.
- Henderson, J. Vernon , Adam Storeygard, and David N. Weil (2012), Measuring Economic Growth from Outer Space, American Economic Review 2012, 102: 994–1028
- Hodler, Roland, and Paul A. Raschky (2014), Regional Favouritism. Quarterly Journal of Economics 129: 995-1033.
- Talavera, Oleksandr and Yuriy Gorodnichenko (2016), How’s DNR Economy Doing, VoxUkraine April 7, 2016
- Xi Li & Deren Li (2014) Can night-time light images play a role in evaluating the Syrian Crisis?, International Journal of Remote Sensing, 35: 6648-6661.
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.
Disclaimer: This FREE Policy Brief is simultaneously published as a column at VoxUkraine.org.
Effects of Trade Wars on Belarus
The trade wars following the 2014 events in Ukraine affected not only the directly involved participants, but also countries like Belarus that were affected through international trade linkages. According to my estimations based on a model outlined in Ossa (2014), these trade wars led to an increase in the trade flow through Belarus and thereby an increase of its tariff revenue. At the same time, because of a ban on imports in the sectors of meat and dairy products, the tariff revenue of Russia declined. As a member of the Eurasian Customs Union (EACU), Belarus can only claim a fixed portion of its total tariff revenue. Since the decline in the tariff revenue of Russia led to a decline in the total tariff revenue of the EACU, there was a decrease in the after-redistribution tariff revenue of Belarus. As a result, Belarusian welfare decreased. To avoid further welfare declines, Belarus should argue for a modification of the redistribution schedule. Alternatively, Belarus could increase its welfare during trade wars by shifting from being a part of the EACU to only being a part of the CIS Free Trade Area (FTA). If Belarus was only part of the CIS FTA, the optimal tariffs during trade wars should be higher than the optimal tariffs without trade wars. The optimal response to the increased trade flow through Belarus is higher tariffs.
Following the political protests in 2014, Ukraine terminated its membership in the CIS Free Trade Area (FTA) and moved towards becoming a part of the EU. The political protests evolved into an armed conflict and a partial loss of Ukrainian territory. These events led to Western countries introducing sanctions against some Russian citizens and enterprises. In response, Russia introduced a ban on imports from EU countries, Australia, Norway, and USA in the sectors of meat products, dairy products, and vegetables, fruits and nut products. In addition, both Ukraine and Russia increased the tariffs on imports from each other in the above-mentioned sectors.
Clearly, the trade wars affected directly involved participants such as the EU countries, Russia, and Ukraine. At the same time, countries like Belarus that were not directly involved in the trade wars, were also affected because of international trade linkages. It is important to understand the influence of trade wars on none-participating countries. To address this question, a framework with many countries and international trade linkages will be utilized and I will in this policy brief present some of my key findings.
Framework and Data
To evaluate the effects of the trade wars, I use the methodology outlined in Ossa (2014). This framework is based on the monopolistic competition market structure that was introduced into international trade by Krugman (1979, 1981). The framework in Ossa (2014) allows for many countries and sectors, and for a prediction of the outcome if one or several countries changes their tariffs. Perroni and Whallye (2000) and Caliendo and Parro (2012) present alternative frameworks with many countries that can also be used to estimate the welfare effects of tariff changes. The important advantage of the framework introduced in Ossa (2014) is that only data on trade flows, domestic production, and tariffs are needed to evaluate the outcomes of a change in tariffs, though the model itself contains other variables like transportation costs, the number of firms, and productivities.
It should also be pointed out that the framework in Ossa (2014) is not an example of a CGE model as it does not contain features such as investment, savings, and taxes. Since the framework in Ossa (2014) is simpler than CGE models, the effects of a tariff change can more easily be tracked and interpreted. On the other hand, this framework does not take into account spillover effects of tariff changes on for example capital formation and trade in assets.
The data on trade flows and domestic production come from the seventh version of the Global Trade Analysis Project database (GTAP 7). The data on tariffs come from the Trade Analysis Information System Data Base (TRAINS). The estimation of the model is done for 47 countries/regions and the sectors of meat and dairy products.
Results
According to my estimations, because of the ban on imports by Russia, the trade flow through Belarus increased. Belarusian imports of meat products are estimated to have increased by 28%, and imports of dairy products by 47%. Such increases in imports mean an increase in the tariff revenue of Belarus. It should be pointed out, however, that the model only tracks the effects of the ban on imports in the sectors of meat and dairy products. An alternative way would be to construct an econometric model that takes into account different factors influencing the trade between the countries. The effects of the decrease in the price of oil and the introduced ban on imports, which happened close in time, could then have been evaluated.
The estimated model further predicts that, because of the ban on imports, the tariff revenue collected by Russia in these two sectors has decreased by 53%. This means that since Belarus can only claim a fixed portion (4.55%) of the total tariff revenue of the EACU, its after-redistribution tariff revenue collected in the meat and dairy product sectors declined by 44.86%, in spite of its increase in before-redistribution tariff revenue by 35%. The decline in Belarus’ after-redistribution tariff revenue is thus estimated to have led to a decrease in welfare by 0.03%. To prevent such a decrease in the future, Belarus should argue for an increase in its share of the total tariff revenue of the EACU.
Furthermore, in addition to the decrease in the tariff revenue, the estimated model predicts that the real wage in Russia decreased by 0.39%, and its welfare by 0.49%.
The introduced ban on imports also affected the European countries that used to export to Russia. The model predicts that the welfare of Latvia declined by 0.38% and that the welfare of Lithuania declined by 0.27%. A substantial portion of the decline in welfare of these countries can be explained by a decrease in their terms of trade. The introduced ban on imports by Russia led to a decline in prices in the countries that exported meat and dairy products to Russia. Lower prices led to a decrease in the proceeds from exports collected by EU countries, and lower proceeds from exports buy less import, implying a decrease in their welfare.
In spite of the increase in tariffs between Russia and Ukraine, the model predicts an increase in the welfare of Ukraine by 0.23% following the formation of the EU-Ukraine Deep and Comprehensive Free Trade Area (DCFTA). An increase in real wages by 0.34% is the main factor contributing to this welfare increase. This is because it is associated with a redirection of Ukrainian exports from Russia towards the EU. The predicted increase in real wages in Ukraine have not materialized so far, presumably because of the ongoing military conflict and because time is needed to redirect the trade flows in response to the changes in the tariffs.
While bearing in mind that the analysis is only based on the sectors of meat and dairy products, Belarus could have increased its welfare during the trade wars if it had shifted from EACU status back to CIS FTA status with tariffs set at before-EACU levels. In this case, Belarus would not have needed to share its tariff revenue with other countries, and would then have increased its tariff revenue by 47.93% instead of the now predicted decline by 44.86%. Similarly, the welfare during trade wars could then have increased by 0.05%, instead of the now predicted decline by 0.03%. Another advantage of moving to CIS FTA status during trade wars is that the real wage could have increased by 0.04% instead of the 0.003% in the case of continued EACU status. Belarus could further have benefitted from moving to CIS FTA status by choosing optimal tariffs. This study suggests that the optimal tariffs of Belarus under CIS FTA status with trade wars are higher than the optimal tariffs under CIS FTA status without trade wars. Higher tariffs is the optimal response to the increased trade flows through Belarus resulting from trade wars.
Conclusion
Although it is optimal to move to CIS FTA status during trade wars, it is optimal to move back to EACU status after the trade wars are over. Therefore, such a policy should be adopted with caution, since the shift back to EACU status will likely not be possible. If it is expected that the trade wars will continue for a long period of time, or if the other members of the EACU will often deviate from the common tariffs, a transition to CIS FTA should be adopted. At the same time, asking for an increase in its share of total tariff revenue of EACU is a feasible strategy for Belarus to follow.
While estimating the effect of a transition from EACU status to CIS FTA status for Belarus during trade wars, the evaluation was done using two sectors affected by counter-sanctions. To evaluate the full welfare effect of this transition, its effect on the other sectors of Belarus should also be estimated, which is a question for the further research.
Traces of Transition: Unfinished Business 25 Years Down the Road?
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.
Conclusion
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.
The Economic Complexity of Transition Economies
‘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.
Conclusion
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.
References
- 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.
Is War Good for a Country’s Political Institutions?
Author: Tom Coupe, KSE.
Recent research suggests that experiencing war violence might make people more likely to turn out during elections. Using data from the conflict in Eastern Ukraine, we show, however, that people who were injured or had close friends or relatives killed or injured were less likely to turn out at the 2014 parliamentary elections. We also show that the impact of violence on turn out and political views depends on the type of violence one experienced.
What Ukrainians Expect From Reforms
Author: Tom Coupé, KSE.
Ukraine needs reforms badly. However, there is a huge difference in how the government, the expert community, and the general public understand reforms. According to a recent survey conducted by a prominent Ukrainian newspaper, people expect that reforms should, in the first place, improve their personal wellbeing. However, research findings beware that in the short run structural changes in the country can worsen economic performance and increase inequality. To reduce the pain of unmet expectations and popular discontent, the government should openly communicate any difficulties to come, and wisely mix the most painfull measures, like the increase of tariffs for the use of public infrastructure, with empowering changes that give citizens a sence of progress, like actions that strengthen democracy and help SMEs to flourish.
Growing Inequalities in Workplace Amenities
Inequality is considered to be a serious detrimental factor for societies’ development. It has been shown to undermine the health of the population, cause civil unrest, and slow down countries’ economic growth. Nizalova’s (2014) paper shows that the focus on the purely monetary component in the studies of inequality is too narrow. In Ukraine, which has had almost no change in income/wage inequality since 1994, the inequality in other workplace dimensions has soared. Nizalova finds that workers in establishments paying higher hourly wages have enjoyed (i) relatively greater reductions in the total workplace injury burden, (ii) greater retention of various benefits/amenities, and (iii) relatively larger increases in wage payment security (de-creased wage arrears). These findings document a high degree of an unequal shift away from work-centered provision of social services, not counter-balanced by the government, and highlight the importance of timely policy intervention as a possible cause of societal disturbances.
Inequality in income, health, and political rights has been on the agenda of many governments and international organisations. It has been shown to lead to tensions in society that can grow into civil unrest, and is named one of the top global risks in the World Economic Forum Global Risk Report, 2013. Country-level comparisons by epidemiologists have documented that more unequal countries have (i) higher rates of mental illness, drug use, and homicide, (ii) a larger incarceration rate, (iii) a larger share of obese population, (iv) higher school drop-out rates, lower socio-economic mobility, lower child wellbeing, and (v) a lower level of trust (Wilkinson and Pickett, 2010). At the macro level, inequality has also been shown to impede sustainable growth (Ostry and Berg, 2011).
Yet, in Ukraine, in spite of a number of continuing severe problems with population health, labor markets, infrustructure, etc., inequality has not been high on the agenda, except for occasional concerns raised by some international organisations and researchers. In our view, there are at least three reasons for this.
First of all, most of the attention in inequality discussions is paid to income inequality. However, in Ukraine after a significant increase in this indicator by the mid-nineties, there has been hardly any dynamics, with the exception of extreme increases in incomes/wealth of a few oligarchs.
Second, and this relates to inequality in any dimension, when people in power are predominantely concerned with self-enrichment, and citizens are not showing their dissatisfaction, or the government has “effective” means of dealing with this dissatisfaction (imprisonment, physical elimination, etc.), as has been the case in Ukraine for many years, those at the lower end of the income distribution have the least chances to attract attention.
Finally, we believe that the reason international organisations have not given much attention to Ukrainian inequality must be related to the fact that the situation in many areas of life has been so dire, i.e. the level of “well-offness” is so low throughout the distribution that the overall level was considered more important than the distribution.
A recent paper by Olena Nizalova (2014) examines the importance of the non-monetary dimensions of work in studies regarding inequality in total returns to work. Nizalova’s paper exploits a unique data set collected by the International Labour Office in Ukraine to study whether there has been a significant change in the non-monetary components of inequality. If this is the case, it can explain the growing tensions in society where the changes in income/wage inequality have been limited.
Non-monetary aspects of inequality
A few academic studies have explored the issue of income/wage inequality in Ukraine and Russia (Ganguli and Terrell, 2006; Galbraith, Krytynskaia, and Wang, 2004; Gorodnichenko, Peter, and Stolyarov, 2010; Lokshin and Ravallion, 2005), and found that, if anything, the change in inequality after 1995 has been quite modest. These results are in line with the dynamics of wage inequality in Ukraine presented in Figure 1, which pictures the ratio of wages in 2nd, 3rd, and 4th quartiles of the wage distribution against those in the 1st quartile.
Figure 1. Log Differences in Hourly Wages Relative to the Lowest Paying Quartile
Source: The authors own calculations based on Ukrainian Labour Flexibility Survey for the period 1994-2004.
However, the measures used in the earlier studies may not reflect the true inequality levels in the society. Indeed, they are omitting the contribution of the non-monetary dimension of work to the overall inequality.
The study of non-monetary working conditions is important for several reasons. First, work is central to people’s lives not only because a major share of household income in most countries comes from labor earnings (Guerriero, 2012), but also because individuals spend a considerable part of their time at work. Thus, earnings inequality can inappropriately reflect the true level of the total inequality in the labor market.
Second, the importance of this direction of research is further highlighted by the development of the ILO “Decent work agenda”. One of its aims is to promote both inclusion and productivity by ensuring that women and men enjoy working conditions, which satisfy several criteria. These criteria include that working conditions are safe, allow adequate free time and rest, take into account family and social values, provide for reasonable compensation in case of lost or reduced income, and permit access to adequate healthcare.
Lastly, inequality in working conditions, and in particular workplace injuries, may directly translate into income and wealth inequality, and, indirectly, affect inequality in future generations.
Ukraine: Inequality in Non-Monetary Work Dimensions Matters
The analysis in Nizalova (2014) shows that establishments that pay higher wages, tend to provide safer and, in general, better working conditions than establishments that pay lower wages. In addition, the latter are much more likely to experience difficulties with the payment of wages and have a higher percentage of workers with severe (more than 3 months) wage arrears. This suggests that the wage inequality may be further exacerbated by the inequality in non-monetary work dimensions.
A further distributive analysis demonstrates that the inequality in non-moneraty work dimensions has been changing noticeably over time. In particular, Figure 2 shows that the burden of workplace injuries, measured as total work days lost due to injuries per 100 Full Time Equivalent (FTE) employees, over time has shifted from being concentrated in the top part of the wage distribution to the lowest part (the way to interpret Figure 2 and all subsequent figures is as follows: the diagonal line in all figures corresponds to the equal distribution of the mentioned workplace characteristic across the wage distribution. The further the actual distribution curve (in red) is from the diagonal, the more unequal it is, with the curve below the diagonal indicating a concentration of the characteristic among higher paying enterprises and the curve above the line – concentration of the characteristic in the lower end of the wage distribution).
Figure 2: Concentration Curves – Total Injury Burden by Year
Source: The authors own calculations based on Ukrainian Labour Flexibility Survey for the period 1994-2004.
Moreover, the distribution of employer-provided benefits has also changed from being almost equally spread across the wage distribution to being more concentrated in the upper part (Figure 3).
Figure 3: Concentration Curves – Amenity Scores by Year
Source: The authors own calculations based on Ukrainian Labour Flexibility Survey for the period 1994-2004.
Notice that this result is not driven by any one particular amenity – it is observed across the whole range of indicators (for example, see Figures 4-6).
Figure 4: Distribution of Transportation Subsidy Provision by Year
Source: The authors own calculations based on Ukrainian Labour Flexibility Survey for the period 1994-2004.
Figure 5: Distribution of Kindergarden Subsidy Provision by Year
Source: The authors own calculations based on Ukrainian Labour Flexibility Survey for the period 1994-2004.
Figure 6: Distribution of Health Service Provision by Year
Source: The authors own calculations based on Ukrainian Labour Flexibility Survey for the period 1994-2004.
Similarly, wage arrears’ (non-payments) concentration has changed from being almost equally distributed across all wage levels to being more concentrated among lower paying establishments (Figure 7).
Figure 7: Distribution of Wage Arrears by Year
Source: The authors own calculations based on Ukrainian Labour Flexibility Survey for the period 1994-2004.
Further, the analysis of distributional shifts in the establishment characteristics over the corresponding period shows significant changes only with respect to firm size, export status, and some sectoral shifts.
Overall, the findings of the paper document an emergence of sizeable inequality in the workplace characteristics in the Ukrainian labor market: workers in poorly paying establishments are facing disproportionately larger risks of on-the-job injury, worse provision of amenities, as well as less security in timely payments of earning.
Conclusion
Although further research on causes of growth in multidimensional inequality in returns to work is required, this study provides two important lessons for the research community and policy makers.
First of all, it highlights the importance of a multi-dimensional approach to labor market returns, since a focus on monetary compensations only may significantly underestimate the true inequality in a society.
Secondly, it draws attention to the need of developing adequate governmental policies to address the inequality of workplace-centered provisions of social services during the transition to market economy. By prioritizing measures to facilitate provision of affordable housing, health care, kindergartens, as well as training opportunities, the government could mitigate increasing inequalities. This would allow the government to avoid significant tensions and conflicts in society, which is an important pre-requisite for ongoing sustainable development.
References
- Bockerman, Petri and Pekka Ilmakunnas. 2006. “Do job disamenities raise wages or ruin job satisfaction?” International Journal of Manpower 27 (3):290–302.
- Clark, Andrew E. and Claudia Senik. 2010. “Who Compares to Whom? The Anatomy of Income Comparisons in Europe.”Economic Journal 120 (544):573–594.
- Galbraith, James K., Ludmila Krytynskaia, and Qifei Wang. 2004. “The Experience of Rising Inequality in Russia and China during the Transition.” European Journal of Comparative Economics 1 (1):87–106
- Ganguli, Ina and Katherine Terrell. 2006. “Institutions, markets and men’s and women’s wage inequality: Evidence from Ukraine.” Journal of Comparative Economics 34 (2):200–227
- Gorodnichenko, Yuriy, Klara Sabirianova Peter, and Dmitriy Stolyarov. 2010. “Inequality and Volatility Moderation in Russia: Evidence from Micro-Level Panel Data on Consumption and Income.” Review of Economic Dynamics 13 (1):209–237
- Guerriero, Marta. 2012. “The Labour Share of Income around the World. Evidence from a Panel Dataset.” URL http://www.sed.manchester.ac.uk/idpm/research/publications/wp/depp/documents/deppwp32.pdf. Working Paper
- Hamermesh, DS. 1999. “Changing inequality in markets for workplace amenities.”Quarterly Journal of Economics 114 (4):1085–1123.
- Hensler, Deborah R., M. Susan Marquis, Allan Abrahamse, Sandra H. Berry, Patricia A. Ebener,Elizabeth Lewis, Edgar Lind, Robert J. MacCoun, Willard G. Manning, Jeannette Rogowski, and Mary E. Vaiana. 1991. “Compensation for Accidental Injuries in the UnitedStates.” RAND Corporation Report Series R3999, Santa Monica, CA: RAND Corporation. URL http://www.rand.org/pubs/reports/R3999
- Keogh, J. P., I. Nuwayhid, J. L. Gordon, and P. W. Gucer. 2000. “The impact of occupational injury on injured worker and family: outcomes of upper extremity cumulative trauma disorders in Maryland workers.” American journal of industrial medicine 38 (5):498–506. Research Support, U.S. Gov’t, P.H.S
- Lokshin, Michael and Martin Ravallion. 2005. “Rich and powerful?: Subjective power and welfare in Russia.” Journal of Economic Behavior & Organization 56 (2):141–172.
- Marquis, M. S. and W. G. Manning. 1999. “Lifetime costs and compensation for injuries.” Inquiry: a journal of medical careorganization, provision and financing 36 (3):244–254. Research Support, Non-U.S. Gov’t.
- Nizalova, Olena Y., 2014. “Inequality in Total Returns to Work in Ukraine: Taking a Closer Look at Workplace (Dis)amenities,” IZA Discussion Papers 8322, Institute for the Study of Labor (IZA).
- Ostry, Jonathan David and Andrew Berg. 2011. “Inequality and Unsustainable Growth: Two Sides of the Same Coin?” IMF Staff Discussion Notes 11/08, International Monetary Fund.
- Rosen, Sherwin. 1986. “The Theory of Equalizing Differences.” In Handbook of Labor Economics, edited by O. Ashenfelter, R. Layard, P.R.G. Layard, and D.E. Card, v.2, chap. 12. North-Holland, 641–692.
- Senik, Claudia. 2009. “Direct evidence on income comparisons and their welfare effects.”Journal of Economic Behavior&Organization 72 (1):408–424.
- Wilkinson, R. and K. Pickett. 2010.The Spirit Level:Why Equality is Better for Everyone. Penguin Books Limited.
Decentralization Reform in Ukraine
The current Ukrainian political system, which is a highly centralized “winner-take-all” system, is one of the main causes of the recent mass street protests. A decentralization reform is needed to make the system more stable by providing people with more impact on policy making, and increasing accountability of the government. A decentralization reform would reduce paternalistic expectations and provide people with more opportunities to take responsibility for public policy design in their region. In addition, it would improve the quality of national politics by introducing more competition and allowing successful regional politics to spread to the national level. However, as all reforms, decentralization bears some risks. This policy brief discusses the benefits and risks of such reform, suggests some ways of mitigation of the risks, and the procedure for reform development.
“In decentralized systems, problems can be solved early and when they are small. And when there are terrible failures in economic management—a bankrupt county, a state ill-prepared for its pension obligations—these do not necessarily bring the national economy to its knees.” / Nassim Taleb
In their path-breaking article Roger Myerson and Tymofiy Mylovanov argue that the underlying reason for the Ukrainian street protests in 2004 and 2014 is a fundamental flaw in the country’s Constitution, namely, the design of its government system. Currently, it is basically a “winner-take-all” system, where a winner of the national elections gains almost a dictator’s power, and then tries to prolong his stay in office with all means.
Such a system – where almost all the power is concentrated in the hands of the central government, and where local authorities, even the elected ones, have very little room for their own decisions – resembles an inverted pyramid and is therefore unstable. A natural way to stabilize the system is to put the pyramid on its foundation – i.e. to provide people with more impact on (and responsibility for!) both local and central government policy.
However, the Ukrainian government has announced a decentralization reform, and has already adopted a Decentralization Concept, which defines the main goals and milestones of the reform. According to the Concept, the legislative base for the decentralization should be developed by the end of 2014. However, it is clear that these plans are unrealistic. This, since on top of Constitutional changes, the reform implies changes to the administrative structure of the country, a redistribution of responsibilities between different levels of local government, and changes to the Tax Code, the Budget Code, and to several other documents. Such a scope of reforms is hardly attainable within the planned timeframe.
So far, the President’s office has developed changes to the Constitution, and the Cabinet of Ministers has drafted changes to the Budget Code. However, both documents miss the main point of the reform – empowering of people (rather than simply delegating some responsibilities from central to local governments). Instead, the drafted law on changes to the Constitution empowers the President, and the drafted changes to the Budget Code are an attempt of the central government to get rid of its “headaches” (e.g. ecological or social housing programs) while at the same time consolidating “electorally valuable” spheres, such as education and healthcare. This Draft Law proposes transferring some revenue sources from central to local levels, and at the same time to extract a part of the revenues that currently belong to local budgets to the central budget. A more detailed analysis of the proposed changes is provided in this article.
To my mind, the main impediment to the decentralization reform is a lack of a systemic approach. The Decentralization Concept does not provide a clear reform path, and changes to the legislation proposed so far look like pieces of a puzzle that do not fit together.
I suggest that the decentralization reform should be developed together with the administrative reform and proceed according to the following algorithm:
- Define functions of the state and distribute them between different levels of government according to a subsidiarity principle; i.e. a function should be transferred to the lowest government level capable of implementing it.
- Estimate the volume of funds needed to implement these functions.
- Assign sufficient revenue sources to local governments.
- If a community is too small to generate a sufficient revenue flow, merge several communities and repeat steps 3-4, keeping the distance between the center of such a united community and its most remote settlement below a defined limit.
- Establish feedback mechanisms through which people in a community could control the authorities and impact their decision-making. These mechanisms are not only elections, but also, more importantly, permanent between-elections activities, such as public hearings/discussions of drafts of local government decisions.
- Use a few communities as pilots and thus find out potential strengths and weaknesses of the proposed reform and make necessary corrections.
The outcome of this algorithm should be a logically connected package of legislative changes rather than a bunch of separate documents.
The development of this reform should be as transparent as possible, and accompanied by wide information and education campaigns about the opportunities that decentralization will provide, and the ways to use these opportunities. These information campaigns are necessary because many Ukrainians now think that decentralization (or federalization) is pushed by the Russian president in order to split Ukraine into parts.
As with all reforms, the decentralization has its potential benefits and risks, which should be accounted for. Fortunately, there exists both a wide academic literature and international experience on this issue.
The economic literature, both theoretical and empirical, does not unambiguously show that “decentralization is good”. Rather, a success of decentralization depends on a number of other factors, such as the presence of democracy (Inman, 2008) and a sufficient accountability of the government (both local and central).
In itself, decentralization does not lead to higher economic growth (e.g. the review of Feld et al, 2013). However, when accompanied by other growth-enhancing reforms, decentralization can positively impact a country’s economic development (Bardhan 2002).
Both the literature and experience of other countries suggest the following major risks of decentralization:
- Decentralization may increase corruption at the local level. If a local official is not accountable to a higher-level government, she may try to extract some rent from her position. This risk can be reduced by a high transparency of the government and working mechanisms of control of citizens over officials.
Indeed, Lessmann and Markwardt (2009) show that decentralization lowers corruption in countries with high levels of freedom of the press, and is harmful for countries where monitoring of the government is not efficient. Besides, Fan, Lin and Treisman (2009) find that “giving local governments a larger stake in locally generated income can reduce their bribe extraction”, i.e. for decentralization to lower corruption, the institutional setup should encourage local officials to create a favorable business environment in their regions.
- Decentralization may intensify secessionist movements. To lower this risk, the largest volume of responsibilities should be transferred to the lowest (community) level. It is rather easy for separatists to buy support of oblast-level officials and get control over an entire oblast. It would be much harder for them to buy every community head in an oblast. Moreover, getting control over an oblast, even rayon by rayon, let alone by community, is practically infeasible.
- Decentralization enhances initial inequality between regions – so the central government has to step in by providing subsidies/subventions to less developed regions (Cai and Treisman, 2005).
At the same time, the “bonuses” of decentralization are worth taking the risks:
- Reduction of tensions between the regions. In the Ukrainian situation, this implies removing grounds for mutual accusations that “one region feeds other regions” or “one region rules the entire country”. If a party that wins a majority in the national elections does not have extensive power over the daily life of people, they can more easily accept the fact this is not the party they voted for.
- Improvement of the national politics by increasing competition between local officials, and between local and central officials. As we know, competition typically increases the quality of a product. Political competition is no exception. As Myerson (2006) notes, “by creating more opportunities for politicians to build reputation as responsible democratic leaders, a federal [decentralized] system can effectively offer an insurance policy against general failure of democracy”. Thus, democracy and decentralization strengthen each other.
- More efficient government. On average, policy decisions will be made closer to their final beneficiaries and hence, will be more fitted to the needs of a certain community. At the same time, all levels of government will work more efficiently.
Decentralization does not imply a weakening of the central government. Rather, it frees its institutions from an unnecessary workload allowing them to concentrate on more strategic tasks, such as:
- protecting people’s rights by establishing a working judicial and security (police and army) systems;
- forming a strategic vision and general directions of the country’s development;
- protecting the country’s interests on the international level.
To make sure that decentralization does not result in feudalization, local officials should be controlled not only by local citizens but also by the central government (law enforcement); strong country-wide political parties would also help to hold the country together.
Conclusions
A decentralization of the Ukrainian political system is currently in the very focus of political, public and research debate.
However, this reform is not likely to be an easy one. The prerequisites for successful decentralization include functioning democratic mechanisms – fair elections, a free press and a strong civil society – resulting in government accountability. Also, for the decentralization reform to succeed, it needs to be coherently bundled with a range of political and administrative reforms (such as the development of a functioning judicial system, deregulation, reduction of rent-seeking opportunities etc.), and development and implementation of such a package is challenging and time-consuming.
At the same time, a wisely designed decentralization process will be highly beneficial for Ukraine, both politically and economically. It will strengthen democracy (by increasing people’s participation) and improve the quality of national politics by introducing more competition into the political system. It is also likely to significantly contribute to economic growth and prosperity, and these benefits make the decentralization reform in Ukraine a challenge worth undertaking despite of all the costs and risks.
References
- Bardhan, Pranab (2002). “Decentralization of Governance and Development,” Journal of Economic Perspectives, American Economic Association, vol. 16(4), pp. 185-205
- Brancati, Dawn (2006). Decentralization: Fueling the Fire or Dampening the Flames of Ethnic Conflict and Secessionism? International Organization. Vol.60, issue 03, pp. 651-685
- Cai, Hongbin and Daniel Treisman (2005). Does competition for capital discipline governments? Decentralization, globalization and public policy. The American Economic Review, Vol. 95, No. 3, Jun.2005
- Cai, Hongbin and Daniel Treisman (2009). Political decentralization and policy experimentation. Quarterly Journal of Political Science. Vol 4. Issue 1.
- Deiwiks, Christa, Cederman, Lars-Erik und Kristian S. Gleditsch (2012). Inequality and Conflict in Federations. Journal of Peace Research. March 2012 vol. 49 no. 2, pp. 289-304
- Enikolopov, Ruben and Ekaterina Zhuravskaya (2007). Decentralization and political institutions. Journal of Public Economics, No. 91, pp. 2261–2290
- Fan, C. Simon, Lin, Chen and Daniel Treisman (2009). Political decentralization and corruption: Evidence from around the world. Journal of Public Economics. Vol.: 93 (2009)
Issue: 1-2, pp: 14-34 - Inman, Robert P. (2008). Federalism’s Values and the Value of Federalism. NBER Working Paper 13735. http://www.nber.org/papers/w13735
- Lars P. Feld, Baskaran, Thushyanthan and Jan Schnellenbach (2013). Fiscal Federalism, Decentralization and Economic Growth: A Meta-Analysis. Public Finance Review 41 (4), 421-445
- Lessmann, Christian and Gunther Markwardt (2009). One Size Fits All? Decentralization, Corruption, and the Monitoring of Bureaucrats, CESIFO Working Paper No. 2662, Cat. 2: Public Choice.
- Myerson, Roger B. (2006). Federalism and Incentives for Success of Democracy. Quarterly Journal of Political Science, 2006, 1: 3–23
- Treisman, Daniel (2006). Fiscal decentralization, governance, and economic performance: a reconsideration. Economics and Politics, July 2006, 18, 2, pp. 219-35.
The Relationship between Education and Migration. The Direct Impact of a Person’s Education on Migration
This brief is based on a section from a large policy report, which investigates to what extent education directly influences major migration decisions. The results indicate that education does not have a clear and persistent effect on most of the migration decisions of Ukrainians — while in 2005-2008 education did not have any effect on the probability of migration at all, in 2010-2012 an inverse relation between qualification and probability of migration appeared. It has been observed that education is positively related to the probability of finding high profile positions, such as professionals, technicians or clerks. Still, the analysis of 2005–2008 data tends to support the “brain-waste”, or better to say, “skills-waste” hypothesis for white-collar Ukrainian migrants but not for blue-collar workers. In 2010-2012 the hypothesis doesn’t hold. *
Trust and Economic Reforms
This brief discusses the importance of trust in economic development. In the aftermath of the 2008 financial crisis, many countries experienced a decline in the level of both general trust and trust and confidence in the government and market institutions. Trust is important for economic growth as it facilitates economic transactions by reducing uncertainty and risk. A lack of trust in the government hinders implementation of structural reforms needed for economic development. Hence, policies aimed at rebuilding trust in the government and institutions become especially important for countries like Ukraine.
Recent events in Ukraine have highlighted an acute crisis of trust in the Ukrainian society (such as trust in the government, politicians, institutions, etc.). Over the past two decades, in the absence of a fair and transparent legal and court system, Ukrainians have become accustomed to relying on informal and often corrupt ways of living and doing business. According to a poll conducted in December 2013, less than 20 percent of the Ukrainian population said that they trust the government, police and courts.
A low level of trust in society is not, however, limited to Ukraine; this problem is also pronounced in many other parts of the world. According to the 2012 Edelman Trust Barometer survey, the general level of trust in most countries surveyed decreased compared to 2011. The most notable decline was in Brazil (36.3%), Japan (33.3%) and Spain (27.5%). These countries also experienced large drops in the level of confidence in the government: Brazil went down by 62.4%, Japan by 51% and Spain by 53.5%. According to the OECD report, generally, less than half (40%) of the citizens trust their government (OECD, 2013).
General trust is important for economic life as it reduces uncertainty and costs associated with economic transactions. Trust affects the functioning of businesses, financial markets, and government intuitions. The level of general trust varies significantly across countries (see Figure 1). While only 3.8 percent of people in Trinidad and Tobago fully trust most people, the Scandinavian countries’ share of trusting people exceeds 60 percent (Algan and Cahuc, 2013).
Economists have in their studies repeatedly appealed to the problem of trust because there are several channels through which trust may influence economic development. First, trust creates favorable conditions for long-term investment and financial market development (Algan and Cahuc, 2013). Second, a higher level of trust in various regulatory authorities increases the level of compliance with the rules and regulations if citizens believe in the fairness of such rules and regulations (Murthy, 2004). In Tabellini (2010), the level of economic development (measured by GDP per capita) of different regions of the EU member countries is compared to their level of trust (defined as in the Figure 1) and respect (defined as the proportion of people who mentioned the quality “tolerance and respect for other people” as being important). Using data from the World Value Survey rounds conducted in the 1990s, he shows that regions with a high level of trust and respect are also the regions that are the most economically developed.
In his Master thesis, the graduate of the Kyiv School of Economics Oleksii Khodenko (Khodenko 2013) analyzed the relationship between the level of trust in the government and the attitude towards market economy (in particular, the attitude towards competition and private property). For this purpose, he used data from the World Values Survey and the European Values Survey. His results have different implications for developed and less developed countries. While a lack of trust in the government in developed countries is transformed into a desire to see more market mechanisms in the economy, this mistrust of the government in developing countries (including Ukraine) undermines the faith in the entire market economy.
Khodenko’s results highlight important policy implications for transition countries: people who grew up in a centrally planned economy tend to underestimate the benefits of the free market and, therefore, only puts confidence in the government and the state as a whole to achieve the development of market mechanisms. Thus a lack of trust hinders, or even prevents implementation of structural economic reforms, which are often “painful” for some groups or for society as a whole. In countries with a low level of trust, the long-term promise of the implemented reforms to improve the lives of people is not perceived as credible. Instead of being viewed by the general public as a today’s sacrifice in the name of future prosperity, they are rather viewed as a deadweight loss (Györffy, 2013).
Figure 1. The Level of Trust in the World
Source: Yann and Cahuc (2013), Figure 1.
Note: Trust is computed as the country average from responses to the trust question in the five waves of the World Values Survey (1981-2008), the four waves of the European Values Survey (1981-2008) and the third wave of the Afrobarometer (2005). The question regarding trust asks: “Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?” Trust is equal to 1 if the respondent answers ”Most people can be trusted” and 0 otherwise.
Moreover, low levels of trust affect all types of structural reforms. Elgin and Garcia (2012) show that the effect of the tax reform on the economy can significantly differ depending on the level of trust in the government; under low levels of trust the announced tax cuts do not lead to exit from the informal sector.
The question is then how to revive or rebuild trust? Knack and Zak (2003) argue that the most efficient policies for building general trust are policies that (1) reduce income inequality since people in countries with more equal income distribution tend to have higher levels of interpersonal trust, and (2) strengthen civil society to increase government accountability. Income inequality often resulting from unequal opportunities can be reduced via increases in educational attainment and income redistribution programs. The presence of a strong civil society with free press ensures that the government is accountable and responsive to its citizens. A government needs to be reliable, open and transparent to effectively address citizens’ demands (OECD, 2013). All these policies cannot be implemented without a fair legal system that guarantees equal treatment of all citizens.
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References
- Algan, Y. and P. Cahuc (2013) “Trust, Growth and Well-being: New Evidence and Policy Implications”, IZA Discussion Paper No. 7464
- Elgin, C. and M. Solis-Garcia (2012), “Public Trust, Taxes and the Informal Sector”, Journal Review of Social, Economic and Administrative Studies, 26(1), pp. 27-44
- Györffy, D. (2013), Institutional Trust and Economic Policy: Lessons from the History of the Euro, Central European University Press
- Knack, S. and P.J. Zak (2003), “Building Trust: Public Policy, Interpersonal Trust, and Economic Development”, Supreme Court Economic Review, 10, pp.91-107
- Khodenko, Oleksii (2013). How Does Confidence in the State Authorities Shape Pro-market Attitudes?
- Murthy, K. (2004), “The Role of Trust in Nurturing Compliance: A Study of Accused Tax Avoiders”, Centre for Tax System Integrity, Working paper No49
- OECD (2013), Government at a Glance 2013, OECD Publishing.
- Tabellini, G. (2010), “Culture and Institutions: Economic Development in the Regions of
- Europe”, Journal of the European Economic Association, 8(4), pp. 677–71