Tag: Ukraine
What Does Ukraine’s Orange Revolution Tell Us About the Impact of Political Turnover on Economic Performance?
Political turnover is a normal, even desirable, feature of competitive politics, yet turnover in a context of weak institutions can create policy uncertainty, disrupt political connections, and threaten the security of property rights. What is the impact of political turnover on economic performance in such an environment? We examine the behavior of over 7,000 enterprises before and after Ukraine’s Orange Revolution—a moment of largely unanticipated political turnover in a country with profoundly weak institutions. We find that the productivity of firms in regions that supported Viktor Yushchenko increased after the Orange Revolution, relative to that of firms in regions that supported Viktor Yanukovych. Our results illustrate that the efficiency consequences of turnover can be large when institutions are weak.
Introduction
Politics in much of the world is a winner-take-all contest. When Viktor Yanukovych fled Kyiv in February 2014, for example, he was joined by a close group of associates overwhelmingly drawn from the country’s Russian-speaking East, including Yanukovych’s home region of Donetsk. The governors who ran Ukraine’s regions under Yanukovych fared no better. Oleksandr Turchynov, who served as acting president from February to June of that year, did what all Ukrainian presidents do: he fired the existing governors and replaced them with figures friendly to the new regime.
What is the impact of such political turnover on economic performance? In principle, replacement of political elites can have profound consequences for enterprise owners and managers, who rely on the support of patrons in government for government contracts, direct and indirect subsidies, the security of property rights, and permits to do business. In a system without effective checks and balances, economic policy can also swing widely as power passes from one group to another. Yet little is known about the impact of such changes on firm productivity, a major driver of economic welfare.
We examine the impact of political turnover on productivity and other aspects of firm performance in “The Productivity Consequences of Political Turnover: Firm-Level Evidence from Ukraine’s Orange Revolution” (Earle and Gehlbach, 2015). Our main finding is that the productivity of firms in regions that supported Yushchenko, the eventual winner of the 2004 presidential election, increased after the Orange Revolution, relative to that of firms in regions that supported Yanukovych, the chosen successor of incumbent President Leonid Kuchma. These results demonstrate that political turnover in a context of weak institutions can have major efficiency consequences as measured by differences in firm productivity.
Ukraine in 2004
Three factors make Ukraine in 2004 an appropriate setting for identifying the effect of political turnover on economic performance. First, Ukraine under Kuchma was a paradigmatic case of “patronal presidentialism,” in which the president “wields not only the powers formally invested in the office but also the ability to selectively direct vast sources of material wealth and power outside of formal institutional channels” (Hale 2005, p. 138). Who won the presidential contest had enormous implications for economic activity.
Second, economic and political power was regionally concentrated in Ukraine’s Russian-speaking East—Yanukovych himself was closely affiliated with oligarchs in Donetsk—while the political opposition represented by Yushchenko had its base in the ethnically Ukrainian and less industrialized West. Voting in Ukraine’s 2004 presidential election reflected this regional divide.
Third, few gave Yushchenko much chance of winning the presidency until the presidential campaign was well underway. In the end, it took not only a highly contested election, but also sustained street protests to wrest power from the existing elite.
Together, these considerations imply not only that political turnover in Ukraine could have an impact on firm performance, but also that any such effect could be observed by comparing the performance of enterprises in regions supportive of the two candidates before and after Yushchenko’s unexpected election victory.
The Orange Revolution and Firm Performance
To analyze the impact of political turnover, we use data on over 7,000 manufacturing enterprises that we track over many years, both before and after the Orange Revolution. We compare the evolution of productivity across firms in regions by vote in the 2004 election that was won by Yushchenko, while controlling for any shocks to particular industries in any year, for constant differences across firms in the level or trend of their productivity, and for regional differences in industrial structure. This design avoids many of the other influences on firm-level productivity that might have coincided with the Orange Revolution.
Our primary finding is that the productivity of firms in regions that supported Yushchenko in 2004 increased after Yushchenko took power, relative to the productivity of firms in regions that supported Yanukovych (and, implicitly, his patron Kuchma, whom Yushchenko succeeded as president). This effect is most pronounced among firms that had the most to gain or lose from presidential turnover: firms in sectors that rely on government contracts; private enterprises, given Ukraine’s weak property rights; and large enterprises. Other measures of economic performance suggest that these results are driven by favorable treatment of particular firms, either before or after the Orange Revolution, rather than by broad changes in economic policy.
Conclusion
Political turnover is often desirable. Nonetheless, our results suggest that the distributional consequences can be profound when institutions are weak, that is, when access to those in power is the primary guarantee of market access, contract enforcement, and property-rights protection. Oscillation of privilege from one region or sector to another is inefficient, as firms initiate or postpone restructuring based on who is in power. The optimal solution, of course, is not to restrict turnover, but to make turnover safe for economic activity. This requires that institutions be reformed to guarantee equal treatment for all economic actors—a difficult process that has proceeded with fits and starts in post-Yanukovych Ukraine.
References
- Earle, John S.; and Scott Gehlbach, 2015. “The Productivity Consequences of Political Turnover,” American Journal of Political Science, 59(3), 708–723.
- Hale, Henry E, 2005. “Regime Cycles: Democracy, Autocracy, and Revolution in Post-Soviet Eurasia,” World Politics, 58(1), 133–65.
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.
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.
Expected Effects of Tobacco Taxation in Five Countries of the Former Soviet Union
Authors: Irina Denisova and Polina Kuznetsova, CEFIR.
In this policy brief, we discuss the results from a study of different dimensions of tobacco taxation policy in five former Soviet Union countries: Belarus, Kazakhstan, Kyrgyz Republic, Russia and Ukraine. We find that the increase in budget revenue from raising excises on filter cigarettes is high in all studied countries. Furthermore, due to a low elasticity of the demand for cigarettes, the increase in excise taxes needs to be substantial to lead to a noticeable improvement in public health.
A Russian Sudden Stop Still a Major Risk
The Russian economy is facing serious challenges in 2015 even after the currency and stock market have strengthened on the back of (expectations of even) higher oil prices. Policy makers that ignore these challenges may be in for a rude awakening when more statistics on the real economy are now coming in. It is time that actions are taken to deal with Russia’s structural problems, mend ties with its neighbors that are also important economic partners, and refocus political priorities towards generating growth and prosperity for its population. In the long run, this is what creates the respect and admiration a great nation deserves.
Recent developments
The value of Russian assets, including shares and the currency, was more or less in free fall in the second half of 2014 and into the beginning of 2015. The annexation of Crimea and continued fighting in Eastern Ukraine and the associated sanctions contributed to a general loss of confidence in Russian assets, but the fall in international oil prices was an even more decisive factor (for a detailed account of the sanctions, see PISM (2015)).
Figure 1 shows how the stock market first took a big hit at the time of the invasion of Crimea, but then recovered before the massive downturn in mid-2014 as oil prices collapsed. The ruble followed a similar path, but with less volatility than the stock market, which is not too surprising given that the Central Bank of Russia (CBR) intervenes to stabilize the currency. However, the ruble had a short time of extreme volatility in mid to end-December when the uncertainty about the impact of financial sanctions was very high.
Figure 1. Oil price, Ruble and Stocks
Sources: CBR, US EIA, MICEX
Financial sanctions were particularly troubling since Russian companies, both private and state owned, have significant external debt that became increasingly hard to refinance. The magnitude of this external debt is also such that it is not a trivial matter for the government or central bank to handle despite the fact that public external debt is very low and international reserves are among the largest in the world. As a matter of fact, external debt was around $250 billion more than then the value of CBR’s international reserves at the peak, but the difference has come down somewhat to around $200 billion as external loans had to be paid back when new external funding was not available at attractive terms.
Sudden Stops
Before turning to the outlook for the Russian economy, a short discussion of sudden stops is warranted. “Sudden stops” is short for sudden stops or sharp reversals in international capital flows. Sudden stops and its effects on the real economy have been analyzed for some time now (see Calvo (1998) for an early contribution). Becker and Mauro (2006) concluded that sudden stops have been the most costly type of shock for emerging market countries in terms of lost GDP in modern history. In their study the average country that experienced a sudden stop had a cumulative loss of income of over 60 percent of its initial GDP before recovering back to its pre-crisis income level.
Sudden stops in capital flows have such large effects on the real economy because of the adverse effects reduced external funding has on imports. A first look at the accounting identity for GDP (GDP=Y=C+I+G+X-M) makes it hard to see how reduced imports can be a problem since imports (M) enter with a negative sign. This in itself suggests that reduced imports should increase GDP. However, imports are used for domestic consumption (C) or investment (I), two factors that enter the same identity with positive signs, which means that when they fall so does GDP. If this were the full story, the net effect on GDP from falling imports would be zero since the positive direct effect from imports would be exactly offset by reduced domestic consumption and investment.
Unfortunately the accounting identity does not make clear the dynamics that follow from this reduction in consumption and investment. For example, the foreign car (or machine) that is no longer imported and will not be sold, will also not require a domestic sales person, annual service, a parking space etc., so the eventual decline in consumption (or investment) will be much larger than the first round effect that is captured by a static accounting relationship. This is one reason why “improvements” in the trade balance stemming from the sudden decrease in imports is not necessarily a good thing for the economy.
Russia is also part of the international financial system with important capital flows both in and out of the country. As such, it is also subject to the risk that changes in sentiment and large capital outflows can affect imports and the real economy. For a time before the global financial crisis, net capital flows to Russia tended to be positive. However, this changed in 2009 and since then most quarters have been showing outflows.
Figure 2. Private Sector Capital Outflows Continue (Q1 2015 in red)
Source: CBR
The speed of outflows picked up dramatically in 2014, reaching more than $150 billion for the year. The general picture of outflows has continued in the first quarter of 2015, with outflows of around $35 billion (which for comparison is twice the $17.5 billion IMF package that was agreed for Ukraine in March 2015). Although Russia still has resources to support a high level of imports, the more capital that leaves, the less money there is to spend and invest in the country.
The Outlook
Everyone knows that Russia generates most of its export revenues from natural resources in general and from oil more specifically. The fact that the health of the economy is closely related to international oil prices is no secret either and Figure 1 showed the tandem cycle of oil prices, the ruble and the stock market. But how important is oil prices as a determinant of GDP growth? This is of course a big question that requires sophisticated thinking and modeling to figure out at a more structural level. But if we are just looking for a back of the envelope estimate, a simple regression of growth of oil is potentially interesting. Perhaps somewhat surprisingly, oil price growth has very high explanatory power: regressing annual changes in GDP per capita in real dollar terms on annual changes in real oil prices (and a constant) for the period 1998 to 2014 generates an R2 of 0.64! Not bad for a one variable macro “model” of the Russian economy. The coefficient on real changes in oil prices is estimated to be 0.15 and hugely significant and the intercept, which could be interpreted as the underlying growth rate in this “model”, of 2.4%.
Using the same IMF data on the real oil price for the first three months of 2015 and comparing that to the average oil price for the full year 2014 implies a drop in the real oil price of 46 percent. Using this oil data as the forecast for all of 2015 and plugging this into the estimated equation suggests that the oil price drop in itself would be associated with a decline in income of almost 7 percent. Adding back the underlying growth rate of just over 2 percent still means a negative growth rate of almost 5 percent in 2015, without even starting to think about sanctions, capital flows or structural problems.
However, there is more data that points in the directions of the economic troubles that lay ahead in 2015, which is trade data. We just discussed the importance of sudden stops and associated drops in imports in explaining large drops in output in emerging markets. Figure 2 already showed the continued capital outflows, and Figure 3 provides a scatter plot of changes in imports and GDP growth. Over the years, Russia has displayed a strong positive correlation between import growth and GDP growth that is in line with the description of sudden stop dynamics.
Figure 3. Imports and GDP Growth (Q1 2015 in red)
Source: Author’s calculations based on CBR and the Federal State Statistics Service (GKS) data
Figure 3 shows the import change in Q1 2015 (i.e., Q1 in 2015 compared to Q1 2014) as a red diamond and puts it on the linear regression line of past observations to get the implied GDP growth number for Q1 2015. First of all, the 36 percent drop in imports is at an all time high for the decade and at roughly the same level as in the worst quarter of 2009 in the global financial crisis. The implied drop in GDP is 10.5 percent (compared with a drop of 9.5 in the worst quarter of 2009). Again, this is not a formal model to generate GDP forecasts, but it is certainly a signal that suggests that the Russian economy has problems to deal with.
Concluding Remarks
The IMF (2015) just released its latest forecast for Russia together with the other countries of the world. The projection for 2015 is a decline of real GDP of 3.8 percent, which is not a great growth number by any means but less negative than what was discussed at the end of 2014. The Economist (2015) in its latest issue is also quoting a banker who says that the situation is not as bad as was previously imagined. The upward revisions have also led to statements among policy makers that seem to suggest that the problems for the Russian economy are behind the country.
Although the free fall associated with the sharp drop in oil prices is halted, recent data on capital flows and imports suggest that the problems for the Russian economy are far from over. If oil prices stay at current levels, capital outflows continue, and imports remain as suppressed as they were in the first quarter, the fall in GDP may be in the same order as in 2009. At that time GDP declined by 8 percentage points, or more than twice the recent forecasts for 2015.
Russian policy makers need to make serious structural reforms and mend ties with its important economic partners near and far to put the country on a more healthy growth trajectory. Simply praying for increasing oil prices is not enough; it is time that Russia becomes the master of its own economic faith.
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References
- Becker, T., and P. Mauro (2006), “Output drops and the shocks that matter”, IMF Working Paper, WP/06/197
- Becker, T. (2014), “A Russian Sudden Stop or Just a Slippery Oil Slope to Stagnation?”, BSR Policy Briefing 4/2014, Centrum Balticum
- Calvo, G. (1998), “Capital Flows and Capital-Market Crises: The Simple Economics of Sudden Stops,” Journal of Applied Economics, Vol. 1, No. 1, pp. 35–54.
- Economist, The (2015), “Russia and the West: How Vladimir Putin tries to stay strong”, April 18-24 issue
- IMF, (2015), World Economic Outlook, April
- PISM, (2015), “Sanctions and Russia”, Polski Instytut Spraw Międzynarodowych, (The Polish Institute of International Affairs)
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.
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.
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- 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. *