Location: Ukraine

Labor Market Adaptation of Internally Displaced People: The Ukrainian Experience

Posted on October 29, 2018 | Policy Brief

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This brief is based on research that investigates the probability of employment among displaced and non-displaced households in a region bordering territory with an ongoing military conflict in Eastern Ukraine. According to the results, internally displaced persons (IDP) are more educated, younger and more active in their job search than locals. Nevertheless, displaced individuals, particularly males, have experienced heavy discrimination. After controlling for personal characteristics, the structure of the household, location, non-labour incomes and endogeneity of displacement, IDP males are 17% less likely to be formally employed two years after resettlement than locals.

Internally displaced persons in Ukraine

In 2014, 23 years after independence, Ukraine suddenly found itself among the top-ten of countries with the largest internally displaced population. During the period 2014–2016, 1.8 million persons registered as internally displaced. Potentially, about 1 million more reallocated to Russia and about 100,000 to other countries nearby, where they sought refugee or labour migrant status (Smal, 2016).

The Ministry of Social Policy of Ukraine (MSPU) has regularly published very general reports on displaced persons. According to these reports, at the end of February 2016, the internally displaced persons in Ukraine included 22,000 individuals from Crimea and over 1.7 million citizens from Eastern Ukraine. These are mostly individuals who registered as IDPs to qualify for financial assistance from the state and some non-monetary benefits. Among them, 60% are retired people, 23.1% are individuals of working age, 12.8% are children and 4.1% are people with disabilities (Smal and Poznyak, 2017). In fact, the MSPU registers not only displaced persons but also those who de facto live in the occupied territories and occasionally travel to territories controlled by the Ukrainian authorities to receive their pension or social benefits (so called ‘pension tourism’). On the other hand, some IDPs did not register either to avoid bureaucracy or because they were unable to prove their status due to lack of documents. Recent publications that are based on surveys portray a more balanced distribution: 15% are retired people, 58% are individuals of working age, 27% are children and 13% are people with disabilities (IOM and the Ukrainian Centre for Social Reforms, 2018).

Only limited information is available about IDPs’ labour market activity. According to the State Employment Service (SES), between March 2014 and January 2016, only 64,300 IDPs or 3.75% referred to the SES for assistance (Smal and Poznyak, 2017). On the one hand, this figure reflects the relatively low reliance of displaced Ukrainians on the SES services in their job search. On the other hand, the geographical variation in the share of SES applicants suggests that Ukraine’s IDPs who moved further from the war zone and their homes were more active in trying to find a job.

Data

Our primary data were collected in June–August 2016 by REACH and provided by the Ukraine Food Security Cluster (UFSC) as a part of the needs assessment in Luhansk and Donetsk oblasts of Ukraine – two regions that were directly affected by the conflict. These two regions have hosted roughly 53% of all IDPs in Ukraine (Smal and Poznyak, 2017). We argue that households that did not move far from the place of conflict are most likely to be driven by conflict only, while long-distance movers may combine economic and forced displacement motives.

The data set offers information on 2500 households interviewed in 233 locations and is statistically representative of the average household in each oblast. It includes respondents currently living in their pre-conflict settlements (non-displaced, NDs) and respondents who report a different place of residence before the conflict (IDPs). The IDP group comprises individuals with registered and unregistered status and from both sides of the current contact line. The non-IDP group includes only households living on the territory controlled by the Ukrainian Government that did not move after the conflict had started.

Our sample covers 1,135 displaced households that came from 131 settlements. Most of the reallocations took place in early summer 2014 with the military escalation of the conflict in Eastern Ukraine. Thus, the average duration of displacement up to the moment of the interview was 637 days (or 21 months). This is a sufficiently long period for adaptation and job search. However, there is enough variation in this indicator – some families left as early as March–April 2014, while others were displaced in June 2016, just a few days before the interviews started.

Results

Simple comparison shows that heads of displaced households are on average almost four years younger than those of non-displaced households (Table 1). In terms of education, displaced households are found to be more educated than non-displaced households, as there are significantly more IDP household heads with tertiary education and significantly fewer individuals with only primary, secondary or vocational degrees. In particular, 37% of IDP household heads hold a university degree compared with 22% of household heads among the local population. This seems to suggest positive displacement selection. IDPs are slightly more likely to be headed by females and unmarried persons, although these differences are statistically insignificant. Displaced households include more children aged under five (0.35 vs. 0.22 children per non-displaced household) and 6 to 17 years (0.42 vs. 0.34, respectively) and fewer members aged over 60 years (0.58 vs 0.66, respectively). There is no difference in the number of working-age adults or disabled individuals per household among IDPs and non-IDPs. The average household size is statistically similar for the groups (2.74 vs. 2.65 persons per IDP and non-IDP household, respectively).

Table 1. Selected descriptive statistics

	Internally displaced households	Non- displaced households

Household head employed	0.43***	0.48***
Household head characteristics
Age (years)	48.10***	52.85***
Male	0.49	0.52
Education
vocational	0.42***	0.49***
university	0.37***	0.22***
Household characteristics
Size (persons)	2.74	2.65
Number of children 0-5	0.35***	0.21***
Number of children 6-17	0.42***	0.34***
Number of members 60+	0.58**	0.66**

IDP payments	0.50***	0***
Humanitarian assistance	0.78***	0.28***

There are further differences in the types of economic activity and occupations among IDPs and non-IDPs. Prior to the conflict, displaced respondents were more likely (than non-displaced persons) to be employed as managers or professionals and less likely to hold positions as factory or skilled agricultural workers. This result also speaks in favor of a positive displacement selection story.

As expected, the conflict has had a negative effect on human capital in the government controlled areas of Donetsk and Luhansk regions. We observe some deskilling at the time of the interviews, which is especially pronounced for IDPs. In particular, the share of managers among the IDPs had reduced from 12% to 5% and that of technicians from 15% to 12%, while the proportion of service and sales employees had increased from 10% to 13%, that of factory workers from 11% to 15% and that of skilled agricultural workers from 2% to 6%.

Considering the economic activity in the current location, we can note that on average the heads of displaced households are 5% less likely to be employed than those of non-displaced households (43% vs. 48%, respectively). In both groups, a large share of respondents report difficulties in their job search, but IDPs are 13% more likely to experience this problem. They report changing their pre-conflict occupation three times more often than non-IDPs (37% vs. 11%).

Government and non-government assistance may also drive the differences in employment. Economic theory states that individuals are less likely to work if they have some backup in the form of non-labour earnings. Financial support and humanitarian assistance are widely used to smooth a displacement shock. At the same time, improperly designed assistance schemes may reduce the stimulus to search for a job.

IDPs are 9% less likely to include earnings in their household’s top three main sources of income than the non-displaced population (46% vs. 55%, respectively), meaning that they rely more on various social payments and pensions. In addition, displaced households may be slightly more reluctant to search for a job due to displacement assistance from the government (received by 50% of IDPs compared with 0% for non-IDP households), although the amounts are quite modest. According to the existing legislation, IDPs can receive regular monthly state payments and one-time state payments. Regular monthly payments can be received by any IDP and cannot exceed UAH 3,000 (~$111) for an ordinary household, UAH 3,400 for a household with disabled people and UAH 5,000 (~$185) for a household with more than 2 children. Eligibility and the size of the one-time payment are determined by the local government. In the data set, 95% of IDPs receive less than UAH 3,000 while the 2016 average monthly wage was UAH 6,000 in Donetsk and UAH 4,600 in Luhansk regions.

In addition, IDPs are three times more likely to receive humanitarian assistance (78% vs. 28% among displaced and non-displaced persons, respectively). This support includes mostly food and winterisation items but also cash (26% among displaced vs. 12% among non-displaced assistance receivers). On the other hand, to cover reallocation and adaptation costs, some IDPs use their financial reserves, and as a result they are by 10 p.p. more likely to report no or already depleted savings. This may increase their stimulus to engage in a more active job search.

After taking into account the observed and unobserved differences between the groups as well as controlling for the location fixed effect, we find that the difference in the probability of employment between displaced and non-displaced persons increases from a casually observed slit of 5% to a chasm of 17.3%. This result suggests that IDPs are [negatively] discriminated despite being younger, more educated, skilled and more ‘able’ in the labour market. Specifically, 7 out of 17 p.p. (41% of the gap) are due to the variation in observed household head characteristics and family composition, while unobserved displacement-related features (such as attitude towards change, activism, mental and physical ability to reallocate) account for 5 p.p. (29%) of the gap. Controlling for particularities of a current location does not substantially affect the estimated differences.

Figure 1. Main results

We re-estimate these regressions using an employment indicator that includes both formal and informal employment (as defined by the respondents), accounting for occasional and irregular employment, including subsistence agricultural work. Since informal work is more common among IDPs, this definition of employment leads to a reduction in the average casually observed gap from 5% to 3%. However, after controlling for all the factors, we obtain the same result – a 17.8% difference between displaced and non-displaced households.

Conclusion

Policy makers and international donors should not be misled by the seemingly comparable probability of employment among IDPs and non-IDPs based on simple statistics. The average 0–5% difference in unconditional employment rates conceals the actual 17% gap in the likelihood of having a job. The contribution of unobserved displacement-related factors in hiding the true gap is large, especially for males seeking formal employment. Without adjusting for it, we would underestimate the real difference in employment probability by one-third to one-half.
Our study produces firm evidence that displaced individuals in Ukraine, particularly males, have been discriminated against in terms of employment. Our results further suggest that male heads of displaced households experience more discrimination in the formal labour market, while the situation is the opposite for females, who are more likely to face unequal treatment in the informal sector. Policy makers and volunteers should take this difference into account in the adaptation of male- and female-headed households.

Humanitarian assistance to displaced individuals was found to have no negative effect on their employment, which suggests that it is provided in an effective manner. Thus, this tool can be used to mitigate the discrimination.

References

IOM and the Ukrainian Centre for Social Reforms. (2018). ’National Monitoring System Report on the Situation of Internally Displaced Persons.’
Smal, V. and O. Poznyak. (2017) ‘Internally displaced persons: social and economic integration in hosting communities’, PLEDDG Project.
Smal, V. 2016. ’Внутрішньо Переміщені Особи: Соціальна та економічна інтеграція в приймаючих громадах.’
Vakhitova, H. and P. Iavorskyi, “Employment of Displaced and Non-displaced Households in Luhansk and Donetsk Oblasts”, Europe-Asia studies, (forthcoming).

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.

Conflict, Minorities and Well-Being

Posted on June 17, 2018 | Policy Brief

We assess the effect of the Russo-Georgian conflict of 2008 and the Ukrainian-Russian conflict of 2014 on the well-being of minorities in Russia. Using the Russian Longitudinal Monitoring Survey (RLMS), we find that the well-being of Georgians in Russia suffered negatively from the 2008 Russo-Georgian conflict. In comparison, we find no general effect of the Ukrainian-Russian conflict of 2014 on the Ukrainian nationals’ happiness. However, the life satisfaction of Ukrainians who reside in the southern regions of Russia in close proximity to Ukraine is negatively affected. We also show that the negative effect of conflict is short-lived with no long-term legacy. Additionally, we analyze the spillover effect of conflict on other minorities in Russia. We find that while the well-being of non-Slavic and migrant minorities who have recently moved to Russia is negatively affected, there is no effect on local minorities who have been living in Russia for at least ten years.

Militarized conflict affects a myriad of socioeconomic outcomes, such as the level of GDP (Bove et al. 2016), household welfare (Justino 2011), generalized trust and trust in central institutions (Grosjean 2014), social capital (Guriev and Melnikov 2016), and election turnout (Coupe and Obrizan 2016). Importantly, conflict has also been found to directly affect individual well-being (Frey 2012, Welsch 2008).

However, previous research studying individual well-being in transition countries largely abstracts from heightened political instability and conflict proneness, while this has been particularly pertinent in transition countries. Examples of transition countries facing various types of conflicts are abound, such as Yugoslavia, Ukraine, Tajikistan, Russia, Armenia, Azerbaijan, Moldova, and so on. Therefore, it is imperative to explore how conflict shapes well-being in transition countries.

In a new paper (Gokmen and Yakovlev, forthcoming), we add to our understanding of well-being in transition in relation to conflict. We focus on the effect of Russo-Georgian conflict of 2008 and the Ukrainian-Russian conflict of 2014 on the well-being of minorities in Russia. The results suggest that the well-being of Georgians in Russia suffered negatively from the 2008 Russo-Georgian conflict. However, we find no general effect of the Ukrainian-Russian conflict of 2014 on the Ukrainian nationals’ happiness, while the life satisfaction of Ukrainians who reside in the southern regions of Russia in close proximity to Ukraine is negatively affected. Additionally, we analyze the spillover effect of conflict on other minorities in Russia. We find that while the well-being of non-slavic and migrant minorities who have recently moved to Russia is negatively affected, there is no effect on local minorities who have been living in Russia for at least ten years.

Data and Results

We employ the Russian Longitudinal Monitoring Survey (RLMS) which contains data on small neighborhoods where respondents live. Starting from 1992, the RLMS provides nationally-representative annual surveys that cover more than 4000 households with 10000 to 22000 individual respondents. The RLMS surveys comprise a broad set of questions, including a variety of individual demographic characteristics, health status, and well-being. Our study utilizes rounds 9 through 24 of the RLMS from 2000 to 2015.

In this survey, we identify minorities with the question of “What nationality do you consider yourself?” Accordingly, anybody who answers this question with a non-Russian nationality is assigned to that minority group.

We employ three measures of well-being. Our main outcome variable is “life satisfaction.” The life satisfaction question is as follows: “To what extent are you satisfied with your life in general at the present time?”, and evaluated on a 1-5 scale from not at all satisfied to fully satisfied. Additionally, we use “job satisfaction” and “health evaluation” as outcomes of well-being.

Our results suggest that our primary indicator of well-being, life satisfaction, for Georgian nationals has gone down in the Russo-Georgian conflict year of 2008 compared to the Russian majority (see Figure 1). The magnitude of the drop in life satisfaction is about 39 percent of the mean life satisfaction. Our estimates for the other two well-being indicators, job satisfaction and health evaluation, also indicate a dip in the conflict year of 2008. Lastly, our estimates show that the negative impact of the conflict does not last long. Although there is a reduction in the well-being of Georgians both on impact in 2008 and in the immediate aftermath in 2009, the rest of the period until 2015 is no different from the pre-2008 period.

Figure 1. Life Satisfaction of Georgian Nationals in Russia

Source: Authors’ own construction based on RLMS data and diff-in-diff estimates.

Furthermore, when we investigate the effect of the Ukrainian-Russian conflict of 2014, we find no negative effect on the life satisfaction of Ukrainians. One explanation for why the happiness of Ukrainians in Russia does not seem to be negatively affected in 2014 is that the degree of integration of Ukrainians into the Russian society is much stronger than the degree of integration of Georgians. On the other hand, our heterogeneity analysis reveals that in the southern parts of Russia closer to the Ukrainian border, where there are more Ukrainians who have ties to Ukraine, Ukrainian nationals are differentially more negatively affected by the 2014 conflict. The differential reduction in the happiness of Ukrainians is about 19 percent of the mean life satisfaction.

Moreover, we also look into whether there is any spillover effects of the Russo-Georgian and the Ukrainian-Russian conflicts on the well-being of other minorities. We first carry out a simple exercise on non-Slavic minorities of Russia. We pick the sample of non-Slavic ex-USSR nationals that are similar to Georgians in their somatic characteristics, such as hair color and complexion. This group of people include the nationals of Azerbaijan, Kazakhstan, Uzbekistan, Kyrgyzstan, Turkmenistan and Tajikistan. We treat this group as “the countries with predominantly non-Slavic population” as their predominant populations are somatically different from the majority Russians, and thus, might either have been subject to discrimination or might have feared a minority backlash to themselves during the times of conflict. This conjecture finds some support below in Figure 2 in terms of violence against minorities. We observe in Figure 2 that hate crimes and murders based on nationality and race peak in 2008.

Our estimates also support the above hypothesis and propose that there is some negative effect of the 2008 conflict on non-slavic minorities’ happiness as well as their job satisfaction, whereas 2014 conflict has no effect.

Figure 2. Hate Murders in Russia over Time

Source: Sova Center

Next, we investigate the spillover effects of conflict on Migrant Minorities. Migrant minorities are minorities who have been living in their residents in Russia for less than 10 years. We conjecture that these minorities, as opposed to the minorities who have been in place for a long time, could be more susceptible to any internal or external conflict between Russia and some other minority group for fear that they themselves could also be affected. Whereas other types of longer-term resident minorities, which we call Local Minorities, are probably less vulnerable since they have had more time to establish their networks, job security, and most likely also have Russian citizenship. Our estimates back up the above conjecture and demonstrate that migrant minorities suffer negatively from the spillover effects of the 2008 conflict onto their well-being captured by any of the three measures, and not from the 2014 conflict, whereas there is no negative impact on local minorities.

Conclusion

In this paper, instead of focusing on the direct impact of conflict on happiness in war-torn areas, we contribute to the discussion on conflict and well-being by scrutinizing the well-being of people whose country of origin experiences conflict, but they themselves are not in the war zone. Additionally, we show that some other minority groups also suffer from such negative spillovers of conflict. Being aware of such negative indirect effects of conflict on well-being is essential for policy makers, politicians and researchers. Most policy analyses ignore such indirect costs of conflict, and this study highlights the bleak fact that the cost of conflict on well-being is probably larger than it has been previously estimated.

References

Bove, V.; L. Elia; and R. P. Smith, 2016. “On the heterogeneous consequences of civil war,” Oxford Economic Papers.
Coupe, T.; and M. Obrizan, 2016. “Violence and political outcomes in Ukraine: Evidence from Sloviansk and Kramatorsk”, Journal of Comparative Economics, 44, 201-212.
Frey, B. S., 2012. “Well-being and war”, International Review of Economics, 59, 363-375.
Gokmen, Gunes; and Evgeny Yakovlev, forthcoming. “War and Well-Being in Transition: Evidence from Two Natural Experiments”, Journal of Comparative Economics.
Grosjean, P., 2014. “Conflict and social and political preferences: Evidence from World War II and civil conflict in 35 European countries” Comparative Economic Studies, 56, 424-451.
Guriev, S.; and N. Melnikov, 2016. “War, inflation, and social capital,” American Economic Review: Papers & Proceedings, 106, 230-35.
Justino, P., 2011. “The impact of armed civil conflict on household welfare and policy,” IDS Working Papers.
Welsch, H., 2008. “The social costs of civil conflict: Evidence from surveys of happiness” Kyklos, 61, 320-340.

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 Does Ukraine’s Orange Revolution Tell Us About the Impact of Political Turnover on Economic Performance?

Posted on November 7, 2016 | Policy Brief

20161107 What Does Ukraines Orange Revolution Image 01

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

Posted on July 18, 2016 | Policy Brief

Satellite view of Europe and Eastern Ukraine at night highlighting city lights, representing the Economic Situation 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

(b) Donetsk
March 2014	March 2015	March 2016

(c) Luhansk
March 2014	March 2015	March 2016

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.
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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.

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Effects of Trade Wars on Belarus

Posted on June 20, 2016 | Policy Brief

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?

Posted on June 13, 2016 | Policy Brief

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

Posted on March 30, 2016 | Policy Brief

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‘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?

Posted on November 30, 2015 | Policy Brief

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

Posted on March 2, 2015 | Policy Brief

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

Posted on December 8, 2014 | Policy Brief

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.
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