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.
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.
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|
|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**|
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.
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.
- 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.
This policy brief assesses the economic situation in the war-affected East of Ukraine. Given that official statistics are not available, we use changes in nighttime light intensity, measured by satellites, to estimate to what extent the war has destroyed the economy, and whether any recovery can be observed since the Minsk II agreement.
This FREE Policy Brief is simultaneously published as a column at VoxUkraine.org/en.
Correct measurement of economic performance is difficult enough in peaceful times and in scenarios, in which reliable economic indicators are available. However, when the necessary data is missing or when its reliability is far from clear, assessing the degree of economic activity – even in the most crude of forms – becomes a significant challenge. And yet, such situations are very frequent, apply to many countries and regions and become most evident at times of military conflicts when data collection is far from a top priority. In the context of the Ukrainian conflict an example of indirectly estimating changes in economic performance can be found in Talavera & Gorodnichenko (2016) who focus on measures of the degree of price integration in the so called Luhansk and Donetsk National Republics (LNR/DNR). In addition, there are various articles using anecdotal evidence to illustrate the economic losses in the East of Ukraine. For example, BBC, 2015 mentions an estimate by the Ukrainian Ministry of Economy that by mid 2015, 50% to 80% of jobs were lost in the so-called Luhansk and Donetsk National Republics, compared to the pre-war situation. Knowing the economic situation in the East is important both to assess the economic viability of the so-called Luhansk and Donetsk National Republics (LNR/DNR) as well as to assess the likely humanitarian situation in the East.
An alternative indirect way to examine the intensity of economic activity is to use measures based on satellite nighttime light intensity images. Nighttime light intensity is closely related to electricity consumption, which often has been used as an indicator of economic activity (e.g. Arora and Lieskovsky, 2014). Nighttime light intensity has been used to assess economic activity in sub-Saharan Africa (Henderson et al., 2012), the impact of the crisis in Syria (Li and Li, 2014) or to study how elected politicians favour their own regions worldwide (Hodler and Raschky, 2014). Henderson et al. (2012) find that among low- and middle-income countries, a one percent change in light roughly corresponds to a one percent change in income. 
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. 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
|March 2014||March 2015||March 2016|
|March 2014||March 2015||March 2016|
|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.
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 suggest 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. 
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.
 ‘The elasticity of growth of lights emanating into space with respect to income growth is close to one (p. 1025)’
 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 publically available for download.
 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 informative judgement on the basis of these data, we focus on the broad picture that emerges from the data, rather than on specific values.
- 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.