Tag: Donbass

Russia’s Real Cost of Crimean Uncertainty

Posted on June 10, 2019 | Policy Brief

Blue sky with fighter jets flying across the sky representing Russias Real Cost of Crimean Uncertainty

The annexation of Crimea has real costs to the Russian economy beyond what is measured by some items in the armed forces’ budget; social spending in the occupied territories; or the cost of building a rather extreme bridge to solve logistics issues. Russia’s real cost of the annexation of Crimea is also associated with the permanent loss of income that the entire Russian population is experiencing due to increased uncertainty, reduced capital flows and investment, and thus a growth rate that is significantly lower than it would have been otherwise. Since the years of lost growth are extremely hard to make up for in later years, there will be a permanent loss of income in Russia that is a significant part of the real cost of annexing Crimea and continuing the fighting in Eastern Ukraine. It is time to stop not only the human bleeding associated with Ukraine, but also the economic.

Estimating the real cost of Russia’s annexation of Crimea and the continued involvement in Eastern Ukraine is complicated since there are many other things going on in the Russian economy at the same time. In particular, oil prices fell from over $100/barrel in late 2013 to $30/barrel in 2016 (Figure 1). Becker (2016) has shown that 60-80 percent of the variation in GDP growth can be explained by changes in oil prices, so this makes it hard to just look at actual data on growth to assess the impact of Crimea and subsequent sanctions and counter sanctions.

Figure 1. Russian GDP and oil price

Source: Becker (2019)

The approach here is instead to focus on one channel that is likely to be important for growth in these circumstances, which is uncertainty and its impact on capital flows and investment.

From uncertainty to growth

The analysis presented here is based on several steps that link uncertainty to GDP growth. All the details of the steps in this analysis are explained at some length in Becker (2019). Although this brief will focus on the main assumptions and estimates that are needed to arrive at the real cost of Crimea, a short description of the steps is as follows.

First of all, in line with basic models of capital flows, investors that can move their money across different markets (here countries) will look at relative returns and volatility between different markets. When relative uncertainty goes up in one market, capital will leave that market.

The next step is that international capital flows affect investment in the domestic market. If capital leaves a country, less money will be available for fixed capital investments.

The final step is that domestic investments is important for growth. Mechanically, in a static, national accounts setting, if investments go down, so does GDP. More long term and dynamically, investments have a supply side effect on growth, and if investments are low, this will affect potential as well as actual growth negatively.

These steps are rather straightforward and saying that uncertainty created by the annexation of Crimea leads to lower growth is trivial. What is not trivial is to provide an actual number on how much growth may have been affected. This requires estimates of a number of coefficients that is the empirical counterparts to the theoretical steps outlined here.

Estimates to link uncertainty to growth

In short, we need three coefficients that link: domestic investments to growth; capital flows to domestic investments; and uncertainty to capital flows.

There are many studies that look at the determinants of growth, so there are plenty of estimates on the first of these coefficients. Here we will use the estimate of Levine and Renelt (1992), that focus on finding robust determinants of growth from a large set of potential explanatory variables. In their preferred specification, growth is explained well by four variables, initial income, population growth, secondary education and the investments to GDP ratio. The coefficient on the latter is 17.5, which means that when the investment to GDP ratio increases by 10 percentage points, GDP grows an extra 1.75 percentage points per year. Becker and Olofsgård (2018) have shown that this model explains the growth experience of 25 transition countries including Russia since 2000 very well, which makes this estimate relevant for the current calculation.

The next coefficient links capital flows to domestic investments. This is also a subject that has been studied in many empirical papers. Recent estimates for transition countries and Russia in Mileva (2008) and Becker (2019) find an effect of FDI on domestic investments that is larger than one, i.e., there are positive spillovers from FDI inflows to domestic investments. Here we will use the estimate from Becker (2019) that finds that 10 extra dollars of FDI inflows are associated with an increase of domestic investments of 15 dollars.

Finally, we need an estimate linking uncertainty with capital flows. There are many studies looking at risk, return and investment in general, and also several studies focusing on international capital flows and uncertainty.  Julio and Yook (2016) look at how political uncertainty around elections affect FDI of US firms and find that FDI to countries with high institutional quality is less affected by electoral uncertainty than others. Becker (2019) estimates how volatility in the Russian stock market index RTS relative to the volatility in the US market’s S&P 500 is associated with net private capital outflows. The estimate suggests that when volatility in the RTS goes up by one standard deviation, this is associated with net private capital outflows of $30 billion.

These estimates now only need one more thing to allow us to estimate how much Crimean uncertainty has impacted growth and this is a measure of the volatility that was created by the annexation of Crimea.

Measuring Crimean uncertainty

In Becker (2019), the measure of volatility that is used in the regression with net capital outflows is the 60-day volatility of the RTS index. Since we now want to isolate the uncertainty created by Crimea related events, we need to take out the volatility that can be explained by other factors in order to arrive at a volatility measure that captures Crimean induced uncertainty. In Becker (2019) this is done by running a regression of RTS volatility on the volatility of international oil prices and the US stock market as represented by the S&P 500. The residual that remains after this regression is the excess volatility of the RTS that cannot be explained by these two external factors. The excess volatility of the RTS index is shown in figure 2.

It is clear that the major peaks in excess volatility are linked to Crimea related events, and in particular to the sanctions introduced at various points in time. From March 2014 to March 2015, there is an average excess volatility of 0.73 standard deviations with a peak of almost 4 when the EU and the USA ban trade with Crimea. This excess volatility is our measure of the uncertainty created by the annexation of Crimea.

Figure 2. RTS excess volatility

Source: Becker (2019)

From Crimean uncertainty to growth

The final step is simply to use our measure of Crimean induced uncertainty together with the estimates that link uncertainty in general to growth.

The estimated excess volatility associated with Crimea is conservatively estimated at 0.7 standard deviations. Using this with the estimate that increasing volatility by one standard deviation is associated with $30 billion in capital outflows, we get that the Crimean uncertainty would lead to $21 billions of capital outflows in one quarter or $84 billions in one year. If this is in the form of reduced FDI flows, we have estimated that this means that domestic investments would fall by a factor of 1.5 or $126 billions.

In this period, Russia had a GDP of $1849bn and fixed capital investments of $392bn. This means that $126 billions in reduced investments correspond to a reduction in the investments to GDP ratio of 7 percentage points (or that the investments to GDP ratio goes from around 21 percent to 14 percent).

Finally, using the estimate of 17.5 from Levine and Renelt, this implies that GDP growth would have been 1.2 percentage points higher without the estimated decline in investments to GDP.

In other words, the Crimean induced uncertainty is estimated to have led to a significant loss of growth that has to be added to all the other costs of the annexation of Crimea and continued fighting in Eastern Ukraine. Note that recent growth in Russia has been just barely above 1 percent per year, so this means that growth has been cut in half by this self-generated uncertainty.

Of course, the 1.2 percentage point estimate of lost growth is based on many model assumptions, but it provides a more sensible estimate of the cost of Crimea than we can get by looking at actual data that is a mix of many other factors that have impacted capital flows, investments and growth over this period.

Policy conclusions

The annexation of Crimea and continued fighting in Eastern Ukraine carry great costs in terms of human suffering. In addition, they also carry real costs to the Russian economy. Not least to people in Russia that see that their incomes are not growing in line with other countries in the world while the value of their rubles has been cut in half. Some of this is due to falling oil prices and other global factors that require reforms that will reorient the economy from natural resource extraction to a more diversified base of income generation. This process will take time even in the best of worlds.

However, one “reform” that can be implemented over night is to stop the fighting in Eastern Ukraine and work with Ukraine and other parties to get out of the current situation of sanctions and counter-sanctions. This would provide a much-needed boost to foreign and domestic investments required to generate high, sustainable growth to the benefit of many Russians as well as neighboring countries looking for a strong economy to do trade and business with.

References

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.

Labor Market Adaptation of Internally Displaced People: The Ukrainian Experience

Posted on October 29, 2018 | Policy Brief

20190506 Do Macroprudential Policy Instruments Image 05

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

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.