Ukraine's Regional Economic Growth and Analysis of Regional Disparities

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2 Ukraine's Regional Economic Growth and Analysis of Regional Disparities Fyodor I. Kushnirsky a and Svitlana V. Maksymenko b Affiliations: a Department of Economics, Temple University, Philadelphia, PA 19122, USA b Department of Economics, University of Pittsburgh, Pittsburgh, PA USA maks@pitt.edu (corresponding author) Abstract Is there evidence that economic growth reduces poverty in Ukraine's regions which lag in industrial and agricultural development? To answer this question and analyze medium-range growth prospects, we build an econometric model consisting of four blocks industry, agriculture, construction, and services for all administrative regions of Ukraine. After adjusting a baseline forecast for a structural break caused by a fall in production, and applying exponential smoothing technique, we identify the top and bottom regional performers in different sectors of the economy. From the policy analysis perspective, we find that the rise in industrial production does not likely affect the level of poverty in the bottom regions. We also find that it is the agricultural growth that could potentially reduce poverty there. The paper discusses some alternative scenarios and development goals for a reduction in entrenched rural poverty in Ukraine. JEL classification: C31, P30, R10 Keywords: Economic growth; Regional development; Poverty; Exponential smoothing; Ukraine. 1

3 1. Introduction Studying Ukraine s regional economy is crucial in view of the attempts to decentralize economic decision making and to reform the national economy. After 25 years of independence, politicians and scholars have never discussed the situation in Ukraine as extensively as recently. Among others, Umland (2011) and Myerson and Mylovanov (2014) stress the need to fundamentally change Ukraine s political, economic, and administrative system to achieve efficiency and reduce regional tension. Many scholars focus on the absence of real political and economic reforms, the persistence of corruption, and structural issues that prevail in the country (Kuzio, 2011, Korostelina, 2014, and VoxUkraine, 2015). It has been emphasized that a more efficient government would produce policy decisions close to final beneficiaries (Sologub, 2014). There is also some caution on decentralization (Cai and Treisman, 2005) because it may let finances flow from poor to well-endowed regions, exacerbating the interregional inequality. The Ukrainian public is quite skeptical on a flurry of activity at decentralization and economic reform in the country. 1 One should understand that not much is going on in Ukraine outside major centers such as Dnipro, Lviv, Kharkiv, Donetsk, Odesa, and especially Kyiv. People in provincial areas do not feel positive changes. On the contrary, prices and tariffs rise, living standards fall, infrastructure keeps crumbling, and business is sagging under the pressure from punitive rules and regulations. 2 The driving causes of rising poverty, especially among the middle class and retirees, are inflation and depreciation of the currency. In June 2015 inflation climbed to 57.5% over June 2014, and hryvnia, which was pegged at a 5:1 to the U.S. dollar in mid-2008, dropped to 25:1 in mid For more detailed discussion, see Kushnirsky (2014). 2 Dzerkalo tuzhnia, 17 July,

4 To link the variation in Ukraine s regional growth and indicators of poverty and to forecast the performance of Ukraine s regional economy consisting of 24 oblasts, the Autonomous Republic of Crimea, and special-status cities of Kyiv and Sevastopol, we build an econometric model covering four economic sectors industry, agriculture, construction, and service by region. The estimation sample covers the data, and the midterm forecast period is Our analysis and forecast of the best and lagging regions in Ukraine may be essential for the rise in territorial economic efficiency and for smoothing the negative consequences of a projected reduction in the distributive function of the central government. The paper is organized as follows. After the introduction, in Section 2 we describe our data, the model, and the methodology. Section 3 offers a brief description of the four blocks of our model. In Section 4 we outline a baseline forecast and, because of a sharp decline in Ukraine s economic output in 2014, we design a nonlinear scenario. This is accomplished by testing the time series for a structural break and by employing exponential smoothing that assigns greater weights to the most recent observations. In Section 5 we consider the regional discrepancies and analyze how variation in industrial and agricultural growth is interconnected with persistent poverty in a number of territorial units. Section 6 concludes. 2. The Data, Model, and Methodology 2.1. The Data and Model Our data originate from the State Statistics Service of Ukraine (Ukrstat) and cover the timeframe 3. We use the Gross Regional Product (GRP) as an aggregate indicator of development in a region. Ukrstat computes the GRP as the sum of gross value added (GVA) 3 In some instances, we use our own estimates employing tools described further in the paper. 3

5 generated by different type of economic activity, adjusted for indirect services of financial intermediation and taxes, less subsidies. The industry data give the sum of GVA for three types of economic activities: mining, processing (manufacturing), and production and distribution of energy, gas, and water. The processing industry, in turn, consists of nine sub-industries, out of which we consider three with the highest output: food processing, metallurgy, and machine building. Ukrstat reports GVA series in nominal prices, and we use indices (growth percentages) to convert the series into 2007 constant prices. We employ splicing time series based on growth rates and levels, interpolation, and shortterm extrapolation. Since agricultural output is reported by Ukrstat in constant 2010 prices, we convert it into constant 2007 prices using regional price indices for To capture the dynamics in crop output, we use the data on grains and leguminous crop production in thousand tons multiplied by 2007 constant prices. The GVA data in construction and services are the sum of output by type of economic activity. Since our sample includes observations up to 2013, we first produce a baseline regional forecast for , and then separate The latter cannot be used for gauging the predictive ability of the model, due to a sharp decline in 2014 production, especially industrial. But it plays an important role in constructing a nonlinear scenario of the growth of Ukraine s regional economy, after testing the time series for structural break and employing exponential smoothing. Our equation estimation is based on the use of autoregressive distributed lag (ARDL(p, q)) model y t y... x 0 1 t 1... p yt p 0xt 1xt 1 q t q e, (1) t which contains lagged values of endogenous variable y and exogenous variables x. Theoretically, a sufficient number of lags of y and x is to be included in order to reduce serial correlation in error 4

6 terms. Whereas there is no justification for long lags in industrial and agricultural production processes, in experimental trials we found a two-period lag adequate for forecasting regional growth. Also, we drop some lagged terms when slope estimates turned out to be highly insignificant. In our ARDL(p, q) model we discount potential regional spillovers. Assuming that standard assumptions on the error term e hold, we use the ordinary least squares for estimating parameters in (1). Altogether, there are over 340 equations in our model and about 50 identities. We use EViews for the estimation The Methodology Given that in the extended sample the highest likelihood of a structural break is in the last observation, we end up with two subsamples, and 2014, with 14 and 1 observations, respectively. In this case the Chow test for structural break is not appropriate because the number of observations in the second subsample is less than the number of parameters. Therefore, we use the predictive Chow test: y i X e i 1 X e i 2 i i i ( A) i ( B) where (A) and (B) are the two subsamples, the total number of observations is n ( n n 1 n 2 ), and the number of parameters on the right side of each equation equals k. The null hypothesis is H :. As n k , the Chow test statistic is ( SSE SSE1) / n F SSE /( n k) 1 1 2, (2) where SSE and SSE1 is the sum of squared residuals in the full sample and larger subsample, respectively. A typical response to a structural break is to use a dummy variable in a pooled time series, which is not a reasonable option in a one-observation subsample. Past statistics are still relevant, 5

7 because they reflect the accumulated production capacities, but their weight cannot be decisive, as a greater emphasis should be placed on a new trend. For this reason, exponential smoothing (ES), which assigns greater weights to more recent observations, is appropriate. By producing a smoothed time series, ES helps harmonize the baseline forecast with the 2014 output. The basic recursive formula of ES is yˆ t y t (1 ) yˆ t 1, (3) where ŷ is a smoothed series of y, and 1 is the smoothing parameter. Single smoothing t t (3) is used when there is no trend in the data; otherwise, it is included as a part of a more complicated smoothing. Since there is a trend but no seasonal variation in our time series, we use a double ES and Holt-Winters-No Seasonal (HW) smoothing. A formula for double ES is: D t yˆ t (1 ) D t 1 (4) ŷ t where is defined by formula (3). The HW series ŷ is ˆ y t l a bl (5) where l is a forward time lag, a is the intercept and b is trend estimated as a t y b ( a a t t (1 )( a t t 1 t 1 b ) (1 b t 1 t 1 ) ) (6) with and the smoothing parameters for the data and trend, respectively. We use both methods (4) and (5)-(6) and, in most instances, choose the one that provides 2 a smaller root mean square error (RMSE) conveniently reported by EView: RMSE ei / n, where e is the difference between the actual and estimated values of the dependent variable. 3. Four Blocks of the Model 6

8 3.1. Industry Industry s share in Ukraine s GDP equals 29%, service and agriculture account for 59% and 12%, respectively (CIA, 2014). The main part of Ukraine s industry belongs to the so-called heavy group A which makes capital goods. The group s output constitutes 70% of Ukraine s industrial output, 4 and a disproportionately smaller light group B produces consumer goods. Ukraine s industrial production is concentrated in four regional complexes: Donbas, Trans- Dnieper, Trans-Black Sea, and Trans-Carpathian (Nemec and Zavoloka, 2009). Table 1 gives 2012 Ukraine s per capita industrial production for five regions with top and bottom outputs, respectively. The share of industrial production in each region of the bottom group is less than 1% in the nation s total. Table Ukraine s Per Capita Industrial Production Region Per Capita Output, UAH Top 5 Regions City of Kyiv Dnipro Donetsk Poltava Zaporizhia Bottom 5 Regions Rivne Kherson 9486 Zakarpattia 7949 Ternopil 7170 Chernivtsi 4542 Ukraine Source: Regions of Ukraine 2013, Part ІI, State Statistics Service of Ukraine: Kyiv, 2013, pp and authors computation. Mining. According to our calculation, the mining industry accounted for 14.7% of Ukrainian industrial production in Based on mineral commodity summaries of the U.S. Geological Survey, more than 5% of the world reserves of iron ore, or 46% of reserves of the 4 Geography of Ukraine. Textbook ( 7

9 former Soviet Union (FSU), is concentrated in Ukraine. While the deposits of oil and natural gas are insufficient, oil refining is well developed. Coal is the main fossil fuel of Ukraine. According to Ukrstat, in 2013 Ukraine mined 84 million tons of coal while in 1990, prior to the collapse of the FSU, almost twice as much 165 million tons. Ukraine's mining industry has been going through a process of restructuring, yet safety in many working mines remains below the standards of neighboring Poland, Russia, and Slovakia. Processing. The processing (manufacturing) industry accounted for 67.6% of Ukrainian industrial production in Country s iron and steel production peaked at 43 million tons in 2007, but after the 2008 financial crisis declined to 30 million tons (Levine et al, 2010). The largest aluminum producer is the Mykolaiv alumina refining plant which supplies 20% of total output. The machine building (MB) complex in Ukraine has more than 3000 plants and factories, accounts for 20% in nation s exports, and sells its products to 54 countries (Nemec and Zavoloka, 2009). The largest heavy MB plants are in Novokramatorsk and Sumy. Outstanding is Ukraine s transport aircraft industry, with the Antonov Design Bureau producing a cargo plane (An-140) and a military transport plane An-70, both of a potential interest to the EU air transportation industry. Energy. Ukraine is the eighth largest producer of electricity in the world; we estimate its contribution to 2014 industrial output at 17.7%. Energy production is based on natural gas (41%), coal (24.3%), petroleum (18.4%), and nuclear energy (12.5%) (Nemec and Zavoloka, 2009). Thermal and nuclear power stations supply 90% of energy; Ukraine has four active nuclear plants constructed in the Soviet period Zaporizska, South-Ukrainian, Rivenska, and Khmelnytska. The Zaporizska nuclear plant, the largest in Europe, produces 20.4% of Ukraine s electricity. 5 The thermal power plants are the largest air and water pollutants in the country. 5 Website of Zaporizska nuclear plant. 8

10 3.2. Agricultural Sector Ukraine utilizes 69% of its land area for agricultural purposes, with arable land occupying over a half of the total (Bogovin, 2006). There are three agro-climatic zones: forest, forest-steppe, and steppe. Steppe, the most intensively cultivated area in Ukraine, claims one of the world largest areas of fertile black soil and accounts for 40% of Ukraine s territory. It is well-known for its rich harvests of wheat, sunflower, corn, and soybeans (Rogovska, 2014). According to Ukrstat, agriculture produces 12% of Ukraine s GDP, down from 17% in 2001, in a sign of structural change. Agricultural exports rose from 4.3 billion USD in 2005 to 17.9 billion USD in 2012, making a quarter of country s exports (Sarna, 2014). Table 2 reports 2012 Ukraine s per capita agricultural production for five regions with top and bottom outputs. Table Ukraine s Per Capita Agricultural Output Region Per Capita Output, UAH Top 5 Regions Cherkasy Poltava 9071 Kirovohrad 8942 Vinnytsia 8868 Kyiv 8602 Bottom 5 Regions Crimea 3395 Odesa 3369 Zakarpattya 3364 Luhansk 2763 Donetsk 2467 Ukraine 4892 Source: Regions of Ukraine 2013, Part ІI, State Statistics Service of Ukraine: Kyiv, 2013, p. 197 and authors computation. Following the disintegration of the FSU, Ukraine s grain output fell, but improved to 60.5 million metric tons in 2014 surpassing a 2011 record of 57 million tons (Mykhailov, 2014). Laborintensive production of sugar beets remains a viable option for many small farms with limited access to agricultural machinery. Production of corn has been promoted in Ukraine beginning with the Khrushchev administration; a half of the total corn area is harvested for grain, and the rest is 9

11 cut for silage. Meat production is responsible for a third of Ukraine s agricultural output. Beef and dairy industry capacity shrank by 60% from the early 1990s, and the cattle population dropped from 10.6 million heads in 2000 to 4.2 million in 2014 (Ukrstat, 2014). Household cattle farms are small, but they continue to supply 65% of milk to domestic market, often directly by producers. An inadequate dairy quality is an obstacle to compete on the EU markets Construction By our estimation, Ukraine s construction sector accounted for 2.9% of country s value added in The majority of provincial population hardly notices new construction, as close to a third of all funding flows to Kyiv, followed by Dnipro, Donetsk, and Odesa. There are some modest gains in residential construction where an average housing space per inhabitant is 23.7 square meters, low in comparison to neighboring countries Service Sector According to Ukrstat, trade and repair, transportation and communications, and real estate contributed the most to the value added of the country s service sector in %, 18.3%, and 11.4%, respectively. Trade and repair. In 2014 the largest trade categories included natural gas (11.2%), pharmaceuticals (7.3%), and tobacco (7.1%). Country s exports in 2014 amounted to 52.5 billion USD, imports to 60.4 billion USD (Ukrstat, 2015). Major export commodities are agricultural products, metals, and chemicals; top imports are energy, machinery, and equipment. Transportation and communications. As of 2014, 47% of Ukraine s railways were electrified (Ukrstat). The total length of Ukraine s shore line is 2000 km, with 18 sea ports; Odesa 6 PMR (2014) Construction Sector in Ukraine 2014, p

12 accounts for 80% of cargo shipments by sea. Ukraine s steppe landscape is good for auto transportation; 97% of public automobile roads have hard coating (Ukrstat, 2014). However, 60% of roads are in need of complete overhaul, with a third of those in a state of emergency. 7 Communications is one of the fastest growing services in the country; about one-third of Ukraine's networks are digital. 4. Baseline Forecast and Nonlinear Scenario Because our baseline forecast could not reflect a war-related slump in economic production in 2014, we use a novel methodology for an appropriate adjustment; the adjusted forecast is called a nonlinear scenario. 8 As Ukrstat explains in reporting the performance of country s economy in 2014, the data do not include temporarily occupied territory of the Autonomous Republic of Crimea, the city of Sevastopol, and a part of the zone of conducting an antiterrorist operation (ATO), meaning the territories of self-proclaimed Donetsk People s Republic (DNR) and Luhansk People s Republic (LNR). Table 3 gives the percentages of Crimea and Donetsk and Luhansk oblasts in Ukraine s 2013 indicators. In the pre-2014 period, the Donetsk oblast used to be densely populated (9.6%), contributing high percentages to GVA in mining (23.4%), processing (21.1%), energy (15.3%), exports (18%), and construction (13.4%). The economy of the Luhansk oblast is less diversified, producing 11.9% of the country s mining output. Table 3. Crimea, Donetsk and Luhansk, Percent in Ukraine s National Economic Indicators Indicator Crimea Donetsk Luhansk Territory Population Labor force Gross value added, 2011 prices Industry Mining The specifics of the baseline forecast are available on request. 11

13 Processing Energy Agriculture Grain Meat, dairy Construction Services Exports Imports Investment Sources: Reports of Ukrstat After positive developments such as the signing in September 2014 of a Minsk Protocol, i.e., an agreement to halt the war in Donbas, there are signs of Ukraine s territorial division becoming less uncertain. To adjust the baseline forecast, we introduce numerical changes that go in both directions. Firstly, since our baseline forecast includes the Crimea economy, which remains under Russia s jurisdiction, we undertake a downward adjustment by excluding Crimea from both the non-linear scenario and the actual 2014 statistics. In the latter case, our decision coincides with the 2014 Ukrstat reporting, even though the latter did not elaborate on its methodology. Secondly, we introduce an adjustment for the Donbas region whose direction is not as straightforward; yet unlike Crimea, Donbas will remain a part of Ukraine, whatever its final status. By keeping Donetsk and Luhansk oblasts in the non-linear scenario, we combine in our nonlinear scenario in-sample data, the Ukrstat 2014 statistics, and the baseline forecast, with an exponential smoothing adjustment. Specific information supporting the assumption of a structural break is quite sketchy, available primarily for the Donbas region. Intuitively, the deeper the decline in regional output, the greater the likelihood of a structural break. Since it is hard to test all 340 regional time series, we use a 10% fall in the 2014 values as a threshold for testing, although the choice is an empirical matter. 9 We use formulas (4) or (5)-(6) for double or Holt-Winters ES with one smoothing 9 E.g., it is interesting that Risso and Carrera (2009) obtained a similar result empirically: their tests suggest a 9% as a structural break point above which inflation significantly slows Mexican economic growth. 12

14 parameter α for the data or two parameters α and β for the data and trend, respectively. Table 4 gives the resulting non-linear scenario for Ukraine s industry forecast. Table 4. Nonlinear Scenario: Ukraine Industry Forecast Region 2014 Actual /2014 Annual % A1. Mining (Top 5 Regions) Dnipro Donetsk Luhansk NA NA Poltava Zhytomyr Ukraine B1. Processing (Top 5 Regions) Donetsk Dnipro Zaporizhia Kharkiv Kyiv City B2. Processing (Bottom 5 Regions) Khmelnytsk Ternopil Kherson Volyn Chernivtsi Ukraine C1. Energy (Top 5 Regions) Donetsk Zaporizhia Dnipro Kharkiv Kyiv City NA NA C2. Energy (Bottom 5 Regions) Ternopil Zhytomyr Volynnia Chernivtsi Zakarpatia Ukraine D1. Industry Total (Top 5 Regions) Dnipro Donetsk Zaporizhia Poltava Kharkiv D2. Industry Total (Bottom 5 Regions) Zakarpatia Ternopil Kherson Volynnia Chernivtsi Ukraine

15 Section A of Table 4 shows that, out of top five oblasts in Ukraine s mining, the fastest growth will take place in Zhytomyr, at an average annual rate of 13.7%, followed by Donetsk with 8.6%. Overall, Ukraine s mining output will be growing by 6.3% annually. Ukraine s processing industry (Section B) will be growing at an average rate of 3.6%; all of the five top regions show a respectable annual growth of at least 4%, with the best growth in the capital city. Of the bottom five regions, only Kherson and Chernivtsi will exhibit a positive growth in processing, leading to a total industrial growth in these two oblasts (Section D). Energy production (Section C) is the least efficient industry in Ukraine. Out of the top five regions, the only two oblasts exhibiting growth are Kharkiv and Zaporizhia. Ukraine s total industrial production will rise at an annual 4.2%. Table 5. Nonlinear Scenario: Ukraine s Agriculture Forecast Region 2014 Actual /2014 Annual % A1. Agriculture Total (Top 5 Regions) Vinnytsia Kyiv Poltava Cherkasy Dnipro A2. Agriculture Total (Bottom 5 Regions) Volyn Ivano-Frankivsk Luhansk Chernivtsi Zakarpatia Ukraine B1. Grain (Top 5 Regions) Poltava Vinnytsia Kharkiv Sumy Khmelnytsk B2. Grain (Bottom 5 Regions) Luhansk Ivano-Frankivsk Volyn Chernivtsi Zakarpatia Ukraine

16 C1. Meat (Top 5 Regions) Cherkasy Kyiv Vinnytsia Dnipro Donetsk C2. Meat (Bottom 5 Regions) Kirovograd Sumy Chernivtsi Odesa Mykolaiv Ukraine As Table 5 indicates, the best agricultural growth is projected for the Dnipro and Cherkasy oblasts (Section A), at respective annual rates of 4.5% and 2.7%. Out of the bottom five oblasts, production will decline in Volyn and Zakarpatia, and will grow at a rate exceeding 1% only in Ivano-Frankivsk. There are two shining spots in grain production when comparing 2017 forecast with 2014 actual output: Khmelnytsk (14.7%) and Ivano-Frankivsk (6.4%). High growth in meat production is expected in Cherkasy and Dnipro oblasts, but all five bottom oblasts will have declining growth, because of falling animal herd. Table 6. Nonlinear Scenario: Ukraine Construction and Service Forecast Region 2014 Actual /2014 Annual % A1. Construction (Top 5 Regions) Kyiv City Donetsk Odesa Dnipro Kyiv A2. Construction (Bottom 5 Regions) Ternopil Kirovograd Sumy Chernihiv Kherson Ukraine B1. Trade and Repair (Top 5 Regions) Kyiv City Donetsk Dnipro Kyiv Lviv

17 B2. Trade and Repair (Bottom 5 Regions) Khmelnytsk Ternopil Rivne Kherson Chernivtsi Ukraine C1. Transportation and Communications (Top 5 Regions) Kyiv City Odesa Donetsk Dnipro Kharkiv C2. Transportation and Communications (Bottom 5 Regions) Chernihiv Ternopil Khmelnytsk Kherson Chernivtsi Ukraine D1. Service Total (Top 5 Regions) Kyiv City Donetsk Dnipro Kharkiv Odesa D2. Service Total (Bottom 5 Regions) Rivne Ternopil Volyn Kherson Chernivtsi Ukraine After a sharp decline in 2014, construction in Ukraine will be recovering at an average rate of 5.8% (Section A of Table 6). The highest growth will take place in Donetsk and Dnipro, both at double-digit annual rates. The bottom five regions will also show impressive growth, even though their starting base is low. Trade and repair (Section B) will be one of the fastest growing industries in Ukraine; almost a third of Ukraine s trade will continue taking place in Kyiv. Ukraine s transportation and communications industry (Section C) will be growing at an annual rate of 4.9%. Overall, Ukraine s services (Section D) will be the best performing sector, because of a double-digit annual growth in all five top regions and in four out of five bottom regions. Overall, our analysis 16

18 illustrates that the construction and service sectors will be advancing in all regions at about the same pace, but as to industrial development and agricultural production, the gap between advanced and lagging regions will only widen. 5. Regional Disparities, Poverty and Growth To further illustrate the pattern of persistent regional disparity, Table 7 gives Ukraine s average monthly wage by region for January All regions are ranked in a descending order, with Kyiv city boasting the largest 7,126 UAH (271 USD), while the country s average is 4,362 UAH (166 USD). The top five regions Kyiv city, Donetsk, Kyiv, Dnipro, and Zaporizhia are in the same position there as in Moreover, the bottom five regions Zhytomyr, Kirovograd, Kherson, Chernivtsi, and Ternopil have also not changed their position for over a decade. Table 7. Ukraine s Average Monthly Wage by Region, January 2016 Region UAH Kyiv City 7126 Donetsk 5142 Kyiv 4453 Dnipro 4445 Ukraine 4362 Zaporizhia 4341 Odesa 4265 Mykolaiv 4120 Poltava 3850 Lviv 3806 Kharkiv 3797 Luhansk 3642 Rivne 3583 Ivano-Frankivsk 3567 Cherkasy 3484 Sumy 3455 Zakarpattia 3419 Vinnytsia 3412 Khmelnytsk 3394 Chernihiv 3365 Volyn

19 Zhytomyr 3297 Kirovohrad 3286 Kherson 3249 Chernivtsi 3139 Ternopil 3008 Source: Ukrstat, 2016 There are many definitions of poverty, with different levels quoted for Ukraine. For example, the Millennium Development Goals of Ukraine (2015) gives an estimate of 24.5% of the population below the national poverty line. According to the Institute of Demography and Social Studies of the National Academy of Sciences of Ukraine, that percent was 26 in 2009, affecting 11.7 million people (Satsyk, 2010). At the regional level, Cheren ko (2009) illustrates a decline in Ukraine s poverty from The German Advisory Group on Economic Reform (2004) considers poverty across oblasts, with the lowest 3% for the Donetsk oblast and the highest 18% for the Rivne oblast. The 2013 Ukrstat data give much higher estimates for these two oblasts: 6% for Donetsk and 29% for Rivne. Ukraine s government uses an officially approved subsistence minimum (SM) to determine the poverty rate and eligibility for exemptions and subsidies. It defines the SM monetary value based on a basket of food, nonfood items and services sufficient, as it claims, for a normal functioning of the human organism. To find eligibility, it calculates total income from a variety of sources such as monetary income, consumption from personal agricultural and nonagricultural production, exemptions and subsidies for municipal services, energy, heating, health care, transport, communications, and food help from relatives and other donors. Thus, the 2016 SM level for the working-age individual is 1,330 UAH (51 USD) and 1,074 UAH for a nonworking- 18

20 age individual (41 USD), while the minimum wage is 1,378 UAH (52 USD). 10 The minimums will gradually rise by an official inflation rate of 12.5% in We divide all 24 oblasts of Ukraine in two groups, top and bottom, based on industry and agriculture performance. According to our calculations, the top 12 regions by industry GVA in 2013 were, in a descending order hereafter: Donetsk, Dnipro, Luhansk, Zaporizzia, Kharkiv, Poltava, Kyiv, Lviv, Mykolaiv, Odesa, Zhytomyr, and Sumy. The bottom 12 regions were: Ivano- Frankivsk, Cherkasy, Vinnytsia, Rivne, Khmelnytsk, Kirivograd, Chernihiv, Zakarpatia, Kherson, Ternopil, Volyn, and Chernivtsi. The top 12 agriculture performers were: Vinnytsia, Kharkiv, Poltava, Kyiv, Cherkasy, Dnipro, Kirovograd, Khmelnytsk, Donetsk, Odesa, Kherson, and Mykolaiv. The bottom 12 agriculture performens were: Zaporizzia, Chernihiv, Zhytomyr, Lviv, Ternopil, Sumy, Rivne, Volyn, Luhansk, Ivano-Frankivsk, Chernivtsi, and Zakarpatia. This classification allows us to identify seven oblasts which are among top performers in both industry and agriculture and, similarly, seven oblasts which are among bottom performers in both categories. The former group consists of Donetsk, Dnipro, Kharkiv, Poltava, Kyiv, Mykolaiv, and Odesa. The group at the bottom of both industrial and agricultural production includes Ivano- Frankivsk, Rivne, Chernihiv, Zakarpatia, Ternopil, Volyn, and Chernivtsi. Identifying the latter group is especially important because of the highest concentration of households at the SM in these oblasts. A major question is what could become the driver out of poverty there. Although economic growth is a necessary condition for poverty reduction, redistributive role of the governments mars the link when it comes to regional development. Since we possess the data on sectoral growth by region, we can circumvent the effect of such a distribution, if any, by Ukraine's central government. That is, the advantage of our research is that we can regress the 10 en.interfax.com ua and AiF-Moscow, Dec. 20,

21 percent of regional poverty on the respective industrial and agricultural output levels, with special attention to the seven oblasts at the bottom of both industrial and agricultural production given above. Our results indicate that the coefficients multiplying the industrial output are insignificant in six out of these seven oblasts, implying that the rise in industrial production does not likely affect the level of poverty there. On the other hand, the role of agricultural production in these oblasts is more in line with the expectation. In five out of the seven oblasts (Ivano-Frankivsk, Rivne, Zakarpatia, Volyn, and Chernivtsi), the coefficients of agricultural output are significant and negative, meaning that the rise in agricultural output indeed leads to a decline in poverty. An exception is the case of Chernihiv and Ternopil oblasts where the respective coefficients are insignificant. Shifting production from agriculture to industry is generally a sign of structural change that causes productivity increase, and mainstream economics views this a necessary condition for enhancing welfare. Yet, given our regression findings, the issue is not so straightforward for Ukraine. Some broader considerations are important, too. As indicated in Section 3.2, the country claims one of the world largest areas of fertile black soil, with agriculture producing 12% of Ukraine s GDP and generating a quarter of country s exports. The leading role of food production will only rise in view of the 2016 implementation of a free trade zone with the European Union, as the deal is expected theoretically to bring more competition, modernization, and foreign direct investment for Ukraine s agro-industrial producers. Paradoxically, despite the abundance of agricultural land, there is no market for it in Ukraine. For over 15 years the Verkhovna Rada extends a ban (moratorium) on sale of agricultural land, with the last extension until January 2017 approved in Out of 42 million hectares of 20

22 agricultural land, 22 million are used by agricultural enterprises which lease it from individual landholders. 11 Most of the latter received a land parcel (pai) of 4-5 hectares during the privatization of state and collective farms in the 1990s. Currently the state owns 10.5 million hectares of land (Novaya Gazeta, Mar. 1, 2016). One would think that it is the task of the legislature to stipulate who can buy the land, who has a priority (e.g., legal entities versus individual purchasers), the ceiling on the size, the participation of non-residents, and other pressing social and legal issues. But the mutual mistrust of the ruling elites in Ukraine is so deep that they would rather not have a market at all than to face the land in possession of someone not to their liking. The assistance of the IMF, with its expertise in land reforms in member countries, might be what Ukraine needs in this respect. When the IMF extends new credit for Ukraine s government, it sets criteria following the letter of its conditionality requirements: raising prices of natural gas and heating, increasing the pension age, privatization of coal mines, restructuring the energy companies, implementation of stress tests for major banks, changing state procurement rules, reducing the number of higher education institutions, and the like. It is hard to understand why the IMF cannot help a borrowing country like Ukraine outline strategic goals for development and pursue these goals, rather than get involved in micromanagement tasks illustrated above. If we imagine for a moment that a fair and efficient agricultural land market is created with the IMF help, this could become a rare achievement of the Fund in Ukraine Conclusion 11 Nizalov, Denys, Land moratorium: Guess who benefits from the status quo? VoxUkraine, Sep. 8, Foreign producers are interested and waiting. Thus, the US Cargill has already invested $100 million in the construction of a grain terminal in port Yuzhnyi (Odesa oblast), 21

23 To analyze and forecast the economy of Ukraine s administrative regions, we build an econometric model consisting of four blocks industry, agriculture, construction, and services with over 340 equations. We produce two types of forecasts: baseline (BF) and its modified version called a nonlinear scenario (NS). The BF follows the trend of a modest growth of Ukraine s economy since 2000 and differentiates its regions by the degree of progress, but it does not take into account the 2014 statistics, which post a sharp decline in economic activity due to war expenditures and territorial losses. For this reason, we build a NS whose essence is to use exponential smoothing that assigns larger weights to more recent observations. This methodology allows us to harmonize all three components of the time series: the in-sample observations, the 2014 actual data, and the values of the BF. The forecasts identify both bright spots and problems in Ukraine s regional economy: The bright spots are mining and the service sector, while energy production and agriculture may not recover from stagnation caused by a 2014 recession. From the policy analysis perspective, we use our model to analyze Ukraine s regional disparities, to establish a link between the level of poverty, on the one hand, and industrial and agricultural growth, on the other, and to identify conditions for a reduction in rural poverty. After dividing all 24 oblasts of Ukraine in two groups based on industry and agriculture production and determining the top and bottom performers, we regress the percent of regional poverty on industrial and agricultural outputs, with a particular attention to seven oblasts at the bottom of both industrial and agricultural production. Our finding is that the rise in industrial production does not likely to affect the level of poverty in the bottom regions. On the other hand, the role of agricultural production is found to be statistically significant for poverty reduction in the Ivano-Frankivsk, Rivne, Zakarpatia, Volyn, and Chernivtsi oblasts. 22

24 The finding that agricultural, not industrial production is significant for the reduction of poverty in the economically lagging oblasts of Ukraine is at the first glance surprising. However, for Ukraine which claims one of the world largest areas of fertile black soil and whose agriculture generates a quarter of country s exports, agricultural sector may remain one of important drivers out of poverty and a source of reducing regional disparities. For the agricultural sector to succeed, rural areas have to become a more attractive place to live. As an illustration to the contrary, with few exceptions Ukraine s rural towns and villages have no hospitals; moreover, nurses are unavailable in many villages, not to mention doctors. Clearly, Ukraine government s policy of abandoning investment in rural healthcare, education, cultural and recreational facilities is unacceptable. 13 Ukrainian agricultural experts estimate that the country s minimum annual investment in agricultural and non-agricultural rural development should amount to 50 billion UAH, whereas actual investment was 7.6 billion UAH in 2011 (Hylka, 2015). Without new tax incentives and availability of low-interest loans for private domestic and foreign investors, calling for the appropriation of government funds for rural infrastructure remains just a wishful thinking. Bibliography Azhaman, Irina (2014), The funding peculiarities of social objects construction in the rural areas of Ukraine, BenYishay, A. and Grosjean, P. (2014). Initial endowments and economic reform in 27 postsocialist countries. Journal of Comparative Economics, 42, Bogovin A.V. (2006). Country pastures/ forage resource profiles: Ukraine. Food and Agriculture Organization of the United Nations: Thus, funding for rural social infrastructure fell fivefold from 2010 to 2014 (Azhaman, 2014) 23

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