Tag: Belarus

The Anatomy of Recession in Belarus

Posted on December 12, 2016 | Policy Brief

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After impressive growth in the 2000s, Belarus’ economy has since the currency crisis of 2011 stalled. Structural issues – dominance of the state sector and directed lending practices – have made growth anemic. Recession for Belarus’ main trading partner and the decline of oil prices has aggravated the long-run problems. We perform growth diagnostics to separate the effects of total factor productivity (TFP) growth from capital accumulation over the recession. We show that, as in the 2000s, capital accumulation had the largest positive effect on growth in Belarus, but TFP gains were very low, or even negative in the years of recession.

During the 2000s, Belarus experienced extraordinarily high growth rates, despite a lack of economic reforms and low performance in the EBRD transition indicators. In Kruk and Bornukova (2014) we show that the growth was extensive in its nature, and mainly driven by capital accumulation. The total factor productivity (TFP) contribution to growth was low. After the currency crisis of 2011 in Belarus, however, growth rates have stagnated. Despite a high investment rate (which declined dramatically only after 2015) the growth rates were below 2 per cent per annum, which is a non-satisfactory performance for a developing economy (see Figure 1). In 2015, Belarus entered its first recession in the last 20 years with GDP declining by 3.9 per cent, and the recession has continued in 2016.

Figure 1. GDP Growth Rates and Investment Rates in Belarus (%), 2005-2015.

Source: Belstat

In the 2000s, the Belarusian government relied on directed-lending programs, and subsidized the interest rates for state-owned enterprises’ (SOE) loans. After the currency crisis of 2011, which many blamed on the loose monetary policies connected to directed-lending programs, the government switched to a so-called modernization policy that underlined the need to invest in new equipment and introduce new technologies. So far this policy have not bear fruits in terms of economic growth, but did it increase efficiency?

Growth Decomposition 2011-2015

Using the standard capital services approach modified for the Belarusian data in Kruk and Bornukova (2014), we decompose Belarusian economic growth in 2011-2015 into the growth of factors (capital and labor) and growth of TFP. We find that the lack of growth in TFP explains the lack of GDP growth and GDP decline over these years.

Figure 2. Gross Value Added Growth Decomposition in Belarus, 2006-2015.

Source: Author’s calculations based on Belstat data. Note: K stands for capital, L for labor, TFP for total factor productivity, and CU for capacity utilization.

A noteworthy fact about the Belarusian growth decomposition is that the direction of growth rate of capital and TFP has been persistently opposite in 2012-2015. Presumably, accelerated capital accumulation vs. stagnating/lowering TFP could be explained by initially insufficient levels of it (i.e. less than steady state). However, this explanation seems to be improper for the Belarusian path. According to our assessments, a capital stock has passed its steady state level at the turn of 2013-2014. Despite this, capital kept growing rapidly, while productivity contracted. An alternative explanation – a growth of the capital stock was secured by specific directed instruments; this artificial capital accumulation caused an endogenous contraction of TFP, as confirmed by the data.

Indeed, a TFP decline could accompany capital accumulation due to expanding allocation and technical inefficiencies. This explains the meltdown of economic growth in Belarus by 2013-2014 and its transition to the negative spectrum later on. In late 2014-2015, this was supplemented by exogenous negative shocks affecting TFP – deteriorating terms of trade and a shrinking energy subsidy from Russia – which caused a rapid dip into recession, which should be classified as structural adjustment.

In 2015-2016, lack of TFP growth and excessive capital accumulation caused further adjustments: firms reduced capital investments radically and contracted capacity utilization. These mechanisms amplified structural recession by a cyclical component.

Sectoral dimension: manufacturing

Out of all the manufacturing industries, only one – manufacturing of electrical, electronic and optical equipment – had positive TFP growth in 2011-2015. On average, manufacturing has lost 4.1% of TFP over this period, with the highest TFP losses in the industries that have always been hallmark for Belarus: manufacturing of machinery (-7.6%) and transport equipment and vehicles (-8.8%). The wood-processing industry has notoriously obtained huge financial aid during the modernization campaign (over 1 billion USD – but Belta (2015) lost 5.6% of TFP over 2011-2015.

We also find that the capital market continues to be distorted by the government interventions, leading to inefficient allocations in the sense that investment is not going to the most efficient industries. On the contrary, there is a negative relationship between the capital growth rate and the TFP growth rate in manufacturing industries. The labor market, which faces less government intervention, functions more efficiently. Labor growth is higher in the industries with higher initial labor productivity.

International comparisons

While comparing the TFPs of Belarusian industries to each other makes little sense (like comparing apples and oranges), comparing them to the TFPs of corresponding industries in other countries might shed some light on the comparative efficiency and competitiveness of the Belarusian economy. Table 1 lists the industries and sectors of the Belarusian economy that are the most and least competitive in a relative TFP sense.

Table 1. TFP winners and losers in Belarus

2014 TFP relative to
Czech Republic Sweden
Winners
Petroleum products 1.98 –
Transport services/communications 1.67 0.70
Trade and repair 1.37 1.77
Financial activities 1.33 –
Chemicals manufacturing 1.17 –
Losers
Transport vehicles 0.72 –
Machinery and equipment 0.70 0.34
Textiles 0.68 0.26
Woodworking 0.56 –
Electricity, gas and water 0.41 0.22
Agriculture 0.40 –

Source: Author’s calculations.

The majority of the industries in the “winners” category are non-tradable (services like communications, finance, trade and repair). Coincidentally, trade, transport and finance also have relatively high shares of private ownership. Another group of winners are rent industries (petroleum benefitting from cheap Russian oil; and chemical industry built on potassium salts extraction).

As for the most of the manufacturing industries, where the government dominates, and where extensive financing was available at subsidized rates, TFP levels are relatively low. While the TFP performance of the manufacturing of transport vehicles, machinery and other equipment was also reported as low in 2010 (Kruk and Bornukova, 2014), the woodworking industry reached high levels of inefficiency after 2010, when the “modernization” program of this industry received a huge influx of capital.

The relative levels of TFP are good predictors of the future exports performance: higher-TFP industries are more competitive in the international markets. The current low relative TFP of the manufacturing sectors suggests that manufacturing exports will not recover in the coming years.

Conclusion

As in the 2000s, Belarus relies on capital accumulation to generate economic growth. In recent years, however, more investments have not generated growth and rather led to losses in TFP, aggravated by external factors. The current recession in Belarus is mainly a structural adjustment, driven by distortive policies of capital accumulation and allocation; and only partially driven by external shocks.

Lack of TFP growth leads to loss of international competitiveness, causing a collapse of exports. Deep structural reforms are necessary to revive growth and recuperate the lost export potential.

References

Spatial Wage Inequality in Belarus

Posted on October 10, 2016 | Policy Brief

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This policy brief summarizes the results of an analysis of wage inequality among the districts of Belarus over the period 2000-2015. The developments in wage inequality varied noticeably by sub-periods: wage disparity decreased in 2000-2005, stayed stable in 2006-2012, and increased again during the last three years. I find evidence for spatial dependency in wages between districts, and increasing separation within districts (between rural and urban population). A decomposition of wage inequality by different quantiles of districts shows that the real wage increase rate in the lower percentiles exceeds the real wage increase rate in the higher percentiles. From a theoretical point of view, my results reject the inverted U-shaped relationship between spatial inequality and economic development for Belarus, and support the hypothesis made by the French economist Thomas Piketty that slow growth rates lead to rise in inequality.

In Belarus, wages make up approximately 60% of household income and account for 46% of GDP. The equality of the wage distribution therefore affects the scale and degree of socio-economic disconnect in the country. On the one hand, too much inequality may dampen long-term growth. On the other hand, too much equality may reduce incentives for productivity improvements.

This policy brief outlines a study (Mazol, 2016), where I examine the wage inequality concern of Belarus using annual Belstat data on district average monthly nominal wages (excluding large cities) from year 2000 to 2015, corrected by the country’s CPI index (using 2000 as the base year).

Characteristics of district wages

According to the Belarusian statistical definitions by the end of 2015, Belarus has 118 districts with an overall population of 4.9 million (excluding large cities), which corresponds to approximately 50% of total population. Average district wages relative to the national mean has increased from 74% in 2000 to 82% in 2005, indicating a catching-up process in wage income between districts and large cities (see Figure 1).

Figure 1. Decomposition of district real wages at the regional level of Belarus

figure-1Source: Author’s own calculations.

However, from 2013, the convergence of wages reverted to divergence (79% in 2015) suggesting that the relatively poor district population have become even poorer in recent years.

District wages differed by 2.8 times in 2000 and by 2.4 times in 2015. The largest number of districts with the lowest wages concentrate in the northern part of Belarus, represented by Vitebsk region with a mostly rural population, whereas districts with the highest wages are mostly in the Minsk and Gomel region, which are the central and most industrialized parts of Belarus (Minsk, Zhlobin, Mozyr and Soligorsk) (see Figure 2).

Figure 2. Map of Belarus’ districts by levels of real wages in 2015

figure-2Source: Author’s own calculations.

However, the common feature in the allocation of different levels of district wages is that the higher/lower wage districts tend to concentrate with similar districts, indicating presence of spatial dependence in the wage distribution.

Spatial interdependencies of district wages

The spatial characteristics are tested using the Global Moran’s I statistic (Moran, 1950). A positive coefficient means that neighboring districts have similar wages and a higher value indicates an increase in the relationship.

The results show that the values of the Global Moran’s I statistic are positive and significant at the 5 percent level for the periods 2000-2008 and 2014-2015 (see Figure 3). This suggests that districts with similar high or low levels of wages tend to concentrate geographically.

Figure 3. Global Moran’s I statistic and GDP growth in Belarus

figure-3Source: Author’s own calculations.

Additionally, starting from 2012, the substantial increase in positive spatial interdependencies in wages between districts coincides with a significant decrease in economic growth. This suggests that the districts of Belarus tend to cluster more closely with each other during economic recessions, indicating a more profound formation of rich and poor clusters of districts. Such a trend could be caused by a lack of public financial resources, which restricts administrative redistribution of financial support in favor of poor districts. As a result, such districts tend to become even poorer (for example, districts in Vitebsk region).

Wage inequality in the districts of Belarus

Overall, the level of wage inequality among the districts of Belarus remains low for the studied period. Moreover, the growth rates of wages in districts with low wages are higher than in the richer districts, indicating presence of a convergence process (see Figure 4). Yet, the differences between these two groups continue to be large. In 2015, the 10th and 90th percentiles of district wages were 4.6 and 6.1 million Belarusian rubles, respectively.

Figure 4. Indexed real wage

figure-4Source: Author’s own calculations.

Regarding inequality dynamics, the country experienced a decline in wage disparity 2000-2005, but from 2013, the inequality in wages started to rise (see Figure 5) and this coincides with an economic slowdown and subsequent recession.

Figure 5. Measures of wage inequality

figure-5Note: CV – coefficient of variation. Source: Author’s own calculations.

Thus, during 2000-2015, Belarus’ accelerating levels of economic growth first led to a decrease in district wage inequality. During a time of high and stable economic growth, the level of district wage inequality was constant. But, during the last years’ negative economic growth, the district wage inequality in Belarus has started to increase again. From a theoretical point of view, these results reject the hypothesis of an inverted-U-shaped relationship between spatial inequality and economic development stated by Kuznets (1955), and confirms the hypothesis stated by the French economist Thomas Piketty (2014) that declining growth rates increase inequality.

Conclusion

My results suggest that spatial wage inequality in Belarus is a persistent phenomenon that has increased in recent years. I found evidence for a spatial dependency in wages between districts and an increasing separation within districts (between rural and urban population). These may lead to a socio-economic instability, growth of shadow economy, and even an emergence of depressed regions (for example, Vitebsk region).

In order to decrease spatial wage inequality and increase overall economic efficiency in the districts of Belarus, the government needs to implement specific policies aimed at facilitating regional drivers of economic growth through the formation of new economic centers at the district level.

References

  • Barro, Robert J.; and Xavier Sala-i-Martin, 1992. “Convergence”. Journal of Political Economy, 100(2), 223-251.
  • Kuznets, Simon, 1955. “Economic growth and income inequality”. American Economic Review, 45(1), 1-28.
  • Mazol, Aleh, 2016. “Spatial wage inequality in Belarus”. BEROC Working Paper Series, WP no. 35, 37 p.
  • Moran, Patrick, 1950. “Notes on continuous stochastic phenomena”. Biometrika, 37(1/2), 17-23.
  • Piketty, Thomas, 2014. “Capital in the Twenty-first Century”. Cambridge, Massachusetts: Harvard University Press, 696 p.
  • Smith Neil, 1984. “Uneven development”. New York, NY: Blackwell, 198 p.
  • World Bank. 2009. World Development Report 2009. “Reshaping economic geography”. Washington, D.C.: The International Bank for Reconstruction and Development, 372 p.

Effects of Trade Wars on Belarus

Posted on June 20, 2016 | Policy Brief

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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

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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.

 

Intermediate and Capital Goods Import and Economic Growth in Belarus

Posted on May 9, 2016 | Policy Brief

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This policy brief presents estimation results of the influence of intermediate and capital goods (ICGs) imports on GDP growth taking into account changes in the exchange rate. The Belarusian economy substantially relies on ICGs imports, and my research indicates that imports of intermediate inputs negatively contribute to Belarus’ economic growth. The findings suggest that a devaluation of national currency can negatively influence both GDP growth and imports of intermediate goods. The negative influence on GDP growth is caused by a lower price competitiveness of the export sector, and the negative influence on imports of intermediate goods is due to a significant increase in the costs of imports.

According to endogenous growth theory technological progress is a key factor that enhances long-run economic growth (Grossman and Helpman, 1994). However, in developing countries scarce commercial activities in R&D limit technological progress (Grossman and Helpman, 1991). From this point of view, imports of ICGs play the same role in the development of the Belarusian economy (taking into account the nature of Belarusian manufacturing, which is mostly to assemble finished goods) as R&D activities in developed countries by transferring foreign technology and innovations (Coe et al., 1997; Mazumdar, 2001). In turn, Belarusian economic policy related to imports of ICGs is seriously conditioned by the foreign exchange constraint.

Imports of ICGs and GDP Growth

Imported ICGs (excluding energy goods) account for approximately 55% of all Belarus’ imports. Starting from 2001 up to 2010 high levels of GDP growth (7-8% on average) were associated with even higher growth levels of ICGs imports (see Figure 1).

Figure 1. Imports of ICGs in 2001-2014

Figure_1Source: Belstat.

However, from 2011, average growth rate of GDP has decreased significantly from 7% in 2006-2010 to 2% in 2011-2014. This was coupled with a substantial drop in the average growth rates of ICGs imports. All these may indicate an insolvency of the current import-led growth (ILG) strategy of Belarus.

Moreover, using an Autoregressive-Distributed Lag (ARDL) approach (Pesaran et al., 2001) to study the long-run relationship between ICGs imports and GDP growth, it was found that a 1% growth in imports of intermediate goods caused a 2.7% decrease in real GDP (Mazol, 2015). The effect of capital goods imports is statistically insignificant.

The Toda-Yamamoto (TY) causality test (Toda and Yamamoto, 1995) clarifies this result, indicating unidirectional causality running from economic growth to imports of intermediate goods, and further to imports of capital goods (see Figure 2).

Figure 2. TY Causality Test

Figure_2Note: * 10% level of significance; ** 5% level of significance; *** 1% level of significance. Source: Author’s own estimations.

Thus, instead of an ILG hypothesis, the findings establish presence of a GLI hypothesis for Belarus, supporting the view that for developing countries, trade is more a consequence of the rapid economic growth than a cause (Rodrik, 1995).

What is the intuition behind these results? The ILG strategy aims to improve efficiency and productivity, and can be appropriate only under two crucial conditions: first, it is necessary to acquire preferably advanced technology from abroad; and, second, there have to exist enough domestic technological capabilities and skilled human capital in order to successfully adapt new technologies from R&D intensive countries.

In Belarus, a violation of the first condition was caused by an ineffective industrial policy aimed to modernize state-owned enterprises (SOEs) (Kruk, 2014). In many cases, capital accumulation was accomplished without appropriate investment appraisal and efficient marketing strategies.

Furthermore, there is serious evidence against the second condition being fulfilled: the share of innovative goods of all shipped goods in the past 4 years have dropped by 5.5 percentage points – from 17.8% to 12.3% (Belstat); and the «brain drain» is still a big problem (mostly due to low salary levels in research areas).

Influence of Exchange Rate Policies

Through the cost of imported intermediates, the exchange rate has an important influence on the price competitiveness of the Belarusian economy. However, the Belarusian exchange rate has fluctuated widely since 2000s (see Figure 3). For example, between 2000 and 2014, the annual percentage change in the nominal effective exchange rate (NEER) has varied from approximately 135% to -2%, and the real effective exchange rate (REER) fluctuated between 23% and 11% annually.

Figure 3. The Exchange Rate 2000-2014

Figure_3Source: Belstat, IFS.

The results from estimated ARDL models (Mazol, 2015) show that while a depreciation of the Belarusian currency negatively influences both the imports of intermediate goods and GDP growth, it does not have a statistically significant effect on the imports of capital goods.

Concerning the influence on intermediate inputs, the explanation is that there are two effects of exchange rate policy on trade. On the one hand, depreciation of national currency leads to growth in the domestic currency price of exports, which motivates national companies to expand production of exports – the derived demand effect. On the other hand, it increases the domestic currency price of imported intermediate inputs, decreasing the quantity of intermediate imports domestics companies can buy – the direct cost effect. The direct cost effect and the derived demand effect have opposite signs (Landon and Smith, 2007).

Additionally, devaluations in Belarus occur in most cases both to import source and export destination countries (first of all Russia). Thus, in the case of imports of intermediate goods, the impact of the direct cost effect is greater than the impact of the derived demand effect, leading to a negative effect on imports of intermediate goods.

Furthermore, the substantial reliance of the Belarusian export sector on imported inputs, combined with above-presented side effects, cause cost-push inflation in the export sector, which decreases its price competitiveness and, overly, the economic growth. This statement is confirmed by the fact that in the period 2002-2011, intermediate inputs were imported both under the permanent expansionary monetary policy and the fixed exchange rate policy (see Figure 3). As a result of such twin strategies, intermediate imports have become more and more expensive, while the price competiveness of Belarusian export goods have steadily declined (taking into account that most of its industrial part is shipped to Russia).

The reason why the exchange rate policy do not seem to have had an effect on capital goods imports is that machinery and equipment were typically imported in accordance with the government’s modernization plans. The realization of these plans often disregarded the current macroeconomic situation in Belarus, and the imports were made just for the sake of importing (to accomplish the plan).

Finally, starting in 2012, depreciation of the Belarusian ruble coincided with the economic recession caused primarily by structural problems that hit the country (Kruk and Bornukova, 2013). Therefore, the increase in flexibility of exchange rate policy had no additional effect on ICGs imports and economic growth in Belarus.

Conclusion

The findings presented here indicate that trade (in terms of ICGs imports) is more a consequence of the rapid economic growth in Belarus rather than a cause. The influence of imports of intermediate goods on GDP growth in the long run is negative. Additionally, the depreciation of the national currency has had a large negative effect on both intermediate imports and economic growth, while its effect on capital goods imports was statistically insignificant.

Thus, Belarusian economic policy based on imported technologies seems ineffective especially in recent years, most probably due to decreasing skills and the ability to imitate and innovate using foreign inputs. Therefore, policy should focus on abolishing the directive industrial management, which has led to a negative influence of ICGs imports on economic growth in Belarus.

Additionally, the country’s export strategy should be refined so that export destinations are different from import sources of intermediate goods that are used for export production. Moreover, the imports of capital goods should contribute to the development of new export markets, and monetary and fiscal policies should be refined in order to promote positive effects of currency valuation changes.

 

References

  • Kruk D., Bornukova K. 2013. Decomposition of economic growth in Belarus. FREE Policy Brief Series, October 2013.
  • Coe D., Helpman E., Hoffmaister A. 1997. North-south R&D spillovers. The Economic Journal 107(440): 134-149.
  • Grossman G., Helpman E. 1991. Innovation and growth in the global economy. The MIT Press, Cambridge MA.
  • Grossman G., Helpman G. 1994. Endogenous innovation in the theory of growth. Journal of Economic Perspectives 8: 23–44.
  • Kruk, D. 2014. Stimulating growth in Belarus: Selecting the right priorities. FREE Policy Brief Series, November 2014.
  • Landon S., Smith C.E. 2007. The exchange rate and machinery and equipment imports: Identifying the impact of import source and export destination country currency valuation changes. North American Journal of Economics and Finance 18: 3–21
  • Mazumdar J. 2001. Imported machinery and growth in LDCs. Journal of Development Economics 65: 209-224.
  • Mazol, A. 2015. Exchange Rate, imports of intermediate and capital goods and GDP growth in Belarus, BEROC Working Paper Series, WP no. 32.
  • Pesaran M.H., Shin Y, Smith R.J. 2001. Bounds testing approaches to the analysis of level relationships. Applied Econometrics 16: 289–326.
  • Rodrik, D. 1995. Getting interventions right how South Korea and Taiwan grew rich. Economic Policy 10: 53-107.
  • Toda H.Y., Yamamoto, T. 1995. Statistical inference in vector auto regressions with possibly integrated processes. Econometrics 66: 225–50.

Non-Tariff Barriers and Trade Integration in the EAEU

Posted on February 29, 2016 | Policy Brief

It is a commonly held view that the Eurasian Economic Union (EAEU) is a political enterprise (Popescu, 2014) that has little economic meaning other than redistribution of oil rents (Knobel, 2015). With a new reality of low oil prices and reduced rents, a legitimate question is how stable this Union is, or whether there is any hope for mutual economic benefits that can provide incentives to all the participants to maintain their membership in the Union? Our answer is yes, there is hope, but only if countries, especially Russia, make progress on deep integration such as services liberalization, trade facilitation, free movement of labor and especially in the reduction of the substantial non-tariff barriers (NTBs). NTBs are hampering trade both within the Union (World Bank, 2012; Vinokurov, 2015), as well as against third country imports. Our research shows (see Knobel et al., 2016) that all the EAEU members will reap benefits from a reduction of NTBs against each other, but they will obtain considerably more substantial gains from a reduction in NTBs against imports from the EU and China.

Since the early stages of creation of the Customs Union (CU) between Belarus, Kazakhstan, and Russia back in 2010, the economic benefits of the CU have been questionable. The main reason for this in Kazakhstan was the increase in its import tariffs in order to implement the common external tariff of the CU, which initially was Russia’s external tariff (Tarr, 2015). Kazakhstan almost doubled its average tariff from 5.3% to 9.5% (Shepoltylo, 2011; Jondosov and Sabyrova, 2011) in the first year of its CU accession. Belarus did not increase its average tariff, but the structure of its tariffs shifted toward a protection of Russian industry.

In 2015, the CU was transformed into the EAEU, and Armenia and Kyrgyz Republic have joined the EAEU. These two countries are WTO members; Kyrgyzstan entered the WTO in 1998, and Armenia in 2001. In 2014, the simple average most-favored nation (MFN) applied tariff rate in Armenia was 3.7%, and 4.6% in the Kyrgyz Republic. Due to differences between Armenia and Kyrgyzstan’s WTO commitments and the EAEU tariff schedule, the new members of the EAEU are not implementing the full EAEU tariff schedule. That is, they have numerous exemptions. However, they have started a WTO commitments modification procedure.

Despite adverse impacts from the higher import prices from implementing the common external tariff of the EAEU in Armenia and the Kyrgyz Republic, there are potentially offsetting gains. Given the importance of remittances to the Kyrgyz Republic, the benefits coming from the right of workers to freely move and legally work inside EAEU likely dominate the tariff issues. Armenia also benefits from the free movement of labor, receives Russian gas free of export duties, and wants to preserve the military guarantee granted by Russia through the six-country Collective Security Treaty Organization.

In the case of Belarus, it receives Russian oil and natural gas free of export-duties, which, when oil prices were high, tended to dominate their calculus. Kazakhstan hopes for more FDI as a platform for selling to the EAEU market; but President Nazarbaev has expressed concerns that the EAEU is not providing net benefits to his country.

To date, the members have judged participation to be in their interest, but with the plunge in the price of oil and gas, the calculus could swing against participation in the EAEU. That is why it is so important to achieve progress with deep integration in the EAEU. One of the most important areas of deep integration for the EAEU is the substantial reduction of non-tariff barriers in goods trade, both between the EAEU members and against third countries. Estimates by the Eurasian Development Bank (Vinokurov et al., 2015) reveal that NTBs account are 15% of the value of intra-union trade flows.

In our paper, Knobel et al (2016), we estimate substantial gains to all the EAEU members from a reduction of NTBs. We employ a global computable general equilibrium model with monopolistic competition in the Helpman-Krugman style based on Balisteri, Yonezawa, Tarr (2014). Estimates of the ad-valorem equivalents of NTBs were based on Vinokurov et al (2015) for the EAEU member countries and Kee, Nicita, Olarreaga (2009) for non-members.

We find that the effects of deep integration are positive for all countries of the EAEU. Armenia’s accession to the EAEU will have a strong positive effect only if coupled with a decrease of non-tariff barriers. Armenian accession is associated with an increase in external tariffs, which causes a negative economic impact and decrease in output.

The effect of deep integration in the EAEU will be even greater if any spillovers effect reducing NTBs for EAEU’s major trading partners are present. Knobel et al. (2016) simulate a 50% decrease in “technical” NTBs inside the EAEU and a 20% spillover effect of reduction NTBs toward either the EU and USA or China. Reduction of NTBs in trade with the EU and the USA dominates the comparable reduction of NTBs with China for all countries of the EAEU in terms of the welfare gain. Armenia’s welfare gain with a spillover effect towards the EU is 1.1% of real consumption compared to 1.02% with a spillover effect towards China. Growth in welfare in Belarus will be 2.7% with a EU spillover versus 2.5% with a spillover effect towards China. Kazakhstan’s gain in real consumption is also greater in the first (EU+USA) case: 0.86% versus 0.66% (with spillover towards China). Russia’s gain in real consumption in the case of a spillover effect with the EU is 2.01% versus 0.63% in the case of China.

Summing up, our findings suggest an answer to the recent concern about stability of the EAEU. We think that eliminating NTB, hampering mutual trade, and decreasing NTBs in either European or Chinese direction could provide mutual economic benefits that could tie countries of the EAEU together, thereby giving a much needed solid economic ground for the Union.

References

The Inevitable Social Security Reforms in Belarus

Posted on February 8, 2016 | Policy Brief

In 2016, Belarus will face the need to reform its social protection policy. The three main directions of reforms will be to departure from subsidized tariffs, to reform the pension system, and to increase unemployment benefits. Needless to say, some of these reforms will be highly unpopular. The government needs not only to cut expenditures, but also to think about new ways of providing targeted social support.

Faced with an anemic growth over the last 5 years and a GDP decline of 3.9 percent in 2015, Belarus has to rethink its economic policy. While the government is so far reluctant to undertake serious structural reforms, the decrease in budget revenues and lack of access to international financing leaves the authorities with few other options than to reform the social security system. This push might actually be a good thing, as social security in Belarus needs to depart from its current non-sustainable model of subsidies for everyone, to a model of focused means-tested social support.

Subsidized Tariffs

A lot of government-set tariffs in Belarus are currently subsidized. Utility service tariffs and transport fees are lower than the costs of providing these services. This is especially true for the heating tariffs, which currently cover 10-20 percent of total costs.

The subsidization policies are inefficient, as they benefit the rich (who consume more) rather than the poor in need of government support. Moreover, in the case of energy tariffs, cross-subsidization leads to higher energy costs for the firms, making them less competitive. Both prospective creditors, the IMF and the Eurasian Fund for Stabilization and Development, demand that the subsidies are gradually removed.

Zhang and Hankinson (2015) estimate the effects of an increase of the heating tariff on welfare. They find that the burden of higher tariffs will mostly fall on low-income groups. In particular, if the heating tariffs increase to 100 percent of the costs, households from the lowest income quintile will spend over 16 percent of their income on energy. Therefore, the authors conclude that the government should introduce a targeted social assistance together with the tariff increase.

While tariff increases were already introduced in the beginning of 2016, a targeted social assistance is still only a project.

Unemployment Benefits

Despite calling itself a social economy, Belarus has inexplicably low unemployment benefits (currently below 10EUR per month in Minsk). These low unemployment benefits contribute to a very low registered unemployment rate – 1 percent in November 2015. The more adequate measure of unemployment, based on labor force surveys, is classified in Belarus. However, large-scale job cuts in the biggest state-owned enterprises suggest that unemployment is a real threat.

Akulava (2015) argues, that given the current situation on the labor market, unemployment benefits should be increased to at least the minimal subsistence level. However, unemployment benefits per se will not solve all the problems in the labor market, and Belarus needs more active labor market policies facilitating the retraining and reallocation of workers.

The IMF has also emphasized the need to introduce proper unemployment insurance. The government has already pre-announced an introduction of increased unemployment benefits, but the details and dates are still unclear. There is a risk that, as many other policies in Belarus, the unemployment support will favor the state-controlled part of the economy and only offer increased support for those laid off from state-owned enterprises.

Pension Reform

The Belarusian pension system has not change much since the Soviet times: it is still a pay-as-you-go redistributive system, with a pension age among the lowest in Europe (55 for women and 60 for men). The Pension Fund first registered a deficit in 2013, and given the ageing population, deficits will only deepen in the future.

Due to relatively low fertility rates (1.6 per woman) and increasing life expectancy, the Belarusian population is quickly ageing: In 2015, there are 4 persons of retirement age per 10 persons of working age, but in 2035, this ratio will be 6 per 10.

Lisenkova and Bornukova (2015) build a demographically accurate overlapping generations model of Belarusian economy to estimate the stability of the pension system. They find that if the current parameters do not change, the deficit of the Pension Fund will explode up to 9% in 2050 (line 55/60 in Figure 1).

Figure 1. The Pension fund deficits under different scenarios, in % of GDP

FREE_brief_Bel_social

Source: Lisenkova and Bornukova, 2015

The authors also estimate different reform scenarios. An increase in the contribution rate and a decrease in the replacement rate (ratio of average pension to the average wage) do not seem feasible, as the current contribution rate of 29 percent is already too high, and the replacement rate is near the minimum set by the National Development Strategy 2020. The most obvious reform is then to increase the pension age for women, who retire 5 years earlier than men, despite having 10 years longer life expectancy. However, as can be seen from line 60/60 in Figure 1, equating the pension ages for women and men will not be enough to curb the deficits. Another simulated reform is to gradually increase pension age to 65 years for everyone, after increasing it to 60 for women only. This reform would mean that the deficits would be kept below 1 percent of GDP (and even generate a small proficit by 2035, although in a very long perspective the deficit will increase to 2 percent again (line 65/65)). In the very long run, Belarus needs to build a fully funded pension system.

The need to increase the pension age is already on the public debate agenda, and the authorities recognize the need for reforms. Needless to say, however, this move will be very unpopular.

Conclusion

The current economic crisis gives an opportunity and incentive to make Belarusian social policy more efficient. This policy brief describes the three major fields of reform. Subsidized tariffs are unfair and inefficient, but before removing subsidies the government should create a targeted system of social assistance to those in need. Increasing unemployment (and demands from the creditors) may force the government to change its unemployment benefit policy. The pension system needs reforms, but these would be difficult to implement due to unpopularity.

▪

References

  • Akulava, M. (2015), ‘Unemployment insurance as a tool of social protection’, BEROC policy paper series, PP no. 32 (in Russian)
  • Lisenkova, K., and Bornukova K. (2015), ‘Effects of Population Ageing on the Pension System in Belarus’, BEROC working paper series, WP no. 28
  • Zhang, F., and Hankinson, D. (2015), ‘Belarus Heat Tariff Reform and Social Impact Mitigation,’ World Bank Publications, The World Bank, number 22574

 

Local Self-Governance in the Republic of Belarus

Posted on June 1, 2015 | Policy Brief

Author: Aleh Mazol, BEROC.

This policy brief summarizes the results of our research on the development of local self-governance in the Republic of Belarus. The aim of this study was to analyze the existing system of local self-governance in the Republic of Belarus and to suggest directions for its improvement. The results show that the development of local self-governance should be directed to the reduction of concentration of the administrative-territorial division, real empowerment of local Councils of Deputies, improvement of the mechanism of alignment and balancing of local budgets, as well as the development of a financial base of local financial management and intergovernmental relations.

Expected Effects of Tobacco Taxation in Five Countries of the Former Soviet Union

Posted on May 18, 2015 | Policy Brief

20150518-Expected-Effects-of-Tobacco-1

Authors: Irina Denisova and Polina Kuznetsova, CEFIR.

In this policy brief, we discuss the results from a study of different dimensions of tobacco taxation policy in five former Soviet Union countries: Belarus, Kazakhstan, Kyrgyz Republic, Russia and Ukraine. We find that the increase in budget revenue from raising excises on filter cigarettes is high in all studied countries. Furthermore, due to a low elasticity of the demand for cigarettes, the increase in excise taxes needs to be substantial to lead to a noticeable improvement in public health.  

The Role of Belarusian Private Sector

Posted on January 19, 2015 | Policy Brief

The development of a private sector and the expansion of its role in the economy is one of the key goals repeatedly announced by the Belarusian authorities. The reforms carried out in Belarus in 2006-2014 moved the country from 106th to 57th position in the World Bank Doing Business ranking. The official statement is that reforms boosted the rapid development of business initiatives and its impact on economic development. Unfortunately, there is no clear confirmation of this statement. The absence of a transparent and clear methodology in Belarusian statistics on how to evaluate the role of the private sector makes it difficult to evaluate the exact input of the Belarusian business in the economy and compare its role to other countries.

In the last 5 years, the Belarusian authorities have repeatedly highlighted the need to develop the private sector, perceiving it as the main source for sustainable economic growth and competitiveness of Belarus in the future.

However, it may be difficult to assess the real role of the private sector in the Belarusian economy. First, existing data do not allow a clear identification of the boundaries between the private and state-owned sectors in Belarus. Furthermore, there are certain methodological differences in identifying and evaluating the private sector between Belarusian official statistics, the World Bank approach and alternative methodologies. These methodological variations combined with data limitations result in significantly different estimates of the role of the private sector for the Belarusian economy. The problem concerns both the evaluation of the role of small and medium enterprises (SMEs) and the private sector in general.

Small and Medium Enterprises

One good example of the abovementioned data issue is the statistics for SMEs sector. Unlike the EU, Belarus does not include individual entrepreneurs to the micro organizations in the SME sector. This results in highly different estimates for the number of SMEs per 1000 inhabitants (Figure 1). If we follow the methodology of the National Statistical Committee of the Republic of Belarus (Belstat), the number is 9.7 firms per 1000 people. However, switching to the EU methodology (IFC report, 2013) raises the number significantly up to 35.9. Moreover, the inclusion of unregistered self-employed individuals involved in the shadow economy (which according to estimations of the authorities amount to at least 100,000 inhabitants) increases the number to 46.5 firms per 1000 people, which is above the level of many European countries.

Figure 1. SME density

figure1Source: own estimations from Belstat data, Eurostat.

Private Sector

As for the private sector in general, the problem here is that the official statistics counts enterprises with mixed form of ownership and state presence to the private sector. This makes it difficult, if at all possible, to obtain the exact input of the private sector to the economy and see the dynamics of its change.

More specifically, there are three potential ways to assess the contribution of the private sector. Unfortunately none of them provides reliable estimates of the role of business. The first method is to use official data. The main problem here is that the private sector according to official statistics includes enterprises with state presence as well as large private companies that are under state control and not totally independent. Thus, the contribution of the private sector calculated based on these figures is likely overestimated.

The second method is to look at enterprises that do not report to the Belarusian ministries, following the methodology of the World Bank used in their evaluation of Belarus machinery industry (Cuaresma et al., 2012). Here, non-ministry reporting enterprises work as a proxy for a private firm, as in this case it doesn’t have to report directly to Belarusian ministries and is independent from the state.

The problem is that the majority of large private enterprises, even though there is no state share in them, are not in this list. In Belarus these enterprises often form a part of state concerns on the one hand and are independent on the other. The example here is JSC “Milavitsa”, one of the largest lingerie producers in EE, which is a part of the Bellegprom concern. Therefore, this methodology likely underestimates the role of the private sector.

The third way is to try to exclude state presence from the official data of the private sector. According to official statistics, the private sector includes several groups of enterprises, such as individual entrepreneurs, legal entities with/without state/foreign presence, etc. However, the absence of a clear distinction between these sub-groups allows for only rough estimates, through the extraction of the state presence.

As a result, all obtained numbers are qualitatively different from each other and there is no clear answer if any of them reflects the real picture.

For example, the contribution of the private sector in total employment according to the three different methods (Figure 2) provides the following results. Officially, in 2013 around 53% of the active labor force worked in the private sector. However, the exclusion of state presence in private property changes the results significantly and the share of the active labor force involved in the private sector drops to a level of 31%, while the non-ministry reporting enterprises employ around 18% of the active labor force.

Figure 2. Private sector in employment (%)

figure2Source: own estimations from Belstat data.

The input of the private sector in the total production volume (Figure 3) is also very diverse depending on the method of evaluation. Official data show that the private sector is responsible for 80% of total production volume. However, the exclusion of state presence decreases the value to a level of just 26%, which is similar to the result demonstrated by the non-ministry reporting enterprises (25%).

Figure 3. Private sector in total production volume (%)

figure3Source: own estimations from Belstat data.

At the same time, the absence of a clear definition of the private sector does not allow for obtaining reliable information about its effectiveness. If we take the rate of return on assets (ROA), again, there is a significant gap in the results of the different methods of estimation (Figure 4). ROA of the private sector according to official statistics is significantly lower than similar indicators based on the data obtained by the other two methods (in 2013: 1.17 vs. 2.4 and 1.3 respectively). Thus, the lower the “measured” state presence, the higher is the productivity of the private sector, especially in comparison with the effectiveness of the state sector (0.25).

Figure 4. Return on Assets (BYR/BYR)

figure4Source: own estimations from Belstat data.

Conclusion

The above discussion has illustrated that diffuseness of data and the definition of the private sector is likely to create troubles for understanding the importance of the private sector in Belarus. This, in turn, may undermine the effectiveness of economic and political measures targeted towards this sector.

The implementation of a clear, unified and transparent methodology of how to estimate the role of business and what exactly can be treated as a private sector in statistics would allow for a better understanding of the obstacles and barriers that the private sector is dealing with, as well as to help developing effective measures of business support. Until then, the official statistics should not stick to just one definition of the private sector. Instead, it can use all three abovementioned gradations, as a better reflection of the realities of Belarusian business.

References

  • Cuaresmo, J., Oberhofer, H., Vincelette, G. (2012).‘Firm Growth and Productivity in Belarus: New Empirical Evidence in the Machine Building Industry’, World Bank, Policy Research Working Paper No. 6005.
  • ‘Business Environment in Belarus 2013.Survey of Commercial Enterprises and Individual Entrepreneurs’, IFC, Report.