Location: Belarus

Institutions and Comparative Advantage in Services Trade

Posted on June 3, 2019 | Policy Brief

High office buildings facing sky representing Institutions and Services Trade

Recent studies have highlighted the role of human capital and good economic institutions in establishing a comparative advantage in trade in complex institutions-intensive goods. We show that the effect of institutions on comparative advantage in services trade is quite different: in fact, countries with bad institutions rely significantly more on services exports. More specifically, as the quality of institutions deteriorates, information technology sector (ICT) services exports as a share of total ICT exports increase significantly and countries with worse institutions get a substantial comparative advantage in the provision of ICT services. This is especially applicable to transitional economies characterized by high, arguably exogenous, human capital at the level of most advanced countries.

Introduction

Recent research in international trade has demonstrated that institutions influence the determination of comparative advantage in the trade of goods. Countries with strong domestic institutions have a significant comparative advantage in producing complex, institutions-intensive goods while countries with weak institutions tend to specialize in less complex goods. Through this channel, weak institutions can hinder growth and development (Nunn and Trefler, 2014).

We argue that the role of institutions in services trade can differ significantly from the one in trade in goods. The intuition behind it is that services provision often relies less on institution-driven factors, such as public infrastructure, availability of large number of inputs, property rights and capital investments than the production of complex goods.

We show, in the case of the information technology sector (ICT), that countries with bad institutions rely significantly more on services exports even after controlling for human capital input requirements and availability. We focus on the ICT sector to isolate the differences in the role of institutions in determining comparative advantage in goods and services. Both ICT goods and services provision are equally intensive in human capital and thus present a good opportunity to study differences between goods and services provision.

Our study is motivated by high ICT services exports (e.g. software development) and low ICT goods exports (e.g. computers, phones, etc.) of transition countries which are known to have high human capital and low institutional indicators.

Institutions and ICT Services Exports

Figure illustrates the high human capital availability of transitions economies and weak domestic institutions relative to other countries. Specifically, we categorize countries into four groups: 23 most developed economies (e.g. USA, Canada, Japan and Western European economies); new members of the European Union (a group of 13 countries including Poland, Slovakia, and Baltic countries); transition economies group consists of 17 mostly post-Soviet countries including Russia, Ukraine, Belarus; the most numerous fourth group includes more than hundred other developing countries.

Figure 1. Institutions quality and schooling by country groups

Source: Authors’ calculations, schooling data from Barro and Lee (2013)

Source: Authors’ calculations, institutional indicators data from the World Bank World Governance Indicators

Figure 1a presents an average number of years of schooling, our measure of human capital, for each country group in 2000 and 2010 (the years are chosen based on data availability). The human capital is at a similar level in the most developed economies, EU-13 and transition economies, but significantly lower in other developing countries. Figure 1b illustrates the average institutional quality for each group in 2000 and 2010. Institutional quality for each country is calculated as an average of six indicators, distributed approximately from -2.5 to 2.5: control of corruption, government effectiveness, political stability, rule of law, regulatory quality, voice and accountability, with a lower value corresponding to worse institutional quality. In contrast to education, the average institutional quality of transition economies, although improving from 2000, remains on average lower than the institutional quality of other developing countries.

Consistent with the literature on institutions and comparative advantage in relationship and investment-intensive goods production, ICT goods export from transition economies is significantly lower than in other countries. In contrast, ICT services exports is at a higher level and faster growth in transition economies than in other countries.

Belarus presents a good motivating example. On the one hand, fundamental education in Belarus is at a level of the most advanced countries, which allows 21 universities in the country to educate about 7,000 graduates in IT industry in a year. On the other hand, ICT services exports in Belarus is thriving: over the last 10 years, the growth of ICT services is an eightfold increase (it was 150M USD in 2008 and 1.2B USD in 2017). Nowadays, Belarus is one of the world leaders in ICT services exports per capita. At the same time, ICT goods export is not growing even close to the level of ICT services exports. Over the same time period, it has grown only by about 30 percent: in 2008 ICT goods export was 105M USD, in 2016 – 140M USD (BELARUS.BY, 2019).

The importance of ICT services exports in transition economies is seen in Figure 2. The figure presents ICT services exports as a share of total exports of ICT goods and services. To obtain values for each country group, we average ICT services shares across countries within each group.

Figure 2. ICT services exports as share of total ICT exports

Source: Authors’ calculations, ICT services export data from Trademap, ICT goods export data from WDI

As Figure 2 shows, the average share of ICT services exports in transition economies is higher than the share of ICT services exports in all other groups of countries. Transition economies, characterized by high human capital and weak institutional quality, specialize in exports of services over goods in their ICT exports. This descriptive evidence suggests that abundant human capital, inherited from the USSR and arguably exogenous, shifts to services within the human capital intensive ICT sector when facing weak institutions.

Empirical panel analysis confirms the descriptive evidence. To test our hypothesis, we use the share of ICT services in total ICT exports as a dependent variable and we show that quality of institutions is a significant determinant. Our regressions show that the higher the quality of institutions is, the lower will the share of ICT services in total ICT exports be. Moreover, regression analysis allows us to quantify this dependence: as the quality of institutions increases by 1, which is approximately the difference between Belarus and Georgia (as can be seen in figure 3 below), the share of ICT goods in total ICT services increases by about 20%.

Institutions as a source of comparative advantage in services

To explore the role of institutions in the relative services provision within a sector further, we look at comparative advantage in exporting ICT services. We incorporate a measure similar to Relative Share measure used in Levchenko (2007) for the analysis of comparative advantage in goods export. The measure effectively compares the share of ICT services export for a given country with the world average. The index of revealed comparative advantage in ICT services over ICT goods is computed for country in the following way:

where is share of ICT services exports in total ICT exports for country, is the export of ICT services for all countries, and is the total ICT export (goods plus services) for all countries.

We look at the revealed comparative advantage index across our group of transition economies in figure 3 and see that even within this group, there is a negative correlation between institutions quality and revealed comparative advantage in ICT services.

Figure 3. Revealed Comparative Advantage and Institutions Quality

Source: Authors’ calculations

Countries with high institutional quality, like Georgia, export relatively more goods compared to services. Countries with low institutional quality, like Ukraine and Belarus, have a comparative advantage in ICT services exports.

We hypothesize that the main mechanism responsible for this is as follows. Poor institutional quality, resulting in, for example, corruption and the impossibility to create binding contracts does not allow the countries to produce complex goods in the ICT industry, while the presence of high human capital in these countries allows them to produce ICT services that much less depend on corruption and contracting inefficiencies but are as intensive in human capital as ICT goods.

For a better understanding of the relationship between institutions and comparative advantage determination, we run panel regressions analysing the probability of having a comparative advantage in ICT services in exports of ICT goods and services as a function of institutional quality. Following Balassa (1965), a country has a comparative advantage in ICT services if the share of services in overall ICT exports is higher than the world average, in other words, revealed comparative advantage index is greater than 1. We find that one unit increase in institutional quality reduces the probability of having a comparative advantage in services by about 25%, which means that a country with institutional quality similar to Georgia is about 25% less likely to have comparative advantage than a country with institutional quality similar to Belarus.

Conclusion

In this brief we have discussed the role of institutions in determining comparative advantage in services. Our study argues that, given high human capital, low quality institutions create comparative advantage in services provision. Since low quality institutions act as an implicit tax on the production of complex goods, rational agents reallocate most resources to the production of services that are less sensitive to the institutional quality, while still requiring high level of human capital. We showed that transition economies are characterized by low institutions quality and high human capital. At the same time, transition economies have the highest share of ICT services export in total ICT export. We also showed that institutions negatively affect comparative advantage in ICT services export. Our results suggest that services exports can be a novel development channel for countries with weak institutional, capital investments and infrastructure. Specialization in high-value added services exports provides opportunity for fostering high human capital.

References

Arshavskiy, Victor, Arevik Gnutzmann-Mkrtchyan and Aleh Mazol, 2019. “Institutions and Comparative Advantage in Service Trade”, Working paper
Balassa, B. (1965). Trade liberalisation and “revealed” comparative advantage 1. The Manchester School of Economics and Social Studies, 33(2), 99-123.
Barro, Robert J. and Jong Wha Lee, 2013. “A new data set of educational attainment in the world, 1950–2010”, Journal of Development Economics, vol. 104, pp 184-198
Levchenko, Andrei A., 2007. “Institutional Quality and International Trade”, Review of Economic Studies, vol. 74, pp 791-819.
Nunn, Nathan and Daniel Trefler, 2014. “Domestic Institutions as a Source of Comparative Advantage”, Handbook of International Economics, Volume 4, Chapter 5, pp 263-315.
BELARUS.BY, 2019. “ИТ в Беларуси”, it-belarus, accessed on May 19, 2019

Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.

Poverty Dynamics in Belarus from 2009 to 2016

Posted on March 4, 2019 | Policy Brief

20190304 Poverty Dynamics in Belarus Image 00

This brief is based on research that studies the incidence and determinants of poverty in Belarus using data from the yearly Household Budget Surveys for 2009-2016. Poverty is evaluated from a consumption perspective applying the cost of basic needs approach. According to the results, in 2015-2016, absolute poverty in Belarus increased twofold and reached 29% of the population. Large household size, high number of children, single mothers and unemployment negatively affect household welfare and increase poverty risk. Moreover, living in rural areas increases the likelihood of being poor and correlates negatively with welfare.

Introduction

Sizeable and increasing poverty poses a threat to social stability and long-term sustainability for every country. Before 2009, Belarus registered over a decade of high and sustainable economic growth that enhanced the average standard of living and raised a substantial number of Belarusians out of poverty. According to the National Statistical Committee of the Republic of Belarus (Belstat), the poverty rate in Belarus (by official definition) has decreased from 41.9% of the population in 2000 down to 6.1% in 2008. The largest reported decline in poverty was in 2001 – from 41.9% to 28.9%.

Since then, Belarus experienced several episodes of economic crises – in 2009, 2011 and 2015-2016 (Kruk and Bornukova, 2014; Mazol, 2017a). Such economic downturns typically introduce considerable survival problems for many households. For example, according to the World Bank, in some countries the poverty rate may reach 50% (World Bank, 2000). In light of this, the small increase (0.3%) in the official poverty measure during these periods casts doubt on the official methodology used for poverty calculations. This brief describes an alternative measure of absolute poverty based on the widely recognized cost of basic needs approach; and summarizes the results of the study of how economic downturns in Belarus influenced welfare and poverty at the household level.

Data and methodology

The data used in this research are pooled cross-sections from 2009 to 2016 of the yearly Belarusian Household Budget Surveys with on average 5000 households in each sample obtained from Belstat. These surveys consist of household and individual questionnaires that contain important data about households including decomposition of expenditures and income by categories, detailed data on consumption of food items, the size, age and gender composition of households, living conditions, etc.

The analysis applies the cost of basic needs approach (Kakwani, 2003). It first estimates the cost of acquiring enough food for adequate nutrition (nutrition requirements for households of different size and demographic composition) per person (food poverty line) and then adds the cost of non-food essentials (absolute poverty line). The calculated poverty lines for each sampled household are compared with the household consumption per person. All measures are preliminary deflated to take into account differences in purchasing power over time and regions of residence.

In contrast, the official poverty measurement compares per capita disposable income of a household with national (official) poverty line, which is the average per capita subsistence minimum budget of a family with two adults and two children (see Table 1).

Table 1. Consumer budgets and absolute poverty line by year in Belarus, in constant BYN

Year	2009	2010	2011	2012	2013	2014	2015	2016
Subsistence minimum budget¹	247	258	293	317	332	362	369	373
Minimum consumer budget²	372	396	367	448	491	517	554	620
Absolute poverty line³	383	395	437	448	468	475	499	520

Source: ¹Belstat; ² Ministry of Labour and Social Protection Republic of Belarus; ³author’s own calculations.

The empirical strategy of the analysis assumes setting the food, non-food and absolute poverty lines using the cost of basic needs approach, estimating poverty measures at the level of entire Belarus and its regions based on Foster-Greer-Thorbecke’s poverty indices (Foster et al., 1984), and analyzing the determinants of welfare and poverty using OLS and probit regressions.

Poverty incidence

The timeline of poverty analysis for Belarus can be subdivided into three periods: crisis of 2009-2011, recovery of 2012-2014, and a crisis of 2015-2016 (see Figure 1).

The results show that during the first period (from 2009 to 2011), absolute poverty at the national level increased from 30.9% to 32.6%. Incidence of absolute poverty for rural and urban areas in 2011 reached 45% and 28% of the population, correspondingly.

Figure 1. Incidence of absolute poverty and GDP per capita growth in Belarus

Source: Author’s own calculations.
Note: Estimates reflect weighted household data.

The second period (from 2012 to 2014) was characterized by a sharp poverty reduction. For example, the absolute national poverty headcount ratio has plummeted from 32.6% in 2011 to 14.9% in 2014, rural poverty dropped by 22.1 percentage points or almost by half and urban poverty decreased by 16.2 percentage points.

In turn, the third period saw a sharp rise in the incidence of poverty. From 2015 to 2016, the headcount ratio for absolute poverty increased by 14.4 percentage points. As a result, in 2016 absolute poverty in Belarus reached 29.3% or almost the same as in 2009 and 2011 (Mazol, 2017b).

Causes and determinants of poverty

The significant increase in poverty in 2015-2016 was due to a combination of several factors, including the household income decline in comparison with its growth in previous years, the increasing need to spend more on food necessities and the growth in food and especially non-food price levels.

As the Figure 2 shows, starting from 2015 there has been a rapid increase in the real cost of non-food budget for Belarusian households, while the food budget has remained almost the same in real terms. Correspondingly, in 2016 the non-food poverty line increased by 14.6%, while the food poverty line went up only by 2.9%.

Figure 2. Real monthly average per capita household expenditure on food and non-food items and real monthly standardized food and non-food poverty lines, 2009-2016, in BYN

Source: Author’s own calculations.
Note: Estimates reflect weighted household data.

Furthermore, as income fell (by 7.2% in 2015-2016), the share of food items in total expenditure increased and real non-food expenditure decreased. This is because household income was not enough to cover both expenditures on food and non-food items. Due to the 2015-2016 economic crisis the cost of meeting the food essentials increased so fast that it has squeezed the non-food budget, leaving insufficient purchasing power for non-food items.

The study also shows that among factors that substantially influence household welfare and poverty at the household level in Belarus are family size, the number of children in a household, presence in the household of economically inactive members. Moreover, single mothers in Belarus appear to be noticeably more vulnerable to macroeconomic shocks than full families both from welfare and poverty perspectives.

Additionally, one of the most important determinants of welfare and poverty in Belarus is spatial location of a household. In particular, poverty highly discriminates against living in rural areas. The poverty incidence for rural areas over 2009-2016 is approximately 10.5 percentage points (or 44%) higher than the national average, while that of the urban areas is nearly 4 percentage points (or 16%) below national average. Moreover, in 2015-2016 urban and rural disparity for poverty widened even more and reached 25.3% for urban vs 40.6% for rural areas.

Finally, two more factors, savings and access to a plot of land, have on average a large positive influence on consumption expenditure and aa negative one on the chance of getting poor.

Conclusion

Poverty alleviation and development reflect economic and social progress in any country. While Belarus initially achieved noticeable progress in this dimension, the economic and social development in recent years seems to increase problem of poverty in Belarus. The estimates show that in 2015-2016, absolute poverty in Belarus increased almost twofold. Household size, large numbers of children in a household, the presence in the household of economically inactive members are all factors that decrease household welfare and increase poverty. Single mothers also appear to be substantially more vulnerable to macroeconomic shocks. Finally, one of the most important determinants of welfare and poverty in Belarus is if a household is rural. These findings are important warning signals for the design of pro-poor policies in Belarus.

References

Foster, J., J. Greer, and E. Thorbecke. (1984). A Class of Decomposable Poverty Measures. Econometrica, 52: 761-766.
Kakwani, N. (2003). Issues in Setting Absolute Poverty Lines, Poverty and Social Development Papers No. 3, June 2003. Asian Development Bank.
Kruk, D., Bornukova, K. (2014). Belarusian Economic Growth Decomposition, BEROC Working Paper Series, WP no. 24.
Mazol, A. 2017a. The Influence of Financial Stress on Economic Activity and Monetary Policy in Belarus, BEROC Working Paper Series, WP no. 40.
Mazol, A. 2017b. Determinants of Poverty With and Without Economic Growth. Explaining Belarus’s Poverty Dynamics during 2009-2016, BEROC Working Paper Series, WP no. 47.
World Bank (2000). Making Transition Work for Everyone: Poverty and Inequality in Europe and Central Asia. Washington DC, The World Bank.

Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.

Development of Belarusian Higher Education Institutions Based on the Entrepreneurial University Framework

Posted on February 8, 2019 | Policy Brief

Photo Of People Doing Handshakes representing Belarusian higher education

In contrast to developed Western countries, higher education institutions (HEIs) in transition economies such as Belarus do not have the pretension to being key actors in cutting-edge innovation and in creating entrepreneurship capital. Rather, they tend to educate job seekers or knowledge workers, as well as to adapt, redevelop and disseminate existing knowledge and technologies. At the same time, policy makers in Belarus have realized that transformation of HEIs is needed to respond to the global challenges. In this regard, this policy brief discusses prerequisites and factors conditioning the development of entrepreneurial HEIs in Belarus.

Capitalizing on state-of-the-art academic research, as well as on the custom-made survey of Belarusian faculty members, the brief concludes that Belarusian policy makers need to create a supportive institutional environment before requiring from HEIs outcomes of the entrepreneurial mission. First-priority measures for the current stance are delineated.

Entrepreneurial University and University 3.0

As a productivity factor, entrepreneurial activities started appearing in economic growth models at the beginning of the twenty-first century (Wennekers & Thurik, 1999; Wong et al., 2005). Consequently, the role of HEIs broadened from educating labor force and knowledge creation to development of “entrepreneurial thinking, action and institutions” (Audretsch, 2014) – HEIs took on the third “entrepreneurial” mission.

Well-studied outcomes of this mission are new firms (academic spin-offs, spin-outs, student-led start-ups), patenting, licensing and the development of entrepreneurial culture and attitudes among graduates and academics.

The concept of an entrepreneurial HEI is multifaceted and is explored within different research streams: from knowledge transfer to entrepreneurship education and HEI management. Consequently, there is no consensus in the understanding of the term “entrepreneurial university” that can, for this policy brief, be broadly defined as a HEI that acts entrepreneurially and is a natural incubator, creating a supportive environment for the startup of businesses by faculty and students, promoting an entrepreneurial culture and attitude for the purpose of responding to challenges of the knowledge-based economy, and facilitating economic and social development.

Figure 1. Evolution of the HEIs’ missions

Source: Adapted from Guerrero & Urbano (2012)

Meanwhile, the concept of “University 3.0” –mostly corresponding to the concept of “Entrepreneurial university” and adopted from J.G. Wissema – started appearing in Russian publications, where the number ‘3’ corresponds to the three HEI missions or to the third generation of HEIs. A possible explanation of this renaming is that, on the one hand, in the post-Soviet context entrepreneurship per se still does not have a positive meaning in a broader society and it is not associated to HEIs. On the other hand, it was expected that such numbering makes the evolution visible. However, this led to speculation on this numbering and gave rise to publications on University 4.0 that should correspond somehow to Industry 4.0 – the current trend of automation and data exchange in manufacturing technologies.

Admittedly, the entrepreneurial mission of HEIs is not associated or equaled to start-ups and knowledge transfer any more, but is increasingly considered as a procedural framework for HEI’s and individual’s behavior.

Belarusian Context

Political, economic, social, technological and legal conditions determine the path and the speed of the evolution of HEIs as well as their contribution to national economies in different stages of economic development. Thus, in Belarus – an efficiency-driven economy, i.e., a country growing due to more efficient production processes and increased product quality (World Economic Forum, 2017), – HEIs are considered to contribute to economic development if they successfully fulfill teaching and research missions. While the outcomes of the third mission are supposed not to be relevant at this stage (Marozau et al., 2016).

However, trying to replicate the success of Western HEIs in the development of the entrepreneurial mission, the Ministry of Education of Belarus initiated the Experimental project on implementation of the “University 3.0” model aimed at the development of research, innovation and entrepreneurial infrastructure of HEIs for the creation of innovative products and commercialization of intellectual activities.

In general, Belarus has a state-dominated well-developed, by some estimates, oversaturated higher education sector that remains mostly rigid and unreformed since the Soviet times. Belarus outperformed all CIS and EU countries except Finland in terms of the number of students per 10,000 population in 2014 (Belstat, 2017) and according to the World Bank has one of highest enrollment rates in tertiary education of about 90%.

Belarusian students have quite high entrepreneurial potential in comparison to other countries participating in the Global University Entrepreneurial Spirit Students‘ Survey (GUESSS). Thus, in five years after graduation, 56.8% intend to be entrepreneurs, while the global average level is 38,2% (Marozau and Apanasovich, 2016). However, curricula of most specialties majors provided by Belarusian HEIs are not supplemented with formal and experiential entrepreneurship education to equip students with entrepreneurial competencies. Innovative methodologies and entrepreneurial approaches to teaching as well as faculty entrepreneurial role models are rare. Moreover, all changes in degree syllabuses need state approval that makes HEIs less flexible and nimble. The situation is further complicated by the fact that supporting entrepreneurial activity has not been an important part of the HEI culture.

Methodological Approach

We conducted online and face-to-face surveys of 48 Belarusian HEI authorities and faculty members that were based on HEInnovate self-assessment tool widely used by policy makers and HEI authorities (see Marozau, 2018).

Overall, emails were sent out to a population of 284 pro-active and advanced representatives of the Belarusian academic community whose email addresses were available in the databases of BEROC and the Association of Business Education. We benefitted from open-ended questions included in the questionnaire to study how representatives of Belarusian HEIs perceived the Entrepreneurial university (University 3.0) concept as well as its conditioning factors and potential outcomes.

Main Findings

First of all, we revealed that the Belarusian academic community is not unanimous in understanding the concept “Entrepreneurial university”. According to the main emphasis provided by respondents, we got the following distribution of answers about what an entrepreneurial is: 12 respondents associated the concept with knowledge transfer and commercialization; 7 respondents stressed the interrelation of teaching, research and innovations; 5 respondents believed that the concept is about earning money; 1 respondent indicated that an entrepreneurial university means developing entrepreneurial competences.

These findings demonstrate the general misunderstanding or fragmented understanding of the phenomenon that may lead to a negative attitude from both HEI staff and policy makers and stress the importance of raising awareness and providing training at least for decision makers and spokesmen.

Figure 2 demonstrates the results of the assessment of Belarusian HEIs against the categories proposed by HEInnovate (1 – very low; 5 – very high).

Figure 2. Assessment of HEIs

20190208 Development of Belarusian Higher Education Figure 2

Source: Author’s own elaborations

We distinguished pairwise between (i) HEIs that participated in the Experimental project and those that did not: (ii) estimates of faculty members that were aware of the concept and those who were not.

Surprisingly, the representatives of HEIs that were left beyond the scope of the Experimental project and those who were aware of the concept perceived their HEIs more advanced in all the areas.

To understand this paradox, we used the chi-square test for independence to discover if there was a relationship between two categorical variables – awareness of the concept and employment at a HEI participating in the Experimental project. Surprisingly, no statistically significant relationship was identified evidencing that implementation of the Experimental project went without raising awareness and wider involvement of faculty.

The analyses of answers to open-ended questions showed that many environmental factors are not only unsupportive to the HEI entrepreneurial development but jeopardize the sustainability of the higher education system in general.

Conclusions

The main conclusions from the study are as follows:

Belarus has not reached the stage of institutional development to foster entrepreneurial HEIs and to expect outcomes of the entrepreneurial mission. To some extent, this explains the skepticism and misunderstanding of the concept of “Entrepreneurial university” (University 3.0).
The main omission of the Experimental project is that the education and training of HEI authorities and faculty are not defined as first-priority measures. Such policy initiatives need to be clear in their objectives, tools, benefits and outcomes as well as evidence-based and open for discussion.
Comprehensive initiatives in this sphere should be developed and implemented in close collaboration with the Ministry of Economy that is responsible for entrepreneurship, the business environment, entrepreneurial infrastructure as well as the State Committee for Science and Technology that is subordinated to the Council of Ministers and deals with the state policy in its sphere.

An important concern here is whether it is currently feasible to have the measures that are relevant and not-for-show rather than half-way initiatives and sticking plaster solutions despite the lack of funding, and absence of elaborate study in the field.

Since the weakest area of Belarusian HEIs according to the HEInnovate tool is the problem of ‘Measuring impact’, the state should reconsider short-term target indicators for HEIs such as export growth rate and workforce productivity growth rate to stimulate investments the entrepreneurial transformation. It is worth monitoring such indicators as number of start-ups/spin-offs founded by graduates/faculty members; number of patents, licenses, trademarks co-owned by a HEI, income from intellectual property; number of R&D projects funded by enterprises etc. Alternatively, the Ministry of Education could adopt the ranking of entrepreneurial and inventive activity of universities used in Russia.
Development of entrepreneurship centers as organizational units at HEIs – ‘one-stop shops’ or ‘single front doors’ for students, faculty, businesses – could be an initial step towards both raising awareness and the integration and coordination of entrepreneurship-related activities within a HEI in order to increase their impact and visibility of these activities.

References

Audretsch, David B., 2014. “From the entrepreneurial university to the university for the entrepreneurial society.” The Journal of Technology Transfer 39(3), 313-321.
Belstat (2017). Education in the Republic of Belarus. Statistical book.
Guerrero, Maribel, and David Urbano, 2012. “The development of an entrepreneurial university.” The journal of technology transfer 37(1), 43-74.
Marozau, Radzivon, Maribel Guerrero, and David Urbano, 2016 “Impacts of universities in different stages of economic development.” Journal of the Knowledge Economy, 1-21.
Marozau, Radzivon and Vladimir Apanasovich, 2016. National GUESSS Report of the Republic of Belarus. http://www.guesssurvey.org/resources/nat_2016/GUESSS_Report_2016_Belarus.pdf
Radzivon Marozau, 2018. Modernization and development of Belarusian higher education institutions based on the entrepreneurial university framework. BEROC Policy Paper Series, PP no.63.
Wennekers, Sander, and Roy Thurik, 1999. “Linking entrepreneurship and economic growth.” Small business economics 13(1), 27-56.
World Economic Forum, 2017. “Global Competitiveness Report 2017-2018”, edited by Klaus Schwab.
Wong, Poh Kam, Yuen Ping Ho, and Erkko Autio, 2005. “Entrepreneurship, innovation and economic growth: Evidence from GEM data.” Small business economics 24(3) 335-350.

Acknowledgments: The author expresses gratitude to Prof. Maribel Guerrero from Newcastle Business School, Northumbria University for her valuable comments and reviews as well as to Yaraslau Kryvoi and Volha Hryniuk from the Ostrogorski Centre (Great Britain) for coordinating the research project that has resulted in this policy brief.

Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.

Towards a More Circular Economy: A Progress Assessment of Belarus

Posted on November 19, 2018 | Policy Brief

20181119 Towards a More Circular Economy Image 02

This policy brief summarizes the results of our study, Shershunovich and Tochitskaya (2018), on the circular economy development in Belarus. The aim of the work was to measure the circularity of the Belarusian economy using European Commission indicators. The analysis reveals that the circular economy in Belarus is still in the initial stage of its development. In 2016, the employment in circular economy sectors in Belarus accounted for 0.49% of total employment, and the investment amounted to only 0.27% of total gross investment. Belarus is also falling behind many European countries in waste recycling.

Introduction

The circular economy represents an economic system based on a business model of reduction, reuse, recirculation and extraction of materials in production, distribution and consumption of goods and services (Batova et al., 2018).

Transition to it offers great opportunities to transform the Belarusian economy and make it more sustainable and environmentally friendly, while preserving primary resources, creating new jobs and increasing competitiveness of enterprises.

In order to encourage the transition to a circular economy, it is important to have a proper monitoring system based on reliable and internationally comparable data. It helps to track progress towards a circular economy, conduct policy impact assessment, and analyze whether measures being taken are sufficient to promote an economy that reduces the generation of waste.

To assess the development of a circular economy in Belarus, a set of the European Commission (EC) indicators was used to capture the evolution of the main elements of closing the materials and products loop. The EC monitoring system comprises 10 indicators which are part of 4 pillars: production and consumption; waste management; secondary raw materials; competitiveness and innovation.

The reasons to use this system for Belarus are as follows: first, there is no set of indicators that provide a comprehensive overview of a circular economy in Belarus, while the EC monitoring framework allows us to capture its main elements, stages, and aspects; second, Eurostat calculates circular economy indicators for the European Union (EU) countries on a regular basis, which proves the high level of their practical application, relevance and robustness; third, the EC is constantly working on their improvement. Thus, the EC set of indicators can be a tool to monitor trends in transition to a circular economy in Belarus.

Tight spots of waste statistics in Belarus

While calculating the circular economy indicators for Belarus the following problems with data affecting the quality of statistics have been identified:

methodological issues;
challenges with recording and coverage;
insufficient degree of international comparability of data, in particular woth the EU countries.

Such methodological problems as the blurred boundaries between the definitions of ‘waste’ and ‘raw materials’, and the lack of criteria for categorizing substances or objects as waste allow enterprises to classify certain substances or objects not as waste and therefore not to file information on them. As a result, less than half of the enterprises which might generate industrial waste, report it. Therefore, the question arises whether the statistical data reflect the real level of waste generation, recycling, and disposal in Belarus.

Data on municipal solid waste (MSW) have proved to be one of the areas of most serious concern. Absence of direct MSW weighing makes the data on it very sensitive to the conversion factor from volume to mass units. The differences between the Belarusian and European waste classifiers and definitions of key concepts (‘waste’, ‘recycling rate’) complicate the data analysis.

In addition, since Belarus is the 3rd world potash fertilizers producer, the share of potash waste in the total volume of waste generation is very high (63-68%). Only a small portion of this type of waste stream is recycled in Belarus (no more than 4%) due to lack of appropriate technologies of potash waste utilization used internationally. As only Germany counting as one of the world’s largest producers of potash fertilizers within the EU, to increase the comparability of data between the EU countries and Belarus, potash waste hasn’t been considered when calculating the circular economy indicators. Given all the above mentioned problems, some of the EU indicators have been adapted to the existing Belarusian statistical data.

Illustration of waste statistics problems

Waste statistics problems result in overestimation or underestimation of some circular economy indicators. A good example is the recycling rate of all waste, excluding major mineral wastes. Belarus, which is a country without a proper legal framework for the circular economy or a well-established secondary raw materials market, had one of the best performances in terms of the recycling rate (72-80%) among the EU countries in 2010-2016. This fact reflects the problems with waste statistics rather than success in waste recycling in Belarus.

Table 1. Recycling rate of all waste excluding major mineral wastes, %, in 2010-2016

Source: for the EU countries and Norway – Eurostat. For Belarus – own calculations based on the data from the RUE “Bel RC «Ecology».

Actual picture of the circular economy development in Belarus

The indicators with minimum distortions in waste statistics show that some elements of the circular economy in Belarus are still in the initial stage of their development (tables 2, 3, 4, 5). Our study reveals that the recycling rate of MSW amounted to 15.4 % in 2014-2016, which is much lower than the EU average in 2014 and 2016. Thus, Belarus has a considerable potential to increase the recycling rate of MSW. The experience of Czechia and Lithuania shows that the MSW recycling rate can be increased relatively fast if efforts are made and resources permit.

Table 2. Recycling rate of MSW, %, in 2010-2016

Source: for the EU countries and Norway – Eurostat. For Belarus – own calculations based on the data from the SE “Operator of SMRs” and Belstat.

In 2016, the recovery rate of construction and demolition waste in Belarus reached 81%, though this indicator fluctuated between 59% and 79% in previous years. However, it can be further improved as in some European countries (Denmark, the Netherlands, Germany, Czechia, Poland and Lithuania) the recovery rate of this type of waste stream exceeds 90%.

Table 3. Recovery rate of construction and demolition waste, %, in 2010-2016

Source: for the EU countries and Norway – Eurostat. For Belarus – own calculations based of the data from the RUE “Bel RC «Ecology».

Despite the fact that the decoupling of economic growth from an increase in waste volumes is an important issue on the international agenda, trends in waste generation in many countries follow a development of GDP. In 2010-2012, the generation of waste excluding major mineral wastes per GDP unit (42-46 kg/thsd of $, PPP) in Belarus (table 4) was comparable with countries such as Czechia, Lithuania, Germany, Denmark, Sweden. However, in 2014 due to waste generation growth, this indicator in Belarus exceeded above-mentioned EU countries and approached the level of Hungary and the Netherlands. It was far above Norway that was the best performer among the European countries and a good example of how a country could really decrease waste generation.

Table 4. Generation of waste excluding major mineral wastes per GDP unit (kg per thsd constant 2011 international $) in 2010-2016

Source: for the EU countries and Norway the data on generation of waste excl. major mineral wastes – Eurostat. For Belarus – own calculations based on the data from the RUE “Bel RC «Ecology». For the EU countries, Norway and Belarus the data on GDP, PPP in constant 2011 international $ – The World Bank.

In 2012, the share of gross investment in the circular economy sectors in Belarus (table 5) decreased in comparison with 2010, however, since 2014 it have shown an upward trend. For the EU countries and Norway this indicator also includes investment in the repair and reuse sector. For Belarus this sector has not been taken into account in calculation due to lack of data. In addition, the gross investment in tangible goods is a bit different from the gross investment in fixed assets used for Belarus as the latter doesn’t include non-produced tangible goods such as land. Yet, even bearing in mind these differences in calculation, the circular economy appeared to be underinvested in Belarus compared to the EU countries and Norway.

Table 5. Gross investment in tangible goods (% of total gross investment) in circular economy sectors in 2010-2016

Source: for the EU countries and Norway – Eurostat. For Belarus – Belstat.

The employment in the circular economy in Belarus accounted for only 0.49% of total employment in 2016, while in the EU countries and Norway this indicator was approaching 3%. This again proves the fact that Belarus has a long way to go towards the creation of a circular economy.

Conclusion

The analysis revealed contradictory results of the circular economy development in Belarus. While the country scores highly across some indicators compared to the EU countries and Norway, this to a large extent reflects the problems with waste statistics, rather than success in waste management. The indicators with minimum distortions in waste statistics show that Belarus is falling behind leading countries in circular economy development. However, in the transition to a circular economy, the monitoring framework is an important component of this process, which permits to track a progress using the system of indicators. In order to ensure that these indicators accurately capture the key trends in the circular economy in Belarus it would seem useful to:

align the definition of ’waste’, ‘recycling rate’ with the international one, identify clear criteria for classifying substances or products as waste and secondary raw materials;
strengthen the accountability of entities for filing reports on waste;
improve the system of MSW and SMRs reporting and recording, and introduce MSW recording based on weighing wherever possible;
consider the option of improving the comparability of Belarus’ waste classifier with the European waste statistical nomenclature.¨

References

Batova, N. et al., 2018. “On the Way to Green Growth: Window Opportunities of Circular Economy”, PP GE no.1.
Belstat. http://www.belstat.gov.by/
Eurostat / Circular economy / Indicators / Main tables. http://ec.europa.eu/eurostat/web/circular-economy/indicators/main-tables
RUE “Bel SRC “Ecology”. http://www.ecoinfo.by
Shershunovich, Y. and I. Tochitskaya, 2018. “Waste Statistics in Belarus: Tight Spots and Broad Scope for Work”, PP GE no.

Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.

Women Entrepreneurs in Belarus: Characteristics, Barriers and Drivers

Posted on May 13, 2018 | Policy Brief

This policy brief summarizes the results of the research on aspects of female entrepreneurship in Belarus. The aim of this work was to shed a light on what the features of female-owned business in Belarus are and whether there are any differences in the motives and barriers it faces compared with male-owned companies. Results show that female-owned companies are smaller in size, less likely to grow fast and less effective in the monetization and promotion of their innovative products and ideas. This is partly due to differences in social roles, motives, decision-making process and macroeconomic factors.

Women’s entrepreneurship is not just a question of gender equality but one of the sources for the sustainable economic development of the country. The presence of women among decision makers is beneficial for companies’ performance, effectiveness and innovativeness, and impacts the growth of profitability of the company (Akulava, 2016; Noland et al., 2016).

Little is known about the state of women’s engagement in economic governance in Belarus. According to the 5^th wave of the BEEPS survey conducted by the World Bank, female top managers operate in around 32.7% of Belarus’ firms and 43.6% of firms have women among their owners (The World Bank, 2013). At the same time EBRD research shows that, on average, for every 10 men taking loans for the development of their own enterprise, only one woman did. Furthermore, the probability of loan rejection is 55% higher for women than for men in Belarus (these average numbers were presented by EBRD representatives during the conference “Business Territory: Women’s View”, Minsk, 2017). Unfortunately there is no information on the size and purpose of the loans, but potentially this may be a sign of discrimination and constraints on women’s economic activity.

We tried to expand the understanding of the role of women in Belarus’ private sector and to uncover individual, social, economic and cultural barriers that affect economic behavior and career choices of women, as well as introduce new drivers for female entrepreneurship in Belarus.

For this purpose we conducted interviews in 3 focus groups with the involvement of women entrepreneurs and also ran a survey that covered 407 owners and top decision-makers in the small and medium enterprises (SMEs).

The data analysis showed that around 30% of businesses belong to women (Table 1). Women tend to choose to operate in wholesale/retail trade, manufacturing, and medical/social services. Trade is the most popular with 28.9% of female-owned companies being part of this industry, while manufacturing stays second (10.1%). Trade also attracts the largest share of the male-owned companies (29.6%), next go manufacturing (23.9%) and construction (18.9%).

Table 1. Sectoral distribution by gender of the owner

	Female-owned	Male-owned
Share in total sample (%)	30.3	69.7
Sectoral distribution
Trade	29.0	29.6
Manufacturing	10.1	23.9
Construction	7.3	18.9
Medical and social services	8.7	1.3
Hotel and catering	8.7	2.5
Transport	7.3	10.1
Other	29.0	13.8

Innovative behavior changes slightly depending on the gender of the owner (33.3% of female- and 38.9% of male-owned companies have implemented innovations during the last 3 years). The measure of implemented innovative activities includes information on whether the company introduced any radical or incremental innovation (product/service/novelty in business processes/new strategy) during the last three years.An average female-owned firm grows much slower than male-owned business (Table 2). The annual sales gain and the sales gain over the last 3 years are 4 times and 2 times smaller respectively. The average number of employees is also smaller among female-owned companies (10 vs. 17 employees). On average, the owner of the male-owned firm has almost 15 years of relevant working and 13 years of managing experience. Similar characteristics for female owners are 12.8 and 9.7 respectively.

However, the realization of the implemented innovations as well as their relevance look more successful among the male-owned businesses. According to the answers in the survey, the profit share due to implemented innovations equals 28.8% among male-owned businesses and just 16.4% among female-owned. Thus, the major part of return is generated by the established business model and not the novelty.

Table 2. Business characteristics by gender of the owner

	Female-owned	Male-owned
Sales growth 1yr (%)	7.6	27.1
Sales growth 3yr (%)	18.4	36.1
Size of the company (employees)	10.6	17.3
Age of the company (years)	8.8	10.2
Relevant experience of the owner (years)	13	14.7
Managing experience of the owner (years)	9.7	12.8
Owners with a higher education (%)	91.3	86.2
Implemented innovation (%)	33.3	38.9
Profit share of implemented innovations (%)	16.4	28.8

One of the potential reasons for differences in characteristics and performance indicators between genders is self-selection, meaning that women are choosing less productive sectors in order to have more flexibility in balancing various social roles they play. In order to check for this, we compare the characteristics mentioned above in three different sectors (manufacturing, wholesale/retail trade and medical/social services) (Table 2a). The male-owned companies form the majority in the manufacturing sector, while medical/social services industry is mostly presented by female-owned business. Finally, the wholesale/retail trade sector is located somewhere in between and is well presented by both female- and male-companies.

Table 2a. Business characteristics by gender of the owner in manufacturing, wholesale/retail trade and medical/social services

	Wholesale/Retail Trade		Manufacturing		Medical and social services
	Female-owned	Male-owned	Female-owned	Male-owned	Female-owned	Male-owned
Sales growth 1yr (%)	9.8	31	2	26.2	10	n/a
Sales growth 3yr (%)	16.4	37.9	5.6	42.3	17.5	n/a
Size of the company (employees)	5.9	14	23.7	19.8	13	8.5
Age of the company (years)	8.8	7.8	16.1	9.2	12.6	16
Relevant experience of the owner (years)	13	13.8	15.3	14.8	15.2	16
Managing experience of the owner (years)	9.8	11.2	12.3	13.3	10.3	22
Owners with a higher education (%)	85	83	100	89.5	100	50
Implemented innovation (%)	35	34.1	57.1	57.9	16.7	50
Profit share of implemented innovations (%)	2.5	25	30	34.1	n/a	n/a

There are differences in size and age of the businesses subject to the industry of the businesses. However, controlling for industry does not reveal any significant changes in the picture in terms of companies’ performance and effectiveness. Male-owned firms are still growing faster and are more successful in promoting implemented innovations Thus, this is likely not an issue of self-selection but of the way male and female owners operate their businesses.

The analysis revealed a number of internal and external barriers creating obstacles for doing business that breaks down into the following categories: social roles, educational patterns, decision-making process and general macroeconomic factors.

Women’s social roles in Belarus

Women in Belarus are mainly at the wheel of domestic responsibilities, which are rarely shared with male partners. According to the survey results, 40% of female and just 9% of male entrepreneurs are responsible for at least 75% of family duties (Table 3). 37% of female and only 0.74% of male owners said that they are in charge for taking care of kids. The same is true for the responsibility to stay at home when kids are sick (32.6% vs. 1.28).

Table 3. Distribution of domestic responsibilities by gender of the owner

	Women	Men
Family duties
less than 25%	10.91	37.5
around 50%	49.10	53.5
more than 75%	40.00	9.00
Kids
taking care of kids	36.96	0.74
staying at home, when kids are sick	32.61	1.48

At the same time, participants of the focus groups admitted that particularly childbirth motivated them to start their own business with flexible working hours and the possibility to work from home, which is generally not possible in corporate business in Belarus. Thus balancing between family and business becomes challenging, impacting career decisions. That motive also appeared in the survey where on average 13% of female and 2.5% of male owners started businesses in order to combine work with parenting. This trend does not change much if we control for industry.

Education

There is no significant gender difference in the educational level of business owners. According to the survey data, 91.3% of female and 86.2% of male owners have a university degree or higher. However, the established social role models of Belarusian women influence both their career and educational choices. Usually girls tend to choose education in arts and humanities, law or economics, rarely going to technical universities. Lack of technical background further prevents their access into hi-tech profitable industries.

Business and economic environment

During the interviews, women stated that “Both men and women businesses face generally the same obstacles in starting up, operational management and strategic development. But in an unfriendly environment – mostly men survive”. Similar messages were obtained from the survey, with almost no significant difference in the estimation of barriers was revealed. The main external barriers mentioned were government control (32.2% of female and 29.3% of male owners), administrative burden (44.1% vs. 41.1%) and tax system (33.5% and 30.5%) (Table 4). Almost all barriers were equally mentioned by the respondents except for corruption. Corruption is the only obstacle that differs between men and women, pointed out by 50% of women, while just 12% of men considered it a problem. We interpret it as women being more risk-averse and less likely do bold and dangerous actions in business like bribing. That corresponds to the literature, which finds women more risk-averse than men (Castillo and Freer, 2018; Croson and Gneezy, 2009).

Table 4. Main obstacles and motives for doing business by gender of the owner

	Women	Men
Main barriers
Government control	32.2	29.3
Administrative burden and legal system	44.1	41.1
Tax system	33.5	30.5
Corruption	49.7	11.8
Human capital	16.1	17.1
Unfair competition	28.5	26.9
Motivation to start-up business
Sudden business opportunity	47.8	42.8
Willingness to earn more	29	34.6
No chance to continue the previous activity	14.5	13.2
Improvement of state’s attitude to entrepreneurs	13	13.2
Possibility to combine work and parenting	13	2.5

Conclusion

The statistical evidence showed that female-owned businesses are smaller in size and grow more slowly compared with male-owned competitors. There are no signs of gender differences in entrepreneurial innovativeness. However, the monetization of implemented innovations is more successful among male-owned companies.

Altogether, the barriers of female entrepreneurship in Belarus are associated with the huge burden of household duties and childcare; hindered access to technical and business education; lack of managerial experience and industry knowledge. The existing exogenous barriers, excessive control, contradictory regulations and unfriendly entrepreneurial ecosystems are seen as additional constraints and contribute to the quality and dynamics of female business.

The obtained results confirm the necessity for adding a gender perspective to SME’s policy support in Belarus as well as for taking it into account when estimating the potential effects of business support programs and policies.

Further research of women entrepreneurship, collection of reliable statistics, comparison of the results with other transition countries are vital. These will give an encouragement to new gender specific initiatives and will contribute to economic growth and innovative perspectives of Belarus.

References

Akulava, M. (2016a). Gender and Innovativeness of the Enterprise: the Case of Transition Countries. Working Paper No. 31.
Castillo, M. and M. Freer. (2018). Revealed differences. Journal of Economic Behavior & Organization, 145: 202-217.
Croson, R. and U. Gneezy. (2009). Gender Differences in Preferences. Journal of Economic Literature, 47(2): 448-474.
Noland, M., Moran, T. and B. R. Kotschwar. (2016). Is gender diversity profitable? Evidence from a global survey. Peterson Institute for International Economics. Working Paper No. 16-3.

Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.

Financial Stress and Economic Contraction in Belarus

Posted on February 12, 2018 | Policy Brief

This brief summarizes the results of an analysis of financial stress episodes in the Belarusian economy. Based on a principal component analysis, I construct a financial stress index for Belarus (BFSI) that incorporates distinctive indicators for the banking sector, exchange market and external debt risks covering the period January 2004 to September 2016. Next, I identify episodes of financial turmoil in Belarus using the BFSI and assess the consequences for the real economy. Finally, I investigate the long-run relationship between financial stress and economic activity in Belarus.

It has become conventional wisdom that a well developed and smoothly operating financial system is critically important for economic growth (see Levine, 2005). It helps in overcoming frictions in the real sector, influencing economic agents’ savings and investment behavior, and therefore enabling the real economy to prosper (Beck, 2014).

In contrast, financial stress to financial system can be defined as the force that influences economic agents through uncertainty and changing expectations of loss in financial markets and financial institutions. It arises from financial shocks such as banking or currency crises (Iling & Ying, 2006). Consequently, the current stress level in the financial system can be quantified by combining a number of key individual stress measures into a single composite indicator – the Financial Stress Index (FSI).

In practice, such indices are already widely used, and allow regulators to maintain financial stability and help investors to assess the overall riskiness of investments in financial instruments of the country. The FSI for Belarus (BFSI) has been estimated for the first time and can be used as an early warning signal of systematic risk in the Belarusian financial sector (Mazol, 2017). In the financial context, systematic risk captures the risk of a cascading failure in the financial sector, caused by inter-linkages within the financial system, resulting in a severe economic downturn.

Construction of the FSI for Belarus

Based on a principal component analysis, the calculated index incorporates distinctive indicators for banking-sector risk estimated by the Banking Sector Fragility Index (BSFI), currency risk assessed by the Exchange Market Pressure Index (EMPI), and the external debt risk proxied by the growth of total external debt.

The BFSI reflects the probability of a crisis (episode of financial stress) – the smaller is the indicator, the better. The stability regime ends, when the BFSI exceeds a predetermined threshold. In particular, episodes of financial stress are determined as the periods when the BFSI is more than one standard deviation above its trend, which is captured by the Hodrick–Prescott filter. The identified episodes of financial stress show that one or more of the BFSI’s subcomponents (banking, external debt or foreign exchange) has changed abruptly.

Episodes of financial stress

During 2004—2016, two episodes of financial stress were detected in the economy of Belarus (see Figure 1). In both cases, there were large devaluations of the Belarusian currency, caused by the need to adjust its real exchange rate.

Figure 1. Episodes of financial stress in Belarus 2004—2016

Source: Author’s own calculations.

The first episode began in December 2008 and ended in May 2009. This episode was mainly a consequence of the global economic and financial crisis that caused a deep recession in Russia, reducing Russia’s demand for import of products from Belarus, further loss of competitiveness due to the sharp depreciation of the Russian ruble and deterioration of the current account balance and the depletion of foreign exchange reserves.

The second episode of financial stress began in December 2011 and ended in May 2012. It was caused by the renewed unbalanced macroeconomic policy aimed primarily at boosting aggregate demand by increasing government spending and accelerating economic growth; and monetary policy aimed at targeting the exchange rate. All this has led to problems in the foreign exchange market that eventually encompassed issues in the banking sector and caused a sharp reduction in foreign exchange reserves.

Financial stress and recessions

Figure 2 shows the contribution of each of the sub-indices to the increase in the BFSI.

Figure 2. The dynamics of components of BFSI during 2004-2016

Source: Author’s own calculations.

The main feature of the graph is that the currency stress is the prevailing factor in the two identified stress episodes. However, while the origins of the second episode were in the currency market, by early 2012, the stress had become much more broad based – the banking stress and the external debt stress contributed significantly to BFSI growth at the same time.

In contrast, since the beginning of 2016 until the end of the observation period, an upward movement in the BSF sub-index was detected indicating that the National Bank of Belarus (NBB) had to be worried about instability in the banking sector, which was mostly related to a loans crisis of state-owned enterprises (SOEs). A loans crisis of SOEs in Belarus means the inability of these enterprises to repay their debts and the need for budget coverage of their obligations and investments in fixed capital (see Figure 3). This happened due to a significantly higher cost of capital for SOEs after the second episode of the financial stress had begun.

Figure 3. Sources of investment financing and overdue loans of Belarusian enterprises

Source: Belstat.

Correspondingly, in the late 2016, the above problems have amplified the external debt stress (lack of external financing) in the economy of Belarus (see Figure 2).

Next, the results showed that financial stress negatively influences economic activity proxied by the index of composite leading indicators (CLI). In particular, an increase by one standard deviation (s.d.) in the BFSI leads to the contraction in the CLI index by 0.5 s.d. (see Mazol, 2017).

Moreover, financial stress has caused significant real output losses. The first episode of financial stress has resulted in the contraction of GDP by 5.9%. Second one has pushed Belarusian economy into a severe recession, which lasted 52 months with cumulative output losses about 12.9% of GDP (see Table 1).

Table 1. Descriptive statistics on episodes of financial stress and recessions in Belarus

Episodes of financial stress	Duration (months)		Output loss^a (% of GDP)	Number of months after start of financial stress to recession
Episodes of financial stress	Financial stress	Recession^b	Output loss^a (% of GDP)
December 2008 – May 2009	6	12	-5.85	0
December 2011 – May 2012	6	52	-12.89	6

Note: a) output loss is measured as GDP below trend during recession; b) a recession is occurred if there was a serious contraction in the economic activity (CLI) during six month or more. Source: Author’s own calculations.

Finally, a great reliance of Belarusian economy on external financing is associated with longer and sharper downturn in the aftermath of second episode of financial stress (see Figure 2).

Conclusion

The study has three policy implications. First, the BFSI may be considered as a comprehensive indicator that successfully determines the main episodes of financial stress in Belarusian economy and can be used to study their macroeconomic consequences.

Second, the BFSI identifies the most salient stress factors for Belarus, thereby showing which financial sectors need to be monitored carefully by national regulator to avoid a critical buildup of risks in the financial system.

Third, efforts to confine financial stress will support the country’s economic activity in the long run, which may include intervention in the foreign exchange market and build up of investor confidence in the economy.

References

Beck, Thorsten, 2014. “Finance, growth, and stability: lessons from the crisis”. Journal of Financial Stability, 10, 1-6.
Illing, Mark; and Ying Liu, 2006. “Measuring financial stress in a developed country: an application to Canada”. Journal of Financial Stability, 2, 243-265.
Levine, Ross, 2005. “Finance and growth: theory and evidence”. In: Aghion, P., Durlauf,S.N. (Eds.), Handbook of Economic Growth, vol. 1A. Elsevier, Amsterdam, 865-934.
Mazol, Aleh, 2017. “The influence of financial stress on economic activity and monetary policy in Belarus”. BEROC Working Paper Series, WP no. 40, 33 p.

Avoiding Corruption and Tax Evasion in Belarus’ Construction Sector

Posted on November 20, 2017 | Policy Brief, Policy Papers

20171119 Avoiding Corruption and Tax Evasion in Belarus Image 01

This brief summarizes our research on the problem of corruption and tax evasion in the construction sector in Belarus. We conducted a survey of construction companies, asking them to estimate the extent of different dimensions of tax evasion and corruption within the sector. The results show the most problematic directions in the sphere. Based on international experiences, we develop recommendations of how to reduce corruption and tax evasion in construction of Belarus.

Shadow economy and the construction sector

The problem of a shadow economy is real for many countries in the world. Many countries try to minimize the level of this illegal activity. But it is very difficult to liquidate tax evasion or envelope wages fully.

In Belarus there is a lot of discussion about corruption and tax evasion limitation. The country ranked 79th in the Corruption Perception Index 2016. The situation in Belarus is much better then in Russia or Kazakhstan, but worse than in Sweden, Finland and Switzerland.

There is lack of systematically updated knowledge about the situation with corruption and tax evasion in the different economic spheres of Belarus. At the same time, there are sectors, which are more prone to develop a shadow economy. One of them is the construction sector. Multilevel chains of relations between contractors and subcontractors, numerous suppliers, and complicated procedures for facility acceptance create possibilities for illegal schemes.

Construction plays an important role in national production. In 2016, the construction sector corresponded to more than 6% of Belarusian GDP. In 2014, this indicator was above 10%. The decline can be explained by a reduction of preferential lending in housing construction and a recession in the economy. Despite the reduction in the share of GDP, around 8% of the total labor force works in construction. More than 90% of the legal entities in the sphere are presented by privately owned enterprises [8].

Taking into account the importance of construction it is necessary to emphasize that reducing the size of the shadow economy could create a better business environment, reduce companies’ expenditures for resolving issues in informal ways, and increase budgetary revenues.

In this brief we present a short summary of our research “Problems of corruption and tax evasion in construction sector in Belarus”, which is forthcoming in the International Journal Entrepreneurship and Sustainability Issues. The project was made in the framework of the project “Corporate engagement in fighting corruption and tax evasion”, financed by the Nordic Council of Ministries.

Method

In order to understand the main issues and challenges in construction sector, we surveyed 50 Belarusian construction companies. We took 20 companies from Minsk and its surrounding region, and 6 organizations from each Belarusian region (Brest, Grodno, Vitebsk, Gomel, and Mogilev). The survey was based on the method used in Putnins and Sauka (2016). This method includes a questionnaire, which helps understanding the actual situation with the shadow economy in the sector. The questions of the survey were divided into three parts.

The first part included neutral questions about economic characteristics of the company, such as number of employees, profit level, the year of establishment, wage levels, and form of ownership.

The second part include more sensitive questions, but which can help us understanding the most problematic issues concerning to corruption and tax evasion. These questions concern such indicators as the level of underreported business income, the degree of underreported number of employees, the percentage of revenue that firms pay in unofficial payments to ‘get things done’, and main barriers to business development. In order to make the answers easier for participants, all the questions deal with the situation in the sector as a whole, and not the company in particular.

The third part of questions concerns the situation in public procurement, and includes the perception of main problems in the sphere.

Survey results

The first part of the survey shows that there has been a decline in many of the economic indicators during the last two years. This may be one factor stimulating the sector’s development of informal activities. Indeed the results of the second part of survey demonstrate that level of shadow economy has significant dimensions. More then 60% of the respondents agree that some firms in the sector received hidden income. More than 50% of the interviewed companies believe that some organizations in the construction sector hire part of their employees unofficially. Wages in “envelopes” is also a problem for the construction companies.

Unregistered firms are a big threat to having a well-developed construction sector. More than 60% of the interviewed companies agree with the existence of unregistered companies. Such non-official organizations create unfair competition in the sector and decrease the level of budget revenues. Many of the unregistered companies work in the sphere of home improvements and renovations.

Figure 1. Estimation of the approximate level of hidden salaries (“wages in the envelopes”) in construction industry

Notes: X-axis is the percentage of respondents that agree with the statement. Source: Results of the survey

The survey results allow us to conclude that the state budget loses part of its corporate income taxes, taxes on wages and social contributions due to the existence of hidden incomes, wages in envelopes, and unregistered companies and employees.

The last, but not the least, question in the second part of the survey was about main obstacles and barriers for operating in the construction sphere. Most of the respondents underlined three groups of barriers. One of them is the administrative challenge, including high level of taxation, inconsequent business legislation, and attitude of the government towards business in general. The second barrier includes economic problems such as lack of funds for business investments, payment behavior of clients, low product or service demand from customers, low access to credits, and inflation. The third group of problems in the construction sector is related to the shadow economy. A large part of the enterprises experiences a problem of high competition from illegal business and corruption. At the same time, a positive thing is that the majority of respondents does not consider crime and racketeering as a threat for the sector.

Figure 2. Estimation of approximate share of unregistered firms production in the total output in construction industry

Notes: The X-axis is the percentage of respondents that agree with the statement. Source: Results of the survey

In the third part of the survey, companies were asked about their participation in public procurement tenders. About 42% of all respondents did not have this experience over the past two years. One of the questions was about competition among construction companies. About 40% of all respondents underlined that they have lost at least one public tender because of unfair competition. Given that only 58% of the companies participated in tenders, we can conclude that unfair competition is a widespread problem for the majority of public procurement auction participants. Imperfect legislation is another problem for the companies. 46% of all respondents believe that the quality of legislation in the sphere is unsatisfactory. Only 12% of the companies did not see any problems in the national legislation.

At the end of the interview, companies were asked to list three main problems in the sphere of public procurement. The answers are shown in Figure 3.

Figure 3. Main problems that companies face when participating in public procurement tenders

Notes: The X-axis is the percentage of respondents that agree with the statement. Source: Results of the survey

The most common answer was corruption. Unfair competition and nepotism were also quite common problems in the public procurement sphere. Among administrative barriers, companies emphasized the complexity of documentation preparation and imperfect legislation. Important economic problems were inflation and unequal conditions for public and private enterprises.

International experiences and recommendations in fighting corruption and tax evasion in the construction sector

Corruption and tax evasion can be stimulated by different factors. One of the main preconditions of the shadow economy in the Belarusian construction sector is inconsistent and frequently changing legislation. For example, public procurements are regulated by the Presidential Decree (Ukaz) on procurement of goods (works, services) in construction. However, this regulation document expires at the end of 2018. Before 2017, such operations were regulated by several legislative acts. Developing understandable and sustainable legislation, which creates clear rules for participants of the market, is very important to increase transparency and openness of the market [11; 12; 13; 15; 18].

Another problem concerns the relations of contractors and sub-contractors. In many cases negotiations between parties are closed and non-transparent. So, it is very difficult to estimate the effectiveness of costs and proper use of funds.

Modern E-Government system adoption can support increased transparency between contractors and sub-contractors, as well as improve the quality of state services. One of the directions in this sphere is the transition towards full electronic document management. [3; 4; 6].

Another risk is related to public procurement procedure. Direct communications between public tender participants and organizers create possibilities for unfair competition. There is substantial international evidence showing that full digitalization of the process would improve the transparency of the public procurement procedure [3; 4; 21]. For example, good reference points for implementation of such digitalization can be the Georgian or Ukrainian experiences of electronic tenders. These two countries have relatively similar institutional environment and heritage as Belarus.

The problem of tax evasion is often related with payments in cash. Such transactions are less transparent and visible for authorities. According to national legislation operations between legal entities should be in cashless form. But there are exceptions to the rule [20]. In this regards the level of tax evasion would be decreased if payments in cash will be minimized.

Another concern is the efficiency of the public procurement procedures. During public procurement auctions in construction, price plays the most important role. The share of “Bid Price” criterion in total volume of all criteria can be up to 50%. The project with the lowest price has the best chance to win the tender. This is not always reasonable. Moreover, some companies hire disabled people that allow them to obtain preferential treatment in the public procurement procedure – for example, apply special correction indicators to the final price. In many cases it is better to install more expensive but high efficiency (more qualitative or ecological) equipment instead of buying cheap but low quality ones. Of course, even in EU legislation, the cost or price of projects is a very important criterion. But then it is often defined as a price-quality ratio. In this regards, the quality of the project can be estimated from the environmental, qualitative or social side [12; 19].

One more issue according to survey results is the problem of unregistered labor force in construction. It can be partly resolved by ID card implementation for all workers and employers in construction sector. In Finland, for example, all workers in construction must have such cards during workdays. Tax authorities can check the availability of the cards at any time [17].

Conclusion

Our survey of Belarusian construction companies confirmed wide exposure of the sector to tax evasion and corruption. The majority of the respondents agreed that some companies hire unregistered workers, pay wages in envelopes, or have hidden income. The most common answer to the main problems in the public procurement sphere was corruption. Based on international experience and national peculiarities, it is advisable to propose the following measures to reduce corruption and tax evasion in construction sector:

Adoption of sustainable legislation.
E-Government system development.
Modernization of the electronic tender system to require no direct contacts between organizers and tender participants.
Reduction of the possibilities of making payments in cash.
Implementation of a price-quality ratio as one of the main criteria for choosing the winner of tenders.
Introduction of ID cards for all employees and employers in the construction sector.

These and other measures are likely to significantly improve the business environment in the construction sector.

References

[1] Anderson, E. 2013. Municipal “Best Practices”: Preventing Fraud, Bribery and Corruption, International Centre for Criminal Law Reform and Criminal Justice Policy. Available on the Internet:http://icclr.law.ubc.ca/sites/icclr.law.ubc.ca/files/publications/pdfs/Municipal%20Best%20Practices%20-%20Preventing%20Fraud%2C%20Bribery%20and%20Corruption%20FINAL.pdf.

[2] Fazekas, M., Toth, I.J., King, L.P. 2013. Corruption manual for beginners: “Corruption techniques” in public procurement with examples from Hungary, Working Paper series: CRCB-WP/2013:01 Version 2.0, Budapest, Hungary. Available on the Internet: http://www.crcb.eu/wp-content/uploads/2013/12/Fazekas-Toth-King_Corruption-manual-for-beginners_v2_2013.pdf.

[3] Krasny, A. 2014. Georgia E-Government. Available on the Internet: https://www2.deloitte.com/content/dam/Deloitte/ua/Documents/public-sector/e-government/Electronic%20government%20of%20Georgia.pdf.

[4] Luzgina, A. International experience of the e-Government System development/ A. Luzgina //Journal of the Belarusian State University. Economics. – Minsk, 2017. – P.76-83.

[5] Luzgina, A., Laukkanen E., Larjavaara I., Viavode I., Volberts J. ,Corporate engagement in fighting corruption and tax evasion in construction sector”, forthcoming in “Entrepreneurship and sustainability issues”

[6] Naumov, A. 2014. Georgia E-experience for Belarus. Available on the Internet: http://e-gov.by/best-practices/elektronnyj-opyt-gruzii-dlya-belarusi.

[7] Official website of Transparency International. Available on the Internet: https://www.transparency.org/.

[8] Official website of Belarusian National Statistical Committee. Available on the Internet: http://www.belstat.gov.by.

[9] Official website of the European Commission. Available on the Internet: https://ec.europa.eu/commission/index_en.

[10] On procurements of goods (works, services) [Electronic source] // Decree of the President of the Republic of Belarus/ 20.10.2016 # 380. Rus.: О закупках товаров (работ, услуг) при строительстве, Указ Президента Республики Беларусь от 20.10.2016, №380. – Mode of access: http://www.pravo.by/document/?guid=3871&p0=P31600380.

[11] On public procurements of goods [Electronic source] // Law of the Republic of Belarus/ 13.07.2012, # 419-З. Rus.: О государственных закупках товаров, работ услуг Закон Республики Беларусь от 13 июля 2012 г. № 419-З. – Mode of access: http://www.pravo.by/document/?guid=3871&p0=h11200419&p1=2.

[12] On organization and conduct of the procurement of goods (works, services) procedures and settlements between customer and contractor in facilities construction [Electronic source] // Resolution of the Council of Ministers of the Republic of Belarus / 31.12.2014, # 88.: Rus: Об организации и проведении процедур закупок товаров (работ, услуг) и расчетах между заказчиком и подрядчиком при строительстве объектов, Постановление Совета Министров Республики Беларусь №88 от 31.12.2014. – Mode of access: http://www.pravo.by/document/?guid=3871&p0=C21400088.

[13] Putnis, J.T., Sauka, A. 2016. Shadow economy index for the Baltic countries 2009 – 2016. The Center for Sustainable Business at SSE Riga. – 47 p.

[14] Pelipas, I., Tochitskaya, I. 2016. Problems of corruption in the assessments of small and medium enterprises. Available on the Internet:

[15] Procurement in construction, what has been changed since January 1, 2017. Available on the Internet: http://www.mas.by/ru/news_ru/view/zakupki-v-stroitelstve-chto-izmenilos-s-1-janvarja-2017-goda-852/

[16] Preventing corruption in public procurements. 2016. OECD Publishing. Available on the Internet: http://www.oecd.org/gov/ethics/Corruption-in-Public-Procurement-Brochure.pdf.

[17] Briganti, F., Machalska, M., Steinmeyer, Heinz-Dietrich, Buelen, W. 2015. Social Identity cards in the European construction industry, edited by Buelen W. Available on the Internet: http://www.efbww.org/pdfs/EFBWW-FIEC%20report%20on%20social%20ID%20cards%20in%20the%20construction%20industry.pdf.

[18] Zaiats, D. 2015. The authorities will strengthen the fight against the shadow economy [Electronic resource]. – Mode of access: https://news.tut.by/economics/465337.html.

[19] On public procurement and repealing Directive 2004/18/EC [Electronic resource]// Directive 2014/24/EU of the European Parliament and of the Council / 26 Februay 2014. – Mode of access: https://news.tut.by/economics/465337.html.

[20] On making amendments and alterations to Instruction on the procedure of conducting cash transactions and the procedure of the cash settlement in Belarusian rubles on the territory of the Republic of Belarus // Resolution of the National Bank of the Republic of Belarus / 31.03.2014. #199. Rus: – О внесении дополнений и изменений в Инструкцию о порядке ведения кассовых операций и порядке расчетов наличными денежными средствами в белорусских рублях на территории Республики Беларусь. Mode of access: http://pravo.by/document/?guid=12551&p0=B21428983&p1=1&p5=0.

[21] Prozorro [Electronic source]. – Mode of access: https: //prozorro.gov.ua/en.

Fiscal Redistribution in Belarus: What Works and What Doesn’t?

Posted on September 30, 2017 | Policy Brief

Belarus proudly calls itself a social state. Indeed, Belarus boasts one of the lowest poverty and inequality levels in the region. Fiscal policy in Belarus is equalizing and pro-poor, effectively redistributing income from rich to poor. As in Russia and many other Post-Soviet states, the equalizing effect of the fiscal policy in Belarus is mostly attributable to the pension system. Some of the other social policies are highly inefficient, failing to redistribute income. The prominent examples are utility subsidies and student stipends, which mainly benefit the upper part of the income distribution. The lack of adequate unemployment benefits is an opportunity to improve the efficiency of the social support system in Belarus.

The Constitution of Belarus characterizes Belarus as a social state, and Belarus takes its social state status seriously. The economic growth in the beginning of the 2000’s was strongly pro-poor (Chubrik, 2007). Poverty according to the national definition (calorie-based poverty line, which in 2015 corresponded to $10.67 PPP per day) declined from 42% in 2000 to 5.7% in 2016, while the poverty according to the international threshold of $3.1 per day in PPP terms is fully eradicated. Belarus also has one of the lowest levels of income inequality in the region with a Gini coefficient of only 0.27 (UNDP, 2016).

How much of the pro-poor and equalizing effects could be attributed to the government policy? Probably it is impossible to give a complete answer to the question. Many non-formalized and not easily quantifiable government policies lead to the decrease in poverty and inequality. For example, the policy of support to state-owned enterprises might have redistributive effects through job creation. However, the absence of access to relevant data makes it impossible to estimate the effects of the policy.

Some of the government policies, on the other hand, are easily quantifiable with available data. Bornukova, Chubrik and Shymanovich (2017) analyze the redistributive effects of fiscal policies in Belarus using the Commitment to Equity methodology (Lustig, 2016). The authors find that the direct taxes and transfers in Belarus (taxes, transfers, and subsidies) are equalizing and pro-poor, lowering the national poverty headcount by 17 percentage points and the income Gini coefficient from 0.41 to 0.27. The high equalizing effect of the fiscal policies in Belarus surpasses those in other developing countries, including Russia where the direct taxes and subsidies reduced the income Gini coefficient by 0.13 (Lopez-Calva et al., 2017). The remaining discussion in this brief is based on the results from Bornukova, Chubrik and Shymanovich (2017), if not otherwise stated.

Fiscal policies and their redistributive effects

Taxation

The two types of direct personal taxes – the personal income tax and the social contributions tax – are both almost flat in Belarus. To fight tax evasion, the Belarusian authorities introduced flat tax rates in 2009, following a successful experiment in Russia. The personal income tax has some small exemptions for families with children, while the social contributions tax has a lower rate for agriculture employees. However, the effect of these deductions is relatively small: the direct taxes decrease the Gini coefficient by only 0.015.

The indirect taxes – the value-added tax, the import duties, and the excises – are weakly regressive, putting the burden of taxation on the poor. This is particularly true for the alcohol and tobacco excises. Again, the main purpose of these taxes is to penalize unwelcome behavior, and not to redistribute income, hence the result is not unexpected, and common for many countries. Overall the indirect taxes in Belarus increase the Gini coefficient by 0.05.

Direct transfers

Direct transfers are responsible for most of the equalizing effects of the fiscal policies. This is not surprising, given that the main purpose of the direct transfers is to fight poverty and provide support for those in need. However, most of the transfers are not need-based or targeted to the poor. Instead they are assigned to households based on their socio-economic characteristics aside income, such as age and maternity status.

Pensions are the main factor of reducing poverty and inequality. They reduced the Gini coefficient by 0.11 and decreased poverty (according to national definition) by 19 percentage points. The incredible effectiveness of the pensions is largely explained by the absence of other sources of income of the retirees. The majority of them does not work, and have no other pension savings or passive income. Pensions in Belarus are also redistributive in nature since they only weakly depend on one’s income during the working life.

Different benefits and privileges also decrease poverty and inequality, but at a much smaller scale. The childcare benefits (for families with children aged 0-3 years) contribute most to the effects, decreasing the Gini coefficient by 0.013 and poverty by 3 percentage points. The variety of privileges does not contribute much due to their relatively small size.

Subsidies

Utilities and transport subsidies are also important elements of the social support system, and their existence is usually justified by the necessity to support those in need. Since the utilities subsidies are incorporated into tariffs and available for everyone independent of need, they are in fact benefitting the rich (i.e. people with big apartments and houses).

Figure 1. Incidence of utilities subsidies by income deciles

Source: Bornukova, Chubrik and Shymanovich, 2017

As seen on Figure 1, upper deciles receive more support through utilities subsidies, and this support is quite substantial, often surpassing $1 per day in PPP. However, as a share of income the utilities subsidies are still progressive, and they in fact decrease the Gini coefficient by the tiny amount of 0.006, and decrease poverty (as any handout). The same is true for transport subsidies.

What could be improved?

Due to the flat nature of direct taxation and an absence of well-targeted needs-based transfers, some of the people in need still fall through the cracks. 1.9% of the population actually becomes poor after we account for the direct taxes and transfers. This headcount increases to 3.3% if we account for indirect taxes.

Another important issue is the efficiency of government transfers and subsidies in fighting poverty and inequality. It is not surprising that pensions have the largest equalizing contribution, as the government spends almost 11% of GDP on pensions. If we account for this fact and look at the efficiency (effect on poverty and inequality per dollar spent), pensions are not the leading program. It is in fact surpassed by different kinds of child support. Given that mothers in Belarus are allowed to take 3 years of unpaid maternity leave, which decreases household income, childcare benefits are relatively efficient.

The unexpected leader in efficiency is unemployment benefits, despite (or maybe due to) their negligible size. Shymanovich (2017) shows that unemployed face high risks of poverty, suggesting that an increase in the size of unemployment benefits and an easier access may bring huge benefits. The current minuscule size of the benefits (around $10-15 per month) is still enough to lift some people out of poverty, and has important equalizing effects, generating the biggest “bang for the buck” out of all benefits.

The student grants (stipends), the utilities subsidy and the transport subsidy have very low efficiency. These programs relocate a lot of funds to the upper deciles of the income distribution. Our calculations show that if all benefits, privileges and subsidies were not available to those in the top two income deciles, the Belarusian budget could save 1.4% of GDP.

Conclusion

Fiscal policies in Belarus are quite effective in redistributing income. Bornukova, Chubrik and Shymanovich (2017) show that the direct taxes and transfers in Belarus result in a decrease of poverty by 17 percentage points, and decrease the Gini coefficient of inequality from 0.41 to 0.27. The pension system has the most important contribution, decreasing poverty by 19 percentage points, and the Gini coefficient by 0.11.

However, the absence of a needs-based, well-targeted social support system leads to many inefficiencies. Direct and indirect taxes lead to impoverishment of 3.3% of population, which is not compensated by direct transfers.

The absence of targeting also leads to 1.4% of GDP redistributed towards the two upper income deciles through benefits, privileges and subsidies. This is, of course, highly inefficient. Better targeting could allow saving these funds or redirecting them to unemployment benefits – the most efficient but a very small benefits program so far.

References

Bornukova, Kateryna, Alexander Chubrik and Gleb Shymanovich, 2017. “Fiscal Incidence in Belarus: a Commitment to Equity Analysis”, BEROC Working Paper Series, WP no. 42
Chubrik, Alexander, 2007. “GDP Growth and Income Dynamics: Who Reaps the Benefits of Economic Growth in Belarus?” In Haiduk, K., Pelipas, I., Chubrik, A. (Eds.) Growth for All? Economy of Belarus: The Challenges Ahead; IPM research Center
Lopez-Calva, L. F., Lustig, N., Matytsin, M., Popova, D., 2017. “Who Benefits from Fiscal Redistribution in Russia?”,in The Distributional Impact of Fiscal Policy: Experience from Developing Countries, edited by Gabriela Inchauste and Nora Lustig (Washington: World Bank, forthcoming).
Gleb Shymanovich, 2017. “Poverty and Vulnerable Groups in Belarus: The Consequences of 2015-2016 Recession (in Russian)”, IPM Research Center Bulletin
UNDP, 2016. “Regional Human Development Report 2016: Progress at Risk”, United Nations Development Programme, Istanbul Regional Hub, Regional Bureau for Europe and the CIS

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Monetary Policy Puzzle in the Presence of a Negative TFP Shock and Unstable Expectations

Posted on May 29, 2017 | Policy Brief

20170528 FREE Policy Brief - Monetary Policy Puzzle Image 01

The Belarusian economy has given birth to a very interesting phenomenon of extremely high real interest rates in a prolonged recession. Despite an expected intuitive guess about the linkage between them (high interest rates cause recession), the reality turned out to be more difficult. The era of high real interest rates was due to past mistakes in economic policy, which undermined the credibility of the latter and gave rise to high and volatile inflation expectations. However, the adverse output path following the too high interest rates was not essential. The recession was mainly predetermined by a negative Total Factor Productivity (TFP) shock. The shock itself forms a disagreeable and contradictive environment for monetary policy. Together with unanchored inflation expectations, this makes monetary policy ineffective and too risky.

Unusually high real rates and recession

Since the painful currency crisis of 2011, the Belarusian monetary environment has become extremely vulnerable in many respects. In 2011 and early 2012, the country faced (once again) a 3-digit inflation rate. While the inflation rate later went down gradually, it was not sufficient to enhance monetary stability in a broader sense. For instance, for nominal interest rates, the level of 20% per annum was an unachievable lower bound until 2016. Moreover, in 2013—2016, upside jumps in the nominal interest rates took place regularly (see Figure 1).

Figure 1.Nominal interest and inflation rates, % per annum

Source: Belstat. Note: Inflation rate is calculated on average basis for last three months on a seasonally adjusted basis and then annualized

Such combination of nominal interest and inflation rates has resulted in an extremely high and volatile level of real interest rates throughout the last 4 years. Real returns at the Belarusian financial market fluctuated in 2013—2016 within the range of 10-30% per annum. For instance, a median (monthly) value of the real interest rate on new loans in 2013—2016 was 17.6% per annum (in the beginning of 2017 it approached the level of 8-10% per annum). So, one may say that the real monetary conditions have been extremely tight in the last couple of years.

At the same time, in 2015—2016 Belarus has dipped into a prolonged and deep recession. During the last two years, the country has lost roughly 7% of its output. The combination of high real interest rates and a recession gave rise to a naive, but acceptable diagnosis: the excessively high interest rates caused (or at least contributed to) the recession. This view became popular in the domestic policy discussions. Furthermore, often this story transformed into a claim that ‘too tight monetary policy causes (or at least contributes to) recession’. Given this pressure, the National bank of Belarus (NBB) became accustomed to justifying its policy stance by considerations of financial stability given financial fragility. So, the economic policy discussion got into the discourse of these two extremes. Finally, it boiled down to the question whether ‘the monetary environment has stabilized enough in order to soften monetary policy’.

However, a naive story about the stance of monetary policy and the business cycle is not (fully) true in the case of Belarus in several respects.

Unanchored expectations drive interest rates

First, high interest rates at the financial market were not because of the excessively high policy rate of the NBB. It happened due to volatile, but still persistently high inflation expectations (Kruk 2017, 2016a). The latter visualized the loss of monetary-policy credibility by the general public.

Before 2016, the level of inflation expectations was persistently higher than the actual inflation, demonstrating an extremely slow (if any) convergence (see Figure 2). At the same time, the ex-ante level of real returns has remained relatively stable. When setting its policy rate, the NBB has taken into consideration existing inflation expectations, otherwise the high expected inflation would have been realized.

Figure 2. Actual and expected inflation, %

Note: Expected inflation has been estimated according to the methodology in Kruk (2016a).

So, in the recent past, the stance of the monetary policy could hardly be accused of generating too tight monetary conditions through the setting of an improper policy rate. The problem was (is) more severe, and one can argue about the inability (and the lack of willingness) of the NBB to anchor inflation expectations.

However, in the late 2016 and early 2017, the expected and actual inflation rates converged, mainly due to a contraction of the former. This introduced more stability into the monetary environment, in a broader sense. Kruk (2017, 2016a) shows that the turn of 2016—2017 has become a breakpoint for the monetary environment to return into a ‘normal’ stance (see Figure 3).

The NBB reacted to the milder monetary environment by a number of reductions in the policy rate (from 18% since August 2016 down to 14% since April 2017). However, a shift of both expected and actual inflation into the range between 5% and 9% may be interpreted as there being room for further reductions.

Figure 3. Classification of monetary environment stance in Belarus, probability estimates

Note: Classification and the methodology for estimates are based on Kruk (2016a). ‘Normal’ regime is characterized by reasonable and relatively stable real interest rates; ‘subnormal’ – too high real interest rate due to ‘inflation expectations premium’; ‘abnormal’ extremely volatile and mainly huge negative real interest rates due to the swings of actual inflation.

Therefore, as of today, one may argue that the long-expected time for a softening of the monetary policy has come, as the ‘expectations overhang’ has disappeared. However, such a view might be too optimistic. Kruk (2017) argues that the convergence of expected and actual inflation rates might be a temporary lucky combination, as there is a lack of evidence supporting a growing credibility of monetary policy among the general public. On the contrary, inflation expectations seem to have shrunk due to a depressed domestic demand and lower consumer confidence. So, even if expectations have contracted, they have not been anchored. Hence, ‘the expectations overhang’ may resurge at any time.

Monetary softening cannot neutralize structural recession

Even if we assume that the ‘expectations overhang’ has disappeared, it would still not mean that there is room for a new monetary stimuli. A naive story about high real interest rates that cause recession glitches once again when interpreting this linkage. Most frequently, countries face a cyclical recession (i.e. caused by temporary demand fluctuations). If that is the case, a negative impact of excessively high interest rates on output path is taken for granted.

However, the Belarusian story of recession is different. Kruk and Bornukova (2014) have shown that the country faced a negative TFP shock, which determined the weakening of the long-term growth rate. Kruk (2016b) shows that due to this shock, the long-term growth rate crossed the zero level approximately at the turn of 2014—2015, and dipped into a negative range later on. Hence, the Belarusian recession that started in 2015 was a combination of a negative contribution from both the long-term dynamics and the business cycle. Furthermore, since the second half of 2016, the negative contribution of the business cycle has faded out, and the recession was determined by the negative TFP shock almost solely (Kruk, 2017) so that, by 2017, the recession has become a purely structural phenomena.

From a monetary policy stance, this gives rise to a new challenge. Although the majority of methodologies still assess the output gap to be negative (but not far away from zero), the output gap will soon be closed automatically because of continuing negative TFP shocks (Kruk, 2017). In a sense, the negative TFP shock contributes to the closing of the output gap in the same way as monetary policy does. However, it does this job in an opposite manner (i.e. by squeezing the trend growth, and not by stimulating the business cycle), it leaves almost no room for monetary policy. It creates a situation where a reasonable loosening of the monetary policy may immediately turn into an excessive one. Taking into account that the dormant inflation expectations can resurge, monetary policy decisions resembles walking on the edge.

Conclusions

Today’s policy discussion in Belarus is extensively concentrated around the search for the best monetary policy to fight the recession. However, this formulation of the problem is a mistake in itself. Today’s contradictions in monetary policy are simply a reflection of the bulk of accumulated structural weaknesses in the economy. Today, monetary policy can hardly do anything to stabilize output. The solutions for ending the recession, and enhancing growth should be found in structural policies, not in the sphere of monetary policy. As for monetary policy, it can, at this moment, hardly contribute to output stabilization (without challenging price stability). To do so, it has to ensure an anchoring of the inflation expectations first.

References

Kruk, D. (2017). Monetary Policy and Financial Stability in Belarus: Current Stance, Challenges, and Perspectives (in Russian), BEROC Policy Paper Series, PP No.43.
Kruk, D. (2016a). SVAR Approach for Extracting Inflation Expectations Given Severe Mnonetary Shocks: Evidence from Belarus, BEROC Working Paper Series, WP No. 39
Kruk, D. (2016b). The Reasons and Characteristics of Recessiion in Belarus: the Role of Structural Factors (in Russian), BEROC Policy Paper Series, PP No. 42.
Kruk, D., Bornukova,K. (2014). Belarusian Economic Growth Decomposition, BEROC Working Paper Series, WP no. 24.

Trade Preferences Removal – The Case of Belarus

Posted on March 27, 2017 | Policy Brief

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How does the removal of trade preferences influence the exports of the affected country? We study this question on the example of Belarus’ loss of trade preferences granted by the EU to developing countries. Our brief argues that trade preferences are most important for simple non-manufactured goods. As a result, removal of trade preferences should increase the manufactured goods in the export structure. Indeed, the overall complexity of Belarusian exports was not harmed by the removal of EU preferences and the manufactured exports increased relative to non-manufactured exports.

Belarus losing trade preferences

As a developing country, Belarus used to receive trade preferences from the US and EU. These preferences grant duty-free imports or provide a discount on the import tariff under the so-called Generalized System of Preferences (GSP). The preferences are provided on a unilateral basis to developing countries and can also be removed on a unilateral basis for various reasons. Their stated objective is to support the economic development of poorer countries (Ornelas 2016). In particular, the US removed Belarus’ preferences in 2000 for worker rights violations. Later, the EU removed the preferences in 2007 for similar reasons. It is a relevant question for policy to understand how the removal of trade preferences affected exports.

This brief discusses the effect of trade preferences removal on the value of Belarus’ exports to the EU and on the structure of exports. Utilization of trade preferences might not be uniform across sectors. In fact, a preference-receiving country should satisfy the Rules of Origin (ROO) requirements and demonstrate that a large enough share of the exported product was produced in the country. This requirement might be more difficult to satisfy for complex manufactured goods with many components from several countries (Hakobyan 2015). Exporters of such products might find satisfying the ROO more costly than what they could gain from receiving an import tariff preference. Exporters of simple or raw products, on the other hand, face a lower cost of demonstrating the origin.

The remainder of the brief develops the hypothesis of a differential impact of trade preferences removal on manufactured and non-manufactured goods; and makes an event study of Belarus’ loss of EU trade preferences in 2007. Our findings suggest that GSP withdrawal affected disproportionally non-manufactured exports, leading to an increase in the manufacturing exports share. This means that harm caused by losing trade preferences was, to some extent, reduced by higher incentives to export more complex manufactured exports.

The complexity of Belarusian exports

To understand the overall structure of Belarusian exports, we first look at the complexity of Belarusian exports over time. Figure 1 presents the economic complexity index (ECI), developed by Hausmann et al. (2014), of exports of Belarus relative to Russia from 1995 to 2014. The ECI measures the diversity and ubiquity of a country’s exports. It considers the number of products a country exports with revealed comparative advantages and how complex these products are. In turn, the complexity of the products is accessed by a so-called product complexity index, PCI. It is determined in an analogous fashion: if few countries are able to export a good and these countries have diversified exports, this product is complex. For example, fertilizers and oil (important exports of Belarus) have low complexity scores, as countries that export these products tend to not have diversified exports.

Figure 1 shows that the difference between the economic complexity of Belarus and Russia increased following the two incidents of Belarus losing trade preferences; first from the US and then from the EU. The incidents of removal of trade preferences are associated with an increase in economic complexity of Belarusian exports relative to Russia. That is, the export of more complex manufactured goods became more important in the export basket of Belarus when it lost the trade preferences. This is consistent with the hypothesis that trade preferences are more important for simpler goods, and following a preference removal their share will go down. Russia is chosen for comparison due to its similarity in economic perspective (economies in transition, similar complexity, GDP trends, dependence on oil and fertilizer prices) and because it also received trade preferences from both the US and EU throughout the considered period.

Figure 1. GSP withdrawal and Export Complexity in Belarus relative to Russia

Note: the figure presents the ECI of Belarus over ECI of Russia in logarithmic form. Source: Authors’ calculations using the ECI data from the Observatory of Economic Complexity.

Export structure of Belarus

To make a first pass at understanding how GSP withdrawal affects the composition of exports, we conduct an event study centered on the year of 2007, when the EU withdrew its GSP preferences for Belarus. We consider the three years before and after the revocation, and benchmark the share of manufacturing exports from Belarus to the EU with its share of manufacturing exports to the US. Since the US had already withdrawn its preferences earlier, its trade regime with Belarus stayed unchanged throughout the period. This makes the US a natural point of comparison to understand the effect of GSP withdrawal.

Findings

As Figure 2 shows, the average share of manufactured products in Belarusian exports to the EU increased slightly after the GSP withdrawal, increasing to 40.4% from its earlier level of 37.9%. At the same time, mineral and fuel exports, though falling slightly, remain the backbone of Belarusian exports accounting for 50% of total exports to Europe. Interestingly, the share of non-fuel exports to the EU remained approximately unchanged at 9%. In other words, the composition of exports to Europe did not drastically change after the GSP withdrawal, as had been anticipated by some ex-ante studies (e.g. BISS 2007).

This comparison alone does not address the question of what might have happened to Belarusian manufacturing exports had the GSP preference not been removed. One possible counterfactual is that the trends in the European export market would have been the same as in the US, where Belarusian manufacturing exports massively lost ground. Their share decreased from 53.4% to 19.3%. Hence, a difference-in-difference estimator would suggest that perhaps the withdrawal of the GSP reduced non-manufacturing export growth to Europe. In turn, the Belarusian manufacturing export share is estimated to be 36.5% higher than it might have been if the GSP had not been withdrawn (statistically significant at the 1% level). This estimate may be a result of trade diversion of non-manufactured goods from the EU to the US. To the extent that non-manufacturing products benefit more from the GSP preferences, these should be stronger affected by trade diversion and would therefore reduce the manufacturing share of Belarus’ exports to the US.

Figure 2. Share of Manufacturing Exports

Note: Manufacturing includes sectors 5, 6, 7 and 8 according to the SITC classification. Source: Authors’ calculations using data from the UN COMTRADE.

Alternatively, one could consider the Belarusian manufacturing export share in relation to Russia, within the European market. For Russia, there is a pattern of declining manufacturing shares. Before 2007, manufacturing accounted for 17.7% of exports to the EU, but afterwards it declined to 14.2%, a 2.5% fall. If Belarus had experienced the same trend, its manufacturing share would have fallen from 37.9% to 34.4%. Instead, Belarusian manufacturing share grew from 37.9 to 40.4%, which suggests that due to the GSP removal, the Belarusian manufacturing export increased by 6%. Given the smaller effect size and the short sample period, this increase is not statistically significant. However, in economic terms, it would still be an important shift.

Conclusion

Although development is one of the main goals of the GSP, there is little evidence that the EU’s Generalized Scheme of Preferences supported the development of advanced industries in Belarus. To the contrary, after the GSP withdrawal the export complexity of Belarus increased relative to that of Russia. There is also some suggestive evidence that the GSP may have encouraged an export profile more focused on non-manufactured products, for which rules of origin are easier to satisfy in practice. More research is clearly needed, not least to analyze other cases of GSP withdrawal for external validity.

Our preliminary findings suggest that GSP in its current form might have created incentives for exporting relatively simple goods, thus creating a risk of “middle-income trap”. Policy implications are twofold: First, the goal of preference programmes like the GSP is development, i.e. more advanced economy with more complex production, and if the preferences in fact foster simple exports, it could create a barrier to development; Second, removal of preferences might have a large negative impact overall but the observation that it removes the previous incentive of producing simple non-manufacturing goods can be seen as positive and thus cushion the negative impact.

References

Belarusian Institute for Strategic Studies (BISS), 2007. “Belarus exclusion from the GSP: possible economic repercussions”, at: http://www.belinstitute.eu.
Hakobyan, Shushanik, 2015. “Accounting for underutilization of trade preference programs: The US generalized system of preferences.” Canadian Journal of Economics/Revue canadienne d’économique, 48.2, 408-436.
Hausmann, Ricardo; Hidalgo, Cesar A., Bustos, Sebastian; Coscia, Michele, Simoes, Alexander, & Yildirim, Muhammed A. (2014). The atlas of economic complexity: Mapping paths to prosperity. Mit Press.
Ornelas, Emanuell, 2016. “Special and differential treatment for developing countries.” Handbook of Commercial Policy 1, 369-432.ilable online, please hyperlink the title.

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