Tag: Belarus

Did the Government Help Belarusian SMEs to Survive in 2020?

Posted on June 14, 2021 | Policy Brief

Enterprises During Pandemic representing Belarus

Capitalizing on the dataset obtained from five waves of the Covideconomy Project business survey, we explore how pandemic-related shocks and state economic policy responses influenced the performance of Belarusian small and medium enterprises (SMEs) in 2020. We find that Belarusian SMEs were left on their own with the COVID-related economic challenges, and only a small portion of enterprises could benefit from state support measures. Only two sectors (Manufacturing and Construction) derived advantages from soft loans provided to state-owned enterprises. The implementation of new, pandemic-adjusted business models did not result in an increase of revenues of Belarusian SMEs, at least not in the short run.

Small and Medium Enterprises During the Pandemic

According to OECD estimates (2020), the small and medium-sized enterprise (SME) sector has been more affected by the COVID-19 pandemic compared to large enterprises. Besides being highly concentrated in the most affected sectors, the main reasons for SMEs experiencing stronger COVID-related shocks are a lower level of cash cushion and limited access to external funds (Goodhart et al., 2021). Next, the stock of supplies and materials, as well as the range of suppliers, are usually lower for SMEs (WTO, 2020). This makes any price changes or abruptions more detrimental for them in comparison to large companies. Lastly, the availability of digital technologies and skills needed to implement new business formats appeared as an additional constraint for the SME sector during the pandemic. Indeed, per the World Bank’s business surveys, the most frequently mentioned effects of COVID-19 on SMEs in Central and Eastern European countries were a drop in sales, liquidity problems, limited access to finance, and breakdowns in supply. In this context, only 35% of SMEs in the region were able to adapt quickly to new conditions by introducing new business models such as online sales, delivery services, and remote work. At the same time, many SMEs in the region laid off employees, reduced wages, or initiated furloughs as alternatives to closing the business altogether.

In this regard, the SME support measures became an extremely important task for national governments to conduce to faster economic recovery and job creation. As a result, a wide range of monetary and non-monetary measures was implemented in various countries to support SMEs.

Internationally, direct support was provided in the form of wage subsidies, cash grants and transfers, tax holidays, reductions, or deferrals that could prevent unemployment growth. In addition, liquidity problems of SMEs were addressed by introducing rental fee deferral or reduction, repayment holidays as well as providing micro and short-term loans.

In many countries, specific measures were aimed to support the digitalization of SMEs (e.g., in China, France, Latvia, Italy, Slovenia, South Korea) by offering subsidies, financial support, training, and consulting services, developing e-commerce sales channels to respond to pandemic-related challenges (OECD, 2020).

Figure 1 demonstrates shares of SMEs in Central and Eastern European countries that benefitted from state support measures and SMEs’ perceived importance of these measures. Wage subsidies (65.1%) and direct cash transfers and grants (47.1%) appeared as the most commonly used measures, while fiscal exemption and reductions were regarded as the most important and relevant ones.

Concurrently, at the macro level, some governments eased requirements on banks’ emergency funds and reduced base rates to provide more and cheaper financial resources as loans for the enterprise sector.

Figure 1: Scope and importance of SME support measures

Source: World Bank data on Belarus, Russia, Poland, Estonia, Latvia, Lithuania, Georgia, Moldova, Slovakia, Czech Republic, Bulgaria, Romania, Hungary.

In general, the scope and target groups of the support programs depended on financial resources at the disposal of governments, access to capital markets, macroeconomic conditions (public debt, exchange rates, unemployment rates), as well as the structure of the economy.

In this brief, we discuss how the macroeconomic environment and the Belarusian government’s policy reaction to the pandemic affected revenues of Belarusian SMEs in 2020.

The Belarusian Economy in 2020

The official statistics reported outstanding results of the Belarusian economy, despite it being expected to be hit harder than other countries in the region. The COVID-19 pandemic-related shocks were aggravated in Belarus by endemic ones: the early-2020 oil-supply dispute with Russia, the sociopolitical crisis that broke out after the presidential elections in August (Bornukova et al., 2021), and the concomitant sharp devaluation of the Belarusian ruble (22.59% to US dollar in 2020) in March and August. Against this backdrop, the 0.9% decrease in GDP, 4.6% increase in real disposable incomes, and stable unemployment rate (at 4.0%) together look like an economic miracle. Some of the rationales behind these figures include the absence of lockdowns and substantial mobility restrictions throughout the year, as well as easy access to bank loans for state-owned enterprises (SOEs) that faced an export shock. At the same time, ad-hoc sampled population and business surveys documented income reductions of Belarusians and a substantial decrease in business revenues in many sectors (Covideconomy project, 2021). Figure 2 displays the shares of SMEs in different sectors whose revenues dropped by more than 20% in the month before being surveyed.

Figure 2. Share of SMEs with loss of revenue >20%  

Source: Own elaboration based on five ways of business surveys

The Belarusian government was substantially restricted in terms of financial resources as well as fiscal and external loan opportunities to extensively support businesses suffering from the COVID-related economic crisis. According to experts’ estimations, Belarus lags behind other Eurasian Economic Union members (Russia, Armenia, Kazakhstan, Kyrgyzstan) in terms of the estimated share of GDP spent on crisis response measures – 1.5% (Russian Academy of Foreign Trade & Research Institute of VEB, 2020). While the most suffering sectors (trade, transportation, hotels, restaurants, tourism, education, leisure, sport, etc.) could benefit from the deferral of profit, real estate and land taxes, as well as rental fees till the end of 2020, obtaining any type of support appeared bureaucratically challenging and imposed exigent obligations for the future. Overall, the support was perceived as negligible and far below expectations both in terms of financial resources saved by businesses and coverage. Thus, in May-October 2020, about 50 thousand businesses (incl. sole proprietors) received cumulative support for a total amount of $26 Million or $536 per business (National Center of Legal Information of the Republic of Belarus, 2020). According to the Covideconomy project, in May-July, less than 5% of SMEs reported getting support from the state.

What Affected Belarusian SMEs?

Motivated by the specific reaction of the Belarusian government and its very limited support to SMEs, we explore what enterprise- and country-level factors affected SME revenues across industries during the pandemic. In pursuit of this objective, we use data obtained from five waves of the business survey conducted within the Covideconomy project (2020) on 359 SMEs amounting to 947 observations, and perform a regression analysis with a set of ordered logistic models. Particularly, we test whether the (i) self-isolation of population, (ii) currency devaluation, (iii) volume of loans provided to SOEs, and (iv) new business models implemented by Belarusian SMEs impacted their revenues.

These hypotheses are based on the following arguments:

  1. In the absence of restrictive measures and lockdowns, entrepreneurs and citizens made conscious decisions about self-isolation and remote work. To minimize personal contact, many people reduced the number of visits to public places as well as various group activities. Such responsible behavior could hurt business income, primarily in the areas of catering, hotels, entertainment, transport, and consumer services, in which SMEs are widely represented.
  2. The sharp devaluation of the Belarusian ruble is, and has traditionally been, a significant problem for Belarusian businesses. The rise in prices of imported goods and services, inflation, and the fall in household incomes in dollar terms harm domestic demand, leading to a drop in sales in many sectors. The exceptions could be export-oriented enterprises, which mostly use materials and supplies produced in Belarus, as well as enterprises that are suppliers and contractors of exporters.
  3. To minimize the impact of the pandemic-related shocks, the Belarusian government continued its habitual practice of providing soft loans for SOEs to maintain their production volumes and pay wages. Arguably, this could bolster demand for SMEs’ goods and services from the side of SOEs’ employees and prevent a deeper recession. In addition, SMEs that were suppliers and contractors of SOEs could also benefit from this policy measure.
  4. The pandemic significantly accelerated SMEs’ processes of finding and realizing opportunities to develop. This became key in the survival of many businesses. We thus expect that the implementation of new business models could have had a positive impact on revenues of SMEs.

In our models, we use the size of SMEs, location in the capital city, and whether a firm belongs to one of the most suffering sectors (HoReCa, Transportation, Leisure & Sport) as control variables. To capture the effect of factors across different sectors, we use interaction terms between the aforementioned factors and dummies indicating different sectors.

The results of the regression analysis (summarized in a stylized way in Table 1) demonstrate that the impact of the selected factors is not consistent across sectors and that none of the factors appear significant when considering the entire sample of SMEs.

Table 1. Impact on SMEs’ revenues

Source: Own estimates based on 947 observations from 359 SMEs.

Not surprisingly, self-isolation behavior negatively affects only the HoReCa and Leisure & Sports sectors. Currency devaluation does not significantly influence the revenues of SMEs. Only the ICT sector, which is export-oriented and does not depend on imported materials, easily adapted to remote work and increased demand for IT-related services and experienced a positive shock. The state policy that provided soft loans to SOEs helped SMEs in the manufacturing and construction sectors that are, supposedly, contractors and suppliers of SOEs. The implementation of new business models did not result in an increase in the revenues of Belarusian SMEs, at least not in the short run. A possible explanation for this finding could be that firms responded by adopting new business models only if they experienced a very steep fall in revenues.

As for the control variables, we find that larger enterprises better adapted to the crisis and their decrease in sales appear smaller. Interestingly, SMEs located in the capital city –  Minsk – suffered more from the crisis in 2020, likely, due to a higher concentration of SMEs in the most affected sectors and a quicker reaction of citizens to economic and political shocks.

Conclusion

Based on our analysis, we can deduce that Belarusian SMEs were left on their own with the COVID-related economic challenges. Only a small share of enterprises could benefit from the state support measures and only two sectors (Manufacturing and Construction) derived advantages from soft loans provided to SOEs.

At the same time, the absence of lockdowns and other restrictions – the laissez-faire approach (Bornukova et al., 2021) – propped up most of the sectors except those that suffered from voluntary self-isolation of customers (HoReCa, Leisure, Sport, Beauty).

The ongoing crisis substantially changes the economic landscape, management practices, and business models of SMEs. The most flexible, competitive, and proactive businesses have been capable of identifying and exploiting the emerged opportunities. From this point of view, Belarusian businesses and entrepreneurs have outstanding experience in surviving and developing during recurrent crises (Marozau et al., 2020). This must be an important pre-condition for the future economic recovery of Belarus.

References

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

Laissez-faire Covid-19: Economic Consequences in Belarus

Posted on March 15, 2021 | Policy Brief

20210301 Addressing the COVID-19 Pandemic FREE Network Policy Brief Image 04

Despite its traditional paternalistic role, the Belarusian government chose minimal reaction to the Covid-19 pandemic. No meaningful economic or social measures were taken in response to the pandemic. We explore a unique dataset to document how major Covid-related shocks affected the earnings of Belarusians in 2020. We utilize the differential timing and sectoral effects of the shocks to identify the impact of Covid-19 on individual socioeconomic outcomes. Not surprisingly, we find that Covid-related shocks increase the probability of an income reduction. This effect is most pronounced for those employed in the private sector. In the absence of a social security net, vulnerable groups had to cope with the economic consequences of the pandemic on their own.

Introduction

Belarus had its first official case of Covid-19 registered on February 27 and its first death on March 31. At first, the increase in newly registered cases was slower than in most other countries, but at the beginning of April Belarus started to catch up. The peak of the first wave was recorded on May 18 with 943 new daily cases. According to the official statistics, the second wave started in September 2020 and was much more severe than the first one, reaching 1,890 new daily cases by the end of December.

Belarusian authorities did not undertake any substantial interventions, such as lockdowns, to fight the spread of the pandemic.  Nevertheless, there were several other key mechanisms through which Covid-19 affected the Belarusian economy. The population’s reaction to the risks of contamination led to a substantial fall in mobility that resulted in decreased sales in retail and services requiring physical interaction. For example, sales in the restaurant industry decreased by 20% in 2020. Lockdowns in major international trade partners such as Russia have led to a decrease in demand for Belarusian exports of goods and transportation services. In the face of these economic challenges, the government focused its attention on supporting full employment and production in state-owned enterprises while ignoring the rest of the economy.

In this brief, we present evidence of the economic effects of Covid-19 in Belarus. We employ a unique dataset on socioeconomic outcomes collected by BEROC to study how individuals are affected by Covid-related shocks in mobility and exports. In order to isolate the effects of these shocks on the well-being of Belarusians, we exploit their timing and sectoral differences.

Measuring Covid-related Shocks

Figure 1 depicts changes in the Yandex self-isolation index which measures the use of Yandex services, including Yandex traffic monitoring and customer mobility compared to the average pre-pandemic day (Yandex DataLens, 2021). Individual everyday mobility started to decline in mid-March, and as the first wave of the pandemic gained momentum, mobility reached its lowest point at the end of April. It started to decline again in November-December 2020 following the second wave.

Figure 1. Yandex self-isolation index in Belarus, 2020

Source: Yandex. The average value during 24 Feb-8 March 2020 set to 100. Seven-day rolling average.

Belarus is a small and open economy with Russia as its main trading partner. The lockdown in Russia that lasted from the end of March until mid-May along with the spring lockdowns in Europe caused a major contraction in external demand for Belarusian goods. Figure 2 shows total physical exports and non-energy physical exports in 2020. The largest difference between total and non-energy exports can be observed in January, February, and March during which Russia and Belarus had an oil-supply dispute. To focus on the effects of the pandemic we use non-energy physical exports to approximate Covid-related exogenous shocks to the economy.

Figure 2. Physical export indices, Belarus

Source: Belstat. December 2019=100.

Income Dynamics

To measure the impact of Covid-19 on Belarusian society, BEROC, in cooperation with the marketing and opinion research company SATIO, conducted a series of online surveys representative of the urban population of Belarus (Covidonomics, 2021). The five waves of the 2020 survey were carried out on April 17-22, May 8-11, June 8-15, September 11-16, and November 25-30.

Respondents were asked about recent changes to their income, and also to specify the reasons for income reduction (if this was the case), including depreciation of the ruble, salary cut, furlough, etc.  Figure 3 depicts the percentage of individuals who reported an income reduction in the previous month for reasons other than currency depreciation by sector of employment. The income reductions peaked in April-June, with the situation relatively stabilizing by September.

Figure 3. Income dynamics by sector

Percentage of respondents reporting income reductions in the previous month for reasons other than currency depreciation, Source: BEROC/SATIO data

The fact that the share of respondents reporting termination peaked at 2.9% in May indicates that firms did not use employment reduction to adapt to the pandemic environment. A big share of respondents employed in the service sector reported domestic demand contraction (fewer orders/clients) as a key factor for their income reduction. The industries that took the hardest hit were hospitality-retail and transportation. In early spring, manufacturing appeared to be one of the most affected industries. However, as exports started to recover in June, the share of manufacturing workers that reported an income reduction decreased significantly, becoming one of the lowest across industries.

Identifying the Effects of Covid-19 Shocks

In this section, we estimate the probability of facing a reduction in individual income as well as the likelihood of being furloughed due to the Covid-19 pandemic.

In 2020, the Belarusian economy suffered due to the oil-supply dispute with Russia, the Covid-19 pandemic, and the national political crisis. To isolate the effects of Covid-19 from those driven by the oil dispute and the political crisis, we add interactions between Covid-related shocks and dummies indicating industries affected by those shocks. This implies three interactions with different binary indicators: exports and manufacturing, exports and transportation, and mobility and hospitality/retail.

To estimate these effects, we use a fixed-effects probit regression controlling for sector of employment, education, age, and gender.

Table 1. Probability of income reduction and furlough

Source: Own estimates from BEROC/Satio data. Controls include age, sector of employment, and education level.

Table 1 shows that individuals employed in the hospitality and retail industry face higher risks of an income reduction due to decreased mobility caused by self-isolation behavior. A 10-percentage-point increase in the self-isolation index is associated with a 1.3 percentage point increase in the probability of income reduction for those employed in the retail and hospitality industry. The interaction term between exports and the manufacturing dummy also appears to be statistically significant for various specifications. A 10-percentage-point decline in physical volumes of exports is associated with a 8.6 percentage point increase in the probability of income reduction for manufacturing workers.

Notably, the private sector employment coefficient shows strong statistical significance which highlights the choice of the authorities to support SOEs, with little to no support for the private sector. Being employed in the private sector increases the probability of facing an income reduction by 7.9 percentage points.

The Gender Dimension

Despite concerns that women experience larger economic losses due to consequences of the pandemic (Dang and Nguyen, 2021; Alon et al., 2020b), we do not find a statistically significant effect of gender in our sample.  In particular, our results offer no evidence of women being more likely to experience an income reduction during the pandemic, similar to findings in Germany (Adams-Prassl et al. 2020c).

While job losses were uncommon during the Covid-19 crisis in Belarus, being furloughed was one of the most common reasons for an income reduction (11.3% of respondents reported being furloughed in May). We also investigate the separate channels through which individuals lose income due to the Covid-related shocks. Notably, the only channel of income reduction that is more prevalent among women than men is through furlough. This finding is consistent with Adams-Prasslet al. (2020a) who argue that this discrepancy can be explained by gender differences in childcare responsibilities.

Conclusion

Belarus is close to unique in having almost no government response to the Covid-19 pandemic. Despite the absence of lockdowns and other restrictions, the Belarusian economy has experienced several Covid-associated shocks. Due to the economy’s openness to trade, it was seriously affected by export contractions. Belarusians have voluntarily reduced their mobility to minimize health risks which has affected the hospitality and retail industry.

We utilize the differential timing and sectoral impact of Covid-related shocks to estimate the pandemic’s effect on the socioeconomic outcomes of individuals. By using a unique dataset, we find evidence that the pandemic increased the likelihood of income reductions for Belarusians, mainly due to the effects of decreased mobility and fall in exports. We also find that those employed in the private sector were more likely to suffer from negative shocks, reflecting the policy choice of the Belarusian government to only provide economic support to the state sector. Finally, we show that, while women are as likely as men to see their income reduced, they are significantly more likely to be furloughed.

Many Belarusians saw their well-being deteriorating as a result of the Covid-19 pandemic. In the absence of unemployment benefits and other social protection mechanisms (Umapathi, 2020), those economically affected had to bear the cost of the shocks on their own.

References

  • Adams-Prassl, A., Boneva, T., Golin, M., and Rauh, C. (2020a). Furloughing. Fiscal Studies, 41(3):591–622.
  • Adams-Prassl, A., Boneva, T., Golin, M., and Rauh, C. (2020b).  Inequality in the impact of the coronavirus shock:  Evidence from real time surveys. Journal of Public Economics, 189:104245.
  • Covidonomics project (2020). BEROC and Satio. http://covideconomy.by/
  • Dang, H.-A. H. and Nguyen, C. V. (2021). Gender inequality during the Covid-19 pandemic: Income, expenditure, savings, and job loss. World Development, 140:105296.
  • Umapathi, N. (2020). Social protection system in Belarus:  perspective. Bankovskiy Vestnik, (3):75–80.  (in Russian).
  • Yandex (2021) Yandex DataLens, https://datalens.yandex.ru/

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.

Circular Economy in Belarus: What Hinders the Transformation?

Posted on December 14, 2020 | Policy Brief

20201214 Circular Economy in Belarus FREE Network Policy Brief Image 02

The transition towards a circular economy has accelerated in response to increasing environmental challenges and the need for more sustainable and cleaner production. Many countries are mainstreaming a circular economy into their policy agenda. In particular, the European Commission’s new Circular Economy Action Plan, adopted in March 2020, will be a key element of the EU Industrial strategy. In Belarus, similar policy agendas that promote circular economy have not been developed yet, however, this concept is now attracting increasingly more attention. Therefore, it is essential to identify barriers that hamper the implementation of circular economy business practices in the country. This policy brief presents the results of a survey that studied 452 Belarusian companies and their prospects and opportunities of circular transformation both within enterprises and at the national level. The findings show that high levels of capital and technology spending and lack of state-provided economic incentives are the most pressing barriers to circular economy development in Belarus. When it comes to enterprises’ own prospects for circular transformation, lack of funding is ranked as the main impediment.

Barriers to Circular Economy Development in Belarus

Despite the fact that there has been an increased interest in the circular economy, evidence suggests that its implementation has been hampered by a variety of barriers. Based on academic literature and business case studies, these barriers can be categorized into several groups (Rizos, et al., 2015; Rizos, et al., 2016; Kirchherr et al., 2018; Ritzén and Sandström, 2017):

  • Cultural barriers (e.g. social, behavioral, and managerial) – a lack of interest, environmental awareness, and/or existing differences in personal values, which hinder the development of a circular economy.
  • Information constraints – a lack of consumer and producer awareness about the key principles and best practices of circular economy implementation;
  • Inadequate regulatory environment – a lack of consistent legal framework, policy support, and incentives for circular economy transition (e.g., through tax relief, fiscal measures, or public procurement);
  • Technological barriers – an absence of a well-managed logistic infrastructure for the collection, extraction, and processing of secondary raw materials (SRM); the lack of standardization and, as a result, lower quality of goods produced from SRM; the absence of knowledge on how circularity can be implemented in a particular industry;
  • Economic impediments – barriers to circular economy transition that are due to low prices for primary raw materials and high investment costs for the implementation of circular business models, as well as lack of funding and restricted access to finance.

This categorization served as the basis for the development of our questionnaire. We surveyed enterprises on the prospects and opportunities relating to their own circular transformation as well as factors constraining the more general development of a circular economy in Belarus. The survey was conducted in 2020 by BEROC and IBB Dortmund and included 452 companies from the Belarusian regions of Brest and Mogilev. The results show that businesses view economic, regulatory, and informational barriers as the most hindering to a circular transformation of Belarus. In particular, the respondents stated that the main impediments are high levels of capital and technology spending (62.8% of respondents), as well as lack of state-provided economic incentives (50.4%). Information constraints are also important as enterprises are not aware of circular technologies and believe that they do not exist (50.4%). Furthermore, there is a lack of knowledge on how to implement circularity in their industry (33.8%) (see Figure 1).

Figure 1. Barriers to circular economy development in Belarus, % of respondents

Source: Figure compiled by the authors based on the survey results.

Respondents also identified barriers that hamper a shift of their own enterprise – rather than that of the entire Belarusian economy – from a linear to a circular business model. According to the survey, the lack of funding is considered as the main barrier to circular transformation among Belarusian companies, as 83.5% of respondents characterized its impact as high or medium. This impediment is followed by the absence of circular technologies that can be applied at the surveyed enterprise (64.9%) and the lack of information and best practice examples with regard to the implementation of circular business models (62.4%). Half of the respondents also indicated that the shift from a linear economy is hampered by the lack of consulting on how to implement circularity (see Figure 2).

Figure 2. Barriers to the circular transformation of the Belarusian enterprises, % respondents

Source: Figure compiled by the authors on the basis of the survey results.

Enterprises identified specific technical challenges associated with possible supply chain constraints. In particular, 40% of respondents raised concerns about the absence of an online database on waste and secondary raw materials, and 39.3% of them worried about possible interruptions in the supply of secondary raw materials.

Stimulus for Circular Transformation in Belarus

Respondents also expressed their views on potential stimulus measures that could be implemented to encourage a transition towards a circular economy in Belarus. Tailored support programs (83.9%), tax incentives (78.5%), and development of infrastructure for the processing of secondary raw materials (76.4%) were identified as the strongest motivators for enterprises’ decision to opt for a circular business model. Other important measures listed by the respondents were revisions of the legislative framework to prioritize the use of secondary raw materials, prevent waste generation, etc. (67.4%) as well as access to consulting on how to implement circularity in a business (62.8%) (Figure 3).

Figure 3. Stimulus for the circular economy development in Belarus, % of respondents

Source: Figure compiled by the authors on the basis of the survey results.

Surveyed enterprises stated that they had already incorporated some circular economy elements in their business model. More than 35% of respondents have used recycled materials in the production process, 19% have recycled products in the production of new materials or products, and around 19% have reused products or embedded raw materials. Moreover, more than 35% of enterprises would be ready to introduce reusage and recycling in their business within the next three years. However, they emphasized that existing regulations should be revised, and economic incentives provided in order to encourage these efforts.

Conclusion

The results confirm that Belarus has potential for circular economy development. Yet, its implementation might be hampered by economic, regulatory, informational, and technological barriers. In particular, the surveyed enterprises stated that high upfront costs, e.g., for technology and equipment, as well as the lack of state economic incentives, are the most pressing impediments to the circular transformation of Belarus. At the company level, lack of funding is seen as the main obstacle in shifting from a linear to a circular business model. Another important barrier is lack of information, as enterprises are not aware of circular technologies and best practice examples.

The results of our survey suggest that, in order to encourage a transition towards a circular economy in Belarus, a tailored support program should be developed, existing regulations revised, and economic incentives provided. The transition will not be possible without mainstreaming a circular economy into Belarus’ policy agenda.

References

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

Belarus Economic Outlook

Posted on October 19, 2020 | Policy Brief

20201019 Belarus Economic Outlook. FREE Network Policy Brief Image of dark streets in Minsk representing Belarusian economic outlook

The Belarus economy was already struggling to generate growth before both the corona pandemic and the political protests following the August presidential election. The lack of growth was the result of an incomplete transition process to modernize the economy combined with a strong reliance on the Russian economy and its dependence on international commodity prices that have not paid off in recent years. With the added political turmoil and, so far, lack of a new political and economic strategy, the economic outlook for Belarus looks grim. Even if a full-blown crisis may be avoided by restrictive economic policies, stagnation will nevertheless be the most likely outcome without fundamental reforms.

Introduction

The Belarus economy was for many years doing very well under president Lukashenko, but since the global financial crisis in 2008/09, this course has been reversed. The downward growth trend has been exacerbated by both slumps in international oil prices (particularly important because of linkages with Russia, see Becker 2016a, 2016b, 2018, 2020), and the COVID-19 pandemic. This is clearly illustrated in Figure 1, which shows how the average growth rate has fallen all the way to a negative one percent in the years since 2015, while the period before the global financial crisis generated an average growth of 8 percent.

The lack of growth in Belarus and its causes has been analyzed in several papers long before the current developments. Akulava (2015) discusses how the government already five years ago understood that it needs to stimulate the private sector to generate growth; Kruk and Bornukova (2016) in turn describe how growth in the boom years was driven by capital accumulation but not improvements in productivity (TFP) that could have sustained growth in more recent years. As for policies to generate growth, Kruk (2014) argues that Belarus should focus on institutional changes that create the right incentives for firms and lead to a more efficient resource allocation rather than simply spend money on new equipment for existing firms. The need for productivity-enhancing reforms is further stressed in Kruk (2019) who points out that there is limited space to stimulate growth by expansionary macroeconomic policies.

Although the political situation after the election is strongly linked to the lack of democracy and freedom, the citizens’ willingness to protest is most likely enforced by the very poor economic performance of recent years. And while the importance of economic developments is sometimes glossed over in the current reporting and narrative of Belarus it will be an important factor in the popularity of any future government in Belarus as well as the current one.

Figure 1. Real GDP growth

Source: IMF World Economic Outlook April 2020.

 Note: The chart is based on the April 2020 version of the IMF’s World Economic Outlook and in the just-released October edition, the 2020 forecast is less negative due to global economic developments. However, this does not change the general downward growth trend Belarus has experienced.

Background

On the structural side, the economy of Belarus is heavily connected to Russian economic developments, which in turn depends on international oil prices (Becker 2016a, 2016b). In the group of FSU countries, Belarus stands out as the country that has the largest share of its exports going to Russia and the largest share of its FDI coming from Russia. On top of that, Belarus enjoys subsidized prices on oil and gas from Russia that benefits not only its exporting refineries but also other energy-intensive industries that are important for generating export revenues.

Figure 2. Exports and FDI shares with Russia and Rest of the World

Source: IMF directions of trade, World Bank development indicators and Central Bank of Russia data on FDI

As a final background note, the importance of SOEs in terms of employment has gone down in recent years but SOEs are still an important provider of jobs in Belarus and another sign of an unfinished transition agenda.

Figure 3. Importance of SOEs

Source: Belstat

To improve growth prospects, this is clearly a sector in need of reforms, including some privatizations, to make it more competitive and less of a drain on government finances. However, this process will need to deal with sensitive employment issue regardless of who is in charge politically.

Furthermore, Marozau, Aginskaya, and Akulava (2020) discuss how the corona pandemic may threaten the jobs of the over 1 million people that are employed by SMEs. The financial constraints of the government make it hard to offer widespread support to SMEs, and the authors argue that the government should target future winners among SMEs rather than the big losers in the crisis.

The challenge of increased unemployment is further exacerbated by the lack of an unemployment benefit system with extensive coverage (Bornukova, 2017). The lack of a well-targeted social security system could lead to a new increase in poverty rates. Mazol (2019) shows how past crises had a negative impact on poverty with absolute poverty increasing almost twofold in 2015/2016.

Recent Developments

The economy in Belarus was facing challenges (like much of the world) this year due to the COVID-19 pandemic well before the political crisis following the August election triggered additional problems. The IMF growth forecast for the year was well into negative numbers and given the (not always stable) links to Russia and thus to oil prices, the longer-term outlook was cloudy as well. Although the IMF’s October forecast shows less negative growth for 2020 (from minus 6 to minus 3 percent as the world is expected to see less of a contraction due to the COVID-19 pandemic), the longer-term outlook is one of stagnation with annual growth of around 1 percent.

For 2020, the economic and political difficulties can be seen in exchange rate developments as well as in the evolution of foreign exchange reserves (Figures 4 and 5).  In some ways, the 25-30 percent depreciation of the currency viz the dollar and euro is not the full story on the currency, since the exchange rate viz the Russian ruble has been much more stable. Given the close links to the Russian economy, this is quite important to note. Indeed, foreign currency reserves (the more liquid part of international reserves) have gone down by some 40% this year but are still at around 3 billion USD.

Additional pressure on the financial system in the past months came from significant withdrawals and people moving their savings to hard currencies after the August election. Krug and Lvovskiy (2020) discuss how this development is driven by political turmoil and also how the lack of trust that is currently generated in the system will lead to further stagnation of the economy. This line of reasoning is supported by Mazol (2018), who shows how financial stress in the past has contributed to costly economic contractions.

Figure 4. Exchange rate indices

(Jan 2020=100)

Source: National Bank of Belarus

 

Figure 5. Foreign exchange reserves

Source: National Bank of Belarus

Outlook and Policy Conclusions

The current economic policy will not generate growth in the short or long term by itself and the current political situation is clearly affecting growth negatively. The current political leadership could of course once again turn to Russia to ask for economic assistance in various forms, including loans, subsidies, or investments. Given the situation in Belarus, this will clearly come at a high political cost that will not necessarily be immediately transparent to people in Belarus or the outside world. Further, a sufficient level of assistance is not bulletproof either – Russia is itself facing difficult economic times ahead, both because of the COVID-19 pandemic and its impact on oil prices but also because of its own inability to generate sustainable growth that is not based on oil, gas and minerals (Becker, 2018, 2020).

How long the political and economic repression can go on without triggering a full-blown meltdown of the financial system in Belarus is anyone’s guess. Unfortunately, a policy mix of more restrictions on financial and exchange transactions in combination with accepting stagnation has been shown to be a model that has “worked” from Cuba, to Iran, Venezuela and North Korea for very long periods of time, so there are no given deadlines for such regimes.

Regardless of short-term policy changes, Russia will remain an important economic player in Belarus for a long time unless something dramatic changes. If there is a transition of political power in Belarus, any new political leadership will have to make careful choices with regard to its relationship with Russia. Quickly cutting ties to its big eastern neighbor could turn out to be very costly for Belarus from an economic perspective given the structure of trade, subsidies, and investments between the two countries.

If the EU (or the West more generally) wants to provide Belarus with a realistic economic alternative to Russia in the short run, it will need to provide substantial funding and strongly support a wide-ranging economic reform program that will need to address transition issues that most of its neighbors did many years ago. This will involve not only selling state assets to foreign investors but also changing the economic system from the ground up, including institutions and management practices. Another important part of the needed change is modern Western education. The importance of higher education institutions (HEI) to generate growth in Belarus is stressed by Marozau (2019), who discusses the role of HEIs in improving productivity and how the universities in Belarus fail to stimulate innovation and entrepreneurship.

The support package may not be cheap for the EU financially but helping the people in Belarus to finally make the transition to a modern, democratic market economy on the doorstep of the EU would certainly be worth it. The question is if the EU will manage to unite around such a policy in a time of COVID-19 lockdowns and economic hardship within its current boundaries. Patience may be required among those that fight for their freedom and a new economic model in Belarus.

References

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

Does Political Illegitimacy in Belarus Imply New Economic Risks?

Posted on October 5, 2020 | Policy Brief

Large group of peaceful demonstration in Belarus that represents Belarus and people who seek to avoid economic risks

Today’s political crisis in Belarus has given rise to the phenomenon classified in political science as political illegitimacy. However, this is not a pure political phenomenon. It causes adverse and severe economic adjustments. In a short-term perspective, it gives rise to numerous risks of financial destabilization. Moreover, it is likely to deepen the current recession and make it protracted. In the long-term, political illegitimacy causes adverse institutional adjustments and erosion of human capital, which is likely to lead a country into a long-lasting depression. We argue that resolving the political crisis in a way that revives trust and legitimacy is the only ‘good’ solution.

Short-term Economic Effects of Political Illegitimacy

Since August 9, 2020, Belarus has been widely discussed worldwide in mass media because of the country’s political crisis. Political scientists classify the current situation in Belarus as a case of political illegitimacy, i.e. there is no consensus in the Belarusian society concerning the recognition and acceptance of a new term for the governing regime.

In turn, the governing regime prefers to ignore the illegitimacy issue. There is an implicit assumption behind this:  illegitimacy is an intangible issue that can hardly result in any tangible threat to the sustainability of the governing regime.

We oppose this view and argue that, at least in an economic dimension, there are numerous channels through which illegitimacy transforms into tangible problems. Inasmuch as the stance of the economy affects political sustainability, it will undermine the latter.

From a short-term perspective, the issue of political illegitimacy has become part of the information accounted for in the decision-making of economic agents in Belarus. Hence, in their economic decisions they either try to struggle against it, or at least to hedge against corresponding adverse effects.

Most evident, the adjustments in decision-making has already visualized in households’ savings behavior. Directly, illegitimacy considerations gave rise to deposit withdrawals from the banking system and enlarged demand for hard currency. Consequently, this led to a rise in depreciation-/inflation-expectations and lowered public trust in the banking system, which in turn has amplified these patterns of the households’ behavior.  In August, Belarus experienced historical peaks in deposit outflows and international reserves were depleted as a result. This has substantially amplified the risks of financial turmoil.

So far, the authorities have curbed the financial stress by implementing a restrictive monetary policy. However, this does not suppress adverse patterns in households’ behavior. It only somewhat allows for a shift of adverse adjustments from financial markets towards the real economy. Moreover, it weakens but does not completely remove the threat of full-fledged financial turmoil, taking into account the systemic financial fragility in Belarus.

In addition to the illegitimacy issue itself, other adverse expectations are likely to give rise to unfavourable trends in households’ consumption behaviour as well. First, household consumption is likely to be dampened as a result of poor consumer confidence and sentiment. Second, additional losses in consumption are likely to occur due to tightening access to credit and progressing financial fragility.

Similar mechanisms are likely to be in place with respect to investment demand. First, poor confidence and sentiment undermine the investment activity of businesses. In Belarus, this channel is likely to be more powerful for private businesses, as investment plans of SOEs (due to their directive nature) are less sensitive to confidence and expectations. Second, investment activity is likely to decline due to deteriorating financial conditions and consequent contraction of credit. This linkage is especially important for the SOEs and housing investments.

The power of adverse consumption and demand trends is still questionable. However, preliminary estimates (introducing negative shocks in addition to scenarios in Kruk, 2020) show that they will reduce the output growth rate by at least 1.5-2.0 percentage points in 2020 Q3-Q4. In other words, they are expected to deepen the current recession and are likely to make it more long-lasting.

Deteriorating payment discipline is one more expected outcome from political illegitimacy. Being amplified by deteriorating financial conditions and economic activity, it can turn into a full-fledged payment crisis and fiscal instability.

Adverse Institutional Adjustments and Effects on Labor Market

Human-to-human interactions based on mutual benefit and trust are the core of a modern market-based economy. Key institutions created to support this interpersonal trust are laws and law-enforcement agencies. If a person does not trust her counterpart in a deal and does not think that she can take him to court to defend her rights, no deal will be signed. When an individual observes unrightful and politically-motivated court decisions in criminal cases, the distrust is also passed on to her beliefs that she would be able to defend her economic rights in the same court. As we observe police violence, tortures, and criminal charges of protesters with no attempt to prosecute those responsible, public trust in the law-enforcement system fades away, and thus all kinds of deals previously supported by a contract-enforcement system cease to exist.

The quality of a judicial system is widely recognized as a powerful determinant to overall institutional quality and the business environment. Hence, poor trust in it would likely undermine business activity directly. Existing businesses are to re-orient towards shorter-term strategies, being reluctant to initiating long-term and risky projects. Moreover, their inclination to geographical diversification of their business activity or even full migration is likely to rise. New entrants – that are extremely important to achieve productivity gains (Foster, Haltiwanger, and Syversen, 2008) – are less likely to start business in the country.

An increase in emigration is a usual consequence of political crisis, especially if it is accompanied by violence and politically-motivated incarcerations. What is unique about the current Belarus crises is that the list of potential emigrees include not only individuals but also firms, especially those working in the IT sector. After 11 August 2020, many IT companies found their employees detained, beaten and tortured. The offices of Yandex, Google and PandaDoc were searched and four top managers working at the latter were detained on tax evasion charges which are likely to be politically-inspired. As of the 18th of September, around 200 IT companies are considering relocation from Belarus and many more are considering partial relocation of their employees to already established foreign offices (Dev.by(2020a)). Results from a recent survey show that 33% of IT specialists have already decided to leave Belarus and the rest indicated that they will leave if the situation worsens (Dev.by(2020b)).

There are several major reasons for why the IT-sector is affected more by the current crises compared to traditional sectors of the Belarusian economy. Firstly, IT companies rarely own physical capital and thus can change their location in a matter of days by simply relocating their employees and laptops. Secondly, the IT labor market is global and mobile, and companies compete for the workers. Therefore, if many workers hold similar strong views on a particular situation, employers are bound to support them to a certain extent. As a result of the latter, many IT companies have openly voiced their disagreement with the election results and the politically motivated violence following the election. High-level employees and owners of major companies have participated in various opposition initiatives and as a result, now face retribution from Lukashenko’s government.

In addition to politically-motivated emigration, we can expect an increase in economically-driven emigration rates as the economy is expected to shrink (Bornukova and Lvovskiy, 2020).

What Is the Way Forward?

The political crisis in Belarus has triggered multidimensional adverse economic adjustments. Nevertheless, the authorities prefer to ignore the links between politics and economics. Hence, they try to overcome the problems with economic policy tools only. However, the room to maneuver with these tools is considerably restricted, and in some cases completely ineffective in suppressing adverse trends.

With respect to the short-term agenda, the authorities cannot offset the adverse trends. They can just mitigate challenges in one dimension and try to re-direct it to another one. For instance, currently the authorities focus on mitigating the probability of a full-fledged financial crisis. This consideration requires restricting monetary conditions. Otherwise, the exchange rate is likely to depreciate, which would be problematic from a corporate debt sustainability perspective. Although being somewhat effective in this regard, this policy mix dampens economic activity. From a financial dimension, the challenge is being re-directed to the real economy.

A similar picture might soon emerge in a fiscal sphere as well. An economic downturn and political crisis can result in a widening income gap. At the same time, the room for maneuver on the expenditure side is constrained. The funds accumulated from the previous periods have to a large extent already been spent to support SOEs. Hence, a further expansion of expenditures is hardly possible, as it would undermine fiscal and public debt sustainability. Therefore, fiscal stimulus is likely to fade away and can gradually even become negative.

Based on estimations in Kruk (2020), before the issue of illegitimacy appeared, the economy was developing according to a scenario of about a 3% drop in GDP in 2020 and a meagre recovery (if any) in 2021. Adding the assumptions associated with adverse adjustments due to the illegitimacy issue into the Kruk (2020) estimates, we show that the recession is likely to deepen by at least 1 percentage point in 2020. In 2021, output losses are likely to expand considerably. In regard to the long-term agenda, the situation is even worse. Conceptual decisions on economic activity by firms and households are closely linked with the issues of trust and legitimacy (Bornukova et al., 2020). Having lost them, the authorities are unlikely to have any effective tools for standing against adverse institutional adjustments and the erosion of human capital. Hence, we may expect that today’s poor growth potential of the Belarusian economy – up to 2.5% of per annum growth (Kruk, 2020) – is likely to weaken further and could even become negative. This means that the stagnation over the recent decade is likely to turn into a long-term depression.

Conclusions

The political crisis and the arising issue of political illegitimacy in Belarus impose severe economic challenges for the country. In a short-term perspective, there are numerous channels that are likely to deepen the recession and make it long-lasting. Moreover, risks to financial stability are progressing rapidly. Hence, there is little room for securing macro stabilization in the near future.

In a long-term perspective, the country is likely to suffer from the disruption of productivity enhancers. It will stem from lower business initiatives and the erosion of human capital. This is a way to a long-term depression.

Standard economic tools are mainly ineffective against both the short-term and long-term challenges. Resolving the political crisis in a way that revives trust and legitimacy is the only ‘good’ solution.

References

  • Bornukova, K. and Lvovskiy, L. (2020). Demography as a Challenge for Economic Growth, Bankauski Vesnik, 680 (3), PP. 31-35.
  • Bornukova, K. Godes, N., and Shcherba, E. (2020). Confidence in the Economy: What is It, How it Works and Why We Need it?, Bankauski Vesnik, 680 (3), PP. 95-99.
  • Foster, L., Haltiwanger, J., and Syversen, Ch. (2008). Reallocation, Firm Turnover, and Efficiency: Selection on Productivity of Profitability? American Economic Review, 98(1), PP. 394-425.
  • Kruk, D. (2020). Short-term Perspective for the Belarusian Economy, BEROC Policy Paper No. 92.
  • Dev.by. (2020a). https://dev.by/news/pochti200-relocate
  • Dev.by. (2020b). https://dev.by/news/opros-relocate-september2020.

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.

The Gender Wage Gap in Belarus: State vs. Private Sector

Posted on September 30, 2019 | Policy Brief

20190830 The Gender Wage Gap in Belarus FREE Network Policy Brief Image 02

This brief is based on research that studies gender difference in wages in Belarus using survey data from 2017. According to the results, the unconditional gender wage differential equals 22.6%. The size of the wage gap is higher in the state sector than in the private sector. Additionally, it increases in the state sector throughout the wage distribution and accelerates at the top percentiles, indicating the presence of a strong glass ceiling effect.

Introduction

The causes and consequences of the gender wage gap in the labor market, that is the difference between the wages earned by women and men, continue to attract increasing attention in empirical studies worldwide.

Belarus’ labor market is not an exception and faces the problem of wage inequality like other neighboring and transition countries. According to the National Statistical Committee of the Republic of Belarus (Belstat), the average gender wage gap in terms of monthly wages was 19% in 2000, it increased up to 23.8% in 2015, and reached 25.4% in 2017.

In this regard, this brief updates the estimates of the gender wage gap in Belarus. And it summarizes the results of the study on what the role of the state and private sectors are in the distribution of gender wage differences in Belarus (Akulava and Mazol, 2018).

Data and methodology

The data used in the research is from the Generations and Gender Survey (GGS) conducted in Belarus in 2017. This survey is a nationally representative dataset that is based on interviews of about 10,000 permanent residents of Belarus, aged 18–79, covering the whole country disaggregated by regions. The GGS contains information on a range of individual (age, gender, marital status, educational attainment, employment status, hours worked, wages earned etc.) and household-level characteristics (household size and composition, land holding, location, asset ownership etc.).

The analysis is based on the typical Mincer model of earnings that estimates individual wage income as a function of various influencing factors using the OLS approach (Mincer, 1974). Specifically, the Mincerian wage equation is defined where the log of the hourly wage rate is regressed on a set of male and female workers’ personal and job characteristics (educational level, working experience, occupational type, organization type, family characteristics, and region).

Next, we use the Oaxaca-Blinder (OB) methodology (Oaxaca, 1973; Blinder, 1973) to identify and quantify the contribution of personal characteristics and the unexplained component (which is referred to as differences in returns) to the wage difference between males and females.

Finally, we apply the Machado-Mata (MM) technique (Machado and Mata, 2005) to look into the nature of the wage gap at various points of the income distribution and also to test the difference for individuals employed in the state or private sectors. For the Machado-Mata procedure, we estimate our specifications at the 10th, 25th, median, 75th and 90th percentiles of the wage distribution.

Results

The analysis shows that women’s wages are lower than men’s wages all over the wage distribution. The average raw gender wage gap equals 22.6% and it increased substantially compared with 9.0% in 1996 and 17.8% in 2006, the numbers obtained in the study conducted by Pastore and Verashchagina (2011).

Figure 1. Gender differential by quantile of the wage distribution

Source: Authors’ estimates based on GGS.

The level of female earnings is lower than the male regardless of the occupational type, educational background, work experience and organizational type. Moreover, the underpayment of women is lower for low earning workers, but increases up to the end of the wage distribution (see Figure 1).

The OB decomposition shows that female educational attainment and job-related experience help to decrease the level of the wage gap slightly (see Table 1).

Table 1. Oaxaca-Blinder decomposition results

Source: Authors’ estimates based on GGS.

However, the occupational choice is leading to an expansion of the difference in earnings. However, its effect is also small, indicating that occupational segregation plays a minor role in explaining the gender wage gap. The major share of the gender wage gap is formed by the unexplained part, which is likely to be attributed to discrimination.

Next, the level of remuneration is higher among private companies. However, contrary to other countries in transition, the average gender wage gap in Belarus in the private sector is lower than in the public sector.

Moreover, the MM decomposition estimates presented in Table 2 demonstrate that the gender wage gap in the state sector shows evidence of the glass ceiling effect (the size of the total wage gap expands at the top of the wage distribution), while no evidence of either glass ceiling or sticky floor (the size of the total wage gap increases at the bottom of the wage distribution) in the private sector.

The negative coefficient near the characteristics part in the private sector shows that female endowments outweighs their male counterparts. Thus, controlling for personal characteristics, if the labor market rewards males and females equally, the wages of females in the private sector should be substantially higher (see Table 2).

Table 2. Machado-Mata decomposition of the observed gender wage gap by organization type

Source: Authors’ estimates based on GGS.

Finally, the results also suggest that female workers are better off being in the private sector at the lowest and the highest percentiles (i.e. the size of the gender wage gap is lower there compared to the 25th and 50th percentile).

A possible explanation for all the above is that institutional differences seem to play a crucial role here. First, Belarusian private firms work under stronger regulation than in other transition economies which makes it harder for them to set low wages. Second, they also operate under stronger competition (compared to state companies), which force them to identify individual productivity more correctly, narrowing the gender difference in pay. In contrast, the paternalistic attitude to women left as a legacy from the Soviet Union further increases the gender wage gap in the public sector.

Conclusion

In this brief, we present new evidence on the existence of a gender wage gap in the Belarusian labor market and analyze the differences in its distribution between the state and private sectors.

Our results show that the unconditional gender wage gap in terms of hourly wages equals 22.6%. Thus, jointly with a previous study (see Pastore and Verashchagina, 2011) and recent official indicators, all these indicate that the pace towards gender equality in Belarus seems to be sluggish. For the moment, all institutional changes accomplished by the Belarusian government to reduce gender discrimination are not enough and require additional efforts to cope with that problem.

However, the gender wage gap is shown to be much wider in the public sector than in the private sector. At the same time the private sector appears to be more attractive than the public sector in the country in terms of the level of remuneration. Therefore, additional structural shifts of the economy accompanied by the growth of competition are needed to induce a further reduction of the gender wage gap.

References

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

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

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.

Losers and Winners of Russian Countersanctions: A welfare analysis

Posted on October 1, 2018 | Policy Brief

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In this brief we provide a quantitative assessment of the consequences of countersanctions introduced by the Russian government in 2014 in response to sectoral restrictive measures initiated by a number of developed countries. Commodity groups that fell under countersanctions included meat, fish, dairy products, fruit and vegetables.  By applying a basic partial equilibrium analysis to data from several sources, including Rosstat, Euromonitor, UN Comtrade, industry reviews etc., we obtain that total consumers’ loss due to countersanctions amounts to 288 bn Rub or 2000 rubles per year for each Russian citizen. Producers capture 63% of this amount, importers 26%, while deadweight loss amounts to 10%. 30% of the transfer from Russian consumers toward importers was acquired by Belarus. The gain of Belarusian importers of cheese is especially impressive – 83% of total importer’s gains on the cheese market.

In August 2014, in response to sectoral sanctions initiated by some countries against Russia, the national government issued resolution No. 778, which prohibited import of processed and raw agricultural products from the United States, the EU, Ukraine and a number of other countries (Norway, Canada, Australia, etc.).

Russian countersanctions were, in particular, imposed on meat, fish, dairy products, fruit and vegetables. Later the list of counter sanctioned goods was edited: inputs for the production of baby food and medicines have been deleted from the ban list, while new items were added. Salt was added to the list in November 2016 and animal fats in October 2017.

The popular idea behind the countersanctions was to limit market access for countries, which supported sectoral sanctions. The other rhetoric of the countersanctions was to support domestic producers via trade restrictions, or by other words – import substitution.

We apply a basic partial equilibrium analysis in order to evaluate the effect of countersanctions on the welfare of main stakeholders – consumers, producers and importers. The overall results are in line with general microeconomic consequences of trade restrictions in a small open economy, that is, we observe a decline in consumer surplus, increase in producer surplus and redistribution across importers. Perhaps, even more interestingly, we are able to provide a numerical assessment of redistribution effects between Russian consumers and producers, on the one hand, and among importers from different countries, on the other.

Partial equilibrium welfare analysis

We apply a framework of the classical analysis of import tariff increases to Russian countersanctions. Countersanctions resulted in increased domestic prices, declining consumption and increased domestic production. Given the increase in prices and declined volumes of consumption, we evaluate the losses by consumers as a decline in consumer surplus. Respectively, given the increase in prices and increase in domestic output we identify the producers gains as an increase in producer surplus. The only difference with a classical analysis is the lack of increase in government revenues. In this case increases in domestic prices were driven by restrictions on trade with historical partners which were substituted by more costly producers. Given the changes in the composition of importers after sanctions, we identify countries which lost and gained access to the Russian market. We use changes in volumes of trade as a measure of respective gains and losses. Figure 1 presents all relevant concepts.

In order to measure all relevant welfare changes, we rely on consumption, production and price data from Rosstat and Euromonitor, trade data from the UN Comtrade database. We use data for 2013 as a benchmark before countersanctions and compare it to 2016. The measures of own price elasticities of Russian demand and supply were taken from the literature. We use real price (in terms of 2013 prices) and volume information for consumption and supply in 2016 as the resulting points on the supply (point C) and demand (point A) curves as shown on Figure 1. Then we restore the consumption and production points on these curves (points F and B) as they would have been in 2013 given the own price elasticities of demand and supply and price level as of 2013.

Figure 1. Visualization of deadweight losses, consumer and producer surplus changes

Welfare analysis

Data

We consider 12 commodity groups that were included in 2014 in the countersanctions list: pork, cheese, poultry, apples, beef, tomatoes, processed meat, fromage frais, butter, oranges, condensed milk, grapes, cream, sour milk products, milk, and bananas.

Prices and volumes information are taken from Rosstat official statistics, which in a few cases were adjusted by data from Euromonitor. Import values were obtained from the UN Comtrade database. The summary of the original data and results of welfare analyses are reported in table 1. Below we discuss in details the situation in three markets – beef, apples and cheese.

Table 1. Summary table of the welfare effects of countersanctions

Group Price (RUR per kg, 2013) Production (thous. tons) Consumption (thous. tons) Elasticity Consumer losses, RUR mn Producer surplus, RUR mn Deadweight loss, RUR mn Importer gains, RUR mn
2016 2013 2016 2013 2016 2013 demand supply
Beef 376 357 238 240 600 897 -0.78 0.1 11311 4388 234 6690
Poultry 109 108 4468 3610 4577 4084 -0.78 0.45 3263 3173 13 77
Pork 286 289 2042 1299 2282 1919 -0.78 0.2 -7167 -6447 38 -757
Milk 55 47 5540 5386 5704 5595 -0.93 0.3 48234 42507 4443 1284
Butter 343 271 251 225 340 340 -0.93 0.18 27468 17680 3370 6419
Cheese 358 283 605 435 748 764 -0.93 0.28 63493 44259 8437 10797
Fromage frais 233 190 407 371 456 457 -0.93 0.3 21803 17104 2600 2099
Apples 84 70 324 313 986 1665 -0.85 0.1 15225 4562 1238 9425
Bananas 61 47 0 0 1141 1165 -0.9 0.1 18967 0 2315 16652
Oranges 65 59 0 0 932 1059 -0.9 0.1 6054 0 272 5782
Grapes 175 131 174 101 366 459 -0.85 0.1 18312 7527 2351 8435
Tomatoes 82 65 1130 863 1583 1718 -0.97 0.1 28824 18177 3290 7357

Data sources: Rosstat, Euromonitor, UN COMTRADE

Bold figures were used to mark the commodity groups with a noticeable consumption growth in 2013-2016, italic figures – for those with consumption decrease, and underlined – for groups where consumption changed insignificantly during the period.

Beef

The Russian beef market experienced a drastic decrease in consumption during two years under countersanctions.  In 2013 constant prices, the average real of 1 kg of beef increased by 5.3% from 357 Rub/kg in 2013 up to 376 Rub/kg in 2016. Domestic output decreased by 0.8% and to 238 thousand tons in 2016 from 240 in 2013. Domestic consumption decreased by 33.1% to 600 thousand tons in 2016 from 897 in 2013. Our estimations indicate that  consumer losses amount to  11.3 bn Rub or 3.5% of beef consumption in 2013; producers’ gains are 4.4 bn Rub or 1.4%; deadweight losses are estimated at 0.2 bn Rub or 0.07%; and importers’ gains equal 6.7 bn Rub or 2.1%.

Out of total 6.7 bn Rub of importers’ gains, importers from Belarus acquire the major share (88%) – 5.9 bn Rub. Importers of beef from India and Colombia gained 0.4 bn Rub (6% of total) and 0.3 bn Rub (5%) respectively. Beef importers from Mongolia gained 0.03 bn Rub, from Kazakhstan – 0.01 bn Rub. Importers of beef from Brazil, Paraguay, Australia, Uruguay, Ukraine, Lithuania, Poland, and Argentina lost market shares in over the period 2013-2016.

Cheese

Average real price for 1 kg of cheese increased by 26.5% up to 358 Rub/kg in 2016 from 283 Rub/kg in 2013, both in constant 2013 prices. Domestic output increased by 39.1%  to 605 thousand tons in 2016 from 435 thous. tons in 2013. Domestic consumption decreased by 2.1% to 748 thous. tons in 2016 from 764 thous. tons in 2013. Our results indicate the following effects of countersanctions on cheese market: consumers’ losses amounted to 63.5 bn Rub or 29.4% of cheese consumption in 2013; producer’s gain is 44.3 bn Rub or 20.5%; deadweight loss is estimated at 8.4 bn Rub or 3.9%; importers’ gains equal 10.8 bn Rub or 5.0%.

Out of a total 10.8 bn Rub of importer’s gains on the cheese market, importers of cheese from Belarus acquired the major share (82.9%) – 9.0 bln Rub, importers of cheese from Argentina gained 0.5 bn Rub (4.8% of total importers’ gain), importers from Uruguay gained 0.4 bn Rub (3.9%), Swiss cheese importers gained 0.2 bn Rub, importers from Armenia – 0.2 bn Rub (1.8%). While importers of cheese from Ukraine, the Netherlands, Germany, Finland, Poland, Lithuania, France, Denmark, Italy, and Estonia lost market access over 2013-2016.

Apples

In 2013 constant prices, average real price for 1 kg of apples increased by 20.0%  up to  84 Rub/kg in 2016 from 70 Rub/kg in 2013. Domestic output increased by 3.5% to 324 thous. tons in 2016 from 313 thous. tons in 2013. Domestic consumption decreased by 40.8% to 986 thous. tons in 2016 from 1665 thous. tons in 2013. According to our analysis, the  effects of countersanctions on the apple market are the following: consumers’ losses amounted to 15.2 bn Rub or 13.1 of apple consumption in 2013; producer’s gain is 4.6 bn Rub or 3.0%; deadweight loss is estimated at  1.2  bn Rub or 1.1%; importers’ gains equal 9.4  bln Rub or 8.1%.

Out of a total 9.4 bn Rub of importer’s gains, importers from Serbia acquired the major share (49.7%) – 4,7 bn Rub, importers of apples from China gained 1.6 bn Rub (16.7% of total importers’ gains), those importing from Macedonia gained 0.8 bn Rub (8.4%), from Azerbaijan 0.6 bn Rub (6.0%), and from South Africa 0.4 bn Rub (4.5% of total importers’ gains). While importers of apples from Poland, Italy, Belgium, and France lost market access.

Overall effects for 12 commodity groups

We calculated the welfare effects for 12 commodity groups: beef, poultry, milk, cheese, cottage cheese, ton butter, dairy products, apples, bananas, oranges, grapes and tomatoes.

Total consumers’ loss due to countersanctions amounts to 288 bn Rub, producers gain 63% out of this amount (182 bn Rub), 26% of total consumers’ loss is redistributed to importers (75 bn Rub), deadweight losses amount to 10% (31 bn Rub).

Distribution of importers’ gains

Belarus is the major beneficiary of Russians countersanctions: its exporters gain 29.4 bn Rub (38%), Ecuador’s exporters are in the second place with 16.4 bln Rub (21). Exporters from Serbia gained 5.1 bn Rub (7%).

Conclusion

There is no doubt that countersanctions were paid out of the pockets of Russian consumers: our estimation of total consumer losses amounts to 288 billion rubles, i.e. each Russian citizen paid 2000 rubles per year.  Out of this sum, Russian producers received 144 billion rubles, i.e. transfer from Russian consumers to producers equals 1260 rubles per person per year. Among Russian sectors, major gains and associated increases in production happened in pork industries (50%), poultry (20%), dairy products (10-30%), fruit and vegetables (10-50%).

The transfer from Russian consumers toward importers from non-sanctioned countries equals 75 billion rubles a year (520 rubles per person per year), out of which 30% was acquired by Belarusian importers. Countersanctions lead to deadweight losses in the efficiency of Russian economy equal to 31 billion rubles or 215 rubles per person per year.

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

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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 5th 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

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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 lossa

(% of GDP)

 Number of months after start of financial stress to recession
Financial

stress

 Recessionb
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.