Tag: SME

Did the Government Help Belarusian SMEs to Survive in 2020?

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.


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.


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 Gender Matter for the Innovativeness of SMEs?

This policy brief summarizes the results of an on-going research project on the gender aspect of companies’ innovativeness in transition countries. The aim of this work is to examine whether there is a gender gap in innovative behavior within the sector of small and medium-sized enterprises (SMEs). The results suggest that the propensity to innovate is higher among companies with a presence of a female owner.   This finding preserves for 5 measures of innovativeness. Thus, female involvement in business might be beneficial for the innovative sustainable development of economy.

The role of small and medium-sized enterprises (SMEs) has increased lately and they are considered one of the main engines of economic growth (Radas and Bosic, 2009). Research on transition economies and development has emphasized the need for strong a SME sector, since it often acts as the backbone of the economy (Lukasc, 2005) and is the largest contributor of employment (Omar et al., 2009). Another important channel through which the SME sector contributes to development is through their innovative activities. Sustainable economic development requires competitive and successful industries. Being innovative is one way to achieve this goal. However, the innovativeness of sectors and industries depends not only on the actions of the largest companies, but also on the SME sector and individual entrepreneurs. Indeed, the latter are often argued to be more dynamic and more ambitious (Chalmers, 1989; Li and Rama, 2015).

The decision to follow an innovative strategy often depends on the company’s leader, their experience and other managerial characteristics. However, the experience of the leader is not the only factor affecting managerial actions – gender also appears to matter (Daunfeldt and Rudholm, 2012). In the absence of clear answers and knowledge about female managerial characteristics, including their innovativeness (Alsos et al., 2013), it is difficult to evaluate their role in modernizing the business society and to distinguish their competitive advantages or disadvantages over male managers and business owners.

The role becomes even more ambiguous for the transition, post-communist economies. The labor market under USSR officially provided equal rights to women. However, in practice women were treated differently than men. While women often had to do the same work as men, the patriarchal society remained with men being regarded as the main decision makers, and women being fully responsible for housework and childcare. This can explain the low presence of women in top-managerial positions and women’s weaker business ties and networks (Welter et al., 2004).

The question of gender and innovation in entrepreneurship has recently starting to attract attention. Earlier, innovativeness was strongly connected and associated with high-tech companies. Thus, innovation research mostly focused on technology-based and capital-intensive industries (Dauzenberg, 2012; Marlow and McAdam, 2012). As a result, innovation behavior in less capital-intensive SMEs was almost entirely overlooked. This can also explain the lack of focus on gender, as men usually dominated the capital-intensive industries (Ljunggren et al., 2010).  In an ongoing research project, I am trying to expand the understanding of gender differences in innovation and SME entrepreneurship with a focus on transition economies and the CIS block in particular.

The idea is to estimate owners’ and CEOs propensity to implement innovations in the organization. The specification of the model follows the literature and uses a probit technique that allows for an estimation of these propensities while taking into account other influencing factors and individual characteristics of firms, their owners and CEOs, which likely affect innovative decisions. The data I use come from the 5th wave of the Business Environment and Enterprise Performance Survey (BEEPS) conducted in 2012-2013. The final dataset covered 5254 SMEs from 30 European and East Asia countries.

The main variable of interest is the innovativeness of the enterprise, proxied by 5 different indicators. The measures of implemented innovative activities are: 1) whether the firms introduced a new product or service during the last 3 years; 2) whether there was any new production process implemented; 3) whether there were any spending on research and development; 4) whether were was an introduction of a new marketing strategy and method; and 5) whether an enterprise implemented new methods in operational management. The usage of 5 indicators instead of one allows me to see whether there is any specific feature of innovativeness that differs by gender.

The list of control variables covers information on the gender of the CEO and owners, number of years of experience of the CEO, age of the firm, type of ownership, focus on internal and external markets, as well as the usage of foreign technologies and certification. I also have information on the share of skilled labor force, the share of females in the organization, and whether the organization bears additional costs on external consulting services and training of employees. Information on industry, country, size of the organization and type of residence is also available.

Unfortunately, the data lacks information on the number of owners, which will prohibit me from estimating the clear gender effects and limits the analysis to the effect of gender diversity among owners.

The obtained results (see Table 1) show that having a female as the only, or one of the, owner(s) increases the propensity of going into uncertainty and implementation of a new good/service by 4.5% in the CIS region and 6.7% in the non-CIS block. However, the effect of having a female CEO is insignificant. This finding contradicts the literature on gender differences in the willingness to take on risk (Wagner, 2001; He et al., 2007; Eckel et al., 2008; Croson and Gneezy, 2009) that mostly demonstrates that women, on average, are more risk-averse than men.

A similar effect is observed for the implementation of a new business process or marketing strategy. The only insignificant difference is the spending on R&D in CIS countries and new managerial methods in non-CIS block. However, these measures of innovativeness raise doubts regarding its applicability for SME sector. A shift from high-intense productions towards services makes it less useful to spend enormous sums of money on technological research. Instead, other innovative actions like the development of human capital are of greater importance.

Table 1. Propensity to innovate

Akulava_tab1Source: Author’s own estimation.


The results show that having a female owner or gender diversity in the ownership structure positively affects the propensity of the organization to follow innovative behaviors and strategies. Therefore, promoting female entrepreneurship and gender equality in ownership seem positive for increasing the innovativeness of companies, and the economy in general, in both the CIS and non-CIS block.


  • Alsos, G.A., Hytti, U., and Ljunggren, E. 2013.Gender and Innovation: State of the Art and a Research Agenda.International Journal of Gender and Entrepreneurship, 5(3):236-256.
  • Chalmers, N. 1989. Industrial Relations in Japan: The Peripheral Workforce. London: Routledge.
  • Croson, R. and Gneezy, U. 2009. “Gender Differences in Preferences”.Journal of Economic Literature.Volume 47, #2.
  • Daunfeldt, S., O., and Rudholm, N., (2012). Does gender diversity in the boardroom improve firm performance? Department of Economics, Dalarna University, SE-781 88 Borlänge, Sweden; and HUI Research, SE-103 29 Stockholm, Sweden.
  • Dautzenberg, K. 2012. Gender differences of business owners in technology-based firms.International Journal of Gender & Entrepreneurship,4:79–98.
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  • Ljundggren, E., Alsos, G.A., Amble, N., Ervik, R., Kvidal, T., Wiik, R. 2010. Gender and innovation: Learning from regional VRI projects. Nordland Research Institute, Norway.
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  • McAdam, M. and Marlow, S. 2008.The Business Incubator and the Female High-Technology Entrepreneur: A Perfect Match? Paper presented at the 2008 International Council for Small Business World Confrence, recipient of the 2008 Best Paper Award for Women Entrepreneurship.
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  • Wagner, M.K. (2001), “Behavioral characteristics related to substance abuse and risk-taking, sensation-seeking, anxiety sensitivity and self-reinforcement”, Addictive Behaviors , Vol. 26, pp. 115-20.
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The Role of Belarusian Private Sector

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

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

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

Small and Medium Enterprises

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

Figure 1. SME density

figure1Source: own estimations from Belstat data, Eurostat.

Private Sector

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

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

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

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

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

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

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

Figure 2. Private sector in employment (%)

figure2Source: own estimations from Belstat data.

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

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

figure3Source: own estimations from Belstat data.

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

Figure 4. Return on Assets (BYR/BYR)

figure4Source: own estimations from Belstat data.


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

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


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