Location: Belarus

Women Entrepreneurs in Belarus: Characteristics, Barriers and Drivers

Posted on May 13, 2018 | Policy Brief

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

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

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

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

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

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

Table 1. Sectoral distribution by gender of the owner

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

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

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

Table 2. Business characteristics by gender of the owner

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

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

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

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

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

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

Women’s social roles in Belarus

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

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

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

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

Education

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

Business and economic environment

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

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

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

Conclusion

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

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

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

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

References

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

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

Financial Stress and Economic Contraction in Belarus

Posted on February 12, 2018 | Policy Brief

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

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

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

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

Construction of the FSI for Belarus

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

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

Episodes of financial stress

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

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

Source: Author’s own calculations.

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

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

Financial stress and recessions

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

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

Source: Author’s own calculations.

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

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

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

Source: Belstat.

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

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

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

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

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

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

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

Conclusion

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

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

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

References

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

Avoiding Corruption and Tax Evasion in Belarus’ Construction Sector

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

20171119 Avoiding Corruption and Tax Evasion in Belarus Image 01

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

Shadow economy and the construction sector

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

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

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

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

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

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

Method

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

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

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

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

Survey results

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Conclusion

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

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

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

References

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Posted on September 30, 2017 | Policy Brief

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

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

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

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

Fiscal policies and their redistributive effects

Taxation

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

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

Direct transfers

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

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

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

Subsidies

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

Figure 1. Incidence of utilities subsidies by income deciles

Source: Bornukova, Chubrik and Shymanovich, 2017

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

What could be improved?

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

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

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

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

Conclusion

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

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

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

References

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

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

Posted on May 29, 2017 | Policy Brief

20170528 FREE Policy Brief - Monetary Policy Puzzle Image 01

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

Unusually high real rates and recession

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

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

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

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

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

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

Unanchored expectations drive interest rates

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

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

Figure 2. Actual and expected inflation, %

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

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

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

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

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

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

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

Monetary softening cannot neutralize structural recession

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

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

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

Conclusions

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

References

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

Trade Preferences Removal – The Case of Belarus

Posted on March 27, 2017 | Policy Brief

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

Belarus losing trade preferences

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

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

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

The complexity of Belarusian exports

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

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

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

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

Export structure of Belarus

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

Findings

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

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

Figure 2. Share of Manufacturing Exports

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

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

Conclusion

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

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

References

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

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The Anatomy of Recession in Belarus

Posted on December 12, 2016 | Policy Brief

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

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

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

Source: Belstat

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

Growth Decomposition 2011-2015

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

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

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

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

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

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

Sectoral dimension: manufacturing

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

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

International comparisons

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

Table 1. TFP winners and losers in Belarus

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

Source: Author’s calculations.

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

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

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

Conclusion

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

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

References

Belta (2015) http://eng.belta.by/president/view/bellesbumprom-group-to-increase-exports-to-1.4-1.5bn-by-late-2017-2860-2014/
Kruk, Dzmitry; and Kateryna Bornukova, 2014. “Belarusian Economic Growth Decomposition”, BEROC working paper series, WP no. 24

Spatial Wage Inequality in Belarus

Posted on October 10, 2016 | Policy Brief

20161010 FREE Policy Brief with Aleh Mazol Image

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

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

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

Characteristics of district wages

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

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

Source: Author’s own calculations.

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

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

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

Source: Author’s own calculations.

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

Spatial interdependencies of district wages

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

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

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

Source: Author’s own calculations.

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

Wage inequality in the districts of Belarus

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

Figure 4. Indexed real wage

Source: Author’s own calculations.

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

Figure 5. Measures of wage inequality

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

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

Conclusion

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

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

References

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

Effects of Trade Wars on Belarus

Posted on June 20, 2016 | Policy Brief

The trade wars following the 2014 events in Ukraine affected not only the directly involved participants, but also countries like Belarus that were affected through international trade linkages. According to my estimations based on a model outlined in Ossa (2014), these trade wars led to an increase in the trade flow through Belarus and thereby an increase of its tariff revenue. At the same time, because of a ban on imports in the sectors of meat and dairy products, the tariff revenue of Russia declined. As a member of the Eurasian Customs Union (EACU), Belarus can only claim a fixed portion of its total tariff revenue. Since the decline in the tariff revenue of Russia led to a decline in the total tariff revenue of the EACU, there was a decrease in the after-redistribution tariff revenue of Belarus. As a result, Belarusian welfare decreased. To avoid further welfare declines, Belarus should argue for a modification of the redistribution schedule. Alternatively, Belarus could increase its welfare during trade wars by shifting from being a part of the EACU to only being a part of the CIS Free Trade Area (FTA). If Belarus was only part of the CIS FTA, the optimal tariffs during trade wars should be higher than the optimal tariffs without trade wars. The optimal response to the increased trade flow through Belarus is higher tariffs.

Following the political protests in 2014, Ukraine terminated its membership in the CIS Free Trade Area (FTA) and moved towards becoming a part of the EU. The political protests evolved into an armed conflict and a partial loss of Ukrainian territory. These events led to Western countries introducing sanctions against some Russian citizens and enterprises. In response, Russia introduced a ban on imports from EU countries, Australia, Norway, and USA in the sectors of meat products, dairy products, and vegetables, fruits and nut products. In addition, both Ukraine and Russia increased the tariffs on imports from each other in the above-mentioned sectors.

Clearly, the trade wars affected directly involved participants such as the EU countries, Russia, and Ukraine. At the same time, countries like Belarus that were not directly involved in the trade wars, were also affected because of international trade linkages. It is important to understand the influence of trade wars on none-participating countries. To address this question, a framework with many countries and international trade linkages will be utilized and I will in this policy brief present some of my key findings.

Framework and Data

To evaluate the effects of the trade wars, I use the methodology outlined in Ossa (2014). This framework is based on the monopolistic competition market structure that was introduced into international trade by Krugman (1979, 1981). The framework in Ossa (2014) allows for many countries and sectors, and for a prediction of the outcome if one or several countries changes their tariffs. Perroni and Whallye (2000) and Caliendo and Parro (2012) present alternative frameworks with many countries that can also be used to estimate the welfare effects of tariff changes. The important advantage of the framework introduced in Ossa (2014) is that only data on trade flows, domestic production, and tariffs are needed to evaluate the outcomes of a change in tariffs, though the model itself contains other variables like transportation costs, the number of firms, and productivities.

It should also be pointed out that the framework in Ossa (2014) is not an example of a CGE model as it does not contain features such as investment, savings, and taxes. Since the framework in Ossa (2014) is simpler than CGE models, the effects of a tariff change can more easily be tracked and interpreted. On the other hand, this framework does not take into account spillover effects of tariff changes on for example capital formation and trade in assets.

The data on trade flows and domestic production come from the seventh version of the Global Trade Analysis Project database (GTAP 7). The data on tariffs come from the Trade Analysis Information System Data Base (TRAINS). The estimation of the model is done for 47 countries/regions and the sectors of meat and dairy products.

Results

According to my estimations, because of the ban on imports by Russia, the trade flow through Belarus increased. Belarusian imports of meat products are estimated to have increased by 28%, and imports of dairy products by 47%. Such increases in imports mean an increase in the tariff revenue of Belarus. It should be pointed out, however, that the model only tracks the effects of the ban on imports in the sectors of meat and dairy products. An alternative way would be to construct an econometric model that takes into account different factors influencing the trade between the countries. The effects of the decrease in the price of oil and the introduced ban on imports, which happened close in time, could then have been evaluated.

The estimated model further predicts that, because of the ban on imports, the tariff revenue collected by Russia in these two sectors has decreased by 53%. This means that since Belarus can only claim a fixed portion (4.55%) of the total tariff revenue of the EACU, its after-redistribution tariff revenue collected in the meat and dairy product sectors declined by 44.86%, in spite of its increase in before-redistribution tariff revenue by 35%. The decline in Belarus’ after-redistribution tariff revenue is thus estimated to have led to a decrease in welfare by 0.03%. To prevent such a decrease in the future, Belarus should argue for an increase in its share of the total tariff revenue of the EACU.

Furthermore, in addition to the decrease in the tariff revenue, the estimated model predicts that the real wage in Russia decreased by 0.39%, and its welfare by 0.49%.

The introduced ban on imports also affected the European countries that used to export to Russia. The model predicts that the welfare of Latvia declined by 0.38% and that the welfare of Lithuania declined by 0.27%. A substantial portion of the decline in welfare of these countries can be explained by a decrease in their terms of trade. The introduced ban on imports by Russia led to a decline in prices in the countries that exported meat and dairy products to Russia. Lower prices led to a decrease in the proceeds from exports collected by EU countries, and lower proceeds from exports buy less import, implying a decrease in their welfare.

In spite of the increase in tariffs between Russia and Ukraine, the model predicts an increase in the welfare of Ukraine by 0.23% following the formation of the EU-Ukraine Deep and Comprehensive Free Trade Area (DCFTA). An increase in real wages by 0.34% is the main factor contributing to this welfare increase. This is because it is associated with a redirection of Ukrainian exports from Russia towards the EU. The predicted increase in real wages in Ukraine have not materialized so far, presumably because of the ongoing military conflict and because time is needed to redirect the trade flows in response to the changes in the tariffs.

While bearing in mind that the analysis is only based on the sectors of meat and dairy products, Belarus could have increased its welfare during the trade wars if it had shifted from EACU status back to CIS FTA status with tariffs set at before-EACU levels. In this case, Belarus would not have needed to share its tariff revenue with other countries, and would then have increased its tariff revenue by 47.93% instead of the now predicted decline by 44.86%. Similarly, the welfare during trade wars could then have increased by 0.05%, instead of the now predicted decline by 0.03%. Another advantage of moving to CIS FTA status during trade wars is that the real wage could have increased by 0.04% instead of the 0.003% in the case of continued EACU status. Belarus could further have benefitted from moving to CIS FTA status by choosing optimal tariffs. This study suggests that the optimal tariffs of Belarus under CIS FTA status with trade wars are higher than the optimal tariffs under CIS FTA status without trade wars. Higher tariffs is the optimal response to the increased trade flows through Belarus resulting from trade wars.

Conclusion

Although it is optimal to move to CIS FTA status during trade wars, it is optimal to move back to EACU status after the trade wars are over. Therefore, such a policy should be adopted with caution, since the shift back to EACU status will likely not be possible. If it is expected that the trade wars will continue for a long period of time, or if the other members of the EACU will often deviate from the common tariffs, a transition to CIS FTA should be adopted. At the same time, asking for an increase in its share of total tariff revenue of EACU is a feasible strategy for Belarus to follow.

While estimating the effect of a transition from EACU status to CIS FTA status for Belarus during trade wars, the evaluation was done using two sectors affected by counter-sanctions. To evaluate the full welfare effect of this transition, its effect on the other sectors of Belarus should also be estimated, which is a question for the further research.

Traces of Transition: Unfinished Business 25 Years Down the Road?

Posted on June 13, 2016 | Policy Brief

This year marks the 25-year anniversary of the breakup of the Soviet Union and the beginning of a transition period, which for some countries remains far from completed. While several Central and Eastern European countries (CEEC) made substantial progress early on and have managed to maintain that momentum until today, the countries in the Commonwealth of Independent States (CIS) remain far from the ideal of a market economy, and also lag behind on most indicators of political, judicial and social progress. This policy brief reports on a discussion on the unfinished business of transition held during a full day conference at the Stockholm School of Economics on May 27, 2016. The event was organized jointly by the Stockholm Institute of Transition Economics (SITE) and the Swedish Ministry for Foreign Affairs, and was the sixth installment of SITE Development Day – a yearly development policy conference.

A region at a crossroads?

25 years have passed since the countries of the former Soviet Union embarked on a historic transition from communism to market economy and democracy. While all transition countries went through a turbulent initial period of high inflation and large output declines, the depth and length of these recessions varied widely across the region and have resulted in income differences that remain until today. Some explanations behind these varied results include initial conditions, external factors and geographic location, but also the speed and extent to which reforms were implemented early on were critical to outcomes. Countries that took on a rapid and bold reform process were rewarded with a faster recovery and income convergence, whereas countries that postponed reforms ended up with a much longer and deeper initial recession and have seen very little income convergence with Western Europe.

The prospect of EU membership is another factor that proved to be a powerful catalyst for reform and upgrading of institutional frameworks. The 10 countries that joined the EU are today, on average, performing better than the non-EU transition countries in basically any indicator of development including GDP per capita, life expectancy, political rights and civil liberties. Even if some of the non-EU countries initially had the political will to reform and started off on an ambitious transition path, the momentum was eventually lost. In Russia, the increasing oil prices of the 2000s brought enormous government revenues that enabled the country to grow without implementing further market reforms, and have effectively led to a situation of no political competition. Ukraine, on the other hand, has changed government 17 times in the past 25 years, and even if the parliament appears to be functioning, very few of the passed laws and suggested reforms have actually been implemented.

Evidently, economic transition takes time and was harder than many initially expected. In some areas of reform, such as liberalization of prices, trade and the exchange rate, progress could be achieved relatively fast. However, in other crucial areas of reform and institution building progress has been slower and more diverse. Private sector development is perhaps the area where the transition countries differ the most. Large-scale privatization remains to be completed in many countries in the CIS. In Belarus, even small-scale privatization has been slow. For the transition countries that were early with large-scale privatization, the current challenges of private sector development are different: As production moves closer to the world technology frontier, competition intensifies and innovation and human capital development become key to survival. These transformational pressures require strong institutions, and a business environment that rewards education and risk taking. It becomes even more important that financial sectors are functioning, that the education system delivers, property rights are protected, regulations are predictable and moderated, and that corruption and crime are under control. While the scale of these challenges differ widely across the region, the need for institutional reforms that reduce inefficiencies and increase returns on private investments and savings, are shared by many.

To increase economic growth and to converge towards Western Europe, the key challenges are to both increase productivity and factor input into production. This involves raising the employment rate, achieving higher labor productivity, and increasing the capital stock per capita. The region’s changing demography, due to lower fertility rates and rebounding life expectancy rates, will increase already high pressures on pension systems, healthcare spending and social assistance. Moreover, the capital stock per capita in a typical transition country is only about a third of that in Western Europe, with particularly wide gaps in terms of investment in infrastructure.

Unlocking human potential: gender in the region

Regardless of how well a country does on average, it also matters how these achievements are distributed among the population. A relatively underexplored aspect of transition is to which extent it has affected men and women differentially. Given the socialist system’s provision of universal access to education and healthcare, and great emphasis on labor market participation for both women and men, these countries rank fairly well in gender inequality indices compared to countries at similar levels of GDP outside the region when the transition process started. Nonetheless, these societies were and have remained predominantly patriarchal. During the last 25 years, most of these countries have only seen a small reduction in the gender wage gap, some even an increase. Several countries have seen increased gender segregation on the labor market, and have implemented “protective” laws that in reality are discriminatory as they for example prohibit women from working in certain occupations, or indirectly lock out mothers from the labor market.

Furthermore, many of the obstacles experienced by small and medium-sized enterprises (SMEs) are more severe for women than for men. Female entrepreneurs in the Eastern Partnership (EaP) countries have less access to external financing, business training and affordable and qualified business support than their male counterparts. While the free trade agreements, DCFTAs, between the EU and Ukraine, Georgia, and Moldova, respectively, have the potential to bring long-term benefits especially for women, these will only be realized if the DCFTAs are fully implemented and gender inequalities are simultaneously addressed. Women constitute a large percentage of the employees in the areas that are the most likely to benefit from the DCFTAs, but stand the risk of being held back by societal attitudes and gender stereotypes. In order to better evaluate and study how these issues develop, gendered-segregated data need to be made available to academics, professionals and the general public.

Conclusion

Looking back 25 years, given the stakes involved, things could have gotten much worse. Even so, for the CIS countries progress has been uneven and disappointing and many of the countries are still struggling with the same challenges they faced in the 1990’s: weak institutions, slow productivity growth, corruption and state capture. Meanwhile, the current migration situation in Europe has revealed that even the institutional development towards democracy, free press and judicial independence in several of the CEEC countries cannot be taken for granted. The transition process is thus far from complete, and the lessons from the economics of transition literature are still highly relevant.

Participants at the conference

Irina Alkhovka, Gender Perspectives.
Bas Bakker, IMF.
Torbjörn Becker, SITE.
Erik Berglöf, Institute of Global Affairs, LSE.
Kateryna Bornukova, Belarusian Research and Outreach Center.
Anne Boschini, Stockholm University.
Irina Denisova, New Economic School.
Stefan Gullgren, Ministry for Foreign Affairs.
Elsa Håstad, Sida.
Eric Livny, International School of Economics.
Michal Myck, Centre for Economic Analysis.
Tymofiy Mylovanov, Kyiv School of Economics.
Olena Nizalova, University of Kent.
Heinz Sjögren, Swedish Chamber of Commerce for Russia and CIS.
Andrea Spear, Independent consultant.
Oscar Stenström, Ministry for Foreign Affairs.
Natalya Volchkova, Centre for Economic and Financial Research.