Location: Belarus
Money as an Economic Category and Its Relationship With Crypto Assets
This brief discusses money in its general definition and describes new types of money arising in the modern era of digitalization, such as electronic money, cryptocurrencies, Central Bank Digital Currencies (CBDC), etc. It provides an overview of some of the legislative approaches trying to deal with new types of money and outlines the benefits and shortcomings arising from allowing for financial operations with digital currency. It also stresses the necessity of a new integrated approach in national and international regulation of cryptocurrencies.
Introduction
Cryptocurrencies have existed for more than 10 years. During this period the interest towards this type of digital money has seen its ups and downs. However, by now, they have become part of modern financial markets. Today, more and more central banks consider the possibility of introducing national digital cash and try to create easy-to-understand and clear regulation for new payment methods. We can observe the rapid transformation of the traditional monetary system. At the same time, there is no clear understanding of how the new monetary system should look like. An essential step towards this understanding is developing a clearer systematization and definition of money, financial funds, cryptocurrencies, fiat money in the traditional and the modern sense. Explaining these concepts is necessary to facilitate effective regulation, the development and supervision of financial markets. Indeed, during rapid financial markets transformation, well-developed regulation is necessary to avoid excessive financial risks and speed up financial sector development.
The Place of Money in the Modern Financial System
Financial resources play an extremely important role in the economy: Monetary systems are like the blood circulation for the body. While there is a common understanding of what money is in the traditional sense, this concept does not take into account the recent development of the financial sector, the penetration of IT technologies, the entry of new non-financial institutions into the financial sector as well as the creation of new products at the intersection of finance and IT. As argued above, a clear and encompassing definition of money, reflecting these developments, is necessary for regulatory purposes both at the national and international level.
Typically, money is defined through its functions, such as a measure of value, means of circulation, means of payment and savings. For example, the Large Economic Dictionary suggests that “Money is the universal equivalent, a special product, used to form expressions of the value of all other goods. Money functions as a medium of exchange and of payments, as a measurement of value, wealth accumulation and world money” (Borisov, 2003). As can be seen, one of the most important characteristics of money is its universality. Money can be exchanged against different goods and services almost without any limitations. At the same time, Tarasov mentioned that money is “legal payment funds, usually consisting of banknotes and coins that are constantly circulating as a medium of exchange in accordance with government rule” (Tarasov, 2012). There are other definitions of money, but they usually describe traditional money.
Along with traditional fiat money, there are other payment methods and electronic money is the most common of them. According to the Belarusian legislation, electronic money is “units of value stored in electronic form, issued in exchange against cash and monetary funds and accepted as a means of payment […]”
Electronic money cannot be described as traditional cash or money on bank accounts. It is not included in the money supply and can be issued only by commercial banks. At the same time, electronic money can perform the same functions as traditional fiat money. Whether or not electronic money can be considered full-fledged money is essentially a legal issue.
Another very important question is dedicated to cryptocurrencies. Cryptocurrencies are usually issued based on blockchain technology (distributed ledger) and can be created (“mined”) by anybody. Hence, electronic money is representative of traditional money, but cryptocurrencies are not.
Taking into account the penetration of information technologies into finance as well as the appearance of electronic money and cryptocurrencies, we can define money as the universal equivalent (measure) of value constituting a legal means of circulation, payment and savings on certain territories within a particular jurisdiction, with a legal status guaranteed by the government (Luzgina, 2018). In this definition, the emphasis is placed on the legitimacy of money because in some countries, operations with digital currencies can be legally interpreted as operations with securities, equity etc., rather than money in the legal sense.
Belarus was one of the first countries that legalized operations with crypto assets. But this does not mean that cryptocurrencies have become the equivalent of national or foreign currencies. According to the Belarusian legislation, people can mine cryptocurrencies, exchange them against Belarusian rubles, foreign currencies, buy, sell and exchange against other tokens (Decree #8, 2018). There is no official permission to use crypto money as a measure of value, means of circulation or payment method. In other words, people cannot use bitcoins for purchasing goods and services. At the same time, cryptocurrencies can be used as traditional financial assets.
It is necessary to emphasize here that the digitalization of the financial sector is an ongoing process. It is very hard to be the leader in the sphere. Despite Belarus being an early mover in the legalization of crypto assets and notwithstanding the existence of a strong IT sector and attractive crypto assets regulation, Belarus is only the 59th among 65 countries in the Fintech Index 2020. Based on the experience of other countries, sustained progress in this area can be achieved by government support, the existence of a well-developed ecosystem and access to financing (Global FinTech Index 2020).
Belarus is not the only country in the world that has limitations on cryptocurrencies’ circulation as fiat money; restrictions differ depending on the jurisdiction. Many central banks consider cryptocurrencies as disruptive technologies with high risks. Regulatory bodies usually cannot control operations with crypto money. That is why cryptocurrencies can be attractive for payments in the grey economy. Moreover, exchange rate fluctuations of cryptocurrencies are very unpredictable. Owners of cryptocurrencies can become very rich as well as very poor within a short period of time.
Central banks can implement limitations to avoid or decrease risks. For example, operations with cryptocurrencies are prohibited in Bangladesh and strongly restricted in India. There are central banks (including the central banks of Malaysia and Austria) that take a neutral position with regards to crypto operations but inform the society about possible risks, including risks of high fluctuations (Luzgina, 2018). At the same time, Japan permits the circulation of cryptocurrencies as a means of payment within its current regulation. That is, the Japanese authorities legalized these digital assets and, supposedly, can keep risks under control.
It is important to understand that these, and other, differences in the approach to crypto assets regulation create barriers for international payments and investment transactions. At the same time, a unification of regulation would contribute to transparency and mitigate the risk of cybercrimes.
Central Bank Digital Currencies: Main Aspects
There is an intense political and academic debate about the future of crypto markets. At the same time, more and more countries begin to think about the introduction of Central Bank Digital Currency (CBDC). Countries like Ukraine, China, Sweden, Canada, Thailand and some others have announced their plans of issuing CBDC. CBDC can be compared with digital cash; it can reduce operational costs and make all money transactions more transparent. But there are some uncertainties: The technology is new and may cause confusion and even disapproval among the population who prefers to use only cash.
One of the most interesting examples of the introduction of CBDC is the case of Uruguay. In 2017-2018, this country realized a pilot project of CBDC (the e-peso). A limited amount of digital currency was issued and only 10,000 citizens joined the project. There was a limited list of stores and businesses that were allowed to work with digital currency and all transactions on the base of mobile phones were done only between registered users. This project has demonstrated several advantages of e-peso circulation. First, the system could work without Internet and provided anonymity but at the same time controllability of all operations. Second, security was the main concern: The person could get access to his/her digital resources even if he/she forgot the password of the digital wallet or lost the mobile phone, but non-authorized access was effectively avoided. Finally, the last but not the least advantage of the system was the exclusion of double charge or falsification during payment transactions. The project lasted half a year and finished successfully. However, transition to the digital currency did not follow.
As of now, many countries only consider or are going to realize pilot studies in this area. The only country that is going to implement CBDC in the foreseeable future is China. The cautious position of many central banks is understandable because CBDC is an analogue of digital cash. The population distrusts such forms of money. Another challenge is that senior citizens often prefer cash for payments and other financial transactions.
Tokens vs. Cryptocurrencies
Bitcoin and other cryptocurrencies present only one kind of digital tokens. According to the Belarussian legislation, a token is an entry in the register of transaction blocks (blockchain), or another distributed information system certified that the owner of a digital sign (token) has rights to civil law objects and (or) presents cryptocurrency. All cryptocurrencies are tokens but not all tokens can be defined as cryptocurrencies. Tokens are issued for multiple purposes. Governments in many countries try to identify all types of operations with tokens for the creation of clear regulation. For example, the Central Bank of Lithuania highlights the differences between issuing tokens in the framework of ICO (Initial Coin Offering) and STO (Security Token Offering). According to the Lithuanian regulation, ICO usually provides for presenting discount programs or using tokens as payment instruments. At the same time, STO includes the issuance of tokens that have features of bonds or other traditional financial instruments and is subject to regulation. In other countries, central banks do not highlight STO and operations regulation with tokens depends on the characteristics and specifics of each project.
Many countries have developed unique principles and rules of tokens regulation. But there are no unified approaches at the international level which makes it difficult for conscientious market participants to work with financial crypto assets over different jurisdictions. Moreover, there are uncertainties and risks that have to be investigated more in detail. Authorities in many countries are afraid of cybercrimes and increasing money laundering operations.
At the same time, many advantages are apparent. For example, in Belarus, crypto platforms get more popular, because they offer attractive financial instruments for the population and companies. On such platforms, companies can attract necessary resources and citizens invest in financial tools with regulated risks.
Figure 1 – Structure of digital, electronic money, tokens and financial means (Luzgina, 2018)
Comment: Fiat electronic money is an electronic analogue of fiat currency. In this case, if we put 100 euros in an electronic wallet, we should see 100 electronic euros after the transaction. At the same time, non-fiat electronic money differs from fiat currency. For example, we can exchange Belarusian ruble against electronic money – V-coin, which is issued by Belgazprombank in cooperation with the mobile operator – A1.
The above discussion results in a number of policy-relevant implications:
- The legal definition of money, financial funds and electronic money should be updated taking into account innovative forms of financial instruments development and the appearance of new financial market participants.
- Old rules and regulatory approaches hinder market development and unregulated space can create additional risks and uncertainties.
- The transition from cash to CBDC is possible but has limitations.
- A unified regulation for cryptocurrencies and other tokens should be developed at the international level for decreasing risks and further developing financial markets.
Conclusion
Financial market transformation is happening very rapidly. The penetration of information technologies in the financial sector created a huge number of new innovative products and simplified financial operations. All these changes have affected the payment system. The creation of electronic and digital currencies makes it necessary to reconsider the future of the traditional monetary system. But even the current regulation has to become more flexible and take into account the rapid growth of new types of financial market participants and products. The development of financial technologies creates additional risks, such as money laundering, money theft or uncontrolled financial operations which go beyond the borders drawn by national jurisdictions very often. Many central banks treat payments with cryptocurrencies and ICO with caution. At the same time, the process cannot be stopped because alternative methods of financial transactions are often more attractive compared with traditional financial services. But the low level of financial and digital literacy among the population combined with outdated legislation can slow down innovative processes in the financial sphere and augment the risks.
References
- “Money: meaning and functions of money – discussed!” (2007). Economics Discussion. Accessed September 12, 2017.
- Tarasov V.I. (2012), “Money, credit, banks”, Minsk: BSU. p. 375.
- “On Digital Economy Development”. Decree No.8 dated December 21, 2017.
- Luzgina A. “Money and monetary funds as economic categories and their relationship with cryptocurrencies”, Bank Bulleting Journal, October 2018. pp.26-35.
- “Japan to provide G20 with the solution for Crypto Regulation”, News Bitcoin.com. Accessed February 28, 2020.
- Central Banks worldwide testing their digital currencies“, News Bitcoin.com. Accessed February 20, 2020.
- Banco Central del Uruguay, 2018. “Uruguayan e-Peso on the context on financial inclusion“, Accessed January 15, 2020.
- “Bank of Lithuania Issues Guidelines for Regulating STO”, (2019). Crowdfund Insider, Accessed February 10, 2020.
- Borisov A.B, (2003). Large Economic Dictionary. Knizhni Mir. p. 895.
- “The Global FinTech Index 2020”, (2019). Accessed March 10, 2020.
- Ting Peng “Turning a crisis into an opportunity, China gets one step closer to CBDC”. Accessed March 25, 2020.
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.
COVID-19 | The Case of Belarus
Belarus is a country with about 9.5 million citizens. The area is 207 thousand sqkm which gives a population density of 45.9 persons/sqkm. The capital is Minsk with around 2 million inhabitants, other major cities are Gomel (0.53mn), Mogilev (0.38mn), Vitebsk (0.38mn), Grodno (0.37mn), Brest (0.35mn). Belarus is a member of the Eurasian Economic Union and is part of the Union State of Russia and Belarus. The national currency is the Belarusian Ruble (BYN).
Different responses to the crisis across countries depend partly on the organization of political authority, as reflected in the level of regional decentralization of decision making in key areas of authority, and the strength and independence of public agencies. In the case of Belarus, the power is highly centralized and most decisions are made either by central government or personally by the president.
It is widely considered that the government in Belarus has a small degree of independence from the president. The authority in charge of dealing with pandemics is the Ministry of Health.
Health Indicators
Belarus had its first officially registered case of Covid-19 on February 27 and the first death on March 31. At first, the increase of the newly registered cases was slower than in most other countries, but in the beginning of April, Belarus started to catch up, reaching 351 officially registered total cases by April 3. As of April 3, officials in Belarus have performed 32000 cases and tried to trace and isolate all the close contacts in the early phase of Covid-19 spread.
Belarus has a relatively high numbers of doctors and hospital beds per capita. There are 4 doctors, 12 nurses and 8 hospital beds per 1000 citizens and 2.3 intensive care units per 10,000 citizens. Government officials claim that there are 22 lung ventilators per 100 thousand persons and that this number can be increased to 38 if necessary.
Financial Indicators
Belarus currently does not have a properly functioning stock exchange, so it is hard to provide any strong evidence on the changes in corporate valuations. The Belarusian ruble started to depreciate in late February of 2020. Figure 1 depicts the recent developments in the exchange rate with respect to US dollar. Since the beginning of 2020, the US dollar went from 2.1 BYN to 2.57 BYN.
Figure 1: USD to BYN exchange rate.
Source: National Bank of Belarus.
The developments that can be seen on Figure 1 are largely due to the depreciation of the Russian Ruble which in turn was caused by decrease in oil prices as the OPEC+ agreement have failed in early March of 2020.
Government Health Policies
The government’s strategy so far was to identify and trace all the Covid-19 cases by performing a large number of tests (32,000 as of April 3) and isolating the first-degree contacts of infected persons. Public events with international participation were forbidden, however this does not apply to other public events and gatherings including football games and music concerts. As of April 4, government officials are still planning to hold the WW2 victory parade on May 9. Borders and airports are not closed, but persons arriving from abroad are advised to self-isolate for 14 days. There is no state-wide closure of schools and universities. The only closed teaching institutions are those which had students with officially confirmed Covid-19.
There is no state-wide quarantine as government officials deem it unnecessary and President Lukashenka calls the situation “Covid hysteria”. Among the measures taken up to date is financial regulatory easing ordered by the National Bank of Belarus. The government also issued a decree that consumer prices should not increase by more than 0.5% per month. In addition to that, the government plans to spend 110 million BYN (42.5 million USD) on economic support measures.
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.
Can Loose Macroeconomic Policies Secure a ‘Growth Injection’ for Belarus?
After a relatively long period of macroeconomic stabilization, Belarus faces the threat of a purposeful deviation from it. However, today there is no room for a ‘growth injection’ by means of monetary policy. Moreover, Belarus still suffers from a problem of unanchored inflation expectations. This prevents monetary policy from being effective and powerful. So, unless inflation expectations have been anchored, any discussion about reshaping monetary policy and making it ‘pro-growth’ is meaningless.
Policy Mix and Macroeconomic Landscape in Belarus
Since 2015, Belarus has considerably improved the quality of its macroeconomic policies. The country has fallen back upon a floating exchange rate, and feasible monetary and fiscal rules. This change followed a long history of voluntary expansionary policy mixes associated with numerous episodes of huge inflation, currency crises, etc.
Due to the new policy mix, the country has been displaying a movement towards macro stability in recent years. For instance, the external position is close to being balanced, the fiscal position has even become positive, while the inflation rate is at historical lows around 5%. For Belarus, these achievements are important, taking in mind a ‘fresh memory’ of price and financial instability. Hence, until recently there were no doubts in the feasibility of the commitments of Belarusian authorities to sound macroeconomic policies.
However, despite a relatively strong macroeconomic performance, the threat of a purposeful and at least temporary deviation from policy commitments seems to strengthen. What is important is that this time, popular simple explanations – e.g. political voluntarism (Belarus will have presidential elections in 2020), a naïve perception of economic policy mechanisms by authorities, etc. – are not sufficient for understanding the phenomenon. Rounds of loosening economic policies tend to be justified as ‘lesser evils’. Exploring some rationality in such a justification requires more insight into the Belarusian macroeconomic landscape.
In recent years, the lack of productivity and output growth has become more evident: in 2015-2019 the average output growth rate has been around 0. The root of the problem is the deficit in productivity and growth (Kruk & Bornukova, 2014; Kruk, 2019), while the rules-based policy mix just uncovered it.
However, this direction of causation tends to be challenged by some policy-makers. In an ’archaic’ manner, the policy mix is accused of blocking any pro-output policy discretion, even if there is a justification for it. For instance, an ‘extra’ need for a ‘growth injection’ may be justified by social challenges. Poor growth in Belarus results in a rather sensitive squeezing of relative levels of well-being in comparison to neighboring countries. Between 2012 and 2019, the well-being shrank from around 78% of the average level in 11 CEE countries down to about 63%. This intensified the labour outflow significantly, including for those employed in socially important industries, say, in healthcare. So, according to this view, the ‘growth injection’ is a lesser evil rather than systemic social threat.
A more advanced ‘accusation’ of the new policy mix assumes that it either causes a too restrictive stance of monetary policy with respect to output or that it ignores complicated transmission channels. For instance, one may argue that too much emphasis on price and financial stability can actually result in undermining them, given the huge debt burden of Belarusian firms. The quality of a considerable portion of the debts in Belarus tends to be sensitive to output growth rates. Hence, according to this argumentation, the monetary policy rule should be ‘more pro-growth’, reflecting the debt-growth-financial stability linkage inside it.
‘Translating’ this policy agenda to a research agenda results in two questions. First, is there room for a more expansionary monetary policy? Second, do financial instability risks require making the monetary policy rule ‘more pro-growth’?
The Monetary Policy Stance: Causality and Causes
Monetary policy, as a rule, aims to be counter-cyclical, i.e. generate expansionary incentives during cyclical downturns, and vice versa. In this respect, its stance should be matched to the estimate of the output gap. From this view, given dominating estimates of a near-zero output gap for 2019 in Belarus (National bank, 2019; Kruk, 2019), today’s monetary policy should be roughly neutral. However, analyzing monetary policy stance together with the estimates of the output gap is not a univocal option, especially given doubts about the consistency of any estimate of the output gap (Coibion et al., 2017).
From this point of view, a direct measurement of the monetary policy stance – matching ex-post real interest rate vs. an ex-ante one – is a worthwhile alternative. If the ex-post real interest exceeds the ex-ante rate, it means that the interest rate policy by a central bank is restrictive, while an opposite situation witnesses its expansionary stance (e.g. Gottschalk, 2001). A methodology for identifying inflation expectations by Kruk (2016) allows detecting restrictive and expansionary stances as well. Moreover, doing it in this way allows simultaneously tracing the stance of actual and expected inflation, and study its possible impact on monetary policy (Figure 1).
Figure 1. Monetary Policy Stance, Actual Inflation and Inflation Expectations in Belarus
Note: Positive sign means restrictive stance of monetary policy, while negative sign means expansionary stance.
Source: Own elaboration according to methodology in Kruk (2016) and based on data from the National Bank of Belarus.
First, this diagnostic shows that the stance of the monetary policy today is roughly neutral, which conforms to the diagnosis based on matching with the output gap. In this respect, it means that there is no room for monetary policy softening today.
However, eventually the situation may change and a need for an expansionary monetary policy may indeed arise. Can the National Bank of Belarus unconditionally satisfy such demand? Second, and the more important conclusion, is that the National Bank cannot. Figure 1 also demonstrates that the monetary policy stance in Belarus is very sensitive to the stance of inflation expectations. From this view, the restrictive monetary policy, say in 2015-2016 and 2018, reflected shocks in inflation expectations. The National Bank had to take a mark-up in the expected inflation in respect to the actual one into account and to transform it to the mark-up of the interest rate. If the National Bank ignores such shocks and nevertheless softens monetary policy, it will undermine price stability due to a powerful transmission effect from expected inflation to the actual one. Moreover, a reverse linkage from actual inflation to the expected one is likely to result in a prolonged inflationary period, causing a so-called ‘abnormal’ stance of the monetary environment (Kruk, 2016).
So, a generalized policy diagnosis for today looks as follows. Monetary policy has reached a roughly neutral level due to a considerable reduction in inflation expectations. The latter, in turn, happened due to a prolonged period of a restrictive policy stance (in 2015-2016), which suppressed actual inflation by means of sacrificing output in a sense (the period of cyclical downturn could have been shorter without such limitations in monetary policy).
Unanchored Expectations Bar a More ‘Pro-Growth’ Policy
A deeper cause of the limited room for monetary policies is unanchored inflation expectations. Statistical properties of the inflation expectations series (Kruk, 2019 and 2016), as well as the polls of households and firms by the National Bank, suggest that despite the reduction of the level of inflation expectations, the issue of it being unanchored is still on the agenda. In this respect, expected inflation in Belarus tends to be sensitive to numerous kinds of actual and information shocks, e.g. domestic and global output dynamics, interest rate levels and spreads, exchange rates, financial stability issues, etc. Hence, unless expectations have been anchored, the monetary policy would still suffer from a lack of power. This means that anchoring inflation expectations is the core precondition for normalizing the monetary environment and the power of any monetary policy.
For the monetary rule, this means that it cannot become more ‘pro-growth’, keeping in mind the risks to financial stability. Otherwise, it can spur price destabilization, which may also trigger financial instability. Hence, the logic of a ‘lesser evil’ does not work. Indeed, there are risks to financial stability stemming from poor growth. But combating them through a more ‘pro-growth’ policy will cause price instability and financial instability stemming from that. But what is more important, the logic of a ‘lesser evil’ itself is doubtful with respect to monetary policy. Recognizing the linkage between monetary policy and financial stability does not mean that risks to the latter should be directly traced by the former. Financial stability issues can and should primarily be tackled through macroprudential tools.
Conclusions
After a relatively long period of macroeconomic stabilization, Belarus faces some risk with respect to it. However, today’s monetary policy stance is roughly neutral in Belarus. Hence, a ‘growth injection’ may result in inflation resurgence. Moreover, even today’s near-neutral monetary policy stance is a considerable achievement, as the country still experiences the challenge of unanchored inflation expectations. This issue is a deep underlying problem, which keeps the monetary policy from being more effective and powerful. So, unless inflation expectations have been anchored, any discussion about reshaping it and making it ‘pro-growth’ is meaningless.
As for today’s justifications for monetary policy softening – poor growth and financial instability risks – they hardly relate with the monetary policy agenda. The challenge of poor growth requires thinking in terms of productivity issues, while financial stability risks in terms of macroprudential tools first.
References
- Coibion, O., Gorodnichenko, Y, Ulate, M. (2017). The Cyclical Sensitivity in Estimates of Potential Output, National Bureau of Economic Research, Working Paper No. 23580.
- Gottschalk, J. (2001). Monetary Conditions in the Euro Area: Useful Indicators of Aggregate Demand Conditions? Kiel Institute for the World Economy Working Paper No. 1037.
- Kruk, D. (2019). Belarusian Economy in Mid-2019: the Results of the Recovery Growth Period, BEROC Policy Paper No. 69.
- Kruk, D. (2016). SVAR Approach for Extracting Inflation Expectations Given Severe Monetary Shocks: Evidence from Belarus BEROC Working Paper No. 39.
- Kruk, D., Bornukova, K. (2014). Belarusian Economic Growth Decomposition, BEROC Working Paper No. 24.
- National Bank of the Republic Belarus (2019). Information on the Dynamics of Consumer Prices and Tariffs and Factors of Changes Therein, 2019Q3.
The Long Shadow of Transition: The State of Democracy in Eastern Europe
In many parts of Eastern Europe, the transition towards stronger political institutions and democratic deepening has been slow and uneven. Weak political checks and balances, corruption and authoritarianism have threatened democracy, economic and social development and adversely impacted peace and stability in Europe at large. This policy brief summarizes the insights from Development Day 2019, a full-day conference organized by SITE at the Stockholm School of Economics on November 12th. The presentations were centred around the current political and business climate in the Eastern European region, throwing light on new developments in the past few years, strides towards and away from democracy, and the challenges as well as possible policy solutions emanating from those.
The State of Democracy in the Region
From a regional perspective, Eastern Europe has seen mixed democratic success over the years with hybrid systems that combine some elements of democracy and autocracy. Based on the V-Dem liberal democracy index, ten transition countries that have joined the EU saw rapid early progress after transition. In comparison, the democratic development in twelve nations of the FSU still outside of the EU has been largely stagnant.
In recent years, however, democracy in some of those EU countries, such as Bulgaria, the Czech Republic, Hungary, Poland and Romania have been in decline. Poland, one of the region’s top performers in terms of GDP growth and life expectancy, has experienced a sharp decline in democracy since 2015. Backlashes have often occurred after elections in which corruption and economic mismanagement have led to the downfall of incumbent governments and a general distrust of the political system. Together with low voter turnout, this created fertile ground for more autocratic forces to gain power helped by demand for strong leadership.
An example from Ukraine illustrated the role of media, both traditional and social, for policy-making. In some countries of the region, traditional media is strictly state-controlled with obvious concerns for democracy. This is less the case in Ukraine, where also social media plays an important role in forming political opinions. The concern is that, as elsewhere, opinions that gain traction on social media may not be impartial or well informed, affecting public perception about policy-making. A recent case showing the popular reaction to an attack on the former governor of the Central Bank suggests that those implementing important reforms may not get due credit when biased and partial information dominates the political discourse on social media.
Another case is the South Caucasian region: Armenia, Georgia and Azerbaijan. The political situation there has been characterized as a “government by day, government by night” dichotomy, implying that the real political power largely lies outside the official political institutions. In Georgia, the situation can be described as a competition between autocracy and democracy, with a feudalistic system in which powerful groups replace one another across time. As a result, trust in political institutions is low, as well as citizens’ political participation.
In the case of Azerbaijan, there is an elected presidency, but in reality, power has been passed on hereditarily, becoming a de facto patrimonial system. Lastly, in Armenia, the new government possesses democratic credentials, but the tensions with neighbouring Azerbaijan and Turkey have given increasing power to the military and important economic powers. Overall, democratisation in these countries has been hindered by a trend for powerful politicians to form parties around themselves and to retain power after the end of their mandates. Also, the historical focus on nation-building in these countries has led to a marked exclusion of minorities and a conflict of national identities.
The last country case in this part of the conference focused on the current political situation in Russia and on the likely outcomes after 2024. The social framework in Russia appears constellated by fears – a fear of a world war, of regime tightening and mass repressions, and of lawlessness – all of them on the rise. Similarly, the economy is suffering, in particular from low business activity, somewhat offset by a boost in social payments. Nonetheless, it was argued that it is not economic concerns, but rather political frustration, that has recently led citizens to take to the street. Despite this, survey data shows that trust in Putin is still over 60%, and that most people would vote for him again. However, survey data also points out that the most likely determinant of this trust is the lack of another reference figure, and that citizens are not averse to the idea of political change in itself. Lastly, Putin will most likely retain some political power after 2024, transiting “from father to grandfather of the nation”.
Voices from the civil society in the region also emphasized the importance of a free media and an active civil society to prevent the backsliding of democracy. With examples from Georgia and Ukraine, it was argued that maintaining the independence of the judiciary, as well as the public prosecutor’s office, can go a long way in building credibility both among citizens and the international community. The European Union can leverage the high trust and hopeful attitudes it benefits from in the region to push crucial reforms more strongly. For example, more than 70% of Georgians would vote for joining the EU if a referendum was held on the topic and the European Union is widely regarded as Georgia’s most important foreign supporter.
Weak Institutions and Business Development
The quality of political and legal institutions strongly affects the business environment, in particular with regards to the protection of property rights, rule of law, regulation and corruption. Research from the European Bank for Reconstruction and Development (EBRD) highlights that the governance gap between Eastern Europe and Central Asia and most advanced economies is still large, even though progress in this area has actually been faster than for other emerging economies since the mid-‘90s. This is measured through enterprise surveys as well as individual surveys. In Albania, for instance, a perception of lower corruption was linked to a decrease in the intention to emigrate equivalent to earning 400$ more per month. Another point concerned the complexity of measuring the business environment and the benefits of firm-level surveys asking firms directly about their own actual experience of regular enforcement. For example, in countries such as Poland, Latvia and Romania the actual experience of business regulation measured via the EBRD’s Business Environment Enterprise Performance Survey, is far worse than one would expect from the World Bank’s well known Doing Business rating.
From the perspective of Swedish firms, trade between Sweden and the region has remained rather flat in the past years, as the complexity and risks of these markets especially discourage SMEs. Business Sweden explained that Swedish firms considering an expansion in these markets are concerned with issues of exchange rate stability, and the institutional-driven presence of unfair competition and of excessive bureaucracy. Moreover, inadequate infrastructure and the presence of bribery and corruption make everyday business operations risky and costly. It was generally emphasized that countries have to create a safe investment environment by reducing corruption, establishing a clear and well enacted regulatory environment, having dependable courts and strengthening domestic resource mobilization. Swedish aid can play a part, but there is a need to develop new ways of delivering aid to make it more effective.
An interesting example is Belarus, that has seen more economic and political stability than most neighbours, but at the same time a lack of both economic and political reforms towards market economy and democracy. Gradually the preference towards private ownership, as opposed to public, has increased in recent years and the country has seen a rising share of the private sector, even without specific privatization reforms. Nonetheless, international businesses are still reluctant to invest due to high taxes, a lack of access to finance as well as to a qualified workforce, but most importantly due to the weak legal system. An exception has been China, and Belarus has looked at the One Belt One Road Initiative as a promising bridge to the EU. Scandals connected with the two main Chinese-invested projects have damped the enthusiasm recently, though.
The economic and political risks of extensively relying on badly diversified energy sources, as is the case with natural gas imports from Russia in many transition states were also discussed. It was shown how some countries such as Ukraine, Poland and Lithuania have improved their energy security by either benefitting from reverse-flow technology and the EU’s bargaining power or building their own LNG terminals to diversify supply sources. However, either of these, as well as other energy security improving solutions are likely to come with an economic cost, though, that not all countries in the region can afford.
A Government Perspective
The main focus of this section was the Swedish government’s new inspiring foreign policy initiative, “Drive for Democracy”. Drawing from a definition of democracy by Kerstin Hesselgren, an early Swedish female parliamentarian, democracy enables countries to realize and utilize the forces of the individual and draw them into a life-giving, value-creating society. It was emphasized that the values of democracy are objectives by themselves (e.g. freedom of expression, respect for human rights) but also that democracy has important positive effects in other areas of human welfare. The Swedish government views democracy as the best foundation for a sustainable society, equality of opportunity and absence of gender or racial bias.
The “Drive for Democracy” specifically identifies Eastern Europe as one of the main frontiers between democracy and autocracy, and the Swedish government promotes human rights and stability through various bilateral programmes through the Swedish International Development Cooperation Agency, Sida, and multilateral initiatives within the EU, such as the Eastern Partnership. It was also emphasized that democracy is a continuous process that can always be improved, as indeed experienced by Sweden. Political rights were granted to women only in 1919 followed by convicts and prisoners in 1933 and to the Roma people only in 1950. Political and democratic rights are thus never once and for all given, and it is crucial that the dividends from democracy are carried forward to the younger generation.
Conclusion
In sum, the day illustrated clearly how democracy engages all segments of society, from the business sector to civil society, and the potential for but also challenges involved for democratic deepening in Eastern Europe. To get more information about the presentations during the day, please visit our website.
Participants at the Conference
- PER OLSSON FRIDH, State Secretary, Ministry for Foreign Affairs.
- ALEXANDER PLEKHANOV, Director for Transition Impact and Global Economics at EBRD.
- TORBJÖRN BECKER, Director, SITE.
- CHLOÉ LE COQ, Associate Professor, SITE and Professor of Economics, University of Paris II Panthéon-Assas.
- THOMAS DE WAAL, Senior Fellow at Carnegie Endowment for International Peace.
- NATALIIA SHAPOVAL, Vice President for Policy Research at Kyiv School of Economics.
- ILONA SOLOGUB, Scientific Editor at VoxUkraine and Director for Policy Research at Kyiv School of Economics.
- KETEVAN VASHAKIDZE, President at Europe Foundation, Georgia.
- MARIA BISTER, Senior Policy Specialist, Sida.
- HENRIK NORBERG, Deputy Director, Ministry for Foreign Affairs.
- YLVA BERG, CEO and President, Business Sweden.
- LARS ANELL, Ambassador and formerly Volvo’s Senior Vice President.
- ERIK BERGLÖF, Professor in Practice and Director of the Institute of Global Affairs, London School of Economics and Political Science.
- KATERYNA BORNUKOVA, Academic Director, BEROC, Minsk.
- ANDREI KOLESNIKOV, Senior Fellow, Carnegie Moscow Center.
Liberal Democracy in Transition – The First 30 Years
This year marks 30 years since the first post-communist election in Poland and the fall of the Berlin Wall. Key events that started a dramatic transition process from totalitarian regimes towards liberal democracy in many countries. This brief presents stylized facts from this process together with some thoughts on how to get this process back on a positive track. In general, the transition countries that joined the EU are still far ahead of the other transition countries in terms of democratic development.
The recent decline in democratic indicators in some EU countries should be taken seriously as they involve reducing freedom of expression and removing constraints on the executive, but should also be discussed in light of the significant progress transition countries entering the EU have shown during the first 30 years of transition. The brief shows that changes in a democracy can happen fast and most often happen around elections, so getting voters engaged in the democratic process is crucially important. This requires politicians that engage the electorate and have an interest in preserving democratic institutions. An important question in the region is what the EU can do to promote this, given its overloaded political agenda. Perhaps it is time for a Greta for democracy to wake up the young and shake up the old.
This brief provides an overview of political developments in transition countries since the first post-communist elections in Poland and the fall of the Berlin Wall 30 years ago. It focuses on establishing stylized facts based on quantitative indices of democracy for a large set of transition countries rather than providing in-depth studies of a small number of countries. The aim of the brief is thus to find common patterns across countries that can inform today’s policy discussion on democracy in the region and inspire future studies of the forces driving democracy in individual transition countries.
The first issue to address is what data to use to establish stylized facts of democratic development in the region. By now, there are several interesting indicators that describe various aspects of democratic development, which are produced by different organizations, academic institutions and private data providers. In this brief, three commonly used and well-respected data providers will be compared in the initial section before we zoom in on more specific factors that make up one of these indices.
The big picture
The three indicators that we look at first are: political rights produced by Freedom House; polity 2 produced by the Polity IV project; and the liberal democracy index produced by the V-Dem project. Figures 1-3 show the unweighted average of these indicators for two groups of countries. The EU10 are the transition countries that became EU members in 2004 and 2007 and include Bulgaria, the Czech Republic, Estonia, Hungary, Lithuania, Latvia, Poland, Romania, Slovakia, and Slovenia. The second group, FSU12, are the 12 countries that came out of the Soviet Union minus the three Baltic countries in the EU10 group, so the FSU12 group consists of Armenia, Azerbaijan, Belarus, Georgia, Kazakhstan, Kyrgyzstan, Moldova, Russia, Tajikistan, Turkmenistan, Ukraine, and Uzbekistan.
Figure 1. Freedom House
Source: Freedom House and author’s calculations
Note: Scale inverted, 1 is best and 7 worst score
Figure 2. Polity IV project
Source: Polity IV project and author’s calculations
Note: Scale from -10 (fully autocratic) to 10 (fully democratic)
Figure 3. V-Dem
Source: V-Dem project and author’s calculations
Note: Scale from 0 to 1 where higher is more democratic
All three indicators convey the message that the democratic transformation in the EU10 group was very rapid in the early years of transition and the indicators have remained at high levels since the mid-90s only to show some decline in the most recent years for two of the three indicators. The FSU12 set of countries have made much less progress in terms of democratic development and remain far behind the EU10 countries in this regard. Overall, there is little evidence at the aggregate level that the democratic gap between the EU10 and FSU12 groups is closing. While the average EU10 country is more or less a full-fledged democracy, the average FSU12 country is at the lower end of the spectrum for all three democracy measures.
The average indicators in Figures 1-3 obviously hide some interesting developments in individual countries and in the following analysis, we will take a closer look at the liberal democracy index at the country level. We will then investigate what sub-indices contribute to changes in the aggregate index in the countries that have experienced significant declines in their liberal democracy scores.
For the first part of the analysis, it is useful to break down the democratic development in two phases. The first phase is from the onset of transition (1989, 1991 or 1993 depending on the specific country) to the time of the global financial crisis in 2009 and the second phase is from 2009 to 2018 (the last data point).
Figure 4. Liberal democracy, the first phase
Source: V-Dem project and author’s calculations
Figures 4 and 5 compare how the liberal democracy indicator changes from the first year of the period (measured on the horizontal axis) to the last year of the period (on the vertical axis). The smaller blue dots are the individual countries that make up the EU10 group while the red dots are the FSU12 countries. The 45-degree line indicates when there is no change between start and end years, while observations that lie below (above) the line indicate a deterioration (improvement) of the liberal democracy index in a specific country.
In the first phase of transition (Figure 4), all of the EU10 countries increased their liberal democracy scores and the average increase for the group was almost 0.5, going from 0.26 to 0.74. This was a result of many of the countries in the group making significant improvements without any countries deteriorating. The FSU12 group had a very different development with the average not changing at all since the few countries that improved (Georgia and Ukraine) were counterbalanced by a significant decline in Belarus and a more modest decline in Armenia.
Figure 5. Liberal democracy, the second phase
Source: V-Dem project and author’s calculations
The very rapid improvement in the liberal democracy index in the EU10 countries in the first phase of transition came to a halt and also reversed in several countries in the second phase of transition. Of course, as they had improved so much in the first period, there was less room for further positive developments, but the rapid decline in some of the countries was still negative news. However, it does point towards that reform momentum was very strong in the EU accession process, but once a country had entered the union, the pressure for liberal democratic reforms has faded.
Overall, the EU10 average fell by 0.1 from 2009 to 2018. This was a result of declining scores in several countries. The particularly large declines in this period have been seen in Hungary (-0.28), Poland (-0.27), Bulgaria (-0.14), the Czech Republic (-0.14), and Romania (-0.12). Again, the average FSU12 score did not change much, although Ukraine (-0.2) put its early success in reverse and lost as much in this period as it had gained earlier.
Country developments
Since much of the current discussion centers on how democracy is being under attack, the figures name the countries that have seen significant declines in the liberal democracy score in the first or second phase of transition. Figures 6 and 7 show the time-series of the liberal democracy index in the countries with significant drops at some stage of the transition process.
Figure 6. FSU12 decliners
Source: V-Dem project and author’s calculations
In many countries, the drop comes suddenly and sharply, with the first and most prominent example being Belarus. There, it only took three years to go from one of the highest ranked FSU12 countries to fall to one of the lowest liberal democracy scores. In Poland, Romania, Bulgaria and Armenia, the process was also very rapid and significant changes happened in 2-3 years.
Figure 7. EU10 decliners
Source: V-Dem project and author’s calculations
In the Czech Republic and Hungary, the period of decline was much longer and in the case of Hungary, the drop was the most significant in the EU10 group. Ukraine stands out as more of an exception with a roller-coaster development in its liberal democracy score that first took it up the list and then back down to where it started. For those familiar with politics in these countries, it is easy to identify the elections and change in government that have occurred at the times the index has started to fall in all of these countries. In other words, the democratic declines have not started with coups but followed election outcomes where in most cases the incumbent leaders have been replaced by a new person or party.
How democracy came under attack
We will now take a closer look at what has been behind the instances of decline in the aggregate index by investigating how the sub-indices have developed in these countries. The sub-indices that build up the liberal democracy index are: freedom of expression and alternative sources of information; freedom of association; share of population with suffrage; clean elections; elected officials; equality before the law and individual liberty; judicial constraints on the executive; and legislative constraints on the executive (the structure is a bit more complex with mid-level indices, see V-Dem 2019a).
Table 1 shows how these indicators have changed in the time period the liberal democracy indicator has fallen significantly (with shorter versions of the longer names listed above but in the same order). The heat map of decline indicated by the different colours is constructed such that positive changes are marked with green, smaller declines are without colour, declines greater that 0.1 but smaller than 0.2 are in yellow and larger declines in red. Note that the liberal democracy index is not an average of the sub-indices but based on a more sophisticated aggregation technique (see V-Dem 2019b). Therefore, the Czech Republic and Bulgaria can have a greater fall in top-level liberal democracy index that what is indicated by the sub-indices.
Table 1. Changes in liberal democracy indicators at times of democratic decline
Source: V-Dem project and author’s calculations
For the countries with the largest changes in the liberal democracy index, it is clear that both freedom of expression and alternative sources of information have come under attack together with reduced judicial and legislative constraints on the executive. Among the EU10 countries, Hungary and Poland stand out in terms of reducing freedom of expression, while Romania has seen most of the decline coming from reducing constraints on the executive. Not surprisingly, Belarus stands out in terms of the overall decline in liberal democracy coming from reducing both freedom of expression and constraints on the executive in the most significant way.
On a more general level, the attack on democracy does differ between the countries, but in the cases where serious declines can be seen, the attack has been particularly focused on information aspects and constraints on the executive. At the same time, all countries let all people vote (suffrage always at 1) and let the one with the most votes get the job (elected officials).
Policy conclusions
This brief has provided some stylized facts on the first 30 years of liberal democracy in transition and some details on how democracy has come under attack in individual countries. It leaves open many questions that require further studies and some of these are indeed ongoing in this project and will be presented in future briefs and policy papers here.
Some observations have already been made here that can inform policy discussions on liberal democratic developments in the region. The first is that changes can happen very rapidly, both in terms of improvements but also in terms of dismantling important democratic institutions, including those that provide constraints on the executive or media that provides unbiased coverage before and after elections. What is also noteworthy is that these changes have almost always happened after an election where a new person or party has come to power, so the democratic system is used to introduce less democracy in this sense.
It is also interesting that in all of the countries, the most easily observed indicators of democracy such as suffrage and having the chief executive or legislature being appointed by elections are given the highest possible scores. In other words, even the most autocratic regime wants to look like a democracy; but as the old saying goes, “it is not who votes that is important, it is who counts”.
The regime changes at election times that have led to declining liberal democracy scores have also in many cases come as a result of the incumbents not doing a great job or voters not turning up to vote. It was enough for Lukashenko in Belarus to promise to deal with corruption and rampant inflation that was a result of the old guard’s mismanagement to turn Belarus into an autocracy. In Hungary, the change of regime came after the Socialist leader was caught on tape saying he had been lying to voters. While in Romania, only 39% voted in the 2016 election. And in Bulgaria, around half of the voters stayed at home in the presidential election the same year.
In sum, both incompetent and corrupt past leaders and disengaged or disillusioned voters are part of the decline in a liberal democracy that we have seen in recent years. It is clearly time for policy makers that are interested in preserving liberal democracy in the region and elsewhere to think hard about how democracy can be saved from illiberal democrats. Part of the answer clearly will have to do with how voters can be engaged in the democratic process and take part in elections. It also involves defending free independent media and the thinkers and doers that contribute to the liberal democracy that we cherish. The question is if the young generation will find a Greta for democracy that can kick-start a new transition to liberal democracy in the region and around the world.
For those readers that want to participate more actively in this discussion and have a chance to be in Stockholm on November 12, SITE is organizing a conference on this theme which is open to the public. For more information on the conference, please visit SITE’s website (see here).
References
- Freedom house data downloaded on Oct 4, 2019, from https://freedomhouse.org/content/freedom-world-data-and-resources
- Freedom house methodological note available at https://freedomhouse.org/report/methodology-freedom-world-2018
- Polity IV project data downloaded on Oct 4, 2019, from http://www.systemicpeace.org/inscrdata.html
- Polity IV project manual available at http://www.systemicpeace.org/inscr/p4manualv2018.pdf
- V-Dem project data downloaded on Sept 24, 2019, from https://www.v-dem.net/en/data/data-version-9/
- Coppedge, Michael, John Gerring, Carl Henrik Knutsen, Staffan I. Lindberg, Jan Teorell, David Altman, Michael Bernhard, M. Steven Fish, Adam Glynn, Allen Hicken, Anna Lührmann, Kyle L. Marquardt, Kelly McMann, Pamela Paxton, Daniel Pemstein, Brigitte Seim, Rachel Sigman, Svend-Erik Skaaning, Jeffrey Staton, Steven Wilson, Agnes Cornell, Lisa Gastaldi, Haakon Gjerløw, Nina Ilchenko, Joshua Krusell, Laura Maxwell, Valeriya Mechkova, Juraj Medzihorsky, Josefine Pernes, Johannes von Römer, Natalia Stepanova, Aksel Sundström, Eitan Tzelgov, Yi-ting Wang, Tore Wig, and Daniel Ziblatt. 2019a. “V-Dem [Country-Year/Country-Date] Dataset v9”, Varieties of Democracy (V-Dem)
- Pemstein, Daniel, Kyle L. Marquardt, Eitan Tzelgov, Yi-ting Wang, Juraj Medzihorsky, Joshua Krusell, Farhad Miri, and Johannes von Römer. 2019b. “The V-Dem Measurement Model: Latent Variable Analysis for Cross-National and Cross-Temporal Expert-Coded Data”, V-Dem Working Paper No. 21. 4th edition. University of Gothenburg: Varieties of Democracy Institute.
The Gender Wage Gap in Belarus: State vs. Private Sector
This brief is based on research that studies gender difference in wages in Belarus using survey data from 2017. According to the results, the unconditional gender wage differential equals 22.6%. The size of the wage gap is higher in the state sector than in the private sector. Additionally, it increases in the state sector throughout the wage distribution and accelerates at the top percentiles, indicating the presence of a strong glass ceiling effect.
Introduction
The causes and consequences of the gender wage gap in the labor market, that is the difference between the wages earned by women and men, continue to attract increasing attention in empirical studies worldwide.
Belarus’ labor market is not an exception and faces the problem of wage inequality like other neighboring and transition countries. According to the National Statistical Committee of the Republic of Belarus (Belstat), the average gender wage gap in terms of monthly wages was 19% in 2000, it increased up to 23.8% in 2015, and reached 25.4% in 2017.
In this regard, this brief updates the estimates of the gender wage gap in Belarus. And it summarizes the results of the study on what the role of the state and private sectors are in the distribution of gender wage differences in Belarus (Akulava and Mazol, 2018).
Data and methodology
The data used in the research is from the Generations and Gender Survey (GGS) conducted in Belarus in 2017. This survey is a nationally representative dataset that is based on interviews of about 10,000 permanent residents of Belarus, aged 18–79, covering the whole country disaggregated by regions. The GGS contains information on a range of individual (age, gender, marital status, educational attainment, employment status, hours worked, wages earned etc.) and household-level characteristics (household size and composition, land holding, location, asset ownership etc.).
The analysis is based on the typical Mincer model of earnings that estimates individual wage income as a function of various influencing factors using the OLS approach (Mincer, 1974). Specifically, the Mincerian wage equation is defined where the log of the hourly wage rate is regressed on a set of male and female workers’ personal and job characteristics (educational level, working experience, occupational type, organization type, family characteristics, and region).
Next, we use the Oaxaca-Blinder (OB) methodology (Oaxaca, 1973; Blinder, 1973) to identify and quantify the contribution of personal characteristics and the unexplained component (which is referred to as differences in returns) to the wage difference between males and females.
Finally, we apply the Machado-Mata (MM) technique (Machado and Mata, 2005) to look into the nature of the wage gap at various points of the income distribution and also to test the difference for individuals employed in the state or private sectors. For the Machado-Mata procedure, we estimate our specifications at the 10th, 25th, median, 75th and 90th percentiles of the wage distribution.
Results
The analysis shows that women’s wages are lower than men’s wages all over the wage distribution. The average raw gender wage gap equals 22.6% and it increased substantially compared with 9.0% in 1996 and 17.8% in 2006, the numbers obtained in the study conducted by Pastore and Verashchagina (2011).
Figure 1. Gender differential by quantile of the wage distribution
Source: Authors’ estimates based on GGS.
The level of female earnings is lower than the male regardless of the occupational type, educational background, work experience and organizational type. Moreover, the underpayment of women is lower for low earning workers, but increases up to the end of the wage distribution (see Figure 1).
The OB decomposition shows that female educational attainment and job-related experience help to decrease the level of the wage gap slightly (see Table 1).
Table 1. Oaxaca-Blinder decomposition results
Source: Authors’ estimates based on GGS.
However, the occupational choice is leading to an expansion of the difference in earnings. However, its effect is also small, indicating that occupational segregation plays a minor role in explaining the gender wage gap. The major share of the gender wage gap is formed by the unexplained part, which is likely to be attributed to discrimination.
Next, the level of remuneration is higher among private companies. However, contrary to other countries in transition, the average gender wage gap in Belarus in the private sector is lower than in the public sector.
Moreover, the MM decomposition estimates presented in Table 2 demonstrate that the gender wage gap in the state sector shows evidence of the glass ceiling effect (the size of the total wage gap expands at the top of the wage distribution), while no evidence of either glass ceiling or sticky floor (the size of the total wage gap increases at the bottom of the wage distribution) in the private sector.
The negative coefficient near the characteristics part in the private sector shows that female endowments outweighs their male counterparts. Thus, controlling for personal characteristics, if the labor market rewards males and females equally, the wages of females in the private sector should be substantially higher (see Table 2).
Table 2. Machado-Mata decomposition of the observed gender wage gap by organization type
Source: Authors’ estimates based on GGS.
Finally, the results also suggest that female workers are better off being in the private sector at the lowest and the highest percentiles (i.e. the size of the gender wage gap is lower there compared to the 25th and 50th percentile).
A possible explanation for all the above is that institutional differences seem to play a crucial role here. First, Belarusian private firms work under stronger regulation than in other transition economies which makes it harder for them to set low wages. Second, they also operate under stronger competition (compared to state companies), which force them to identify individual productivity more correctly, narrowing the gender difference in pay. In contrast, the paternalistic attitude to women left as a legacy from the Soviet Union further increases the gender wage gap in the public sector.
Conclusion
In this brief, we present new evidence on the existence of a gender wage gap in the Belarusian labor market and analyze the differences in its distribution between the state and private sectors.
Our results show that the unconditional gender wage gap in terms of hourly wages equals 22.6%. Thus, jointly with a previous study (see Pastore and Verashchagina, 2011) and recent official indicators, all these indicate that the pace towards gender equality in Belarus seems to be sluggish. For the moment, all institutional changes accomplished by the Belarusian government to reduce gender discrimination are not enough and require additional efforts to cope with that problem.
However, the gender wage gap is shown to be much wider in the public sector than in the private sector. At the same time the private sector appears to be more attractive than the public sector in the country in terms of the level of remuneration. Therefore, additional structural shifts of the economy accompanied by the growth of competition are needed to induce a further reduction of the gender wage gap.
References
- Akulava, M. and A. Mazol. (2018). What Forms Gender Wage Gap in Belarus? BEROC Working Paper Series, WP no. 55.
- Blinder, A. (1973). Wage Discrimination: Reduced Form and Structural Estimates. Journal of Human Resources, 8, 436-455.
- Machado, J., and J. Mata. (2005). Counterfactual Decomposition of Changes in Wage Distributions Using Quantile Regression. Journal of Applied Econometrics, 20(4), 445‑465.
- Mincer, J. (1974). Schooling, Experience, and Earnings. New York: Columbia University.
- Oaxaca, R. (1973). Male-Female Wage Differentials in Urban Labor Markets. International Economic Review, 14(3), 693-709.
- Pastore, F., and A. Verashchagina. (2011). When Does Transition Increase the Gender Wage Gap? An application to Belarus. The Economics of Transition, 19(2), 333-369.
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.
Institutions and Comparative Advantage in Services Trade
Recent studies have highlighted the role of human capital and good economic institutions in establishing a comparative advantage in trade in complex institutions-intensive goods. We show that the effect of institutions on comparative advantage in services trade is quite different: in fact, countries with bad institutions rely significantly more on services exports. More specifically, as the quality of institutions deteriorates, information technology sector (ICT) services exports as a share of total ICT exports increase significantly and countries with worse institutions get a substantial comparative advantage in the provision of ICT services. This is especially applicable to transitional economies characterized by high, arguably exogenous, human capital at the level of most advanced countries.
Introduction
Recent research in international trade has demonstrated that institutions influence the determination of comparative advantage in the trade of goods. Countries with strong domestic institutions have a significant comparative advantage in producing complex, institutions-intensive goods while countries with weak institutions tend to specialize in less complex goods. Through this channel, weak institutions can hinder growth and development (Nunn and Trefler, 2014).
We argue that the role of institutions in services trade can differ significantly from the one in trade in goods. The intuition behind it is that services provision often relies less on institution-driven factors, such as public infrastructure, availability of large number of inputs, property rights and capital investments than the production of complex goods.
We show, in the case of the information technology sector (ICT), that countries with bad institutions rely significantly more on services exports even after controlling for human capital input requirements and availability. We focus on the ICT sector to isolate the differences in the role of institutions in determining comparative advantage in goods and services. Both ICT goods and services provision are equally intensive in human capital and thus present a good opportunity to study differences between goods and services provision.
Our study is motivated by high ICT services exports (e.g. software development) and low ICT goods exports (e.g. computers, phones, etc.) of transition countries which are known to have high human capital and low institutional indicators.
Institutions and ICT Services Exports
Figure illustrates the high human capital availability of transitions economies and weak domestic institutions relative to other countries. Specifically, we categorize countries into four groups: 23 most developed economies (e.g. USA, Canada, Japan and Western European economies); new members of the European Union (a group of 13 countries including Poland, Slovakia, and Baltic countries); transition economies group consists of 17 mostly post-Soviet countries including Russia, Ukraine, Belarus; the most numerous fourth group includes more than hundred other developing countries.
Figure 1. Institutions quality and schooling by country groups
1a
Source: Authors’ calculations, schooling data from Barro and Lee (2013)
1b
Source: Authors’ calculations, institutional indicators data from the World Bank World Governance Indicators
Figure 1a presents an average number of years of schooling, our measure of human capital, for each country group in 2000 and 2010 (the years are chosen based on data availability). The human capital is at a similar level in the most developed economies, EU-13 and transition economies, but significantly lower in other developing countries. Figure 1b illustrates the average institutional quality for each group in 2000 and 2010. Institutional quality for each country is calculated as an average of six indicators, distributed approximately from -2.5 to 2.5: control of corruption, government effectiveness, political stability, rule of law, regulatory quality, voice and accountability, with a lower value corresponding to worse institutional quality. In contrast to education, the average institutional quality of transition economies, although improving from 2000, remains on average lower than the institutional quality of other developing countries.
Consistent with the literature on institutions and comparative advantage in relationship and investment-intensive goods production, ICT goods export from transition economies is significantly lower than in other countries. In contrast, ICT services exports is at a higher level and faster growth in transition economies than in other countries.
Belarus presents a good motivating example. On the one hand, fundamental education in Belarus is at a level of the most advanced countries, which allows 21 universities in the country to educate about 7,000 graduates in IT industry in a year. On the other hand, ICT services exports in Belarus is thriving: over the last 10 years, the growth of ICT services is an eightfold increase (it was 150M USD in 2008 and 1.2B USD in 2017). Nowadays, Belarus is one of the world leaders in ICT services exports per capita. At the same time, ICT goods export is not growing even close to the level of ICT services exports. Over the same time period, it has grown only by about 30 percent: in 2008 ICT goods export was 105M USD, in 2016 – 140M USD (BELARUS.BY, 2019).
The importance of ICT services exports in transition economies is seen in Figure 2. The figure presents ICT services exports as a share of total exports of ICT goods and services. To obtain values for each country group, we average ICT services shares across countries within each group.
Figure 2. ICT services exports as share of total ICT exports
Source: Authors’ calculations, ICT services export data from Trademap, ICT goods export data from WDI
As Figure 2 shows, the average share of ICT services exports in transition economies is higher than the share of ICT services exports in all other groups of countries. Transition economies, characterized by high human capital and weak institutional quality, specialize in exports of services over goods in their ICT exports. This descriptive evidence suggests that abundant human capital, inherited from the USSR and arguably exogenous, shifts to services within the human capital intensive ICT sector when facing weak institutions.
Empirical panel analysis confirms the descriptive evidence. To test our hypothesis, we use the share of ICT services in total ICT exports as a dependent variable and we show that quality of institutions is a significant determinant. Our regressions show that the higher the quality of institutions is, the lower will the share of ICT services in total ICT exports be. Moreover, regression analysis allows us to quantify this dependence: as the quality of institutions increases by 1, which is approximately the difference between Belarus and Georgia (as can be seen in figure 3 below), the share of ICT goods in total ICT services increases by about 20%.
Institutions as a source of comparative advantage in services
To explore the role of institutions in the relative services provision within a sector further, we look at comparative advantage in exporting ICT services. We incorporate a measure similar to Relative Share measure used in Levchenko (2007) for the analysis of comparative advantage in goods export. The measure effectively compares the share of ICT services export for a given country with the world average. The index of revealed comparative advantage in ICT services over ICT goods is computed for country in the following way:
where is share of ICT services exports in total ICT exports for country, is the export of ICT services for all countries, and is the total ICT export (goods plus services) for all countries.
We look at the revealed comparative advantage index across our group of transition economies in figure 3 and see that even within this group, there is a negative correlation between institutions quality and revealed comparative advantage in ICT services.
Figure 3. Revealed Comparative Advantage and Institutions Quality
Source: Authors’ calculations
Countries with high institutional quality, like Georgia, export relatively more goods compared to services. Countries with low institutional quality, like Ukraine and Belarus, have a comparative advantage in ICT services exports.
We hypothesize that the main mechanism responsible for this is as follows. Poor institutional quality, resulting in, for example, corruption and the impossibility to create binding contracts does not allow the countries to produce complex goods in the ICT industry, while the presence of high human capital in these countries allows them to produce ICT services that much less depend on corruption and contracting inefficiencies but are as intensive in human capital as ICT goods.
For a better understanding of the relationship between institutions and comparative advantage determination, we run panel regressions analysing the probability of having a comparative advantage in ICT services in exports of ICT goods and services as a function of institutional quality. Following Balassa (1965), a country has a comparative advantage in ICT services if the share of services in overall ICT exports is higher than the world average, in other words, revealed comparative advantage index is greater than 1. We find that one unit increase in institutional quality reduces the probability of having a comparative advantage in services by about 25%, which means that a country with institutional quality similar to Georgia is about 25% less likely to have comparative advantage than a country with institutional quality similar to Belarus.
Conclusion
In this brief we have discussed the role of institutions in determining comparative advantage in services. Our study argues that, given high human capital, low quality institutions create comparative advantage in services provision. Since low quality institutions act as an implicit tax on the production of complex goods, rational agents reallocate most resources to the production of services that are less sensitive to the institutional quality, while still requiring high level of human capital. We showed that transition economies are characterized by low institutions quality and high human capital. At the same time, transition economies have the highest share of ICT services export in total ICT export. We also showed that institutions negatively affect comparative advantage in ICT services export. Our results suggest that services exports can be a novel development channel for countries with weak institutional, capital investments and infrastructure. Specialization in high-value added services exports provides opportunity for fostering high human capital.
References
- Arshavskiy, Victor, Arevik Gnutzmann-Mkrtchyan and Aleh Mazol, 2019. “Institutions and Comparative Advantage in Service Trade”, Working paper
- Balassa, B. (1965). Trade liberalisation and “revealed” comparative advantage 1. The Manchester School of Economics and Social Studies, 33(2), 99-123.
- Barro, Robert J. and Jong Wha Lee, 2013. “A new data set of educational attainment in the world, 1950–2010”, Journal of Development Economics, vol. 104, pp 184-198
- Levchenko, Andrei A., 2007. “Institutional Quality and International Trade”, Review of Economic Studies, vol. 74, pp 791-819.
- Nunn, Nathan and Daniel Trefler, 2014. “Domestic Institutions as a Source of Comparative Advantage”, Handbook of International Economics, Volume 4, Chapter 5, pp 263-315.
- BELARUS.BY, 2019. “ИТ в Беларуси”, it-belarus, accessed on May 19, 2019
Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.
Poverty Dynamics in Belarus from 2009 to 2016
This brief is based on research that studies the incidence and determinants of poverty in Belarus using data from the yearly Household Budget Surveys for 2009-2016. Poverty is evaluated from a consumption perspective applying the cost of basic needs approach. According to the results, in 2015-2016, absolute poverty in Belarus increased twofold and reached 29% of the population. Large household size, high number of children, single mothers and unemployment negatively affect household welfare and increase poverty risk. Moreover, living in rural areas increases the likelihood of being poor and correlates negatively with welfare.
Introduction
Sizeable and increasing poverty poses a threat to social stability and long-term sustainability for every country. Before 2009, Belarus registered over a decade of high and sustainable economic growth that enhanced the average standard of living and raised a substantial number of Belarusians out of poverty. According to the National Statistical Committee of the Republic of Belarus (Belstat), the poverty rate in Belarus (by official definition) has decreased from 41.9% of the population in 2000 down to 6.1% in 2008. The largest reported decline in poverty was in 2001 – from 41.9% to 28.9%.
Since then, Belarus experienced several episodes of economic crises – in 2009, 2011 and 2015-2016 (Kruk and Bornukova, 2014; Mazol, 2017a). Such economic downturns typically introduce considerable survival problems for many households. For example, according to the World Bank, in some countries the poverty rate may reach 50% (World Bank, 2000). In light of this, the small increase (0.3%) in the official poverty measure during these periods casts doubt on the official methodology used for poverty calculations. This brief describes an alternative measure of absolute poverty based on the widely recognized cost of basic needs approach; and summarizes the results of the study of how economic downturns in Belarus influenced welfare and poverty at the household level.
Data and methodology
The data used in this research are pooled cross-sections from 2009 to 2016 of the yearly Belarusian Household Budget Surveys with on average 5000 households in each sample obtained from Belstat. These surveys consist of household and individual questionnaires that contain important data about households including decomposition of expenditures and income by categories, detailed data on consumption of food items, the size, age and gender composition of households, living conditions, etc.
The analysis applies the cost of basic needs approach (Kakwani, 2003). It first estimates the cost of acquiring enough food for adequate nutrition (nutrition requirements for households of different size and demographic composition) per person (food poverty line) and then adds the cost of non-food essentials (absolute poverty line). The calculated poverty lines for each sampled household are compared with the household consumption per person. All measures are preliminary deflated to take into account differences in purchasing power over time and regions of residence.
In contrast, the official poverty measurement compares per capita disposable income of a household with national (official) poverty line, which is the average per capita subsistence minimum budget of a family with two adults and two children (see Table 1).
Table 1. Consumer budgets and absolute poverty line by year in Belarus, in constant BYN
| Year | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 |
| Subsistence minimum budget1 | 247 | 258 | 293 | 317 | 332 | 362 | 369 | 373 |
| Minimum consumer budget2 | 372 | 396 | 367 | 448 | 491 | 517 | 554 | 620 |
| Absolute poverty line3 | 383 | 395 | 437 | 448 | 468 | 475 | 499 | 520 |
Source: 1 Belstat; 2 Ministry of Labour and Social Protection Republic of Belarus; 3 author’s own calculations.
The empirical strategy of the analysis assumes setting the food, non-food and absolute poverty lines using the cost of basic needs approach, estimating poverty measures at the level of entire Belarus and its regions based on Foster-Greer-Thorbecke’s poverty indices (Foster et al., 1984), and analyzing the determinants of welfare and poverty using OLS and probit regressions.
Poverty incidence
The timeline of poverty analysis for Belarus can be subdivided into three periods: crisis of 2009-2011, recovery of 2012-2014, and a crisis of 2015-2016 (see Figure 1).
The results show that during the first period (from 2009 to 2011), absolute poverty at the national level increased from 30.9% to 32.6%. Incidence of absolute poverty for rural and urban areas in 2011 reached 45% and 28% of the population, correspondingly.
Figure 1. Incidence of absolute poverty and GDP per capita growth in Belarus
Source: Author’s own calculations.
Note: Estimates reflect weighted household data.
The second period (from 2012 to 2014) was characterized by a sharp poverty reduction. For example, the absolute national poverty headcount ratio has plummeted from 32.6% in 2011 to 14.9% in 2014, rural poverty dropped by 22.1 percentage points or almost by half and urban poverty decreased by 16.2 percentage points.
In turn, the third period saw a sharp rise in the incidence of poverty. From 2015 to 2016, the headcount ratio for absolute poverty increased by 14.4 percentage points. As a result, in 2016 absolute poverty in Belarus reached 29.3% or almost the same as in 2009 and 2011 (Mazol, 2017b).
Causes and determinants of poverty
The significant increase in poverty in 2015-2016 was due to a combination of several factors, including the household income decline in comparison with its growth in previous years, the increasing need to spend more on food necessities and the growth in food and especially non-food price levels.
As the Figure 2 shows, starting from 2015 there has been a rapid increase in the real cost of non-food budget for Belarusian households, while the food budget has remained almost the same in real terms. Correspondingly, in 2016 the non-food poverty line increased by 14.6%, while the food poverty line went up only by 2.9%.
Figure 2. Real monthly average per capita household expenditure on food and non-food items and real monthly standardized food and non-food poverty lines, 2009-2016, in BYN
Source: Author’s own calculations.
Note: Estimates reflect weighted household data.
Furthermore, as income fell (by 7.2% in 2015-2016), the share of food items in total expenditure increased and real non-food expenditure decreased. This is because household income was not enough to cover both expenditures on food and non-food items. Due to the 2015-2016 economic crisis the cost of meeting the food essentials increased so fast that it has squeezed the non-food budget, leaving insufficient purchasing power for non-food items.
The study also shows that among factors that substantially influence household welfare and poverty at the household level in Belarus are family size, the number of children in a household, presence in the household of economically inactive members. Moreover, single mothers in Belarus appear to be noticeably more vulnerable to macroeconomic shocks than full families both from welfare and poverty perspectives.
Additionally, one of the most important determinants of welfare and poverty in Belarus is spatial location of a household. In particular, poverty highly discriminates against living in rural areas. The poverty incidence for rural areas over 2009-2016 is approximately 10.5 percentage points (or 44%) higher than the national average, while that of the urban areas is nearly 4 percentage points (or 16%) below national average. Moreover, in 2015-2016 urban and rural disparity for poverty widened even more and reached 25.3% for urban vs 40.6% for rural areas.
Finally, two more factors, savings and access to a plot of land, have on average a large positive influence on consumption expenditure and aa negative one on the chance of getting poor.
Conclusion
Poverty alleviation and development reflect economic and social progress in any country. While Belarus initially achieved noticeable progress in this dimension, the economic and social development in recent years seems to increase problem of poverty in Belarus. The estimates show that in 2015-2016, absolute poverty in Belarus increased almost twofold. Household size, large numbers of children in a household, the presence in the household of economically inactive members are all factors that decrease household welfare and increase poverty. Single mothers also appear to be substantially more vulnerable to macroeconomic shocks. Finally, one of the most important determinants of welfare and poverty in Belarus is if a household is rural. These findings are important warning signals for the design of pro-poor policies in Belarus.
References
- Foster, J., J. Greer, and E. Thorbecke. (1984). A Class of Decomposable Poverty Measures. Econometrica, 52: 761-766.
- Kakwani, N. (2003). Issues in Setting Absolute Poverty Lines, Poverty and Social Development Papers No. 3, June 2003. Asian Development Bank.
- Kruk, D., Bornukova, K. (2014). Belarusian Economic Growth Decomposition, BEROC Working Paper Series, WP no. 24.
- Mazol, A. 2017a. The Influence of Financial Stress on Economic Activity and Monetary Policy in Belarus, BEROC Working Paper Series, WP no. 40.
- Mazol, A. 2017b. Determinants of Poverty With and Without Economic Growth. Explaining Belarus’s Poverty Dynamics during 2009-2016, BEROC Working Paper Series, WP no. 47.
- World Bank (2000). Making Transition Work for Everyone: Poverty and Inequality in Europe and Central Asia. Washington DC, The World Bank.
Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.
Development of Belarusian Higher Education Institutions Based on the Entrepreneurial University Framework
In contrast to developed Western countries, higher education institutions (HEIs) in transition economies such as Belarus do not have the pretension to being key actors in cutting-edge innovation and in creating entrepreneurship capital. Rather, they tend to educate job seekers or knowledge workers, as well as to adapt, redevelop and disseminate existing knowledge and technologies. At the same time, policy makers in Belarus have realized that transformation of HEIs is needed to respond to the global challenges. In this regard, this policy brief discusses prerequisites and factors conditioning the development of entrepreneurial HEIs in Belarus.
Capitalizing on state-of-the-art academic research, as well as on the custom-made survey of Belarusian faculty members, the brief concludes that Belarusian policy makers need to create a supportive institutional environment before requiring from HEIs outcomes of the entrepreneurial mission. First-priority measures for the current stance are delineated.
Entrepreneurial University and University 3.0
As a productivity factor, entrepreneurial activities started appearing in economic growth models at the beginning of the twenty-first century (Wennekers & Thurik, 1999; Wong et al., 2005). Consequently, the role of HEIs broadened from educating labor force and knowledge creation to development of “entrepreneurial thinking, action and institutions” (Audretsch, 2014) – HEIs took on the third “entrepreneurial” mission.
Well-studied outcomes of this mission are new firms (academic spin-offs, spin-outs, student-led start-ups), patenting, licensing and the development of entrepreneurial culture and attitudes among graduates and academics.
The concept of an entrepreneurial HEI is multifaceted and is explored within different research streams: from knowledge transfer to entrepreneurship education and HEI management. Consequently, there is no consensus in the understanding of the term “entrepreneurial university” that can, for this policy brief, be broadly defined as a HEI that acts entrepreneurially and is a natural incubator, creating a supportive environment for the startup of businesses by faculty and students, promoting an entrepreneurial culture and attitude for the purpose of responding to challenges of the knowledge-based economy, and facilitating economic and social development.
Figure 1. Evolution of the HEIs’ missions
Source: Adapted from Guerrero & Urbano (2012)
Meanwhile, the concept of “University 3.0” –mostly corresponding to the concept of “Entrepreneurial university” and adopted from J.G. Wissema – started appearing in Russian publications, where the number ‘3’ corresponds to the three HEI missions or to the third generation of HEIs. A possible explanation of this renaming is that, on the one hand, in the post-Soviet context entrepreneurship per se still does not have a positive meaning in a broader society and it is not associated to HEIs. On the other hand, it was expected that such numbering makes the evolution visible. However, this led to speculation on this numbering and gave rise to publications on University 4.0 that should correspond somehow to Industry 4.0 – the current trend of automation and data exchange in manufacturing technologies.
Admittedly, the entrepreneurial mission of HEIs is not associated or equaled to start-ups and knowledge transfer any more, but is increasingly considered as a procedural framework for HEI’s and individual’s behavior.
Belarusian Context
Political, economic, social, technological and legal conditions determine the path and the speed of the evolution of HEIs as well as their contribution to national economies in different stages of economic development. Thus, in Belarus – an efficiency-driven economy, i.e., a country growing due to more efficient production processes and increased product quality (World Economic Forum, 2017), – HEIs are considered to contribute to economic development if they successfully fulfill teaching and research missions. While the outcomes of the third mission are supposed not to be relevant at this stage (Marozau et al., 2016).
However, trying to replicate the success of Western HEIs in the development of the entrepreneurial mission, the Ministry of Education of Belarus initiated the Experimental project on implementation of the “University 3.0” model aimed at the development of research, innovation and entrepreneurial infrastructure of HEIs for the creation of innovative products and commercialization of intellectual activities.
In general, Belarus has a state-dominated well-developed, by some estimates, oversaturated higher education sector that remains mostly rigid and unreformed since the Soviet times. Belarus outperformed all CIS and EU countries except Finland in terms of the number of students per 10,000 population in 2014 (Belstat, 2017) and according to the World Bank has one of highest enrollment rates in tertiary education of about 90%.
Belarusian students have quite high entrepreneurial potential in comparison to other countries participating in the Global University Entrepreneurial Spirit Students‘ Survey (GUESSS). Thus, in five years after graduation, 56.8% intend to be entrepreneurs, while the global average level is 38,2% (Marozau and Apanasovich, 2016). However, curricula of most specialties majors provided by Belarusian HEIs are not supplemented with formal and experiential entrepreneurship education to equip students with entrepreneurial competencies. Innovative methodologies and entrepreneurial approaches to teaching as well as faculty entrepreneurial role models are rare. Moreover, all changes in degree syllabuses need state approval that makes HEIs less flexible and nimble. The situation is further complicated by the fact that supporting entrepreneurial activity has not been an important part of the HEI culture.
Methodological Approach
We conducted online and face-to-face surveys of 48 Belarusian HEI authorities and faculty members that were based on HEInnovate self-assessment tool widely used by policy makers and HEI authorities (see Marozau, 2018).
Overall, emails were sent out to a population of 284 pro-active and advanced representatives of the Belarusian academic community whose email addresses were available in the databases of BEROC and the Association of Business Education. We benefitted from open-ended questions included in the questionnaire to study how representatives of Belarusian HEIs perceived the Entrepreneurial university (University 3.0) concept as well as its conditioning factors and potential outcomes.
Main Findings
First of all, we revealed that the Belarusian academic community is not unanimous in understanding the concept “Entrepreneurial university”. According to the main emphasis provided by respondents, we got the following distribution of answers about what an entrepreneurial is: 12 respondents associated the concept with knowledge transfer and commercialization; 7 respondents stressed the interrelation of teaching, research and innovations; 5 respondents believed that the concept is about earning money; 1 respondent indicated that an entrepreneurial university means developing entrepreneurial competences.
These findings demonstrate the general misunderstanding or fragmented understanding of the phenomenon that may lead to a negative attitude from both HEI staff and policy makers and stress the importance of raising awareness and providing training at least for decision makers and spokesmen.
Figure 2 demonstrates the results of the assessment of Belarusian HEIs against the categories proposed by HEInnovate (1 – very low; 5 – very high).
Figure 2. Assessment of HEIs
Source: Author’s own elaborations
We distinguished pairwise between (i) HEIs that participated in the Experimental project and those that did not: (ii) estimates of faculty members that were aware of the concept and those who were not.
Surprisingly, the representatives of HEIs that were left beyond the scope of the Experimental project and those who were aware of the concept perceived their HEIs more advanced in all the areas.
To understand this paradox, we used the chi-square test for independence to discover if there was a relationship between two categorical variables – awareness of the concept and employment at a HEI participating in the Experimental project. Surprisingly, no statistically significant relationship was identified evidencing that implementation of the Experimental project went without raising awareness and wider involvement of faculty.
The analyses of answers to open-ended questions showed that many environmental factors are not only unsupportive to the HEI entrepreneurial development but jeopardize the sustainability of the higher education system in general.
Conclusions
The main conclusions from the study are as follows:
- Belarus has not reached the stage of institutional development to foster entrepreneurial HEIs and to expect outcomes of the entrepreneurial mission. To some extent, this explains the skepticism and misunderstanding of the concept of “Entrepreneurial university” (University 3.0).
- The main omission of the Experimental project is that the education and training of HEI authorities and faculty are not defined as first-priority measures. Such policy initiatives need to be clear in their objectives, tools, benefits and outcomes as well as evidence-based and open for discussion.
- Comprehensive initiatives in this sphere should be developed and implemented in close collaboration with the Ministry of Economy that is responsible for entrepreneurship, the business environment, entrepreneurial infrastructure as well as the State Committee for Science and Technology that is subordinated to the Council of Ministers and deals with the state policy in its sphere.
An important concern here is whether it is currently feasible to have the measures that are relevant and not-for-show rather than half-way initiatives and sticking plaster solutions despite the lack of funding, and absence of elaborate study in the field.
- Since the weakest area of Belarusian HEIs according to the HEInnovate tool is the problem of ‘Measuring impact’, the state should reconsider short-term target indicators for HEIs such as export growth rate and workforce productivity growth rate to stimulate investments the entrepreneurial transformation. It is worth monitoring such indicators as number of start-ups/spin-offs founded by graduates/faculty members; number of patents, licenses, trademarks co-owned by a HEI, income from intellectual property; number of R&D projects funded by enterprises etc. Alternatively, the Ministry of Education could adopt the ranking of entrepreneurial and inventive activity of universities used in Russia.
- Development of entrepreneurship centers as organizational units at HEIs – ‘one-stop shops’ or ‘single front doors’ for students, faculty, businesses – could be an initial step towards both raising awareness and the integration and coordination of entrepreneurship-related activities within a HEI in order to increase their impact and visibility of these activities.
References
- Audretsch, David B., 2014. “From the entrepreneurial university to the university for the entrepreneurial society.” The Journal of Technology Transfer 39(3), 313-321.
- Belstat (2017). Education in the Republic of Belarus. Statistical book.
- Guerrero, Maribel, and David Urbano, 2012. “The development of an entrepreneurial university.” The journal of technology transfer 37(1), 43-74.
- Marozau, Radzivon, Maribel Guerrero, and David Urbano, 2016 “Impacts of universities in different stages of economic development.” Journal of the Knowledge Economy, 1-21.
- Marozau, Radzivon and Vladimir Apanasovich, 2016. National GUESSS Report of the Republic of Belarus. http://www.guesssurvey.org/resources/nat_2016/GUESSS_Report_2016_Belarus.pdf
- Radzivon Marozau, 2018. Modernization and development of Belarusian higher education institutions based on the entrepreneurial university framework. BEROC Policy Paper Series, PP no.63.
- Wennekers, Sander, and Roy Thurik, 1999. “Linking entrepreneurship and economic growth.” Small business economics 13(1), 27-56.
- World Economic Forum, 2017. “Global Competitiveness Report 2017-2018”, edited by Klaus Schwab.
- Wong, Poh Kam, Yuen Ping Ho, and Erkko Autio, 2005. “Entrepreneurship, innovation and economic growth: Evidence from GEM data.” Small business economics 24(3) 335-350.
Acknowledgments: The author expresses gratitude to Prof. Maribel Guerrero from Newcastle Business School, Northumbria University for her valuable comments and reviews as well as to Yaraslau Kryvoi and Volha Hryniuk from the Ostrogorski Centre (Great Britain) for coordinating the research project that has resulted in this policy brief.
Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.
Towards a More Circular Economy: A Progress Assessment of Belarus
This policy brief summarizes the results of our study, Shershunovich and Tochitskaya (2018), on the circular economy development in Belarus. The aim of the work was to measure the circularity of the Belarusian economy using European Commission indicators. The analysis reveals that the circular economy in Belarus is still in the initial stage of its development. In 2016, the employment in circular economy sectors in Belarus accounted for 0.49% of total employment, and the investment amounted to only 0.27% of total gross investment. Belarus is also falling behind many European countries in waste recycling.
Introduction
The circular economy represents an economic system based on a business model of reduction, reuse, recirculation and extraction of materials in production, distribution and consumption of goods and services (Batova et al., 2018).
Transition to it offers great opportunities to transform the Belarusian economy and make it more sustainable and environmentally friendly, while preserving primary resources, creating new jobs and increasing competitiveness of enterprises.
In order to encourage the transition to a circular economy, it is important to have a proper monitoring system based on reliable and internationally comparable data. It helps to track progress towards a circular economy, conduct policy impact assessment, and analyze whether measures being taken are sufficient to promote an economy that reduces the generation of waste.
To assess the development of a circular economy in Belarus, a set of the European Commission (EC) indicators was used to capture the evolution of the main elements of closing the materials and products loop. The EC monitoring system comprises 10 indicators which are part of 4 pillars: production and consumption; waste management; secondary raw materials; competitiveness and innovation.
The reasons to use this system for Belarus are as follows: first, there is no set of indicators that provide a comprehensive overview of a circular economy in Belarus, while the EC monitoring framework allows us to capture its main elements, stages, and aspects; second, Eurostat calculates circular economy indicators for the European Union (EU) countries on a regular basis, which proves the high level of their practical application, relevance and robustness; third, the EC is constantly working on their improvement. Thus, the EC set of indicators can be a tool to monitor trends in transition to a circular economy in Belarus.
Tight spots of waste statistics in Belarus
While calculating the circular economy indicators for Belarus the following problems with data affecting the quality of statistics have been identified:
- methodological issues;
- challenges with recording and coverage;
- insufficient degree of international comparability of data, in particular woth the EU countries.
Such methodological problems as the blurred boundaries between the definitions of ‘waste’ and ‘raw materials’, and the lack of criteria for categorizing substances or objects as waste allow enterprises to classify certain substances or objects not as waste and therefore not to file information on them. As a result, less than half of the enterprises which might generate industrial waste, report it. Therefore, the question arises whether the statistical data reflect the real level of waste generation, recycling, and disposal in Belarus.
Data on municipal solid waste (MSW) have proved to be one of the areas of most serious concern. Absence of direct MSW weighing makes the data on it very sensitive to the conversion factor from volume to mass units. The differences between the Belarusian and European waste classifiers and definitions of key concepts (‘waste’, ‘recycling rate’) complicate the data analysis.
In addition, since Belarus is the 3rd world potash fertilizers producer, the share of potash waste in the total volume of waste generation is very high (63-68%). Only a small portion of this type of waste stream is recycled in Belarus (no more than 4%) due to lack of appropriate technologies of potash waste utilization used internationally. As only Germany counting as one of the world’s largest producers of potash fertilizers within the EU, to increase the comparability of data between the EU countries and Belarus, potash waste hasn’t been considered when calculating the circular economy indicators. Given all the above mentioned problems, some of the EU indicators have been adapted to the existing Belarusian statistical data.
Illustration of waste statistics problems
Waste statistics problems result in overestimation or underestimation of some circular economy indicators. A good example is the recycling rate of all waste, excluding major mineral wastes. Belarus, which is a country without a proper legal framework for the circular economy or a well-established secondary raw materials market, had one of the best performances in terms of the recycling rate (72-80%) among the EU countries in 2010-2016. This fact reflects the problems with waste statistics rather than success in waste recycling in Belarus.
Table 1. Recycling rate of all waste excluding major mineral wastes, %, in 2010-2016
Source: for the EU countries and Norway – Eurostat. For Belarus – own calculations based on the data from the RUE “Bel RC «Ecology».
Actual picture of the circular economy development in Belarus
The indicators with minimum distortions in waste statistics show that some elements of the circular economy in Belarus are still in the initial stage of their development (tables 2, 3, 4, 5). Our study reveals that the recycling rate of MSW amounted to 15.4 % in 2014-2016, which is much lower than the EU average in 2014 and 2016. Thus, Belarus has a considerable potential to increase the recycling rate of MSW. The experience of Czechia and Lithuania shows that the MSW recycling rate can be increased relatively fast if efforts are made and resources permit.
Table 2. Recycling rate of MSW, %, in 2010-2016
Source: for the EU countries and Norway – Eurostat. For Belarus – own calculations based on the data from the SE “Operator of SMRs” and Belstat.
In 2016, the recovery rate of construction and demolition waste in Belarus reached 81%, though this indicator fluctuated between 59% and 79% in previous years. However, it can be further improved as in some European countries (Denmark, the Netherlands, Germany, Czechia, Poland and Lithuania) the recovery rate of this type of waste stream exceeds 90%.
Table 3. Recovery rate of construction and demolition waste, %, in 2010-2016
Source: for the EU countries and Norway – Eurostat. For Belarus – own calculations based of the data from the RUE “Bel RC «Ecology».
Despite the fact that the decoupling of economic growth from an increase in waste volumes is an important issue on the international agenda, trends in waste generation in many countries follow a development of GDP. In 2010-2012, the generation of waste excluding major mineral wastes per GDP unit (42-46 kg/thsd of $, PPP) in Belarus (table 4) was comparable with countries such as Czechia, Lithuania, Germany, Denmark, Sweden. However, in 2014 due to waste generation growth, this indicator in Belarus exceeded above-mentioned EU countries and approached the level of Hungary and the Netherlands. It was far above Norway that was the best performer among the European countries and a good example of how a country could really decrease waste generation.
Table 4. Generation of waste excluding major mineral wastes per GDP unit (kg per thsd constant 2011 international $) in 2010-2016
Source: for the EU countries and Norway the data on generation of waste excl. major mineral wastes – Eurostat. For Belarus – own calculations based on the data from the RUE “Bel RC «Ecology». For the EU countries, Norway and Belarus the data on GDP, PPP in constant 2011 international $ – The World Bank.
In 2012, the share of gross investment in the circular economy sectors in Belarus (table 5) decreased in comparison with 2010, however, since 2014 it have shown an upward trend. For the EU countries and Norway this indicator also includes investment in the repair and reuse sector. For Belarus this sector has not been taken into account in calculation due to lack of data. In addition, the gross investment in tangible goods is a bit different from the gross investment in fixed assets used for Belarus as the latter doesn’t include non-produced tangible goods such as land. Yet, even bearing in mind these differences in calculation, the circular economy appeared to be underinvested in Belarus compared to the EU countries and Norway.
Table 5. Gross investment in tangible goods (% of total gross investment) in circular economy sectors in 2010-2016
Source: for the EU countries and Norway – Eurostat. For Belarus – Belstat.
The employment in the circular economy in Belarus accounted for only 0.49% of total employment in 2016, while in the EU countries and Norway this indicator was approaching 3%. This again proves the fact that Belarus has a long way to go towards the creation of a circular economy.
Conclusion
The analysis revealed contradictory results of the circular economy development in Belarus. While the country scores highly across some indicators compared to the EU countries and Norway, this to a large extent reflects the problems with waste statistics, rather than success in waste management. The indicators with minimum distortions in waste statistics show that Belarus is falling behind leading countries in circular economy development. However, in the transition to a circular economy, the monitoring framework is an important component of this process, which permits to track a progress using the system of indicators. In order to ensure that these indicators accurately capture the key trends in the circular economy in Belarus it would seem useful to:
- align the definition of ’waste’, ‘recycling rate’ with the international one, identify clear criteria for classifying substances or products as waste and secondary raw materials;
- strengthen the accountability of entities for filing reports on waste;
- improve the system of MSW and SMRs reporting and recording, and introduce MSW recording based on weighing wherever possible;
- consider the option of improving the comparability of Belarus’ waste classifier with the European waste statistical nomenclature.¨
References
- Batova, N. et al., 2018. “On the Way to Green Growth: Window Opportunities of Circular Economy”, PP GE no.1.
- Belstat. http://www.belstat.gov.by/
- Eurostat / Circular economy / Indicators / Main tables. http://ec.europa.eu/eurostat/web/circular-economy/indicators/main-tables
- RUE “Bel SRC “Ecology”. http://www.ecoinfo.by
- Shershunovich, Y. and I. Tochitskaya, 2018. “Waste Statistics in Belarus: Tight Spots and Broad Scope for Work”, PP GE no.
Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.