Project: FREE policy brief

Addressing the Covid-19 Pandemic: Policy Responses Across Eastern Europe

Posted on June 1, 2020 | Policy Brief

20200601 Addressing the Covid-19 Pandemic FREE. Network Image 01

The world holds its breath as Covid-19 continues to spread and challenge local health care systems as well as local economies. The focus of international media has mostly been on China and then Western Europe and the US. However, countries around the Baltic Sea, Eastern Europe and the Caucasus differ from the West with respect to their socio-economic development, trade integration, and political systems. The webinar “Addressing the Covid-19 Pandemic in Eastern Europe: Policy Responses Across Eastern Europe” hosted by the the Forum for Research on Eastern Europe and Emerging Economies (FREE) Network on May 28, 2020 aimed to fill this gap in the current discourse and give voice to experts from Latvia, Russia, Georgia, Belarus, Poland, Ukraine as well as Sweden, in order to contextualize their countries’ policy choices and experiences in the crisis. Policy recommendations can only be of preliminary nature at this point of time. Yet, it becomes clear that even though transition countries have fared relatively well during the health crisis, they will not be spared from the ensuing economic crisis and will require policy tools which are adapted to the local context.

Introduction

Less than six months after the outbreak of the Covid-19 crisis in China, the pandemic has spread across the globe. The epicenter has moved from Asia to Europe and the US, and in late May 2020 some voices are warning that it is now shifting towards Latin America. While the world´s eyes have been on Milan and Paris, little has been said about how the new EU member states and countries to the East of the European Union cope with the pandemic. Some observers have claimed the emergence of a new “iron curtain” in the corona crisis; Eastern Europe, the Baltic States and the Caucasus having been relatively unscathed compared to the West. Persisting differences in trade and travel patterns, demographic and socio-economic differences, as well as differences in trust levels could account for such an observation.

Yet, the most recent statistics suggest that this may be a premature interpretation and the overall picture is much more heterogeneous. Infections in Russia seem to be rising quickly, Georgia by contrast has turned out to be one of the top students.

Figure 1: Total confirmed Covid-19 cases vs. deaths per million.

Source: Our World in Data, 2020. • CC BYa.
Note: Data includes the most recent numbers as of May 25, 2020. Both measures are expressed per million people of the country’s population. The confirmed counts are lower than the totals. The main reason for this is limited testing.

On May 28, 2020, the Forum for Research on Eastern Europe and Emerging Economies (FREE) Network hosted a webinar with its member institutes: BEROC in Belarus, BICEPS in Latvia, CEFIR@NES in Russia, CenEA in Poland, ISET-PI in Georgia, KSE in Ukraine, and SITE in Sweden to discuss how their countries have fared in the corona crisis so far. The webinar provided an opportunity to share experiences and to add some interpretations and insights to the crude statistics, which often become unintelligible in the current overflow of information.

Figure 2: FREE Network Countries.

Source: SITE 2020.

The webinar started with Torbjörn Becker, director of SITE, introducing recent developments in terms of health statistics in the region and the research being done within the framework of the FREE Network.

SITE on Sweden

Jesper Roine, Professor at the Stockholm School of Economics and SITE, then presented the case of Sweden, the country which – with regards to death rates – has surpassed all other FREE Network countries by far. The Swedish case has been very controversially discussed in international media throughout the pandemic. Yet, the common claim that in Sweden everything was “business as usual” is not true, according to Roine. Compared to its direct neighboring countries Finland, Denmark and Norway, Sweden has chosen a relatively lenient approach to Covid-19, but high schools and universities have moved to distance learning since March and working from home is highly encouraged. Mobility reports show that Swedes have reduced their movement a lot, but less so than their Scandinavian neighbors. Roine confirmed that the Swedish health policy has been dominated by the public health agency, Folkhälsomyndigheten. Even though this is the default option in Swedish law, Roine stressed that this does not mean that the government’s hands are tied.

He presented two preliminary conclusions regarding the impact of the Swedish strategy: first, Sweden’s mitigation strategy has worked relatively well; the public health system is seriously strained but not overwhelmed. Yet, Roine said that the “lack of testing [remained] a mystery”, even for advocates of the current mitigation strategy. Second, in Roine’s opinion the attempt to protect the elderly has failed. The virus has spread to numerous nursing homes and excess death rates indicate that mortality has increased sharply for citizens above 65 years of age, much less for other age groups. Geographically, Stockholm has been the center of the epidemic. Other parts of the country have been affected to a much lesser degree.

BICEPS on Latvia

Sergejs Gubins, Research Fellow at the Baltic International Centre for Economic Policy Studies (BICEPS) presented the Latvian experience of the corona crisis. A small country of about 2 million inhabitants, Latvia currently presents the second lowest Covid-19 mortality rate within the EU. Gubins related this to the Latvian government’s quick and determined policy reaction. After the first cases were reported in early March, schools and universities were closed, public gatherings forbidden, international travel halted, and a two-meter social distance rule imposed. Given the success of this strategy, Latvia has started to loosen its restrictions. A “Baltic Schengen area” was announced very recently and travel among the Baltic states is now possible again. The economic support package announced by the government amounts to 45 percent of GDP and includes a large equity investment in the airline airBaltic as well as important investments in infrastructure. According to Gubins, the current policy discussion focuses on the accessibility and size of help funds, widely deemed insufficient. Furthermore, the economic outlook of the country in terms of unemployment rates and GDP growth is bleak despite its success in containing the virus.

CEFIR on Russia

According to Natalia Volchkova, Director of the Centre for Economic and Financial Research (CEFIR) at the New Economic School in Moscow, Russia has pursued a “standard European strategy” in its fight against Covid-19. Two new hospitals exclusively for Covid-19 patients were created in Moscow, the current epicenter of the pandemic, and nearby. Most money spent on health care went to these new facilities, less was transferred as bonuses to medical workers. Russia has emphasized testing: around 10 million tests were performed; close to 400,000 cases of Covid-19 were confirmed. On May 27, free antibody testing was started in Moscow and is to be extended to other parts of the country. State-financed testing will serve to measure the potential degree of immunization of the population. While cases have started to decline in Moscow, other regions of Russia lag behind and are still expected to peak.

Volchkova stressed the role of the Russian shadow economy, which has been severely hit by the crisis. The size of the informal sector makes it difficult for the Kremlin to pass efficient support packages for the economy. Another policy problem lies in the weakness of the social security net, particularly unemployment benefits are hard to obtain. Therefore, most policy measures have focused on companies. Family allowances are the government’s second heavily used tool, which to Volchkova’s mind is an efficient policy choice. She concluded that the current help measures may already amount to 3 percent of GDP.

ISET-PI on Georgia

As of May 28, 2020, Georgia had only reported 12 corona deaths. According to Yaroslava V. Babych, Lead Economist at ISET Policy Institute in Tbilisi, the key explanation for Georgia’s relative success in the corona crisis is that, as in Latvia, testing started very early. She explained that even before Georgia’s neighbor Iran confirmed an outbreak of Covid-19, passengers’ temperatures were taken at the border crossing. The government in Tbilisi then soon imposed harsh quarantine measures, local quarantines in regional hotspots, a shutdown of public transport, an evening curfew and very high fines. Compliance with the measures was very high. Orthodox Easter celebrations were allowed to take place under strict hygiene measures and did not result in a spike in infection rates.

The country, largely reliant on tourism and agriculture, is now focusing on the economic consequences of the crisis. According to Babych, Georgia holds the ambition to become the first European country to open up to international tourism again from July 1, 2020. The government is also determined to avoid another meltdown of the important construction sector, as happened in 2008 – 2009. However, similar to the Russian case, Babych identified two factors which crucially weaken the Georgian economy: the lack of automatic stabilizers in the form of unemployment benefits and the large informal sector. Policymakers have therefore resorted to monthly cash payments to those who stopped paying income tax around March and fixing prices for specific food products. While the effectiveness of these measures still has to be evaluated, the policy discourse in Georgia has moved on to the socio-economic consequences of the crisis.

BEROC on Belarus

Lev Lvovskiy, Senior Research Fellow at the Belarusian Economic Research and Outreach Center (BEROC), provided an overview of the Belarusian policy measures. According to Lvovskiy, Belarus has a high number of nurses and doctors and a relatively efficient “Soviet style of fighting pandemics”. There have been hardly any restrictions to public gatherings and events, both the Orthodox and the Catholic Easter festivities were maintained, as were soccer games and the national Victory parade. Initially, the official policy was to trace and isolate cases, but this did not prove to be very efficient, supposedly due to poor enforcement. Lvovskiy said that testing is rare which is why statistics on the spread of the virus and its effects remain of questionable quality.

While Belarus disposed of a solid health care system, it was not well prepared economically, which explains why the government has not been very proactive in Lvovskiy’s opinion. The Belarusian industrial production decreased by 7 percent in April 2020 compared to the same month the year before; unemployment has started to increase, yet, there are no significant unemployment benefits. Increasing the height of unemployment pay is the key policy issue under discussion in Minsk but in the absence of international loans, the government´s hands are tied. The issue is urgent: the most recent BEROC survey suggests that 46% of individuals living in urban areas have already seen their income decrease. Lvovskiy’s preliminary conclusion is that the Belarusian policy response to the Covid-19 crisis was not as bad as expected by many international observers: the health crisis has mostly been contained. But like in the Georgian case, the socio-economic implications of the crisis are becoming more pressing now.

CenEA on Poland

Michal Myck, Director of the Centre for Economic Analysis (CenEA) in Szczecin, explained that Poland also successfully avoided a spike in infection rates thanks to a quick policy response. Poland was one of the first countries to impose international travel restrictions and very harsh social distancing measures, yet, infection rates remain higher than in other FREE Network countries. Since the second half of April, most measures have been lifted and the spread of the virus seems under control and concentrated in the region of Silesia.

All limitations were implemented without invoking a state of emergency. Myck suggested that the government may have made this choice because the presidential elections would have been automatically postponed otherwise, an outcome the government wanted to avoid. The elections were eventually postponed, but doubts persist with regards to the constitutional validity of the way this decision was taken. Myck stressed the persisting political uncertainty. Economic policy in Poland has focused on protecting jobs and providing liquidity to enterprises. State loans have been primarily directed to SMEs and will be partly written off, conditional on continued activity and employment. In Myck’s opinion, the economic outcome for Poland will depend on whether investments from and exports to Western Europe quickly resume or not.

KSE on Ukraine

Tymofiy Mylovanov, President of the Kyiv School of Economics and former Minister of Economic Development, Trade and Agriculture, stressed that in the first few weeks of the pandemic, Ukraine enforced harsher policy measures than its neighbors. The lock down was almost complete, with only grocery stores and pharmacies allowed to open. Compliance was high during the first few weeks but then started to decline.

The government allocated 3 percent of GDP to a Covid-19 support fund, there has been a lot of deregulation on the labor market, but the central bank’s key interest rate remains at 8 percent. Pressure for a looser monetary policy increases according to Mylovanov, as GDP has fallen by 1.2 percent and unemployment is expected to reach up to 10 percent by the end of the year.

Mylovanov’s thoughts about Ukraine’s economic prospects are mixed: average salaries continue to grow during the crisis which may be explained by the fact that low-skilled employees get laid off first, suggesting a potentially long-lasting change of the composition of the workforce. At the same time, the political situation is volatile with local elections coming up in October 2020 and public pressure mounting. As Poland, Ukraine did not declare a state of emergency. While Mylovanov thinks that the policy response could have been better, he is optimistic that Ukraine was better prepared to Covid-19 than to previous crises and will not have to resort to international loans.

Preliminary Conclusions

It is too early to draw any definite conclusions, but undoubtedly, a lot can be learned from the very diverse experiences of the corona crisis in the region. The former Soviet countries have a different historical and political legacy than Western European countries and accordingly, have found different ways of handling the crisis. Some have been more successful than their Western neighbors. But even those countries which have not faced a large health crisis have been severely hit economically and are likely to suffer economic hardship in the future.

The lack of a strong tradition of unemployment benefits and automatic stabilizers renders countries like Georgia, Belarus and Russia particularly vulnerable to the economic crisis which will inevitably follow the Covid-19 outbreak. In some countries, the corona shock may also accelerate or trigger political changes. In the view of this, the FREE Network will organize a series of follow-up webinars and briefs on more specific corona-related topics, with the aim of contextualizing statistics and providing a wider picture of the socio-economic consequences and policy implications of the crisis.

Please find a full recording of the webinar below. Updates on further events will be posted on the FREE website and on social media channels (Facebook, Twitter).

List of Speakers

  • Jesper Roine, Professor at the Stockholm Institute of Transition Economics (SITE / Sweden)
  • Sergejs Gubins, Research Fellow at the Baltic International Centre for Economic Policy Studies (BICEPS / Latvia)
  • Natalia Volchkova, Director of the Centre for Economic and Financial Research at New Economic School (CEFIR@NES / Russia)
  • Yaroslava V. Babych, Lead Economist at ISET Policy Institute (ISET / Georgia)
  • Tymofiy Mylovanov, President at the Kyiv School of Economics (KSE / Ukraine)
  • Lev Lvovskiy, Senior Research Fellow at the Belarusian Economic Research and Outreach Center (BEROC / Belarus)
  • Michal Myck, Director of the Centre for Economic Analysis (CenEA / Poland)
  • Torbjörn Becker, Director of the Stockholm Institute of Transition Economics (SITE)

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.

Supporting Measures for Belarusian SMEs: the Context of the Covid-19 Pandemic

Posted on May 25, 2020 | Policy Brief

Image of numerous different coloured umbrellas photographed from above representing Belarusian SMEs

In the context of the evolving global economic crisis, governments are “competing” with each other in the complexity and scale of measures to support the economy and, in particular, small and medium-sized enterprise (hereafter  SMEs). The main goal of these measures is, on the one hand, to prevent a significant increase in unemployment and a consequent social strain, and, on the other hand, to ensure economic recovery driven by the most efficient enterprises.

Belarusian SMEs, which currently employ more than 1.3 million people, usually respond faster and more extensively than the state companies to the downturn in the economy by laying off employees. At the same time, they are also expected to be more sensitive reacting to governmental support policies. In this regard, the policy brief discusses the role and response of SMEs in the period of crises and delineates short- and medium-term measures.

Why are SMEs in the Focus During Economic Downturns?

SMEs often become the focus of state policy in a period of adverse and unstable economic situations and the recent pandemic is not an exception. This special attention can be motivated by the following basic assumptions:

1) SMEs are more flexible and respond faster to both negative and positive trends in the economy (Muller at al., 2018);

2) the activity of SMEs is more labor-intensive compared to large enterprises (Beck et al., 2005; Cravo et al., 2012);

3) a period of economic uncertainty creates new opportunities (new niches, exits of competitors from the market) that can be used by the most proactive SMEs (Cowling et al., 2015).

Based on these assumptions, a large share of SMEs on the one hand makes the economy more resilient in crises and, on the other hand, contributes to the volatility of unemployment. As a result, governments try to support SMEs to prevent a rapid increase in unemployment due to staff cuts and bankruptcy and, simultaneously, to maintain a competitive environment that creates incentives for innovation.

Typically, governments have substantial experience and proven tools to uphold large public and too-big-to-fail private enterprises, while supporting a heterogeneous population of SMEs requires additional study and field tests.

At the same time, the design, the scope, and the coverage of support policies should be introduced having in mind the possible reactions of various types of SMEs to the economic hardship.  Indeed, during an economic decline even in the worst hit sectors, businesses and SMEs in particular may react by implementing three basic strategies:

1) reducing costs by firing employees, cutting wages and by increasing productivity;

2) increasing revenue by introducing innovations (product, process, organizational, marketing), diversification, and entering new markets;

3) suspension of activities or liquidation of an enterprise (OECD, 2009).

Definitely, any government aims for the largest possible share of enterprises that pursue the second strategy leading to job creation and significant added value.

Policy Responses in the Period of the Pandemic

Due to the urgency of adoption and the weak predictability of the epidemiological situation, most of the proposed SME-support packages around the world are designed for the short term and are poorly targeted. Based on the study of already announced measures, the OECD (2020) has developed a comprehensive classification and sequence of SME-support measures undertaken by governments:

1. Health measures, and information for SMEs on how to adhere to them;

2. Measures to address liquidity by deferring payments (taxes, social security & pension contribution, rental, utilities);

3. Measures to provide extra and more easily available credit to strengthen SME resilience;

4. Measures to mitigate the consequences of lay-offs by extending possibilities for temporary redundancies and wage subsidies;

5. Structural policies (digitalization, training and education for SMEs, support in finding and entering new markets etc.).

Unfortunately, the government of Belarus has started discussing and implementing some of these measures only partially and in a rather non-specific way. Instead of this, we argue that all the measures should be targeted and adjusted to different sectors. To further expand and analyze our point, BEROC developed and commissioned an express random-sample survey of 100 Belarusian SMEs on April 13-27 in order to elaborate and justify relevant support measures (BEROC, 2020).

Belarusian SMEs in the Pandemic

The financial situation of Belarusian SMEs by sector and their response to the crisis manifested in implementing innovative approaches and new business models are illustrated in Figure 1.

Figure 1. Decrease of revenues and response of SMEs

Note: Area of circles is proportional to the number of SME employees in a sector.
Source: Own elaboration based on the survey.

SMEs operating in hotels, restaurants, catering (HoReCa), education, sport & leisure as well as transportation (the right lower rectangle) are characterized by a substantial decrease of revenues and low adaptability. At the same time SMEs in the communication and IT sector and scientific, technological and consulting sectors demonstrate a high degree of adaptability that may be related to some extend to managerial competencies and human capital in general which is concentrated in these sectors.

As an implication for policy makers and SMEs’ leaders, possible support measures (based on OECD classification) and business strategies are summed up in Table 1.

Table 1. Support measures and business strategies for Belarusian SMEs

Group Sectors Recommended strategy Relevant Measure (number in the OECD classification)
A. Decrease of revenues + slow adaptation Construction,

wholesale trade & retail

manufacturing

 Re-configuring supply chains, entering new niches, business process optimization 2,3,5
B. Decrease of revenues + active adaptation Communication & IT

Scientific, technological, consulting services

 Focusing on development of anti-crisis solutions in B2B and B2C segments 2,4
C. Substantial decrease of revenues + slow adaptation Transportation

HoReCa

Education

Leisure, beauty & sport

 «Conservation» or liquidation of a business 2,3,5
D. Substantial decrease of revenues + active adaptation Not identified in the survey Diversification to adjacent market segments 2,4,5
E. No changes or growth of revenue Agriculture & Forestry

E-commerce, pharmacy, online services, online games…

 Expansion to new markets while competitors are on quarantine. 5

Source: Own elaboration based on the survey.

The main measure to support SMEs in the short term (items 2-4 in the OECD classification) can be:

  • Deferral, reduction or suspension of contributions to the social security fund (groups B, C) – this will save jobs in the short term;
  • Wage subsidies that will allow paying minimum wages and keeping staff (groups A, C)
  • Rent and utility deferrals or at least payment in arrears – for groups A, C – combined with the support of building owners. This will significantly reduce costs in the face of falling revenues instead of reducing labor costs;
  • Loan holidays and preferential conditions for SMEs (group D). This will provide liquidity for enterprises that according to banks’estimates will be able to develop in the medium term;
  • Temporary repeal of fines for late payment of taxes and contribution to the social security fund (groups A-D).

As for the medium-term measures, the most relevant ones are as follows:

  • Expanding the coverage and improving the quality of business education (including digitalization of business) by means of providing vouchers and/or grants;
  • Subsidies to unemployed people for starting up a business combined with basic training on entrepreneurship;
  • Export support by developing infrastructure for certification and international marketing as well as providing export loans (Marozau et. al., 2020).

Conclusion

The Belarusian government is substantially restricted in terms of financial resources, fiscal and external debt opportunities to extensively support businesses suffering from the economic crisis. Therefore, formal and economically justified criteria for selecting sectors, as well as individual businesses and individual entrepreneurs should be developed. Meanwhile, the beneficiaries of the state support should not be the most affected businesses, but rather the most forward-looking ones. This so-called “picking winners” approach (Gonzalez-Pernia et al., 2018) would conduce to faster economic recovery and job creation driven by the private sector and, particularly, by SMEs. This is probably the main argument in favor of supporting small and medium-sized businesses in the crisis.

References

  • Beck, T., Demirguc-Kunt, A., Levine, R. (2005). “SMEs, Growth and Poverty: Cross- country evidence.” Journal of Economic Growth, 10(3), 199-229.
  • BEROC. (2020). “SME Survey Results”, Access mode http://covideconomy.by/business. Access date: May 19, 2020).
  • Cowling, M., Liu, W., Ledger, A., & Zhang, N. (2015). “What really happens to small and medium-sized enterprises in a global economic recession? UK evidence on sales and job dynamics.” International Small Business Journal, 33(5), 488-513.
  • Cravo, T.A., Gourlay, A., Becker, B. (2012). “SMEs and Regional Economic Growth in Brazil.” Small Business Economics, 38 (2), 217-230.
  • González-Pernía, J. L., Guerrero, M., Jung, A., & Pena-Legazkue. (2018). “Economic recession shake-out and entrepreneurship: Evidence from Spain.” BRQ Business Research Quarterly, 21(3), 153-167.
  • Marozau, R., Akulava, M., Aginskaya, H., (2020). “Measures to support small and medium-sized businesses in Belarus in the context of the pandemic and global recession.” BEROC Policy Paper Series, PP no.89.
  • Muller, P., Mattes, A., Klitou, D., Lonkeu, O., Ramada, P., Ruiz, F.A., Devnani, S., Farrenkopf, J., Makowska, A., Mankovska, N., Robonn, N., Steigertahl, I. (2018). Annual report on European SMEs 2017/2018. The 10th Anniversary of the Small Business Act. European Commission.
  • OECD. (2020). “COVID-19: SME Policy Responses.” OECD Centre for Entrepreneurship, SMEs, Regions and Cities (CFE). Access mode https://read.oecd-ilibrary.org/view/?ref=119_119680-di6h3qgi4x&title=Covid-19_SME_Policy_Responses. Access date: May 19, 2020.
  • OECD. (2009). “The Impact of the Global Crisis on SME and Entrepreneurship Financing and Policy Responses.” OECD – Centre for Entrepreneurship, SMEs and Local Development, Paris.

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 and Gender Inequality in Russia

Posted on May 14, 2020 | Policy Brief

20200514 Covid-19 and Gender Inequality in Russia FREE Network Policy Brief Image 01

Gender inequality is a complex phenomenon characterized by significant and persistent differences in social and economic indicators for women and men. In connection with the Covid-19 pandemic and unprecedented quarantine measures around the world, economists are thinking not only about the obvious global consequences for the global economy but also about the indirect effects, including those through gender-related changes in the labor market. What will the consequences of this crisis on the labor market be in the long run, especially on its gender and family-related components? In this brief, we look at the potential effects of the Covid-19 epidemic and the associated quarantine on gender inequality in Russia.

Introduction

Gender inequality is a complex phenomenon characterized by significant and persistent differences in social and economic indicators for women and men. These may be differences in access to education and medicine, labor market participation, wages, entrepreneurship, participation in politics and public administration, and the distribution of domestic unpaid labor within the family. Reducing gender inequality (like any other form of inequality) correlates with increases in GDP.

The prevalence and scale of gender inequality is, on average, lower in developed countries than in developing countries, and although there is a general tendency for gender gaps to narrow over time, this does not happen simultaneously and equally in all countries. According to the Global Gender Gap Index (2020), which ranks more than 150 countries, the five countries with the best indicators include Iceland, Norway, Finland, Sweden, and Nicaragua, while Congo, Syria, Pakistan, Iraq, and Yemen are in the very bottom. As of 2020, Russia is located approximately in the middle, being the 81st, right between El Salvador and Ethiopia.

In connection with the Covid-19 pandemic and unprecedented quarantine measures around the world, economists are thinking not only about the obvious global consequences for the global economy but also about the indirect effects, including those through gender-related changes in the labor market. A study of World War II, for example, shows that even short-term gender differences in the labor market can have long-term consequences (Goldin and Olivetti, 2013). What will the consequences of this crisis on the labor market be in the long run, especially on its gender and family-related components? In this brief, we look at the potential effects of the Covid-19 epidemic and the associated quarantine on gender inequality in Russia.

Heterogeneous Cross-Sectoral Effects

Economists are now discussing two main channels that can influence gender inequality (Alon et al., 2020). The first one works through differential risk of losing jobs and salaries for women and men due to the disproportionate impact of the epidemic and quarantine on sectors which predominantly employ each gender. The direction of this effect is not easy to predict. On the one hand, the current crisis differs from ordinary recessions in that the service sector, where more women are traditionally employed, is now suffering more than usual. However, it is very important to emphasize what kind of services we are talking about: restaurants and salons are not the whole of the Russian economy. According to the Russian Statistical Agency (Rosstat) 49% of all employed women in 2019 worked in three sectors – trade, healthcare, and education. At the same time, hotels, restaurants, and other services (which include hair and beauty salons) provided less than 8% of women’s employment.

Therefore, from the point of view of assessing the risk of job loss, it makes sense to consider state-financed sectors, where employees are likely to be retained, separately. Among the private businesses, two (non-mutually exclusive) types of sectors are likely to suffer the least. First, the critical ones that do not stop their activity during quarantine (for example, food retail, private medical centers). And second, those that are characterized both by a high ability to work “remotely” and continue to have sufficient demand for their goods and services – either directly or through value chains (see e.g. Volchkova, 2020). For example, agriculture, manufacturing and hotels are worse off in this combination than the financial sector, science, administration, and some types of online education. At the level of the individual characteristics of the employee, even when comparing the same occupations, the possibility of remote work positively correlates with the level of education, wealth, working for a company (rather than self-employment), and being female (according to Saltiel, 2020, for developing countries).

According to the same data from Rosstat, it turns out that about 49% of all women and 40% of all men worked in the “state-financed” and “remote-work” sectors (or 69% against 52%, if we add the trade sector). This is of course an overestimate, since not every job within a sector is characterized by state-financing or remoteness, but it likely represents the relative propensity across genders, which is of our interest. This relative propensity is mostly due to the much higher employment of women compared to men in health and education (approximately 4 to 1 in both sectors). In general, this may mean that the risk of job loss is now higher for men, and not for women as was predicted using US data by Alon et al. (2020), given the gender structure of employment by industry in the US. This rough assessment does not account for different opportunities for women and men to quickly find a new job, especially in the areas of high demand. For example, if the need for delivery workers has increased, and men are more likely to take this job, then it may be easier for them to quickly find a new job. This adaptive effect would unlikely overturn the original difference, because the number of such jobs is also limited.

The Effect of Childcare Facilities Closure

The second channel, likely having a multiplicative effect on the first, operates through the unexpected closure of children’s educational institutions (kindergartens and schools). These effects may be different depending on family composition. While before the pandemic, working parents could send their children to kindergarten and school, this opportunity is now completely unavailable. In the case of online education, not all children are independent enough to learn at home, especially primary school students. At the same time, other childcare support (e.g. from nannies, grandparents and other relatives, etc.) can also be significantly limited due to social distancing and self-isolation, although Russia is in a better position in this regard compared to many developed countries because grandparents traditionally help more in raising children. (It is interesting that in developed countries, the possibility of outsourcing household chores – childcare, cleaning, etc. – is one of the important explanatory factors for higher fertility among more educated women, compared with less educated ones, (see Hazan and Zoabi, 2015)).

Naturally, the situation with closed childcare and educational institutions will not affect the productivity of people without young children. According to the latest census in 2010, about 88 million people, which is as much as 75% of the total adult population of the country, do not live together with children under 18 years old. Also, most likely there will not be a big negative effect on families with children where one of the parents (most often the mother) or another individual in the household (a grandparent) took care of the child at home before the quarantine.

For all other families, the critical problem is juggling childcare with work. The most vulnerable categories of the population here are single mothers and single fathers (and there are about 5 and 0.6 million in Russia, respectively), especially those who do not have any outside help.

Among families with small children where both parents work, several important factors can be identified. On the one hand, according to developed countries, even in families where both parents work, women spend more time on household chores and childcare than men (Doepke and Kindermann, 2019). If one believes that the initial factors that affected this distribution of domestic work (such as traditional norms and role models or the relative income of spouses) have not disappeared, then the sharply increased burden of household chores will disproportionately fall on women. This can lead to a decrease in the relative productivity of women compared to men in the labor market and a greater risk of dismissal. In the long run, this can also negatively affect gender inequality, as even a temporary exit from the labor market may be accompanied by human capital losses and a worse career path in the future.

The Interaction of Both Effects

On the other hand, the opposite situation is also possible. If, due to the disproportionate effect of quarantine on various sectors of the economy, which has been discussed above, women have a lower risk of losing their jobs, then it is possible that at least temporarily, a significant part of the childcare will fall on men. This situation can also happen in families where the woman works in critical sectors of the economy (especially in healthcare) and the man works remotely from home.

Economists have suggested several mechanisms for the effect of short-term additional interaction between fathers and children on long-term participation in their upbringing: there is more information about children’s needs, learning-by-doing, and greater attachment to children. For example, the data from Canada shows that the introduction of 5 weeks of parental leave for fathers led to a more even distribution of domestic labor in households and a greater likelihood of the mother’s participation in the labor market, even 1-3 years after the fact (Patnaik, 2019). Moreover, even if there are not many families like this in the country, the new social norms can gradually spread in society through so-called “peer effects”. Dahl et al. (2014), for example, show using Norwegian data that the brothers and colleagues of men who took parental leave were 11-15% more likely to take it in the future, relative to brothers and colleagues of men who did not take such leave.

Other Hypotheses

Another major consequence of the epidemic and quarantine is the potential upsurge in domestic violence. Several European countries have already noticed an increase in such crimes (European Parliament, 2020), and some crisis centers in Russia have also reported an increase in calls to helplines. Economists identify different triggers for this behavior (Peterman et al., 2020). This may be a direct consequence of quarantine, which increases the time spent by the potential victim and abuser in a closed space, and the inability to seek immediate help, both psychological and medical. Indirect effects can also work through an increased risk of depression and post-traumatic stress syndrome, which were well documented for previous epidemics such as SARS and swine flu. and that may happen due to job loss, reduced income, general economic uncertainty, or a direct fear of getting sick.

These effects disproportionately affect women (and children); therefore, additional resources should be dedicated to identifying such crimes, strengthening support structures for women, and increasing the availability of reporting options without attracting the attention of an abuser (for example, such a warning system may be installed in pharmacies – a place where a woman can go to alone).

Economists have yet to accurately measure and test all these mechanisms, which interact with each other in complex combinations, but it is now clear that very different scenarios are possible, including the positive ones – of a long-run decrease in gender inequality.

References

  • Alon T.,  Doepke M., Olmstead-Rumsey J., and Tertilt M. “The impact of Covid-19 on gender equality”, Covid Economics, Issue 4, 14 April 2020.
  • Dahl G.B., Løken K.V., Mogstad M. “Peer Effects in Program Participation”, American Economic Review 104(7): 2049–2074 (2014).
  • Doepke M. and Kindermann F. “Bargaining over Babies: Theory, Evidence, and Policy Implications”, American Economic Review, 109(9): 3264–3306 (2019).
  • Goldin C. and Olivetti C. “Shocking Labor Supply: A Reassessment of the Role of World War II on Women’s Labor Supply”, American Economic Review, 103(3): 257-262 (2013).
  • Hazan M. and Zoabi H. “Do highly educated women choose smaller families?” Economic Journal, 125(587): 1191-1226 (2015).
  • Patnaik A. “Reserving Time for Daddy: The Consequences of Fathers’ Quotas”, Journal of Labor Economics, 37(4): 1009-1059 (2019).
  • Peterman A., Potts A., O’Donnell M., Thompson K., Shah N., Oertelt-Prigione S., and van Gelder N. “Pandemics and Violence Against Women and Children”, Center for Global Development working paper, 1 April 2020.
  • Saltiel F. “Who can work from home in developing countries?” Covid Economics, Issue 6, 17 April 2020.
  • Volchkova N. “Who should receive government support during Covid-19 crisis”, in “Economic Policy during Covid-19”, April 2020.
  • European Parliament. “COVID-19: Stopping the rise in domestic violence during lockdown”, Press Release  7 April 2020.
  • Rosstat, “Russian census 2010”.
  • Rosstat, “Russian labor force survey 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.

Covid-19: News for Europe’s Energy Security

Posted on May 7, 2020 | Policy Brief

Black and white image of natural gas processing plant representing Energy security Europe

While there has been a lot of attention on the effect of Covid-19-related developments in the oil market, the effect on the natural gas market has almost evaded media attention. For the EU, however, the gas market and especially the impact of the pandemic on the gas relationship with its largest gas supplier, Russia, is of high relevance. This brief discusses the potential implications of Covid-19 on this relationship both under the pandemic and during the expected slow economic recovery. We argue that, while in the short run the security of Russian gas supply is likely to improve, this is unlikely to be the case in the aftermath of the pandemic. To ensure gas supply security in post-pandemic markets, the EU may need to finally implement the long-awaited “speaking with one-voice” energy policy.

Introduction

The ongoing coronavirus pandemic will not only affect human lives, but also bring new economic and political challenges. The energy sector, and in particular the dramatic decrease of oil prices, has been in the news since the beginning of the Covid-19 crisis. But discussions have so far rarely touched the natural gas market, despite the pandemic taking its toll also on this market. As for oil, the demand and price have been negatively affected by the economic slowdown. While not as drastic as for oil, the price of natural gas in the EU has declined by approximately 40% since the beginning of 2020 (World Bank, 2020). However, the impact of the pandemic is likely to be quite different in oil and gas markets. There are multiple reasons for that; for example, oil and oil products are predominantly consumed by the transport sector while natural gas is mostly used in the power sector, the industry and households, and these sectors were differently affected by the Covid-19 pandemic.

Understanding the impact of the pandemic on the gas market is especially interesting from the European point of view, given that natural gas accounts for 25% of total energy consumption and two thirds of this gas is imported. The imports are also very concentrated, with the main supplier Russia providing around 40% of the gas, compared to 25% of the crude oil. This dependency, as well as a long history of tensions with third parties (Ukraine and Belarus) on the Russian gas transit routes, has made the EU’s concerns about the security of Russian gas supply much more pronounced than for oil (see Le Coq and Paltseva, 2012). The combination of these factors – i.e. the importance of natural gas for the EU and the long-standing concern about gas supply security warrant an analysis of the short and mid-term effect of the Covid-19 pandemic on the gas market, and, specifically, on the EU-Russia gas relationship. This brief discusses how the pandemic-driven decline in gas demand, and the potential shift in the balance of power between the parties may affect both the dependency on, and the transit of, Russian gas.

EU Dependency on Russian Gas Under the Covid-19 Pandemic

As is well known, Covid-19 and the associated lockdowns imposed by many EU Member States, have caused a slowdown in most economies and a decline in energy demand. However, for natural gas, the effect is likely to be significantly smaller than for oil. While we do not yet have statistics for the EU’s gas demand in recent months, the Norwegian energy consultancy Rystad Energy has predicted the decline of gas demand to be around 4% for March and April 2020. This forecast was given quite early in the course of the pandemic, and is very likely an underestimation; still, it is very different from the one for oil, with the demand drop estimated to be a whopping 34% in April.

One reason why we do not observe a sizable decrease in gas demand is that the natural gas is used in electricity generation, especially as a base-load fuel to compensate for the intermittency of green energy sources, such as sun and wind. With the reduced electricity demand, renewable power generation has become relatively more important in the electricity supply in many countries. Since mid-March 2020, the share of renewable power generation across the EU is 46%, nine percent higher than during the same period last year (Energy Transition Lab, 2020). Interestingly, in France, Germany, Belgium, the Netherlands, the Czech Republic, Poland and Hungary, the absolute volume of electricity generation by renewable sources even increased relative to the same period in 2019, despite declining energy demand. One potential channel, anecdotally recorded for Germany could be higher solar generation due to cleaner skies resulting from the decline in emissions because of lower fossil energy consumption. A higher volume of a renewable generation often requires more back-up power to maintain grid stability. While natural gas is not the only back-up source, this need might still limit the decline in gas demand (or even increase it like e.g. in the Czech Republic). Of course, cheaper gas prices may also play a role: for example, Slovakia and Romania experienced an increase in gas-based generation, but a drop in the renewable generation since mid-March 2020 relative to the same period in 2019. Finally, another reason for the moderate gas demand decline is its residential use – which is likely to be sustained due to the lockdown regime introduced by many countries.

When it comes to Russian gas imports, the official statistics since mid-March – roughly the beginning of lockdown policies across the EU – are not available yet. However, we can with some reservation look at the evolution of the volume of gas sales to the EU disclosed by Gazprom (2020). There was a very sizable decrease in Russian gas imports by the EU – of more than 21% – as compared to the same period last year but it started before the lockdown: January 2020 recorded a drop of 34% and February of 20%). This suggests that the current decrease in Russian gas imports is only marginally related to the pandemic, and more related to the overall gas market situation (such as relatively full gas storage in the EU in 2020, a warm winter, an increase in LNG imports, etc.).

It is, however, likely that the negative effect of the pandemic on Russian gas imports by the EU will be noticeably higher than it currently appears in the Gazprom data, thereby further decreasing the EU’s dependency on Russian gas. Moreover, since demand and prices decrease, substituting for Russian gas, were there a supply interruption, should be relatively easy and cheap with the current excess capacity of the natural gas market and the substantial storage in the EU.

Another reason for the improvement in the security of Russian gas supply to the EU is the observation that Russia’s dependency on oil and gas exports in combination with pandemic-associated factors may lead to a substantial economic downturn in Russia (Becker, 2020). In these dire circumstances, Russia is unlikely to further risk its gas export revenues by pursuing geopolitical goals through the means of gas supply and gas transit. For all these reasons, one may expect the security of Russian gas supply to the EU to improve during the pandemic.

However, the EU dependency on Russian gas may still be a concern due to medium-run effects of Covid-19. First of all, while the gas prices have been in decline for roughly a year now, the recent decrease in natural gas prices has accelerated the negative impact on the unconventional natural gas industry. For example, the US natural gas rig count has declined by 20% since mid-March 2020, which accounts for more than a third of the 54% year-to-year decline (Ycharts.com, 2020). Similarly, nearly 42% of Australian gas resources could be uneconomic under the current gas prices, according to Rystad Energy. While gas prices are unlikely to stay low forever, the industry will need time to recover even if/when the natural gas demand rises again. Moreover, the East-Asian markets are likely to be served first, as they are expected to recover from the pandemic shock before Europe. This dynamic, coupled with historically higher LNG prices in Asia may delay the LNG flows to Europe. A shortage of LNG in Europe, in turn, is likely to hinder any diversification strategy from Russian gas, weakening the EU’s bargaining power. The new Russia-China gas pipeline, “Power of Siberia”, operational since the end of 2019, will also be used to satisfy the post-Covid-19 Chinese gas demand which is likely to recover before demand picks up in the EU. Its use will then allow Russia to be less reliant on exporting gas to the EU, further contributing to the EU’s gas security concerns.

Transit of Russian Gas to the EU: Covid-19 Effect

The EU’s energy security also depends on the reliability of Russian gas transit to the EU. There are currently 5 transit routes connecting Russia to the EU (plus the routes that are serving the Baltic states and Finland without further transit), see Figure 1. Three onshore routes connect Russia to the EU via Ukraine and Belarus. There has been a history of gas transit disputes associated with these routes, at times threatening the Russian gas supply to the EU. Two newer offshore pipelines, Nord Stream 1 (in operation since 2011) and TurkStream (in operation since 2020) connect Russia directly to Germany, and to the South-East of Europe via Turkey. Further, one more offshore route to Germany, Nord Stream 2, is currently underway, with the operations announced to start in the first quarter of 2021. All three offshore projects are expected to not suffer from geopolitical transit issues.

In relation to the Covid-19 pandemic, there are likely to be two major effects on Russian gas transit. First, the inauguration of Nord Stream 2 is likely to be further delayed. Nord Stream 2 is 50% financed by Gazprom, and this financing scheme may be difficult to sustain after the fall in oil and gas prices and a significant decrease of Gazprom’s export revenues. Indeed, while the statistics for March and April 2020 are not yet available, the Russian customs statistics suggests that the USD value of gas exports from Russia in January-February 2020 has decreased by 45% relative to the same period last year. Because Nord Stream 2 could facilitate gas delivery to the EU in case of a transit conflicts, its expected delay may negatively impact the EU’s gas security.

Additionally, the Covid-19 related demand drop may impact the utilization of Russia-EU gas routes, driven by the current agreements between Russia and the transit countries. Russia and Ukraine have just signed a transit agreement for the next 5 years. This agreement was widely perceived as a diplomatic success of the EU (that facilitated the deal), given the historically difficult geopolitical relation between Ukraine and Russia. One of the new features of this agreement is of particular interest within the Covid-19 context. Unlike for previous deals, Russia agreed to prepay a fixed volume of gas transit, 178.1 mcm/day for 2020, and 110 mcm/day units for 2021-24 (Pirani et al., 2020). So, underutilization of this route is costly for Russia.

Figure 1. Gas supply Routes to the EU.

Source: Ukrainian Liaison Office in Brussels

With decreased demand due to Covid-19, warmer weather in the coming months and almost full gas storages in the EU, this contractual feature may affect how Russia allocates its gas exports across the routes. At least, in the short term, it may undermine Russian gas transit via the Belarus-Poland route. The concern about the utilization of this route in relation to the new Russia-Ukraine transit agreement has already been raised by Pirani et al. (2020). The Covid-19-associated decrease in gas demand is likely to make this concern much more real. Russia may use the Belarus-Poland pipeline sporadically, e.g. to adjust for the seasonal spikes in demand, without long-term capacity booking. Recent gas tensions between Russia and Poland (e.g. Poland winning in the arbitration court against Gazprom (RFE/RL, 2020), and Poland repeatedly expressing opinions and exercising legislative effort restricting the usage of Nord Stream 1 and construction of Nord Stream 2) may further exacerbate the issue.

In the medium term, however, when the EU gas demand has recovered but Nord Stream 2 is not yet in place, the Belarus-Poland route is likely to prove useful for Russia, at least starting from 2021 (when prepaid volumes of Russian gas transit via Ukraine will decline according to their agreement).

The transit contract between Russia and Poland is to be renewed in mid-May 2020, and as of now, it is unclear if, and how it will be written and whether the Belarus-Poland transit route will be used to a substantial degree or only marginally. If transit through the Belarus-Poland route is limited, it will imply poorer route diversification for a major part of European consumers of Russian gas, thereby lowering their security of Russian gas supply.   This may also put another strain on the bargaining power allocation within the EU and the EU’s intended common energy policy of “speaking with one voice” with external energy suppliers like Russia.

Conclusion

Summing up, the decrease in demand of natural gas, as well as other factors associated with the ongoing Covid-19 pandemic, such as economic recession and turbulence in stock markets, are likely to have noticeable implications for the security of Russian gas supplies to the EU in the short term. On the one hand, even if the current pandemic-associated decrease in demand of gas from Russia seems rather moderate, the ultimate negative effect on Russian gas imports by the EU is likely to be larger. Lower imports from Russia are likely to improve the security of supply, both through lower import dependency of the EU, and through improved market opportunities due to the current market’s overcapacity. On the other hand, in the medium run, lower demand also negatively affects the non-conventional gas industry, undermining the diversification opportunities to LNG, and, consequently, natural gas energy security. Further, a fall in the gas demand by the EU coupled with the newly signed transit agreement between Russia and Ukraine may potentially cause underusage of the Belarus-Poland transit route, thereby putting a strain on the diversification of Russian gas import routes to the EU and on the power balance within the EU.

Energy security might be even more of a concern in the post coronavirus period when the economy is slowly recovering, and cheap and guaranteed energy supply is crucial. To ensure this supply, national efforts combined with an EU-wide policy coordination would be required. The long-discussed “speaking with one voice” common energy policy may finally need to materialize in order to facilitate reliable access to natural gas.

References

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

Quota or not Quota? On Increasing Women’s Representation in Politics

Posted on May 4, 2020 | Policy Brief

20200504 FREE Network Policy Brief image with dozen people standing at the seaside in sunset representing Women’s Representation in Politics Image 01

All over the world, politics remains one of the most male-dominated spheres in society, in spite of the substantial progress made in achieving more gender balance in the last decades. A large number of countries worldwide have adopted some form of electoral gender quotas to accelerate this progress, but the empirical evidence on the effectiveness of such policy tools is mixed.

In this policy brief, we first discuss the potential impacts of gender quotas. Quotas may (a) increase women’s representation in political positions, or decrease it, if there are backlash effects; (b) improve or worsen the quality of selected politicians; and (c) bring about important policy changes, given the wealth of empirical evidence of gender differences in policy preferences, with, for instance, women appearing more concerned about health and the health system than men. We then provide an overview of the empirical evidence on quota impacts in the economics literature, and contextualize these findings with a special focus on the countries of the FREE (Forum for Eastern Europe and Emerging Economies) network. We end with policy advice on the design of gender quotas in the domain of politics.

Quotas in the World and in the FREE Network Region

According to the International Institute for Democracy and Electoral Assistance (IDEA), 127 countries worldwide currently use quotas with the goal of increasing the presence of women in governmental institutions. Broadly speaking electoral gender quotas can be classified into seat reservation and candidate lists quotas. The former limit the competition for a governmental seat to women, whereas the latter prescribe a minimum representation of women in electoral lists. Candidate quotas can be legislated, i.e. they constitute a legal requirement, or voluntary, whereby parties adopt quotas in their internal statute.

Table 1: Share of women in national parliaments (in %) FREE Network countries

Source: World Bank Data (2020).

The popularity of gender quotas is, however, not uniformly distributed across the globe. For example, while political gender representation is far from equal in most countries of FREE network region (see e.g. table 1), out of these countries only Armenia, Poland and Sweden dispose of electoral gender quotas (see figure 1).

Figure 1: Gender quotas in the FREE Network region

Note: the FREE Network region is marked in light red, the countries in the region with quota are marked in dark red. Source: SITE, 2020.

Since 2011, Armenia has had a legislated candidate quota of 40% for its National Assembly. This quota replaced a previous quota of 15%, passed in 2005 – one of the requirements to enter the Council of Europe (Itano 2007). Poland has also had a legislated candidate quota of 35% for the Lower House (the Sejm) as well as for subnational elections since 2011 (IDEA 2020; World Bank 2019). Sweden, the fourth most gender equal country worldwide according to the 2020 ranking of the World Economic Forum, and ninth in the women’s political empowerment sub-index, does not have legislated quotas. However, political parties themselves have decided to adopt voluntary quotas: the ruling Social Democrats use a zipper system in which the two sexes alternate on party lists; the Left Party has a minimum 50% quota for women, while the Green Party has a 50% gender quota (IDEA 2020). The Swedish Moderates, Liberals, Center parties and the extreme-right Swedish Democrats currently do not have gender quotas. The Swedish Democrats entered the parliamentary elections in 2018 with the highest share of male candidates observed among the Swedish parties – 70% (SVT 2020; SVT 2018).

In spite of their popularity among policy-makers worldwide, the merits of quotas are still largely debated. Opponents of gender quotas are often concerned about their effects on the meritocratic selection of politicians. Another common criticism is that nominating more female candidates may not automatically translate into more women in powerful positions. For instance, the shares of women in the Armenian and the Polish Parliament are 24 and 29% respectively (World Bank 2019), well below the national legislated candidate quota (it bears noting, however, that these shares have been growing over the last ten years, as shown in Figure 3). The respective shares of female ministers are 7% and 23% (Government of the Republic of Armenia 2020; OECD 2020,).

Figure 2: Share of women in national parliaments (in %)

Source: The authors’ own rendering of World Bank Data (2020).

Why is increasing women’s political participation considered a policy objective of utmost importance in many countries worldwide, and how can gender quotas help achieving it? In this brief we contribute to the ongoing debate on the merits of gender quotas, by offering an overview of their potential effects and by critically reviewing the empirical evidence from the most recent academic literature.

Which Effects Can We Expect From Quotas?

The primary objective of electoral quotas is to reduce gender gaps in representation in electoral lists and in the targeted representative institutions. Quotas can also activate trickle-up mechanisms, whereby gender gaps decrease in positions that are not directly targeted by the quota. The trickle up effect occurs, for instance, if women’s networks within parties or in governmental organizations help the promotion of female leaders. Furthermore, gender quotas may help to improve the quality of politicians. As noted by, among others, Bertrand (2018), a society likely improves the quality of its leaders when it enlarges the pool where those leaders are chosen from. A critical underlying assumption in this line of argument is that there are no major differences in the distribution of “political talent” between women and men. However, even with equal distribution of political talent, if the supply of women willing to enter politics is very limited and there are not enough qualified women to fill the quota positions, the average quality of a “quota” politician may end up being lower than that of her colleagues – and quotas may have the unintended consequence of reinforcing stereotypes against female politicians. This, in turn, may ultimately imply lower promotion rates of women to key positions and/or worse electoral support of female politicians, thereby undermining women’s political empowerment at various levels.

One of the most popular arguments in favor of the adoption of gender quotas is that women’s political preferences may not be adequately represented by male-dominated political bodies. Gender quotas, by increasing female representation among politicians (and possibly among voters), can thus help closing a potential gap in substantial representation. A large body of literature has documented gender differences in policy preferences, by considering, e.g. the size and composition of government spending after the expansion of suffrage to women (Kenny and Lott 1999), voting records in referenda (Funk and Gathmann 2015), survey data (see, e.g. Bagues and Campa 2020), or women’s contributions to legislative amendments (Lippmann 2020). In this historical moment when the world is plagued by a pandemic, the most important gender difference to emphasize seems to be in the area of health. Exploiting the federal referenda held between 1981 and 2001 in Switzerland, Funk and Gathmann (2015) show that Swiss women are more likely to be in favor of health, unemployment and social security spending than men, and less likely to be in favor of military spending. Similarly, based on survey data from a sample of nearly 60,000 Spanish residents, Bagues and Campa (2020) find that women are significantly more likely than men to report that the health system is one of the problems that affects them the most. Likewise, Lippmann (2020) analyzes the contribution of French legislators to amendments and finds that women are 25% more likely than men to initiate at least one amendment related to health issues. This gender difference regarding health policy is also visible in the European Social Survey (ESS), which covers a representative sample of the population of 19 European countries. When asked to give a general opinion on the current state of health services in their country, female respondents turn out to be significantly less satisfied than male respondents on average. The difference is statistically significant, albeit not particularly large (12% of a standard deviation) and holds in most of the countries included in the ESS. One potential reason behind this noticeable difference in satisfaction with health services is that women also report lower health status than men (10% of a standard deviation and statistically significant).

Figure 3: Self-reported satisfaction with the current state of national health services

Source: The authors’ own rendering of the ESS (2018).

A natural question to ask in spring 2020 is whether a world with more women among political leaders would have had health systems better equipped to face a pandemic. While we will never have a definite answer to this question, studies of the impacts of gender quotas can help assessing whether the gender of political leaders matters for policy decisions.

What is the Empirical Evidence on the Effects of Quotas?

Quotas increase women’s representation in electoral lists, but only when they are binding and appropriately enforced (i.e. the cost for parties of not complying with the quota must be high enough). Yet, when quotas are limited to the composition of electoral lists, the strategic positioning of female candidates in “not-winning” positions tends to undermine the quota effect on the election of women (see Esteve-Volart and Bagues, 2012, and Bagues and Campa, 2020). This seems to be the case of Poland: According to Gwiazda (2017), the lack of a placement mandate obliging parties to put women in the top positions of a party list, is indeed one reason why the Polish quota has not translated into a higher share of female representatives.

The evidence on the spill-over of quotas to higher positions is mixed. Two studies find that candidate quotas in Italy and Sweden increased the probability that women reach leadership positions, above and beyond the quota mandate (De Paola et al. 2010, O’Brien and Rickne, 2016). Bagues and Campa (2020), however, fail to establish similar evidence in Spain.

In studies of developing countries, Beaman et al. (2009) find that seat reservation in India improved male voters’ perception of female leaders, as well as women’s probability of being elected once the reservation was removed. Conversely, experimental evidence from Lesotho suggests that, if anything, a quota-mandated female representative reduces women’s self-reported engagement with local politics (see Clayton, 2015).

An increasing number of studies also examine the quota impact on the quality of the elected politicians, proxied by different measures. Baltrunaite et al. (2014) find that a gender quota improved the average education of elected politicians in Italy, and Besley et al. (2017) provide similar evidence looking at a measure of labor market performance in Sweden. Bagues and Campa (2020), studying candidate quotas in Spain, fail to find an improvement in the quality of politicians, measured by their education and electoral performance; however, their assessment is that the quota did not decrease quality either, contrary to the expectation of many quota opponents. However, Chattopadhyay and Duflo (2004) find that, in the context of seat reservations in rural India, quota candidates are less educated.

Finally, the evidence on whether gender quotas bring about policy change is scarce. Chattopadhyay and Duflo (2004) show that the reservation of the most important seat in Indian villages brought policy choices closer to women’s preferences. In Spanish municipalities, Bagues and Campa (2020) fail to find significant increases in the share of “female expenditures” (issues women have been found to care more about than men, based on surveys) over two legislatures when candidate quotas were used.

Conclusion

Gender quotas are a popular policy tool used to close existing gender gaps in political empowerment, which are large in many countries in the FREE Network. A growing economics literature on the impacts of gender quotas helps assessing what objectives policy-makers may be pursuing when they adopt them, and under which conditions these objectives can be achieved. There is a number of lessons to be learned from this literature.

First, the design of the quota is crucial for it to achieve its primary objective, which is to increase women’s presence in the targeted political positions. Placement mandates, for instance, are particularly important in the design of candidate quotas to avoid that women are strategically placed at the end of the ballot. Second, policy-makers need to take the local context into account. Whether a candidate quota can generate spill-overs to higher-level positions likely depends on the degree of centralization of political parties for instance; where party leaders are very powerful, we may be less likely to see an increase in the share of female leaders following the adoption of a candidate quota. Third, the question when gender quotas successfully bring about policy change needs additional investigation. Different factors likely play a role, such as: the type of position targeted by the quota (legislative or executive, local or national, etc.); the extent of the increase in representation achieved; the magnitude of the gender difference in preferences; the type of decision-making process prevailing (majority voting or unanimity); how the selection of politicians is affected by the quota; and how women’s influence on policy is measured. Studies that systematically vary some of these factors will improve our understanding of this area of research. Fourth, there is no overwhelming evidence of negative effects of gender quotas in a number of dimensions, at least over a medium-term horizon.

The case for adopting and testing different forms of gender quotas, perhaps in combination with additional measures, is therefore relatively strong. Overall, our assessment is that quotas will have to remain in policy-makers’ toolbox for some time if the worldwide effort to close the persisting gender gaps in political empowerment is to continue.

References

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

The Swedish Exceptions: Early Lessons From Sweden’s Different Approach to COVID-19 – Insights From a SITE-LSE Webinar

Posted on April 27, 2020 | Policy Brief

Image of sunrise in Stockholm street representing Sweden's different approach to COVID-19 response

Sweden’s policy in the Corona crisis has been subject to a lot of discussion in international media recently. Some point to the country portraying “the Swedish way” as a valid policy alternative to the forced lock-down of society, others criticize the Swedish government for being imprudent. Given the pace with which the virus spreads and considering the volatility of current events, it is pre-mature to draw any definite conclusions. But it is certainly time to start an informed policy discussion. The webinar “The Swedish Exceptions: Early Lessons from Sweden’s different approach to COVID-19, jointly organized by the Stockholm Institute of Transition Economics (SITE) and the London School of Economics (LSE) on April 22, 2020” brought together academics from different relevant disciplines from Scandinavia, the UK and the US . The webinar allowed to discern a few of the motivations behind the Swedish policy choices as well as a number of criteria which will serve to measure the success of governments’ responses to the Covid-19 pandemic in the future.

Understanding the Swedish Approach to Covid-19

Much has been written and said about the Swedish reluctance to impose a strict lock-down on the country: the Swedish government has so far relied mostly on expert recommendations, avoiding from more stringent policies such as the strict lock-downs imposed by for instance Sweden’s neighboring countries Norway and Denmark (more on Sweden in the Covid-19 crisis here). The majority of the speakers in the webinar agree that the Swedish policy in the Corona crisis has been an outlier, even with respect to traditional Swedish policy: Peter Baldwin, historian and professor at the University of New York and the University of California, Los Angeles, argued that Sweden has had an interventionist tradition with respect to social and health policy in the past. “Native policy traditions” therefore do not explain why Sweden has chosen this policy course in his view.

While it seems difficult to pin down historical or ideological reasons behind the Swedish policy stance with respect to Covid-19, Lars Trägårdh, professor of social history at Ersta Sköndal Bräcke University College in Stockholm, pointed out that even though the legal differences may seem stark, the difference in the policy impact may be smaller than expected, the crucial factor being the degree of compliance with a certain measure or recommendation and not its legal force. Trägårdh further argued that, since it may take many months to develop a vaccine, the sustainability of a given policy strategy is essential. According to him, a policy relying on voluntary compliance as the Swedish one rather than legal obligation, may therefore yield comparable effects in the short and medium run and could even turn out to be more successful in the long run.

Trägårdh argued that the true exceptionality of the Swedish response to the global pandemic has been the choice to not close elementary schools. This policy choice can be explained above all by the concern for children’s rights: for smaller children, digital learning simply is not a valid option. As declared by the government on several occasions, another reason is that parents working in professions such as healthcare may be induced to stay at home if schools are closed. Finally, Trägårdh cited a recent study from Iceland which suggests that the effect of closing schools on limiting the spread of the virus may be relatively small.

Later in the discussion, another potential argument in favor of the Swedish strategy emerged: Professor Sara Hagemann from the LSE School of Public Policy described the difficulty of leaving a lock-down, which Denmark is currently experiencing. The question which measures are to be lifted and which sectors of the economy are to be opened first has caused considerably more controversy than imposing the initial lock-down. In contrast, the public debate in Sweden can immediately focus on dealing with the long-term consequences of the crisis according to Trägårdh.

The significance of the concept of “herd immunity” (meaning the protection from disease arising from large percentage of the population having developed immunity) for the Swedish strategy is unclear. Baldwin pointed out that even though Swedish authorities have declared not targeting herd immunity, many measures implicitly seem to be aiming for this outcome.

Results of the Swedish Approach Until Today

Tom Britton, professor of mathematics at Stockholm University, agreed that the Swedish response to the Covid-19 crisis came late and that there has been too little testing. However, he argued that the government’s policy has been consistent, focusing on reducing the spread of the virus and protecting risk groups and especially the elderly. Whether Sweden has achieved the latter goal is still up to discussion, though. As of April 2020, reported infections and deaths in nursing homes had increased, which according to Trägårdh has been the major failure of the Swedish policy response up until today. Yet, the speakers agreed that the Swedish government’s measures have received a lot of public support within Sweden so far, which is a non-negligible factor for the long-term success of the strategy.

General Policy Conclusions

Professor Ole Petter Ottersen, president of the Karolinska Institute in Stockholm, Sweden’s largest centre of medical research, stressed the speed with which the virus has been spreading: the rapid development forces policymakers to quickly take decisions based on limited information. Given the lack of data, Ottersen called for politicians to practice humility and acknowledge the uncertainty surrounding policy choices. According to him, it will take years to evaluate whether the Swedish model or the Norwegian model of a quick and strict lock-down is better suited to fight the pandemic.

Policymakers around the globe face a dilemma: for sustainable crisis management and given countries’ interdependency, measures meant to fight the spread of Covid-19 should be aligned internationally and taken cooperatively. Yet, as Hagemann pointed out, it is clear that one policy cannot fit all: countries differ for instance with respect to their socio-economic structure, health care quality and availability, demographics, and with respect to the point in time when they were hit by the virus. This is not only the case between countries, but even within countries, which could justify a differentiated approach between rural and urban areas in some instances. In other words, all models and policy recommendations have to be adapted to the specific local setting. A strategy which allows for making local adjustments while maintaining a global perspective will be a major challenge for policymakers in the coming months and, likely, years.

Britton stressed the importance of understanding the limits of the models being used. Their predictions depend on a lot of assumptions regarding for instance how individuals behave and to what extent rules and regulations are being respected. Anti-body tests will soon provide more data on the actual spread of the virus, but even then, major questions, such as how to treat a potential trade-off between preventing deaths from Covid-19 vs. the socio-economic and health costs caused by a lock-down, will remain unanswered. This trade-off is country specific as well: Hagemann argued that Sweden and the other Nordic countries have quite successfully implemented remote working and learning options. This, however, will not be feasible in most developing countries, for instance, which necessarily affects the cost-benefit analysis of the available policy options.

Further, data collection and availability undoubtedly need to improve. As long as no better instruments of analysis are available, both scientists and politicians should be transparent about the simplifying assumptions and models they base their policy recommendations and decisions on.

Finally, despite their different academic backgrounds, all experts agreed on the need to take into account the indirect consequences of both the spread of the virus and the policy measures implemented to fight it. Covid-19 is likely to reinforce social inequities. For instance, it has been shown that in Stockholm, immigrant communities have been hit the hardest. As soon as the imminent health crisis is under control, the policy focus, therefore, has to shift towards the socio-economic consequences of the crisis.

Acknowledgements

The Stockholm Institute of Transition Economics wishes to express its appreciation to the speakers for their contributions to the policy debate, to the London School of Economics for the successful cooperation in organizing the event, and to the audience for its engaging questions and interest in the topic.

List of Speakers:

  • Peter Baldwin, New York University and University of California, Los Angeles
  • Tom Britton, Stockholm University
  • Sara Hagemann, London School of Economics
  • Ole Petter Ottersen, Karolinska Institute, Stockholm
  • Lars Trägårdh, Ersta Sköndal Bräcke University College, Stockholm
  • Erik Berglöf, London School of Economics (moderator)

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.

Money as an Economic Category and Its Relationship With Crypto Assets

Posted on April 20, 2020 | Policy Brief

Image with two bitcoin coins representing money and 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:

  1. 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.
  2. Old rules and regulatory approaches hinder market development and unregulated space can create additional risks and uncertainties.
  3. The transition from cash to CBDC is possible but has limitations.
  4. 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

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.

Russia Economic Update — Brace for the Covid-19 Impact!

Posted on April 15, 2020 | Policy Brief

A view of central Moscow City at dawn representing the sudden stop of Russian economy

Russia’s oil dependence will once again contribute to an economic downturn that most certainly will follow the Covid-19 outbreak in Russia as in other countries. The decline in oil prices alone could lead to a drop in GDP of more than 8 percent. On the positive side, Russia manages its macro economy well. However, its fiscal reserves are not unlimited and the recent massive fall in oil prices has not been matched by a similar decline in the ruble exchange rate which means potential extra problems for the budget. Furthermore, monetary policy will have less of a role to play in dealing with this type of crisis. This means that Russia like other countries will face difficult trade-offs in dealing with the crisis at a time when some of the previously announced economic policy changes have not been well received by the public.

Introduction

The corona virus crisis will destroy both lives and economies as it spreads across the globe. Fortunately, the corona virus death toll in Russia so far is relatively modest compared to many other countries, but the economy is most certainly heading for very difficult times. This is (again) due to the fact that the Russian economy is too dependent on the developments of international oil prices (see e.g. Becker, 2016a,b). In recent years, Russia had to deal with two severe declines in oil prices that hit its economy, first in connection with the global financial crises 2008/09, and second, in 2014/15, when there was a fall in oil prices simultaneously with Russia being hit by international sanctions after the illegal annexation of Crimea. Although these episodes were very costly for the Russian economy, they also provided important lessons for policy makers on fiscal, monetary and exchange rate policies that come in handy today. They also contributed with data on the relationship between large movements in oil prices and the effects they had on GDP growth in Russia. This is useful at this stage to assess what can happen with the economy after the significant decline in oil prices that has followed in the course of the corona outbreak.

Dramatic Decline in Oil Prices

We still do not know when this crisis will be over, but when it comes to the fall in international oil prices the start has been far more severe than the two crises referred to above. Since the beginning of 2020, oil prices have fallen from around $60/barrel to around $15/barrel or as Figure 1 shows, a barrel is now worth around 25 percent of what it was worth three months ago. Furthermore, prices are rather volatile and will continue to be so and there will most certainly also be periods of sharp increases in oil prices going forward – but the overall result for the year compared to the previous year is most likely a very sharp fall in prices. This decline in oil prices has so far been much more dramatic than the two previous crisis episodes the Russian economy has experienced under Putin as president or prime minister.

Figure 1. Oil price developments in recent crises

Note: This graph is based on the European Brent spot price FOB published by the U.S. Energy Information Administration and the axis shows trading days, so that the graph covers the period from January 1 to March 30. Different qualities of oil of course have different prices, but the patterns shown here are similar for other oil prices as well.

Exchange Rate and Stock Market

As in previous crises, the Russian stock market and exchange rate are following the evolution of oil prices. However, neither the stock market, nor the exchange rate has fallen as rapidly as oil prices. This can be due to many factors, but one likely explanation is that investors think that the decline in oil prices will not last for as long as it has in past crises. Whether this assumption is correct remains to be seen of course, but if oil prices stay low for an extended period, we can expect to see further declines in both the exchange rate and stock market.

Figure 2. Oil prices, exchange rate and stock market

Sources: Oil prices as in Figure 1, the exchange rate from Central Bank of Russia, RTS index from Moscow Stock Exchange.

The fact that the exchange rate this time has “only” depreciated by 20 percent when oil prices have fallen by 70-80 percent means that the oil price measured in rubles has fallen much more dramatically in this crisis compared to the previous ones. In the 2008/09 global financial crisis, the oil price in ruble terms was, in the end, unchanged compared to the start of the crisis. In 2014/15 this was not the case, but the decline in the ruble oil price was a more modest 25 percent compared to the 60 percent drop right now. This has serious implications for the government’s budget which is ruble-based and highly dependent on oil revenues.

Economic Policy

The Russian government now has plenty of experience in dealing with crises. The first lesson after the crisis at the end of the 90s was to have enough fiscal resources to deal with a crisis without having to go to the IMF again. The second lesson came in the global financial crisis when the fixed exchange rate had to be abandoned to avoid depleting the central bank’s international reserves. A prudent fiscal policy backed by the National Wealth Fund and a flexible exchange rate is still the backbone of the macroeconomic policies that can help mitigate the impact of lower oil prices.

The central bank is pursuing inflation targeting and uses a 4 percent inflation rate as the target that guides its policy decisions. The main tool is setting the key interest rate at a rate that will achieve the inflation target. The key interest rate is currently 6 percent, significantly down from the high of 17 percent in January 2015. The central bank states clearly in its monetary policy documents that “Monetary policy lays the groundwork for economic development; however, it cannot be a source of a sustainable rise in economic potential” (see page 6 in Central Bank of Russia, 2020). This implies that the central bank will only lower the key interest rate if inflation falls, not to support growth or try to achieve other, potentially conflicting goals. This is good news for macroeconomic stability but may become an issue of political tension if there is a serious downturn in the economy while inflation remains higher than the target rate.

In mid-2019, the National Wealth Fund was doubled and went from $60 billion to just over $120 billion (Ministry of Finance, 2020). This was done as a one-off transfer of surplus funds from the government’s budget. However, at its peak in the global financial crisis, the combined reserve fund and wealth fund that existed then had assets of over $220 billion but by the start of 2011, the assets were down to $111 billion. In other words, a year and a half into that crisis episode, the government had used an amount from the funds that roughly corresponds to the total amount available in the National Wealth Fund today. The fiscal space is, therefore, less impressive than it may look at a first glace and just burning through the cash in the National Wealth Fund is not a sustainable fiscal policy if this crisis continues a few more months.

Instead, the government will have to plan other measures as soon as the most immediate spending to deal with the crisis is done. This will entail difficult trade-offs since the health system will need increased resources at the same time as households and companies will need support to mitigate the impact from lost jobs and closed businesses in the wake of corona-induced shut-downs rather than the decline in oil prices, so adding to the pressure coming from declining oil prices. Increasing taxes in a time of already depressed purchasing power and profits is also not an appealing option and although there are still tax increases in the pipeline, the government has announced that these will not come in effect this year. Like in many other countries, the Russian government is proposing several measures to support the economy that will be discussed in more detail in a forthcoming FREE policy brief. However, these measures will add to the costs of the government at a time of falling revenues. From an economic perspective, reallocating resources from the military and security sectors to other parts of the economy seems like an obvious choice under these circumstances, but most likely not the outcome of this process given the government’s geopolitical and domestic power ambitions. Again, the fiscal reserves will allow postponing these harder decisions, but if the crisis goes on for some time, alternative measures such as borrowing domestically or internationally will most certainly be discussed also in Russia. However, many governments will be in need of borrowing on international markets going forward and the rates required to access this type of funding may not be very attractive and still force domestic budget reallocations.

Growth Impact of the Oil Price Fall

It is of course too early in the crisis to make very precise forecasts on how the economy will fare in 2020. This will in the end crucially depend on how the Covid-19 pandemic develops and on government responses to the crisis not only in Russia but also in the rest of the world. A partial analysis of the impact of falling oil prices can however be done with the models presented in Becker (2016a) which link changes in oil prices to growth. This paper shows a few alternative specifications that differ in the GDP measure being in dollars or real rubles, and in some other dimensions. All specifications are highly statistically significant and able to explain between 60 and 90 percent of variations in GDP growth in the period 2000-2015. Focusing on the relationship between the percentage change in oil prices and growth in real ruble GDP, the estimated coefficient is 0.14. This implies that for every 10-percentage point drop of oil prices, GDP growth goes down by 1.4 percent. Currently, oil prices have declined by 75 percent since the beginning of the year. However, the model estimates are based on comparing how average oil prices change between years so this is the numbers we need to compute and compare. The average price of Brent oil (which is used in this model) was $64/barrel in 2019 but we obviously do not know what the average oil price will be this year. We therefore need to first “forecast” oil prices for the rest of the year before we can compute the impact on growth. If we make the simple assumption that oil prices stay at the current level and take into account that they were significantly higher the first couple of months this year, the average price would end up being $25/barrel. That would amount to a 60 percent decline in average oil prices between 2019 and 2020. The partial effect of this oil price decline would therefore make Russian real GDP drop by 8.5 percent in 2020. Again, this is the partial effect based on the estimated coefficient in a linear relationship between oil price changes and real GDP growth. In plainer English, we are not looking at the first order effect of closing stores etc. to avoid the virus from spreading but only the additional effect that we think will come from falling oil prices. In addition, the effect this massive decline in oil prices is assumed to have on GDP is derived by a coefficient that is estimated on smaller changes in oil prices and real GDP. Nevertheless, this exercise provides a first, and rather daunting, assessment of what can happen to GDP given the decline in oil prices alone.

Concluding Remarks with OPEC and IEA update

This brief has provided a first assessment of how the Russian economy may be impacted by the massive decline in oil prices that has followed in the course of the corona pandemic. It has shown that the economic downturn this time can be significantly worse than both the 2008/09 and the 2014/15 crises. A base line estimate suggests that GDP may fall by more than 8 percent only because of the fall in oil prices. The above calculation obviously includes neither the impact the health situation will have on companies or households, nor the government’s ability to mitigate the negative consequences. If the other problems the economy is facing as a direct result of the health crisis also lead to a significant decline in supply and demand, Russia could easily see real GDP declining by more than 10 percent in 2020.

Our estimate is an important reminder that Russia’s continued oil dependency is a risk to the economy and its citizens. Now is not the time for ambitious structural and institutional changes to generate growth, but hopefully the urgent crisis period passes without policy makers forgetting the risks the country’s oil dependence entails. They learnt the fiscal and monetary lessons well from past crises, now is the time to learn something new. The most appealing road to sustainable economic growth is still building credible property rights institutions and rule of law in a framework that would make Russia the innovative business-oriented superpower it could be.

A few days after the first version of this brief was published, oil prices started to rise as the OPEC together with Russia started discussions to cut production to support oil prices. A tentative agreement was reached which is supposed to cut production by 10 million barrels per day in May and June, the largest cut in OPEC’s history. Had this movements in prices continued, the forecast for the Russian economy would have been affected. However, this recovery in prices was soon reversed and oil prices started to fall again. The decline continued on April 15 as the International Energy Agency presented a dire forecast of oil demand and stated that this year may be the worst year ever in terms of declining demand. All in all, the price movements that have followed the OPEC meeting and the statements of the IEA do not change the baseline prediction this brief has provided.

References

Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes. This brief was first published on April 6, 2020 and then revised on April 15, 2020.

Recipient Type and the Effectiveness of Informational Campaigns: The Case of Meat

Posted on March 30, 2020 | Policy Brief

A picture of meat on the table table, indoor, sitting representing Informational campaigns that affect meat consumption

While global population growth has been accelerating during the last decades, the number of humans currently living on the planet is dwarfed by the amount of farm animals alive at any time, and even more by the quantity we slaughter for meat every year. According to the latest FAO statistics, this latter number is estimated at around 75 billion. Even ignoring animal welfare, this is severely affecting the health of the planet and our own. What should be done about this?

Externalities of Meat Consumption

Mankind has been butchering and eating animals for at least 3,4 million years (McPherron et al., 2010). Evolutionary biology theories claim that complementing our diet with meat contributed to the spectacular growth of our brain (Fonseca-Azevedo et al., 2012). Anthropological theories suggest that the necessity of hunting drove the development of tool building, language and social structures. The domestication of animals (and plants) around 10,000 years ago led to a jump in the history of civilization. In other words, eating meat is a large part of what made us human. However, during the last century, we took this to unsustainable levels. All in all, the agricultural sector accounts for 25 to 30% of global CO2 emissions, second only to the energy and transport sector, and 60% of non-CO2 emissions, in particular methane, which is much more efficient than CO2 at warming up the planet. A third to half of these emissions, depending on whether or not we include the share related to land use, comes from livestock production. Large scale factory farms, which cater to the ever-increasing global demand for cheap meat, are also responsible for other externalities, including distorted resource use (in particular of water and fertile land); local pollution of air and waterways, with consequences for neighbouring ecosystems and human health; abuse of antibiotics, which threatens their effectiveness with dramatic implications for the whole spectrum of modern medicine. The cheap and overabundant animal products with worsened nutritional properties, which result from these production methods are also behind the epidemic of “welfare diseases” such as diabetes, cardiovascular conditions and some types of cancer (Mozaffarian, 2016).

So, what should we do about this? Economics is very clear on this point. In the presence of externalities, market prices do not reflect social costs; therefore, the market mechanism fails, and decisions taken on the basis of these prices are suboptimal. If applied to meat consumption, this principle implies that, first of all, consumers and producers must pay for the emissions (and other externalities) they cause. Today’s carbon pricing systems, whether in the form of a tax like in Sweden or tradable emission permits like in EU, exempt the agricultural sector for various reasons. Moreover, as already mentioned there is more to meat than carbon emissions. Another FREE brief (Perrotta, 2011) makes the case for a meat (consumption) tax. Multiple teams of researchers (Wirsenius, Hedenus, and Mohlin, 2011; Edjabou and Smed, 2013; Gren, 2015; Andersson, 2019) have come as far as to compute the optimal level of such a tax, in different contexts and under different assumptions. There are also drawbacks to this approach, though. Climate-change curbing policy is in general an area where policy makers at all levels find it hard to converge to policies of strong incentives, such as taxes and regulation. Interventions targeting food production or dietary choices, in particular, are likely to face strong opposition from producers and consumers alike. It is therefore worth considering the alternative – or at least complementary – strategy of information and awareness campaigns.

The Power of Information

Given that a climate policy agenda of strong incentives is so fraught with obstacles, the potential for information to spark voluntary action would be very valuable. There is a catch here, however. Information about the benefits of an action often fails to encourage that action. Consider the case in point: for decades now, we have observed a persistence and increase of meat eating despite mounting evidence and widespread information on the ills of meat production and consumption. Indeed, this well-known weakness of informational interventions has contributed to the rising importance and application of alternative approaches. One example is the popularity of the so-called nudges (Thaler and Sunstein, 2009), modifications in the choice architecture that can subtly push agents towards an action without actually limiting the available alternatives. There is ample research on where and why the chain from information to action might get interrupted, and established evidence that the effectiveness of information depends on a variety of factors such as recipients’ prior beliefs, the sender’s credibility, and the non-informative content of the message, such as the emotional evocativeness of imagery (see a survey in DellaVigna and Gentzkow, 2010). Taking a step back to the stage before, namely the question whether information does reach the intended beneficiaries in the first place, at least three aspects of this have been investigated: limited attention, active avoidance, and selective retaining of information on the part of the recipients. In a new working paper (Berlin and Mandl, 2020), we investigate the role of individual type for selective information retention. We ask whether certain types of agents, in our case vegetarians, retain more of the information they are exposed to, even when exposed to a similar context and the same incentives to retain information as everyone else (so that hopefully the competing channels of limited attention and active avoidance can be neutralized). This has relevance for the possibility of tailoring the policy message, similar to the marketing theories of market segmentation. In contrast to well-developed marketing practices in the private sector, this potential has so far not been exploited in policy design. To the best of our knowledge, this mechanism has not been investigated in a real-life incentivized setting outside the lab before.

Natural Experiment in Class

We exploit a natural experiment in the context of higher education. A class of college students was assigned an essay about their plan for a Christmas dinner menu, after being exposed to a lecture and reading materials on the externalities of meat production, so that they could decide to make use of this information. The essays were to be written in randomly assigned groups of three, making the type combination, i.e. the presence of one or more vegetarian group members, a random group characteristic. We hypothesize that there is a difference in how carnivores and vegetarians deal with the provided information about the food industry. In particular, we test whether groups that include a vegetarian student recall a larger share of the information than groups made up only of carnivores. The essay was mandatory, and moreover it awarded study credits toward the final grade of the course (10/100 points). This constitutes a sizeable incentive and possibly provides a stronger motivation for information retention as compared to the average monetary rewards which lab experiments rely on. To measure the share of information retained, we preregistered a list of 30 words in both English and Swedish related to the learning outcomes of the lecture. We then used a script to measure how many of the 30 words appear in each essay. We call this number the essay’s score, separate and independent from the teacher’s assigned grade, which is of relevance for the student. The teacher-assigned grade, reflecting general comprehension of the topic rather than just the presence of keywords, is expected to be correlated with the score, but not perfectly. We also expect the grade to capture the ability of the students to a higher degree compared to the score, as the automatized word count fails to consider the context in which the words are mentioned.

Results

Figure 1. Group score by treatment status

Source: Berlin and Mandl (2020).

On average, groups including a vegetarian student scored higher (4.8) than groups with all meat-eaters (4.3), but not significantly so. The estimated Cohen’s d (0.347), a standard measure of effect size used to indicate the standardized difference between two means, is much smaller than the minimum detectable effect in our sample, which we estimated at 0.8. In other words, we do not have the statistical power to either accept or reject the null hypothesis. The reason is that the treated group displays larger variation in score outcomes, possibly due to the smaller than anticipated sample size: only 11 students out of almost 300 identified themselves as vegetarians or vegan (non meat-eaters), which is a much smaller proportion than what the latest survey of young adults in Sweden estimates (17%, Djurens Rätt, 2018).

Looking beyond the mean at the details of our data reveals an interesting pattern. As the Figure shows, the distribution of achieved scores among the vegetarian groups is bimodal: a lower-level concentration of scores is close to the mode of the control distribution, but there is an almost as large mass at a higher level. This might suggest that, quite understandably, (attention and) performance, in terms of recall, is affected by several factors beyond the type. In other words, not all the individuals with the relevant type display increased retention of information. While many vegetarians remain close to the mode for the meat-eating type, a large fraction obtains double the score, suggesting a substantial though heterogenous increase in the retention of information.

We also use regression analysis in order to control for potential omitted variables and net out some of the variation in the score data that is not related to our variable of interest (such as group size and ability). Robustness checks were performed with different specifications and alternative outcome variables, but the main conclusion remains the same: mean performance, in terms of information retention, is higher for the vegetarian type but not significantly so. However, these results should not be interpreted as a rejection of our original hypothesis about the importance of type for information retention, as our analysis is empirically underpowered due to the low number of vegetarians in the sample. More importantly, the method we propose is highly appropriate, easily replicable and cheap.

Conclusion

Information interventions are low-cost and can be effective. Understanding how they can be tweaked for best effect is an area of crucial research interest, in particular for such an area as climate-change curbing policy. We provide an easy and cheap method to investigate this further and hope that more future research will pursue this avenue.

References

  • Andersson, Julius J., 2019. “The ‘meatigation’ of Climate Change: Environmental and Distributional Effects of a Greenhouse Gas Tax on Animal Food Products.” London School of Economics
  • Berlin, Maria P. and  Benjamin Mandl , 2020. “Selective attention and the importance of types for information campaigns”, SITE Working Paper Series. 53.
  • DellaVigna, Stefano and Matthew Gentzkow, 2010. “Persuasion: empirical evidence.”, Annu. Rev. Econ. 2 (1), 643–669.
  • Djurens Rätt, 2018. “Opinionundersökning, Våren 2018.” Novus.
  • Fonseca-Azevedo, Karina and Suzana Herculano-Houzel, 2012. “Tradeoff between brain and body mass.” Proceedings of the National Academy of Sciences. 109 (45), 18571-18576
  • McPherron, Shannon P. et al., 2010. “Evidence for stone-tool-assisted consumption of animal tissues before 3.39 million years ago at Dikika, Ethiopia.” Nature 466, 857–860.
  • Mozaffarian, Dariush, 2016. “Dietary and policy priorities for cardiovascular disease, diabetes, and obesity: a comprehensive review.” Circulation. 133(2), 187–225.
  • Perrotta, Maria, 2011. “Tax Meat to Save the Baltic Sea.” FREE Policy Brief Series.
  • Säll, Sarah and Ing-Marie Gren, 2015. “Effects of an environmental tax on meat and dairy consumption in Sweden.” Food Policy. 55, 41-53.
  • Thaler, Richard H. and Cass R. Sunstein, 2009. “Nudge: Improving Decisions About Health, Wealth, and Happiness.” Penguin Group.

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

The Shadow Economy in Russia: New Estimates and Comparisons with Nearby Countries

Posted on March 16, 2020 | Policy Brief

Image of a metro elevator going upwards representing shadow economy of Russia

We apply a new method to measure the shadow economy in Russia during the period 2017-2018 and provide evidence on the main factors that influence involvement in the shadow economy. Drawing on a methodology developed by Putnins and Sauka (2015), we estimate that the size of the shadow economy in Russia is 44.7% of GDP in 2018. This is similar to the size of the shadow economy in countries such as Kyrgyzstan, Kosovo, Ukraine, and Romania, but higher than the level of the Baltic countries. Our findings are largely consistent with other less direct approaches for estimating the shadow economy. An advantage of our approach is that it can provide more detailed information on the components of the shadow economy.

Introduction and Approach to Measuring the Shadow Economy

The aim of the Shadow Economy Index, which has now been estimated in a number of countries, is to measure the size of shadow economies and explore the main factors that influence participation in the shadow economy. We use the term “shadow economy” to refer to all legal production of goods and services produced by registered firms that is deliberately concealed from public authorities (OECD, 2002; Schneider, Buehn and Montenegro, 2010).

The Shadow Economy Index draws on a survey-based methodology developed by Putnins and Sauka (2015). It combines estimates of business income that has been concealed from authorities, unregistered employees, and ‘envelope’ wages. The approach exploits the fact that entrepreneurs and business leaders are in a unique position in that they have knowledge about the amount of business income that is concealed from authorities, the number of employees that work for them unofficially, and the extent to which they pay wages informally to avoid taxes.

The challenge for such methods is to elicit maximally truthful responses about these sensitive issues, otherwise, the size of the shadow economy will be underestimated. To address this challenge, we use a number of survey and data collection techniques shown in previous studies to be effective in eliciting more truthful responses (e.g. Gerxhani, 2007; Kazemier and van Eck, 1992; Hanousek and Palda, 2004). While the full details can be found in Putnins and Sauka (2015), they include confidentiality with respect to the identities of respondents, framing the survey as a study of satisfaction with government policy, phrasing misreporting questions indirectly about “similar firms in the industry” rather than the respondent’s actual firm, gradually introducing the most sensitive questions after less sensitive questions, excluding inconsistent responses, and controlling for factors that correlate with a potential untruthful response such as tolerance towards misreporting.

The Index measures the size of the shadow economy as a percentage of GDP. Computing the Index involves three steps:

  • (i) estimate the degree of underreporting of employee remuneration and underreporting of firms’ operating income using the survey responses;
  • (ii) estimate each firm’s shadow production as a weighted average of its underreported employee remuneration and underreported operating income, with weights reflecting the proportions of employee remuneration and firms’ operating income in the composition of GDP; and
  • (iii) calculate a production-weighted average of shadow production across firms.

The survey about shadow activity in Russia from 2017 to 2018 was conducted between February and March 2019. We use random stratified sampling to construct samples that are representative of the population of firms in Russia drawing on the official company register and covering all regions in Russia. In total, 500 phone interviews were conducted with owners, directors, and managers of companies in Russia. We use the same methodology to collect data in other countries, which we compare with Russia, conducting a minimum of 500 interviews in each country.

Size of the Shadow Economy in Russia and Nearby Countries

The estimated size of the shadow economy in Russia is 44.7% of GDP in 2018. Our estimates suggest that the year before, in 2017, the shadow economy was slightly larger with 45.8% of GDP, although the annual change is not statistically significant. For comparison with nearby countries, using the same approach, high levels of shadow economy are also found in Kyrgyzstan (44.5% of GDP in 2018), Kosovo (39.5% of GDP in 2018), Ukraine (38.2% of GDP in 2018), and Romania (33.35% of GDP in 2016), but considerably lower levels are found in the Baltic countries, especially Estonia (16.7% of GDP in 2018). See Table 1 for the full set of estimates.

The estimates using our direct micro-level approach to measuring the shadow economy are largely consistent with other less direct approaches for estimating the size of the shadow economies, such as Schneider (2019). An advantage of the direct micro-level approach is that it is able to provide more detailed information on the components of the shadow economy, which we turn to next.

Components and Determinants of the Shadow Economy in Russia

We find that envelope wages and underreporting of business profits stand out as the two largest components of the Russian shadow economy. Underreporting of salaries or so-called ‘envelope wages’ in Russia are approximately 38.7% of the true wage on average in 2018, whereas approximately 33.8% of business income (actual profits) are underreported. Unofficial employees in Russia as a percentage of the actual number of employees are estimated 28.2% in 2018.

Some companies in Russia, rather than simply concealing part of the income or employees, are completely unregistered and therefore also contribute to the shadow economy. We estimate that such companies make up 6.1% of all enterprises in Russia.

Our findings also suggest that there is a very high level of bribery in Russia: the magnitude of bribery (percentage of revenue spent on ‘getting things done’) is estimated to be 26.4%, whereas the percentage of the contract value that firms typically offer as a bribe to secure a contract with the government in Russia is 20.6% in 2018. We also find that more than one-third of companies in Russia pay more than 25% of the revenue or contract value in bribes.

We find that the size of the shadow economy in all sectors of the Russian economy is close to 40% with somewhat higher levels in the construction and wholesale sectors, controlling for other factors. Using regression analysis, we find that entrepreneurs that view tax evasion as a tolerated behaviour tend to engage in more informal activity, as do entrepreneurs that are more dissatisfied with the tax system and the government. This result offers some insights into why the size of the shadow economy in Russia is so large – it is at least in part due to relatively high dissatisfaction of entrepreneurs with the business legislation and the government’s tax policy. We also find some evidence that higher perceived detection probabilities and, in particular, more severe penalties for tax evasion reduce the level of tax evasion, suggesting increased penalties and better detection methods as possible policy tools for reducing the size of the shadow economy.

Finally, while firms of all sizes participate in the shadow economy, we find that younger firms tend to do so to a greater extent than older firms. The results support the notion that young firms use tax evasion as a means of being competitive against larger and more established competitors.

Acknowledgments

This research was supported by a Marie Curie Research and Innovation Staff Exchange scheme within the H2020 Programme (grant acronym: SHADOW, no: 778118).

References

  • Gerxhani, K. (2007). “Did you pay your taxes?” How (not) to conduct tax evasion surveys in transition countries. Social Indicators Research 80, pp. 555-581.
  • Hanousek, J. and Palda, F. (2004). Quality of government services and the civic duty to pay taxes in the Czech and Slovak Republics, and other transition countries. Kyklos 57, pp. 237-252.
  • Kazemier, B. & van Eck, R. (1992). Survey investigations of the hidden economy. Journal of Economic Psychology 13, pp. 569-587.
  • Lechmann, E. and D. Nikulin (2017). Shadow Economy Index in Poland. Gdansk University of Technology, Poland: Gdansk.
  • Lysa, O. et al. (2019) Shadow Economy Index in Ukraine. SHADOW: an exploration of the nature of informal economies and shadow practices in the former USSR region. Kyiv International Institute of Sociology, Ukraine: Kyiv.
  • Mustafa, I., Pula J.S., Krasniqi, B., Sauka, A., Berisha, G., Pula, L., Lajqui, S. and Jahja, S. (2019) Analysis of the Shadow Economy in Kosova. Kosova Academy of Sciences and Arts, Kosova: Pristina.
  • OECD, 2002. Measuring the Non-Observed Economy: A Handbook. OECD, Paris, France.
  • Putnins, T.J. and Sauka, A. (2019). Shadow Economy Index for the ‘Baltic Countries 2019-2018. SSE Riga: Riga, Latvia.
  • Putnins, T.J., A. Sauka and A. Davidescu (2020, forthcoming). Shadow Economy Index for Moldova and Romania, 2015-2018. SSE Riga, National Scientific Research Institute for Labour and Social Protection.
  • Putnins, T.J. and Sauka, A. (2015). Measuring the shadow economy using company managers. Journal of Comparative Economics 43, pp. 471-490.
  • SIAR (2019). Shadow Economy Index for Kyrgyzstan. SHADOW: an exploration of the nature of informal economies and shadow practices in the former USSR region. SIAR research and consulting, Kyrgyzstan: Bishkek.
  • Schneider, F. (2019) Calculation of the Size and Development of the Shadow Economy of 35 Mostly OECD Countries up to 2018. Unpublished manuscript.
  • Schneider, F., Buehn, A. and Montenegro, C. (2010). New estimates for the shadow economies all over the world. International Economic Journal 24, pp. 443-461.

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.