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Dimensions of Well-being

Posted on September 20, 2021 | Policy Brief

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This brief summarizes the insights shared in the online workshop “Dimensions of Well-being“, where participants presented and discussed their latest research relating to the dimensions of well-being. The two-day workshop was organized by the Stockholm Institute of Transition Economics (SITE) as part of the Forum for Research on Gender Economics (FROGEE) and took place on 28-29 June, 2021.

Introduction

It has been roughly 18 months since the first cases of Covid-19 were reported in Europe. So far the total number of deaths worldwide has passed 4.4 million (John Hopkins University, 2021), unemployment is trending upward in most countries (ILOSTAT, 2021), roughly half of the world’s students have been affected by school closures (UNESCO, 2021), and an alarming increase in domestic violence has been reported across the globe (UN Women, 2020).

It is safe to say that this pandemic crisis has had a multifaceted impact on our lives. Identifying what factors contribute to overall well-being and understanding how they interact with one another is central in designing and implementing solid and effective recovery policies.

Stockholm Institute of Transition Economics invited international experts to an online workshop where they discussed and presented their recent research relating to the dimensions of well-being. The workshop was organized as part of the Forum for Research on Gender Economics (FROGEE).

Well-being in a Pandemic

The government response policies intended to contain the spread of Covid-19 have undoubtedly had a major impact on society. However, estimating the overall effect of these policies on individuals’ well-being is not necessarily straightforward. Economic support policies likely have a positive effect on income and decrease poverty. But at the same time, other responses such as lockdowns and mobility restrictions may not only have an opposite effect on these outcomes but also influence other known determinants of well-being such as social life or education.

Anthony Lepinteur, researcher at the University of Luxembourg, presented his recent work on the well-being consequences of the pandemic policy responses in Germany, France, Spain, Italy, and Sweden. Lepinteur and co-authors link survey data on subjective well-being measures to data on government economic policy and stringency indices. The former index records financial policies such as income support, furlough schemes, and debt relief while the latter measures the strictness of Covid-19 containment and closure policies. The results show that more stringent policies reduce life satisfaction, and this negative effect is stronger for women, the unemployed, and those with relatively high incomes. Economic support policies are found to have no significant impact on reported life satisfaction.

As many countries have experienced major disruptions in many sectors of their economy, concerns have been raised about deteriorating labor markets and the effect this might have on living conditions and, ultimately, the well-being of individuals. Knar Khachatryan, associate professor at the American University of Armenia, shared research studying the impact of Covid-19 on multidimensional deprivation from labor market opportunities in Armenia. Knachatryan and co-authors base their analysis on two surveys from 2018 and 2020. To measure labor market opportunities, they adopt the “Alkire-Foster method” to develop a multidimensional index of labor market deprivation – a basket of indicators explaining an individual’s degree of labor market opportunities (e.g. education, employment status, income, type of work contract, and union membership). With respect to this index, they find that education is the most important determinant of multidimensional labor market deprivation – those having less than a bachelor’s degree are very likely to be deprived in terms of labor market opportunities. The results also show that the pandemic has widened the gender gap in labor opportunities. The number of people classified as deprived has increased more for women than men during the pandemic. This is primarily because women experienced stronger income reductions and more frequent job losses.

Thesia Garner, researcher at the U.S. Bureau of Labor Statistics, discussed how ex-ante levels of well-being have affected the outcomes of economic support policies during the pandemic. More specifically, her study investigates the role of individual’s well-being in determining their reported use of economic impact payments (EIP) in the U.S. Garner and co-author assess well-being using both objective measures (e.g. income sources, employment status) and subjective ones (e.g. depression, financial difficulty, expectations about job-loss or eviction). The findings show that those who report lower levels of subjective well-being are more likely to use the EIP to pay off debt, and this likelihood increases as the well-being measures worsen. Respondents who report having experiences of financial difficulty and negative expectations about the economy are more likely to spend the stimulus on nondurables and tend to allocate it to a wider range of spending categories.

In contrast to the U.S. and most other countries in the world, Belarus’ government offered very little support to its citizens during the pandemic. Lev Lvovskiy, researcher at BEROC, presented findings on how different sectors of the Belarusian economy and society were affected by the pandemic. Using the BEROC/Satio survey data, Lvovskiy and co-authors examine that the country still had sharp drops in mobility and economic shocks mainly caused by lockdowns of major trade partners. The pandemic significantly increased the probability of income reductions and they show that financial distress associates with the incidence of depression of Belarusians.

Gender and Wellbeing

Another central topic discussed at the workshop concerned the gender aspects of well-being and other related topics from gender economics.

An essential channel through which gender differences in well-being can arise is unequal representation in politics. Sonia Bhalotra, professor at the University of Warwick, presented a study on the relationship between maternal mortality and women’s political power in 174 countries. Maternal mortality is the leading cause of death and disability for women aged 15-44, and significantly higher in low-income countries – at levels similar to what high-income countries had in the early 1900s. Bhalotra and co-authors document that the costs of providing access to prenatal health services, antibiotics, and skilled birth attendance are relatively low. They therefore argue that there are likely other barriers to adopting these solutions. Male policymakers might have a weaker preference for preventing maternal mortality or less information on its prevalence and treatment. To gain insight, the authors use a staggered event-study approach and study the effect of gender quota implementations on the maternal mortality ratio (MMR, maternal mortality per birth). They find that, in countries that adopted quotas, the MMR declined by 10% following implementation, and this effect is stronger for larger quotas. Focusing on the mechanisms, the results show that gender quotas lead to a 5-8 percentage point (p.p.) increase in skilled birth attendance, a 4-8 p.p. increase in prenatal care utilization, 6-7 % decline in birth rates, and an increase in girl’s education by 0.5 years.

Elizaveta Pronkina, researcher at Université Paris-Dauphine, also shared findings relating to gender and politics but from a historical perspective.  Her research studies historic institutional differences across communist regimes and women’s work experiences. The paper focuses on Lithuania and Poland, two countries that experienced different gender policies under a communist regime. After the second world war, Lithuania was controlled by the central government of the Soviet Union while Poland’s government was able to preserve its independence although being part of the Soviet bloc. Based on anecdotal evidence, the two countries had the same religious and political policies but different enforcement – Lithuania faced a hard and Poland a soft form of communism. To isolate the impact of the Soviet policies on women’s life decisions and account for differences in the countries’ pre-communist era, the authors only include regions that were part of the Russian empire until the end of the first world war. The findings show that women living under the Soviet regime were more likely to educate themselves and have on average two additional years of work experience (by 50 years of age).

A productive environment and reliable social interactions at work are also likely to be formative elements of people’s well-being, and gender might factor in here. Yuki Takahashi,  PhD candidate in economics at the University of Bologna, presented his paper on how being corrected by others affects one’s willingness to collaborate with them in future work, as well as gender differences in these responses. Takahashi conducts a quasi-experimental design in which roughly 3000 participants individually and collectively solve a puzzle. The setting allows the researcher to observe individual ability, number of corrections, as well as whether the corrections were good (i.e., a mistake was corrected), or bad (i.e., a good move was corrected). The study analyzes how the different factors affect an individual’s likelihood of being selected as a collaborator in a last puzzle-solving stage where both participants win cash earnings based on joint performance. The results show that both genders respond negatively to a correction, but women more so than men. Men are less likely to collaborate with a person who has corrected their mistake, particularly men with high ability. The gender of the corrector is found not to matter.

Domestic violence (DV) is another gender aspect of well-being that has become particularly concerning during the pandemic. For many victims, lockdowns and curfews have meant more exposure to their perpetrator. Mobility restrictions have also implied more social isolation from family members and friends as well as increased economic distress, two other factors known to exacerbate DV. In a preliminary study presented by Damian Clarke, associate professor at the University of Chile, he and co-authors address the relationship between DV and quarantines in Chile. They use longitudinal data on police DV hotline calls and use of women’s shelters to measure DV incidence, criminal complaints of DV to police to measure reporting, and mobile phone data to measure mobility. Exploiting municipal variation in the timing of lockdown entry and exit, the study shows that lockdowns lead to more DV incidence and less reporting. DV shelter use increased on average by 11% with entry and reversed with exit. DV calls to the police hotline increased by 86% and persists after lockdown exit. DV crime reports decrease by 5% and increases by 10% with exit. Moreover, the authors document that lockdowns activate both DV mechanisms – increased economic distress and decreased mobility. In municipalities where lockdowns had a stronger impact on unemployment and mobility, they also find larger changes in DV.

Expectations About the Future and Parenthood

Two other studies presented at the workshop discussed the relationship between future expectations and well-being. Claudius Garten, researcher at the Technical University of Dortmund, presented findings on the role of homeownership. Garten and co-authors utilize individual-level survey data from 2007 covering 14 European countries. It contains information on homeownership status and wellbeing measures expressed as respondents’ expectations about future living standards five years from today. They find that expectations about future living standards are higher among homeowners relative to renters and strongly associated with the value of housing assets, suggesting that material security through housing ownership works as a channel for future wellbeing. Garten further argued that since most countries included in the sample have experienced rising house property prices and increased rents since 2007, the divergence between renters and owners is likely to be even more significant today, especially in urban areas.

The second presentation that discussed expectations about well-being in later life was by Alina Schmitz, researcher at the Technical University of Dortmund. Unlike housing, which is seen as a form of material security, Schmitz’s study focuses on the role of health infrastructure quality. Availability of care services may be seen as a safety net in case of illness and care dependency and should thus have a positive effect on wellbeing. The study performs a multilevel analysis on the individual, regional and, country level using micro-survey data on individuals’ life satisfaction and macro-data on the availability of long-term care beds, covering 96 regions from six European countries in 2015. The results show that the quality of care infrastructure is significantly related to the wellbeing of those aged above 50. Moreover, care infrastructure is particularly important for the wellbeing of those with health limitations (i.e. those who require that infrastructure either now or in the future).

Parenthood is another factor that is commonly thought of as a source of happiness. Contrary to this idea, European populations are aging rapidly and the young today have fewer children than the generations before them. The reason why people choose to have few children could be several – e.g. high opportunity costs and/or low benefits of having a large family. Is the fertility rate we see in the developed world today a result of the well-being-maximizing decisions of individuals? This is the main question asked in the paper presented by Barbara Pertold-Gebicka, assistant professor at the Institute of Economic Studies at Charles University. Her study utilizes European survey data to investigate the effect of having an additional unplanned child in five developed countries. To measure the effect of an additional unplanned child and deal with the fact that happy individuals tend to have more children, Pertold-Gebicka and co-author compare people who had twin births in their second pregnancy with parents of two children. Apart from life satisfaction, the most common wellbeing measure, the authors construct a second measure of wellbeing denoted as the happiness index – normalized value summarizing five questions about feelings over the last 5 months, interpreted as the relative frequency of positive feelings. They find no significant effect of having a third child on the well-being of parents. However, when separately looking at groups divided by age of children, they find that the effect of having an additional child on well-being is negative for fathers of younger children and positive for those of teenagers. For the parents of younger children, they show that the negative effect of having a third child is likely driven by increased feelings of nervousness and problems relating to accommodation.

Measuring Inequality and Social Deprivation

Some aspects of wellbeing such as feelings of unfairness or social connections can be quite ambiguous to study as they depend on context and are hard to quantify.

Nicolai Suppa, researcher at the Centre for Demographic Studies at the UAB, presented his research aimed to improve the measurement of deprivation in social participation (DSP) and complementing previous work with an additional outcome variable measuring a different dimension of deprivation. The study uses German survey data to measure how often common social activities are performed and then uses an intersectional approach (similar to the “Alkire-Foster method”) to assign individuals as deprived based on if and how often they practice these activities. The findings show that while the DSP measure correlates positively both with income poverty and material deprivation measures, it identifies a different sample of individuals. Being deprived in terms of social participation is associated with a significant loss of life satisfaction, a magnitude comparable to the loss of being unemployed.

Ingrid Bleynat, researcher at Kings College London, also discussed how to improve measurement but presented a study focusing on a different dimension of well-being, inequality. While quantitative approaches may give little account of the detailed mechanisms of inequality and its multidimensionality, qualitative studies often focus on a subset of the population which make results difficult to generalize. Bleynat and co-authors suggest a mixed approach, combining quantitative and qualitative assessments of inequality. They utilize neighborhood-level data on average household income in Mexico City to randomly select five households in each decile of the income distribution and conduct semi-structured interviews in these households to better understand the nuances of inequality. Based on these interviews they construct two qualitative measures. The first is called inequality of lived experiences and measures qualitative experiences in work, education, and health services across the income distribution. The second is called lived experiences of inequality, and measures feelings of stigma, discrimination, and social hierarchy across gender, ethnicity and location. The quantitative data confirms that Mexico City is highly unequal across the income distribution in terms of not only income but also social factors such as housing, health and food security. The results concerning the qualitative measures, such as inequalities in lived experiences or lived experiences of inequality confirm the existing understanding – e.g., that households belonging to the lower deciles are more likely to be mistreated in the public health sector, have a hostile school environment, and worse working conditions, or that women across the income distribution bear most of the childcare responsibilities, – but provide nuanced details on the interaction between material inequality and the reported experiences.

Conclusion

There is no doubt that the impact of Covid-19 on our well-being has been many-sided, and the presentations of the workshop have clearly demonstrated the broad spectrum of related problems and concerns, as well as their variation across institutional, social, political, economic, and cultural contexts.

Although we are well underway, further research and comprehensive data collection on how people have coped with and responded to the pandemic is needed to design sensible recovery policies and incentivize governments to implement them.

On behalf of the Stockholm Institute of Transition Economics, we would like to thank the experts who shared their insightful research and participated in “Dimensions of Well-being“.

List of Participants

Part 1 | Online Workshop on Dimensions of Well-being

Part 2 | Online Workshop on Dimensions of Well-being

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.

IMF’s New SDR Allocation—Why Belarus Is “Getting Money From the Fund”

Posted on September 9, 2021 | Policy Brief

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Why is the IMF sending $1bn to Belarus as the country is falling deeper into repression and authoritarianism? The short answer is that Belarus, together with 189 other countries, is a member of the IMF and the institution has decided to make a $650bn allocation of SDRs to its members in proportion to their quotas in the IMF. Belarus has a quota of 0.14 and will thus receive an injection of around $1bn to its reserves. In other words, this is not a decision to support the Belarus government as such but a general decision by the IMF members to support a global recovery after the Covid-19 pandemic. That said, it still means that the leaders of Belarus are given an asset worth $1bn that can be used without conditions, but the underlying reason to support the recovery in low- and middle-income countries still makes this palatable.

Introduction

On August 2, 2021, the board of the IMF approved the largest-ever SDR allocation to its 190 member countries. Belarus is one of the members that, by this decision, will get a boost of reserve assets of almost $1 billion. This has raised the question in some circles of “why is the IMF giving money to Belarus”. This brief provides a short background on IMF SDR allocations; how this may be used by the autocratic regime of Belarus; and why the general SDR allocation still makes sense.

SDR Allocations

For most people, an IMF “SDR allocation” is just another mysterious acronym that means very little. Therefore, a short introduction to the concept is warranted. SDR is short for Special Drawing Rights and is the IMF’s own reserve asset and unit of account, with a value that was first linked to gold but is now based on a basket of other currencies (IMF 2021). More specifically, the value of the SDR is based on a basket that consists of the U.S. dollar, euro, yen, pound sterling, and Chinese renminbi (since 2016). Table 1 shows the amounts of each currency and the value of the SDR based on exchange rates for August 26, 2021. In short, on that date, 1 SDR was worth approximately 1.42 U.S. dollars. Since the cross-exchange rates in the basket vary over time, so does the value of the SDR (see Figure 1).

Table 1. The SDR basket

Source: IMF (https://www.imf.org/external/np/fin/data/rms_sdrv.aspx)

Figure 1. SDR valuation

Source: IMF’s IFS database

The next issue is how SDRs are allocated among the IMF members. This is determined by the IMF’s Articles of Agreement and is done to provide reserve assets to its member countries. A new SDR allocation requires an 85 percent majority in the board to pass, and SDRs are then allocated to members based on their quotas. IMF quotas, in turn, are basically the stake the different member countries have in the Fund and are roughly based on the size of the economy of the country relative to other members.  Since several countries joined the IMF after the general SDR allocations in 1981, a special allocation was done in 2009 to allow new member countries to join the SDR Department on more equal terms. There was also a large general allocation in 2009 during the global financial crisis and in 2021 in response to the COVID-19 pandemic (Figure 2). The latter one is by far the largest and given the exchange rate in Table 1, the SDR456.5 billion is equivalent to around $650 billion.

 Figure 2. SDR allocations

The final issue to address in this section is why the SDR allocations matter at all. The answer is that SDRs can be exchanged for other currencies that, in turn, can be used to buy goods and services in international markets, including vaccines, other medical equipment, services, or food. When countries use the SDRs in this way, there is a cost in terms of the interest rate countries pay on SDRs. However, this interest rate is very low compared to other types of borrowing, so it is a cheap way of getting more foreign currency to spend (see Figure 3). In other words, for countries lacking access to foreign exchange at reasonable costs, the SDR allocation is a very welcome addition to their spending power.

Figure 3. Interest rate on SDR

Source: IMF’s Finance Department

Belarus and the IMF

Belarus became a member of the IMF in July 1992, shortly after the dissolution of the Soviet Union. Its quota in the IMF is SDR 681.5 million (or a share of 0.14 percent of total).

Belarus has had two IMF programs so far, the first in the early 1990s and the second in the wake of the global financial crisis in 2009. In the latter program, the IMF board approved a $2.5 billion loan “in support of the country’s efforts to adjust to external shocks” on January 12, 2009 (IMF, 2009a). The loan was then increased to a total of $3.5 billion in June 2009 (IMF, 2009b).

Despite the need for reforms and external funding, Belarus could not reach an agreement with the IMF on continued funding and instead repaid the loans to the Fund between 2012 and 2015. At the heart of this was the fact that for a country to get financial support in a regular Fund program, conditions will apply and will not always be stated explicitly, including on how to deal with human rights issues that are outside the Fund’s mandate. Therefore, the previous money from the Fund to Belarus was fundamentally different from the general SDR allocation described here, which is money without strings attached.

As the Covid-19 pandemic hit economies across the globe, Belarus approached the Fund in March 2020 to seek financial assistance. According to various reports, Belarus could not reach an agreement with the IMF due to conditions on how the pandemic was to be handled (IMF, 2020).

The new SDR allocation is however NOT subject to any conditionality but distributed to IMF members in proportion to their quotas. For Belarus, this means a new SDR allocation of 0.14 percent of the total SDR 456.5 billion, equivalent to around $900 million. As explained above, the SDR allocation can be exchanged for dollars, euros, or other currencies that can then be used to buy whatever the regime in Belarus likes. It could be vaccines, food, and medical equipment, but it could also be guns, ammunition, or tear gas to the security forces. In other words, this is money that can be spent in any way the government decides and the only price for this is a very small interest charge (see Figure 3) that comes with not keeping the SDRs as a reserve asset.

Concluding Remarks

The IMF is a member institution with 190 countries that is governed by its Articles of Agreement. This dictates that a new general SDR allocation should be distributed to its members according to their quotas. New SDR allocations are rare but have been used before to handle global economic crises. The current SDR allocation is designed to help low- and middle-income countries to deal with the economic side of the Covid-19 by making more foreign exchange available at a low cost. Helping countries with limited reserves to deal with the crisis and ensure that they can secure imports of vital goods and services makes perfect sense. The fact that this general support in certain instances will go to regimes like the one in Belarus that we currently think do not warrant the support of the global community is unfortunate. In a perfect world, the IMF would be able to impose conditions on human rights and democracy for any type of financial support, but this is not the world we live in. Therefore, the conclusion is not to stop helping a global recovery but to do more to support the alternatives to autocratic regimes across the world with other instruments.

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.

Vaccination Progress and the Opening Up of Economies

Posted on July 5, 2021 | Policy Brief

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In this brief, we report on the FREE network webinar on the state of vaccinations and the challenges ahead for opening up economies while containing the pandemic, held on June 22, 2021. The current state of the pandemic in each respective country was presented, suggesting that infection rates have gone down quite substantially recently in all countries of the network, except in Russia which is currently facing a surge in infections driven by the delta-version of the virus. Vaccination progress is very uneven, limited by lacking access to vaccines (primarily Ukraine and Georgia) and vaccine scepticism among the population (primarily in Russia and Belarus but for certain groups also in Latvia, Poland and to some extent Sweden). This also creates challenges for governments eager to open their societies to benefit their economies and ease the social consequences of the restrictions on mobility and social gatherings. Finally, the medium to long term consequences for labour markets reveal challenges but also potential opportunities through wider availability of work–from-home policies. 

Background

In many countries in Europe, citizens and governments are starting to see an end to the most intense impact of the Covid-19 pandemic on their societies. Infection and death rates are coming down and governments are starting to put in place policies for a gradual opening up of societies, as reflected in the Covid-19 stringency index developed by Oxford University. These developments are partially seasonal, but also largely a function of the progress of vaccination programs reaching an increasing share of the adult population. These developments, though, are taking place to different degrees and at different pace across countries.  This is very evident at a global level, but also within Europe and among the countries represented in the FREE network. This has implications for the development within Europe as a whole, but also for the persistent inequalities we see across countries.   

Short overview of the current situation

The current epidemiological situation in Latvia, Sweden, Ukraine, and Georgia looks pretty similar in terms of Covid-19 cases and deaths but when it comes to the vaccination status there is substantial variation.

Latvia experienced a somewhat weaker third wave in the spring of 2021 after being hit badly in the second wave during the fall and winter of 2020 (see Figure 1). The Latvian government started vaccinating at the beginning of 2021, and by early June, 26% of the Latvian population had been fully vaccinated.

Sweden, that chose a somewhat controversial strategy to the pandemic built on individual responsibility, had reached almost 15 thousand Covid-19 deaths by the end of June of 2021, the second highest among the FREE network member countries relative to population size. The spread of the pandemic has slowed down substantially, though, during the early summer, and the percentage of fully vaccinated is about to reach 30% of the population.

Figure 1. Cumulative Covid-19 deaths 

Source: Aggregated data sources from the COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, compiled by Our World in Data.

Following a severe second wave, the number of infected in Ukraine started to go down in the winter of 2020, with the total deaths settling at about 27 thousand in the month of February. Then the third wave hit in the spring, but the number of new daily cases has decreased again and is currently three times lower than at the beginning of the lastwave. However, a large part of the reduction is likely not thanks to successful epidemiological policies but rather due to low detection rates and seasonal variation. 

In June 2021, Georgia faces a similar situation as Ukraine and Latvia, with the number of cumulative Covid-19 deaths per million inhabitants reaching around 1300 (in total 2500 people) following a rather detrimental spring 2021 wave. At the moment, both Georgia and Ukraine have very low vaccination coverage relative to other countries in the region(see Figure 5).

In contrast to the above countries, Russia started vaccinating early. Unfortunately, the country is now experiencing an increase in the number of cases (as can be seen in Figure 2), contrary to most other countries in the region. This negative development is likely due to the fact that the new Covid-19 delta variant is spreading in the country, particularly in Moscow and St. Petersburg. Despite the early start to vaccinations, though, the total number of vaccinated people remains low, only reaching 10.5% of the population.

Figure 2. New Covid-19 cases

Source: Aggregated data sources from the COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, compiled by Our World in Data.

In some ways similar to Sweden, the government of Belarus did not impose any formal restrictions on individuals’ mobility. According to the official statistics, in the month of June, the rise in the cumulative number of covid-19 deaths and new daily infections has declined rapidly and reached about 400 deceased and 800 infections per one million inhabitants, respectively. Vaccination goes slowly, and by now, around 8% of the population has gotten the first dose and 5% have received the second.

There were two major waves in Poland during the autumn 2020 and spring 2021. In the latter period, the country experienced a vast number of deaths.  As can be seen in Figure 3, the excess mortality P-score – the percentage difference between the weekly number of deaths in 2020-2021 and the average number of deaths over the years 2015-2019 – peaked in November 2020, reaching approximately 115%. The excess deaths numbers in Poland were also the highest among the FREE Network countries in the Spring of 2021, culminating at about 70% higher compared to the baseline. By mid-June, the number of deaths and cases have steeply declined and 36% of the country’s population is fully vaccinated.

Figure 3. Excess deaths

Turning to the economy, after a devastating year, almost all countries are expected to bounce back by the end of 2021 according to the IMF (see Figure 4). Much of these predictions build on the expectations that governments across the region will lift Covid-19 restrictions. These forecasts may not be unrealistic for the countries where vaccinations have come relatively far and restrictions have started to ease. However, for countries where vaccination rates remain low and new variations of the virus is spreading, the downside risk is still very present, and forecasts contain much uncertainty.

 Figure 4. GDP-growth

Vaccination challenges

Since immunization plays such a central role in re-opening the economy and society going back to normal, issues related to vaccinations were an important and recurring topic at the event. The variation in progress and speed is substantial across the countries, though.

Ukraine and Georgia are still facing big challenges with vaccine availability and have fully vaccinated only 1.3% and 2.3% of the population by the end of June, respectively. Vaccination rates have in the recent month started to pick up, but both countries face an uphill battle before reaching levels close to the more successful countries.

Figure 5. Percent fully vaccinated

Other countries a bit further ahead in the vaccine race are still facing difficulties in increasing the vaccination coverage, though not so much due to lack of availability but instead because of vaccine skepticism. In Belarus, a country that initially had bottleneck issues similar to Ukraine and Georgia, all citizens have the opportunity to get vaccinated. However, Lev Lvovskiy, Senior Research Fellow at BEROC in Belarus, argued that vaccination rates are still low largely because many Belarusians feel reluctant towards the vaccine at offer (Sputnik V).

This vaccination scepticism turns out to be a common theme in many countries. According to different survey results presented by the participants at the webinar, the percentage of people willing or planning to get vaccinated is 30% in Belarus and 44% in Russia. In Latvia, this number also varies significantly across different groups as vaccination rates are significantly lower among older age cohorts and in regions with a higher share of Russian-speaking residents, according to Sergejs Gubins, Research Fellow at BICEPS in Latvia.

Webinar participants discussed potential solutions to these issues. First, there seemed to be consensus that offering people the opportunity to choose which vaccine they get will likely be effective in increasing the uptake rate. Second, governments need to improve their communication regarding the benefits of vaccinations to the public. Several countries in the region, such as Poland and Belarus, have had statements made by officials that deviate from one another, potentially harming the government’s credibility with regards to vaccine recommendations. In Belarus, there have even been government sponsored disinformation campaigns against particular vaccines. In Latvia, the main problem is rather the need to reach and convince groups who are generally more reluctant to get vaccinated. Iurii Ganychenko, Senior Researcher at KSE in Ukraine, exemplified how Ukraine has attempted to overcome this problem by launching campaigns specifically designed to persuade certain age cohorts to get vaccinated. Natalya Volchkova, Director of CEFIR at NES in Russia, argued that new, more modern channels of information, such as professional influencers, need to be explored and that the current model of information delivery is not working.

Giorgi Papava, Lead Economist at ISET PI in Georgia, suggested that researchers can contribute to solving vaccine uptake issues by studying incentive mechanisms such as monetary rewards for those taking the vaccine, for instance in the form of lottery tickets. 

Labour markets looking forward

Participants at the webinar also discussed how the pandemic has affected labour markets and whether its consequences will bring about any long-term changes.

Regarding unemployment statistics, Michal Myck, the Director of CenEA in Poland, made the important point that some of the relatively low unemployment numbers that we have seen in the region during this pandemic are misleading. This is because the traditional definition of being unemployed implies that an individual is actively searching for work, and lockdowns and other mobility restrictions have limited this possibility. Official data on unemployment thus underestimates the drop in employment that has happened, as those losing their jobs in many cases have left the labour market altogether. We thus need to see how labor markets will develop in the next couple of months as economies open up to give a more precise verdict.

Jesper Roine, Professor at SITE in Sweden, stressed that unemployment will be the biggest challenge for Sweden since its economy depends on high labor force participation and high employment rates. He explained that the pandemic and economic crisis has disproportionately affected the labor market status of certain groups. Foreign-born and young people, two groups with relatively high unemployment rates already prior to the pandemic, have become unemployed to an even greater extent. Many are worried that these groups will face issues with re-entering the labour market as in particular long-term unemployment has increased. At the same time, there have been more positive discussions about structural changes to the labour market following the pandemic. Particularly how more employers will allow for distance work, a step already confirmed by several large Swedish firms for instance.

In Russia, a country with a labour market that allowed for very little distance work before the pandemic, similar discussions are now taking place. Natalya Volchkova reported that, in Russia, the number of vacancies which assumed distance-work increased by 10% each month starting from last year, according to one of Russia’s leading job-search platforms HeadHunter. These developments could be particularly beneficial for the regional development in Russia, as firms in more remote regions can hire workers living in other parts of the country.

Concluding Remarks

It has been over a year since the Covid-19 virus was declared a pandemic by the World Health Organization. This webinar highlighted that, though vaccination campaigns in principle have been rolled out across the region, their reach varies greatly, and countries are facing different challenges of re-opening and recovering from the pandemic recession. Ukraine and Georgia have gotten a very slow start to their vaccination effort due to a combination of lack of access to vaccines and vaccine skepticism. Countries like Belarus and Latvia have had better access to vaccines but are suffering from widespread vaccine skepticism, in particular in some segments of the population and to certain vaccines. Russia, which is also dealing with a broad reluctance towards vaccines, is on top of that dealing with a surge in infections caused by the delta-version of the virus.

IMF Economic Outlook suggests that most economies in the region are expected to bounce back in their GDP growth in 2021. While this positive prognosis is encouraging, the webinar reminded us that there is a great deal of uncertainty remaining not only from an epidemiological perspective but also in terms of the medium to long-term economic consequences of the pandemic.

Participants

  • Iurii Ganychenko, Senior Researcher at Kyiv School of Economics (KSE/Ukraine)
  • Sergejs Gubins, Research Fellow at the Baltic International Centre for Economic Policy Studies (BICEPS/ Latvia)
  • Natalya Volchkova, Director of the Centre for Economic and Financial Research at New Economic School (CEFIR at NES/ Russia)
  • Giorgi Papava, Lead Economist at the ISET Policy Institute (ISET PI/ Georgia)
  • Lev Lvovskiy, Senior Research Fellow at the Belarusian Economic Research and Outreach Center (BEROC/ Belarus)
  • Jesper Roine, Professor at the Stockholm Institute of Transition Economics (SITE / Sweden)
  • Michal Myck, Director of the Centre for Economic Analysis (CenEA / Poland)
  • Anders Olofsgård, Deputy Director of SITE and Associate Professor at the Stockholm School of Economics (SITE / Sweden)

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.

Creative Industries: Impact on the Development of Ukraine’s Economy

Posted on June 21, 2021 | Policy Brief

Image of coloured umbrellas representing impact of creative industries

This brief is based on research investigating the effects of creative industries on the development of the Ukrainian economy. The results indicate that capital investment in creative industries has a significantly greater effect on economic growth than a simple increase in the consumption of the respective industry’s products. Thus, we conclude that to achieve a more substantial economic effect of spending in creative industries, it is necessary not only to increase the expenditures in these industries and boost consumption of their products but also to support these industries in developing production capacity. The underlying study “Creative Industries: Impact on the Development of Ukraine’s Economy” was prepared by the Kyiv School of Economics in cooperation with the Ministry of Culture and Information Policy of Ukraine. The first results from the study were presented at the international forum “Creative Ukraine” in 2020.

Background

In 2019, the United Nations (UN) General Assembly declared 2021 as the International Year of Creative Economy for Sustainable Development. This nomination was a recognition of the growing role of creative industries in the economic development of both developed and developing countries. The program of events taking place under the theme of the International Year of the Creative Economy for Sustainable Development includes forums, conferences, and intergovernmental meetings, which intend to draw attention to the problems that hinder the development of creative industries (CI) and the opportunities that these areas create.

The importance of CIs, which lie at the crossroads of art, business, and technology, is constantly growing both at the national level and in terms of international competition between countries. CIs have become a strategic direction for increasing competitiveness, productivity, employment, and sustainable economic growth (UNCTAD 2019) [1]. Exceptional rates of growth in turnover, creation of new jobs, and resilience to the economic crisis make creative industries an attractive area for investment at both the private and governmental levels. (UNCTAD 2004) [2]. On the other hand, the scope of knowledge about the economic role of CIs and their impact on the development of other sectors of the economy is quite limited.

This brief describes the economic effect of spending in CIs. Particularly, using input-output and computable general equilibrium models, we outline CI multiplier effects on the development of other industries and discuss implications for government support of CI.

Creative Industries in Ukraine

Although the term creative industry is becoming more common, countries have different approaches to the definition. There have been attempts to introduce an international standard, but the goal has not yet been achieved [3].

Ukrainian law define CIs as “types of economic activity aimed at creating added value and jobs through cultural (artistic) and/or creative expression”.

Currently, the Cabinet Ministers of Ukraine list 34 basic economic activities belonging to CIs, including visual arts, performing arts, publishing, design, fashion, IT, audiovisual arts, architecture, advertising, libraries, archives and museums, folk arts and crafts.

The gross value added (GVA) of CIs in Ukraine is growing rapidly. In 2013, the GVA of creative industries amounted to UAH 31 billion (3% of total value added), and in 2019 it amounted to UAH 117.2 billion (3.9% of total value added) (Figure 1). The number of companies and employees in the field of CI is also growing rapidly. In 2019, there were 205.5 thousand business entities and more than 350 thousand employees. 

Figure 1. Gross value added of CI in Ukraine

Source: State Statistics Service of Ukraine

Most GVA of CIs is generated by information technology (IT) activities. In 2019, the IT sector generated UAH 63.7 billion of GVA or 54.3% of the national CI GVA (Figure 2). In second place, there is Advertising, ¢Marketing and PR – UAH 20.2 billion of GVA or 17% of national GVA. In third place with a small gap there is Audiovisual Art – UAH 19.4 billion of GVA or 17% of national GVA.

Figure 2. Structure of Gross Value Added CI in Ukraine, 2019.

Source: State Statistics Service of Ukraine

Methodology and Data

To assess the economic effect of creative industries, we employ a computable general equilibrium (CGE) approach. CGE estimates a general equilibrium model of an economy using real-life economic data. It models interactions of individual markets – such as manufactured goods, services, and factors of production – encompassing the entire economic system. In doing so, the model takes into account reactions of economic agents – economic sectors, households, government, external sectors – and assumes that markets are perfectly competitive. The resulting set of simultaneous equations then employs real data from the economy in question to estimate the equilibrium in these markets by balancing supply and demand in all markets via the appropriate choice of prices.

In this way, the CGE model is a good reflection of a studied economy. In particular, in application to our research question, it allows us to distinguish the economic impact of additional consumption and capital investments in creative industries, and therefore to form reasonably precise recommendations for policy measures. This feature makes the CGE approach much more relevant than the alternative methods, such as the input-output approach.

Limitations of the CGE approach include increased analytical difficulty and computational demands, calibration and the use of estimated parameters, etc.

Data utilized by the CGE model are given by the Social Accounting Matrix (SAM). The SAM structure is related to the input-output table. Each row and column reflects the income and expenses of a particular economic agent. The main principle of SAM is balance, i.e., income from the sale of goods and services equals expenditures.

As a result, the availability of input-output table data is a crucial factor for our analysis. The State Statistics Service of Ukraine publishes an input-output table for 42 industries, which is not sufficient to distinguish creative industries from other sectors of the economy. To compensate for these deficiencies, we use the following sources:

  • input-output table for Ukraine for 2018.
  • input-output table for Poland for 2015 (latest available) to approximate the intermediate consumption of creative industries, not available from Ukrainian input-output tables.
  • annual report on state budget expenditures of Ukraine for 2018.
  • balance of payments of Ukraine for 2018.
  • structural business statistics of Ukrainian enterprises in part of gross value added and sales volume for 2018.

Results

The results of the CGE model suggest a strong effect of investment in CIs.  The sizes of the multipliers across the most creative industries are similar. The exception is the programming industry, for which for a one hryvnia investment leads to a total GDP growth of 3.2 hryvnias. This value is the highest among all sectors of the economy, not only among the CIs. For the rest of the CIs, the multiplier ranges from 1.9-2.2, which is comparable to the multipliers of the construction and finance and insurance sector (Figure 3). Accordingly, the increase in GDP for one hryvnia of investment by the industry is:

  • UAH 2.2 for libraries, museums, archives.
  • UAH 2.1 for publishing.
  • UAH 2.1 for architecture.
  • UAH 2.0 for performing and other arts.
  • UAH 2.0 for production of jewellery, costume jewellery, musical instruments.
  • UAH 2.0 for public relations, marketing, advertising.
  • UAH 2.0 for design, photography, translation.
  • UAH 1.9 for audiovisual and audio art.

Figure 3. GDP change per one hryvnia of capital expenditures*

* Estimated assuming 5% increase in capital Source: Our calculations are based on data from State Statistics Service of Ukraine and Poland, as described in the data section.

While the above results are obtained by estimating GDP response to a 5% increase in capital, the results are quite similar for different sizes of investments.

Conclusion

Our estimations show that investment in creative industries has a considerable impact on GDP. Investment in the IT sector has the highest multiplier, even compared to “non-creative” sectors of the economy. Other CIs’ multipliers can be compared to the construction and finance and insurance sector. Therefore, the results suggest that creative industries offer a highly valuable investment opportunity.

We also find that increase in capital investment in a creative industry has a stronger positive impact on GDP than an increase in the consumption of the respective industry’s products. An immediate policy implication of this finding is that, to achieve a more significant economic effect of government spending in creative industries, it is necessary not only to increase the expenditures on these industries or boost consumption of their products but also to support them in expanding production capacity.

References

  • Nikolaeva, O., Onoprienko, A., Taran, S., Sholomitskyi, Y. and Iavorskyi, P., 2020. Creative Industries: Impact on the Development of Ukraine’s Economy. Ministry of Culture and Information Policy of Ukraine.
  • UNCTAD, 2019. How can the creative economy help power development? https://unctad.org/news/how-creative-economy-can-help-power-development
  • UNCTAD, 2004. Creative Industries and Development. https://unctad.org/system/files/official-document/tdxibpd13_en.pdf

 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.

Did the Government Help Belarusian SMEs to Survive in 2020?

Posted on June 14, 2021 | Policy Brief

Enterprises During Pandemic representing Belarus

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

Small and Medium Enterprises During the Pandemic

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

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

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

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

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

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

Figure 1: Scope and importance of SME support measures

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

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

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

The Belarusian Economy in 2020

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

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

Source: Own elaboration based on five ways of business surveys

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

What Affected Belarusian SMEs?

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

These hypotheses are based on the following arguments:

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

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

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

Table 1. Impact on SMEs’ revenues

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

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

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

Conclusion

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

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

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

References

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

Green Concerns and Salience of Environmental Issues in Eastern Europe

Posted on June 7, 2021 | Policy Brief

Flooded street in Germany representing climate change risk perceptions

Changes in individual behavior are an essential component of the planet’s effort to reduce carbon emissions. But such changes would not be possible without individuals acknowledging the threat of anthropogenic climate change. This brief discusses the climate change risk perceptions across Europe. We show that people in Eastern Europe are, on average, less concerned about climate change than those in Western Europe. Using detailed survey data, we find evidence that the personal experience of extreme weather events is a key driver of green concern, and even more so in the non-EU Eastern part of Europe. We argue that this association might be explained by the relatively low quality and informativeness of public messages concerning global warming in this part of Europe. If information is scarce or perceived as biased, personal experience will resonate more.

Introduction

Climate change is one of the main threats to humanity. Tackling it entails a combined effort from all parts of society, from regulatory changes and industries adopting new greener business models to consumers adjusting their behavior. While an individual’s contribution to climate change may appear insignificant, research shows that the aggregate effect of mobilizing already known changes in consumer behavior may allow the European Union (EU) to reduce its carbon footprint by about 25% (Moran et al., 2020).

However, the first step for people to adjust their consumption patterns is to acknowledge the threat of anthropogenic climate change. Public ignorance about climate change’s impacts remains high across the world. Furthermore, citizens of more polluting countries are often relatively less concerned about climate change. This lack of awareness is not well-understood, in part due to the multi-dimensional local factors affecting it (Farrell et al., 2019).

This brief discusses the potential drivers of climate risk perceptions, focusing on the differences between Western Europe, Eastern European states that are part of the EU, and non-EU Eastern European countries. We first present the climate change concerns across these regions. We then discuss to which extent the country’s pollution exposure measures and individuals’ socio-economic characteristics can explain these differences. We show that the personal experience of extreme weather events is a key driver of green concern, and even more so in the non-EU part of Eastern Europe. We relate this result to the relatively low salience and informativeness of public messages concerning climate in this part of Europe and discuss potential policy implications.

Green Concerns and Pollution Exposure Across Europe

Figure 1 compares, across Europe, the share of poll respondents who see climate change as a major threat, based on the data from the Lloyd’s Register Foundation World Risk Poll 2020.  While there is a significant variation in climate risk perception within each region, respondents in Eastern Europe are, on average, less concerned about climate change than those in Western Europe. We observe a similar pattern between the EU and non-EU parts of Eastern Europe. 

Exposure to pollution does not seem to clearly explain these differences. Moreover, the patterns of correlation between climate concern and pollution differ across regions and measures of pollution exposure. The left panel of Figure 2 presents averages across the regions for two pollution measures: carbon emissions (which is, perhaps, reflecting climate threat in general) and air quality (which is more directly associated with health risks). We can see that CO2 emissions are the highest in the non-EU part of Eastern Europe, the least environmentally concerned region. Still, the EU part of Eastern Europe has the lowest average emissions per capita across the three regions (this ranking likely results from the interaction between reliance on fossil fuels, industrial structure, and level of development across the three regions). At the same time, when it comes to the average air quality (measured as the percentage of population exposed to at least 10 micrograms of PM2.5/m3), the non-EU EasternEuropean region is doing better than its EU counterpart, which is more climate concerned. Here, better average air quality in the non-EU Eastern European region is due to its relatively low population density, and consequently, low PM2.5 exposure in large parts of Russia. (See, more on the air quality gap within the EU in Lehne, 2021).

Figure 1: Climate concerns in Eastern and Western Europe

Source: Authors’ calculations based on Lloyd’s Register Foundation World Risk Poll 2020, question 5 “Do you think that climate change is a very serious threat, a somewhat serious threat, or not a threat at all to the people in this country in the next 20 years?”. Averages are calculated with population-representative weights.

The right panel of Figure 2 shows correlations between (country-level) climate concerns and pollution. For CO2, the correlation is negative in all three regions, suggesting that, within each region, more emitting countries are less concerned. This negative correlation, however, is the strongest in the EU-part of Eastern Europe and almost absent in the non-EU part. The differences between the regions are even more striking for the correlation between climate concerns and air quality: both in Western Europe and in the EU part of Eastern Europe, citizens of countries with worse air quality are more concerned about climate change. However, in non-EU Eastern Europe, the relation is the exact opposite: lower concerns about climate change go hand-in-hand with worse quality of air.

Figure 2: Emissions vs. Climate concerns in Eastern and Western Europe, 2018

Source: Authors’ calculations based on www.climatewatchdata.org, OECD and World Risk Poll 2020. The climate concern variable is a country-level weighted average of answers “Very high risk” to the World Risk Poll 2020 question 5, see note to Figure 1.

Green Concerns and Socio-economic Characteristics

Lower climate concerns in EU-part of the Eastern bloc have been documented before; they are often explained by the Eastern-European economies’ high reliance on coal and other fossil fuels, low-income levels, and other immediate problems that lower the priority of climate issues (e.g., Lorenzoni and Pidgeon 2006, Poortinga et al., 2018, or Marquart-Pyatt et al., 2019). Additionally, the literature suggests that climate beliefs are linked to individuals’ socio-economic characteristics, such as level of education, income, or gender (see, e.g., Poortinga, 2019), which may be different across the regions.

However, the regional differences in climate beliefs also persist when we use individual-level data and control for respondents’ individual characteristics, as well as for country-level variables, such as GDP per capita, oil, gas, and coal dependence of the economies, and exposure to emissions (at the country level, as our individual data does not have this information). This is illustrated in Column 1 of Table 1.

Table 1: Climate change beliefs determinants, individual-level cross-section data.

Source: This is an outcome of logistic regression. Experience =1 if the respondent answered “yes” to the World Risk Poll 2020 question L8D “Have you or someone you personally know, experienced serious harm from severe weather events, such as floods or violent storms in the past TWO years?” Media Freedom is based on 2018 Freedom House data, and scores media between 0 (worst) and 4 (best). Controls include age, gender, education, personal feelings about household income, income quantile, urban/rural, size of household, number of children under 15, las well as log of GDP per capita, log of CO2 per capita, mean exposure to PM2.5, and oil, gas and coal rents as a share of GDP. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

In what follows, we explore another key driver, the personal experience of extreme weather events. While there is a sizable literature on the effect of experience on climate beliefs, that factor was never, to our knowledge, considered to understand the difference in climate risk perception between the EU- and non-EU parts of Eastern Europe.  

Green Concern and Salience of Environmental Issues

In line with the recent climate risk perceptions literature (e.g., Van der Linden, 2015), we show that personal experience increases the likelihood of considering climate change as a major threat across all three regions (see column 2 in Table 1). The association is stronger in the EU part of Eastern Europe and even more so in the non-EU part (even if the difference between the last two is not statistically significant). This finding is confirmed when we control for (observable and unobservable) country-specific effects, such as social norms, via the inclusion of country-level fixed effects. In this case, extreme weather events make respondents more climate-conscious within each country (Column 3 of Table 1). In this specification, the effect differs statistically between the two groups of Eastern-European countries, even if only at a 10% significance level. To put it differently, the impact of personal experience with extreme weather events seems to close a sizable part of the gap in climate risk perceptions across the regions and more so in the non-EU part of Eastern Europe.

Our preferred explanation for this finding is that personal experience resonates with the quality and informativeness of public messages concerning global warming. If information is scarce or perceived as biased, personal experience will resonate more. The low political salience of environmental issues in Eastern Europe, inherited from its Soviet past (McCright, 2015), and lower media quality in Eastern Europe (see e.g., Zuang, 2021) are likely to affect the quality of public discourse concerning the risks of climate change, and, consequently, the information available to individuals.

The climate-related legislative effort across Eastern Europe reflects the low political importance of climate change in the region. According to the data from Grantham Research Institute on Climate Change and the Environment, non-EU transition countries, on average, have adopted 8 climate-related laws and policies, while the corresponding figure is 11.5 for EU transition countries and 18 for the countries in Western Europe. Further, Figure 3 shows a positive correlation between climate change concerns and the number of climate-related laws for Western Europe and the EU-part of Eastern Europe but a negative one for the non-EU part of Eastern Europe and Caucasus countries. One possible interpretation of these differences is that climate change is relatively low on the political agenda of (populist) regimes in the non-EU part of Eastern Europe, as climate-related legislative activity (proxied by, admittedly rough, a measure of the number of laws) does not reflect the intensity of population climate preferences.

Figure 3: Climate concern vs. Climate legislation

Source: Authors’ calculations based on climate legislation data from Grantham Research Institute on Climate Change and the Environment, and World Risk Poll 2020

Regarding the influence of media quality, column (4) of Table 1 shows that the effect of personal experience on climate change concern is negatively correlated with media freedom. One interpretation could be that individuals in countries with freer media infer less from their extreme weather experience because more accurate media coverage about climate risks improves the population’s knowledge on the issue.

Of course, the causality of the climate belief-experience relationship could also go in the other direction – people who are more concerned about climate change could be more likely to interpret their personal experience as weather-related extreme events. It is impossible to distinguish with the data at hand. However, Myers et al. (2013) show that both channels are present in the US, and the former channel dominates for the people less engaged in the climate issue. Stretching this finding to the Eastern Europe case, we argue that more precise information on the importance of climate change may partially have the same effect as experience – i.e., it will increase people’s awareness and concern about the consequences of global warming.

Conclusion

This brief addresses the differences in climate change beliefs between Eastern and Western Europe, as well as within Eastern Europe. It discusses the determinants of these differences and stresses the importance of personal experience, especially in the non-EU part of Eastern Europe. It relates this finding to the relatively low accuracy of information and quality of public discourse about climate change in the region.

We know already that tackling climate change requires reliable and accurate sources of information. This is especially crucial given what we outline in this brief. This issue resonates with the current social science analysis of the diffusion of climate change denial (see e.g., Farell et al., 2019, on the significant organized effort in spreading misinformation about climate change). Such contrarian information that relays uncertainty and doubt regarding the severity of the global climate change threat could have a severe impact, especially in situations with low political salience of climate change, like in non-EU Eastern Europe. A significant effort of both governments and civil society is needed to provide adequate information and mobilize the population in our common fight against climate change.

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.

Jurisdictional Competition for FDI in Developing and Developed Countries

Posted on May 31, 2021 | Policy Brief

20210531 Jurisdictional Competition FREE Network Image 01

This brief is based on research studying jurisdictional competition between countries and its influence on the inflow of foreign direct investments (FDI). The study compares jurisdictional competition among the developing Central and Eastern European (CEE) countries with competition among developed EU countries. As instruments of jurisdictional competition for FDI, we consider governments’ efforts to improve the rule of law, corporate governance, and tax policies. The results suggest the presence of proactive jurisdictional competition via the quality of corporate governance regulation both in the CEE and the EU countries. The CEE states also attract FDI by competing in tax policies.

Introduction

The determinants of FDI inflows have been examined in numerous studies. A substantial number of them consider the influence of institutions, which are defined as particular organizational entities, procedural devices, and regulatory frameworks (IMF, 2003).

The quality of institutions is a particularly important FDI determinant for less-developed countries because the poor institutional quality and weak law enforcement increase the costs of running a business, create barriers for financial market efficiency, and increase the probability of foreign assets expropriation (Blonigen, 2005).

However, governments interested in attracting FDI to boost job creation, new technologies, and tax revenues to their countries are not only concerned about the internal institutional environment. They are also competing with other countries in attracting foreign investments, engaging in what is often referred to as “jurisdictional competition”. In a broad sense,  this can be thought of as governments’ efforts to outcompete one another in offering foreign companies more favorable institutional and fiscal conditions for capital placements.

This brief summarizes the results of a study on the jurisdictional competition for FDI among the developing CEE and among developed EU countries (Mazol and Mazol, 2021). The research explores the precondition for proactive jurisdictional competition between economies for FDI – namely, how the economic and institutional environment within a country impacts the inflow of FDI both domestically and to its neighboring states, – by using a spatial econometric approach. The brief emphasizes the difference in the FDI policy responses implemented by developing CEE and developed EU countries.

Data and Methodology

In our econometric analysis, we use the FDI inward stock (i.e., the value of capital and reserves in the economy attributable to a parent enterprise resident in a different economy) as the dependent variable. The explanatory variables indicating jurisdictional competition include quality of corporate governance, rule of law, political stability, and tax policy. We employ balanced panel datasets for 26 developing CEE countries and 15 developed EU countries for the period 2006-2018. The dataset is derived from the World Bank and UNCTAD databases.

The analysis is based on a panel spatial Durbin error model (SDEM) with fixed effects (LeSage, 2014). Parameter estimates in the SDEM contain a range of information on the relationships between spatial units (in our case, countries). A change in a single observation associated with any given explanatory variable will affect the spatial unit itself (a direct effect) and potentially affect all other spatial units indirectly (a spillover effect) (Elhorst, 2014). The spatial spillover effect is viewed here as the impact of the change in the institutional or economic factor in one country on the performance of other economies (LeSage & Pace, 2009).

In our case, the direct effect is the effect on the FDI in country i of the changes in the studied instrument of jurisdictional competition in country i. The spillover effect is the change in FDI in country j following a change in the studied instrument of jurisdictional competition in country i.

Results

The results of our estimation are suggestive of a proactive jurisdictional competition in taxes among the CEE countries and in corporate governance quality both among the CEE and EU countries. Analyses of other factors (i.e., political stability and rule of law) show no significant interrelation between policy measures implemented by neighboring countries in order to attract FDI.

The precondition for the presence of proactive jurisdictional competition in a particular factor is to have statistical significance in both its direct and spillover effects (Elhorst and Freret, 2009). Such findings may indicate that policy measures in one economy trigger a policy response in a neighboring economy, which, in turn, influences the level of FDI in both countries.

Table 1. Estimation results of SDEM models – direct effects

Notes: *** – significance at 1% level, **  – significance at 5% level, *  – significance at 10% level. ln – denotes the logarithm of the underlying variable. lagt – denotes lagged underlying variable by one period (year) in time. Values of t statistics in parenthesis. CEE countries: Albania, Armenia, Azerbaijan, Belarus, Estonia, Bosnia and Herzegovina, Bulgaria, Croatia, Czech Republic, Georgia, Hungary, Kazakhstan, Kyrgyz Republic, Latvia, Lithuania, Macedonia, Moldova, Poland, Romania, Russia, Serbia, Slovakia, Slovenia, Tajikistan, Ukraine, Uzbekistan. EU countries: Austria, Belgium, Denmark, Finland, France, Germany, Greece, Italy, Luxembourg, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland. Source: Author’s estimates based on World Bank and UNCTAD databases.

Our results for the direct and indirect response to a tax policy in CEE countries illustrate this logic. Decreasing tax_rateincreases FDI to the CEE economy enacting this change (see Table 1), as well as to its neighboring countries (see Table 2). This finding is consistent with jurisdictional competition in taxes. That is, a reduction in domestic tax_rate may entail a decrease in the tax rate of a neighboring economy, resulting in a subsequent increase in FDI. (To explicitly confirm the suggested channel, further tax policy analysis would be needed). Interestingly, our results suggest that jurisdictional competition in taxes is only present among CEE economies, but not among EU countries.

In turn, an increase in corp_governance, a measure of corporate governance quality, increases FDI in neighboring countries both in the EU and in the CEE region (see Table 2).  A possible interpretation is that an increase in corp_governance in one country may entail an increase in corp_governance in its neighboring economies, resulting in a subsequent increase in FDI.  This result suggests proactive competition via corporate governance policy both among the EU countries and the CEE countries.

However, the direct effect differs between the regions. In the EU, an increase in corp_governance increases FDI to the EU economy in question, in line with common wisdom (see Table 1). At the same time, in the CEE region, an increase in corp_governance is followed by a decrease in FDI to that country.

Table 2. Estimation results of SDEM models – spillover effects

Notes: ***  – significance at 1% level, **  – significance at 5% level, *  – significance at 10% level. ln – denotes the logarithm of the underlying variable. Values of t statistics in parenthesis. lagt_lags – denotes spatially lagged underlying variable (multiplied by spatial weight matrix) lagged by one period (year) in time. Source: Author’s estimates based on World Bank and UNCTAD databases.

One potential explanation for the negative direct effect of corporate governance quality on FDI in the CEE economies is that improved corporate governance practices can block certain types of FDI, leaving behind foreign investors with a lower “threshold for corruption”. This may decrease FDI to the CEE country in question. However, once the jurisdictional competition results in an improvement of corporate governance across the region, it ultimately has a positive spillover effect.

The above explanation is in line with the theory of regulatory capture (Stigler, 1971), which suggests that the decisions made by public officials might be shaped and sometimes distorted by the efforts of rent-seeking interest groups to increase their influence.

Finally, the estimates do not indicate that the other studied institutional factors, rule of law and political stability, are applied as instruments of jurisdictional competition as neither groups of countries show significant spillover effects. The results, however, show that these factors influence the FDI inflow via the direct effect. More specifically, an increase in political_stability positively influences the FDI inflow to the economies in question, both in CEE and the EU, while rule_of_law is positive and significant only for the CEE countries. If investors are not as responsive to changes in rule_of_law when the initial level is high, the fact that EU countries typically have a higher rule_of_law value compared to CEE countries might explain why this estimate is insignificant for the EU countries.

Conclusion

This brief, first, presents new evidence on the relationship between different economic and institutional factors and FDI using a spatial econometric approach; second, it analyzes the possible existence of jurisdictional competition among developing CEE countries and developed EU countries as well as its effect on FDI.

The results suggest proactive jurisdictional competition in FDI determinants such as corporate governance quality and tax rates. CEE countries competing with one another use both these instruments of jurisdictional competition, while EU countries compete only via corporate governance quality. Furthermore, foreign investors are not sensitive to the quality of rule of law in the EU countries, while this instrument is more important for the FDI inflow to CEE economies.

Our results stress that officials responsible for the FDI policy implementation should pay more attention to the policies undertaken by neighboring governments as such external policies can make their own strategies to attract FDI to their economy less effective.

References

  • Blanton, S., and R. Blanton. (2007). What Attracts Foreign Investors? An Examination of Human Rights and Foreign Direct Investment. The Journal of Politics, 69(1), 143-155.
  • Blonigen, B. (2005). A Review of the Empirical Literature on FDI Determinants. Atlantic Economic Journal, 33(4), 383-403.
  • Elhorst, J. (2014). Spatial Econometrics from Cross-Sectional Data to Spatial Panels. Berlin: Springer.
  • Elhorst, J., and S. Freret. (2009). Evidence of Political Yardstick Competition in France Using a Two-Regime Spatial Durbin Model with Fixed Effects December. Journal of Regional Science, 49(5), 931-951.
  • IMF (2003). World Economic Outlook 2003. International Monetary Fund: Washington DC.
  • LeSage, J. (2014). What Regional Scientists Need to Know About Spatial Econometrics? Working Paper, Texas State University-San Marcos, San Marcos.
  • LeSage, J., and R. Pace. (2009). Introduction to Spatial Econometrics. Boca Raton, FL: CRC Press, Taylor and Francis Group.
  • Mazol, A., and S. Mazol. (2021). Competition of Jurisdictions for FDI: Does Developing and Developed Countries Response Different to Economic Challenges? BEROC Working Paper Series, WP no. 73.
  • Stigler, G. (1971). The Theory of Economic Regulation. Bell Journal of Economic and Management Science, 2, 3-21.

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.

Enemies of the People

Posted on May 24, 2021 | Policy Brief

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From the early days of the Soviet Union, the regime designated the educated elite as Enemies of the People. They were political opponents and considered a threat to the regime. Between the late 1920s and early 1950s, millions of enemies of the people were rounded up and forcedly resettled to remote locations within the GULAG, a system of labor camps spread across the Soviet Union. In recent research (Toews and Vezina, 2021), we show that these forced relocations have long-term consequences on local economies. Places close to camps that hosted more enemies of the people among prisoners are more prosperous today. We suggest that this result can be explained by the intergenerational transmission of education and a resulting positive effect on local development, which can still be observed to this day.

Historical Background

Targeting the educated elite, collectively referring to them as Enemies of the People and advocating their imprisonment, can be traced back to the beginning of the Russian Revolution in 1917. After consolidating power a decade later, Stalin launched the expansion of the GULAG system, which at its peak consisted of more than a hundred camps with over 1.5 million prisoners (see Figure 1). A large number of historians extensively described this dark episode in Russian history (Applebaum (2012), Khlevniuk (2004), and Solzhenitsyn (1974)). During the darkest hours of this episode, the Great Terror, 1.5 million enemies were arrested in just about two years. While half were executed immediately, the other half were forcedly allocated to GULAG camps spread across the Soviet Union and mixed with non-political prisoners (see Figure 2). Enemies accounted for about a third of GULAG prisoners after the Great Terror. As a result, education levels were higher in the GULAG than in society. In 1939, the share of GULAG prisoners with tertiary education was 1.8%, while, according to the Soviet Census of the same year, only 0.6% of the population had tertiary education.

After Stalin’s death, labor camps started closing rapidly, but many ex-prisoners settled close to the campsites. New cities were created and existing cities in the proximity of camps started growing fast (Mikhailova, 2012). Enemies remained once freed for a combination of political, economic, and psychological reasons. Politically, they were constrained in their choice of location by Stalin-era restrictions on mobility. Economically, they had few outside options and could keep on working for the camps’ industrial projects. On the psychological level, prisoners had become attached to the location of the camp, as Solzhenitsyn (1974) clearly puts it: “Exile relieved us of the need to choose a place of residence for ourselves, and so from troublesome uncertainties and errors. No place would have been right, except that to which they had sent us.”.

Figure 1. Location and size of camps in the Soviet Gulag system

Notes: The circles are proportional to the prisoner population of camps. The data is from the State Archive of the Russian Federation (GARF) and Memorial

Enemies of the People and Local Prosperity

At the heart of our analysis is a dataset on GULAG camps which we collected at the State Archive of the Russian Federation (GARF). It allows us to differentiate between prisoners who were imprisoned for political reasons (Enemies of the People) and those arrested for non-political crimes. The share of enemies varied greatly across camps, and we argue that this variation was quasi-random. We back this up by the historical narrative, according to which the resettlement process was driven by political rather than economic forces, suggesting that strategic placements played little role in the allocation of enemies (Khlevniuk (1995) and Ertz (2008)). Moreover, while the forced nature of allocation to camps allows us to rule out endogenous location decisions, we also show that neither economic activities nor geographic attributes, such as climatic conditions, soil quality, or the availability of resources, predict the share of enemies across camps.

To estimate the long-run effects of enemies on local prosperity, we link the location of camps in 1952, the year before Stalin’s death and at the peak of the GULAG system, to post-Soviet data covering the period 2000-2018.

Figure 2. The rise and fall of the Gulag

Notes: The solid line shows the number of Gulag camps while the dashed line shows the total number of prisoners in the Gulag. The two vertical dash lines indicate the years that can define the start and end of the Gulag, starting with Stalin’s 5-year plan in 1928 and ending with Stalin’s death in 1953. The shaded areas show specific periods of marked change for the Gulag, starting with dekulakization in 1929, when when Stalin announced the liquidation of the kulaks as a class and 1.8 million well-off peasants were relocated or executed. The Great Terror of 1936-1938, also referred to as the Great Purge, is the most brutal episode under Stalin’s rule, when 1.5 million enemies were arrested, and half of them executed. The Gulag’s prisoner population went down during WW2, as non-political prisoners were enlisted in the Red Army, and as the conditions in camps deteriorated and mortality increased. Source: Memorial.

In particular, the camp level information is linked to the location of firms from the Russian firm census (2018), data on night-lights (2000-2015), as well as data from household and firm-level surveys (2016 and 2011-2014, respectively). Our results suggest that one standard deviation (28 percentage point) increase in the share of enemies of the people increases night-lights intensity per capita by 58%, profits per employee by 65%, and average wages by 22%. A large number of specifications confirm the relationship depicted in Figure 3, which illustrates the positive association between the share of enemies across camps and night-lights intensity per capita.

Figure 3. Share of enemies vs. night lights per capita across Gulags

Notes: The scatters show the relationship between the share of enemies in camps in 1952 and night lights per capita within 30 km of camps in 2000 and 2015. Each circle is a 30km-radius area around a camp, and the size of the circles is proportional to the camps’ prisoner populations. The biggest circle is Khabarovsk. The solid lines show the linear fit, and the shaded areas show the 95% confidence interval. Areas near camps with a higher share of enemies have brighter night lights per capita both in 2000 and 2015. The data on Gulags is from the State Archive of the Russian Federation (GARF) and the data on night lights is from the DMSP-OLS satellite program and made available by the Earth Observation Group and the NOAA National Geophysical Data Center. The data on population is from the gridded population of the world from SEDAC.

Intergenerational Transmission

We suggest that the relationship between enemies and modern prosperity is due to the long-run persistence of high education levels, notably via intergenerational transmission, and their role in increasing firm productivity. For the identification of the intergenerational link, we rely on a household survey collected by the EBRD in which interviewees are explicitly asked whether their grandparents have been imprisoned for political reasons during Soviet times. Exploiting this information, we show that the grandchildren of enemies of the people are today relatively more educated. We also find that grandchildren of enemies are more likely to be residing near camps that had a higher share of enemies of the people among prisoners in 1952. An alternative explanation for our results could be that the leadership of the Soviet Union may have strategically chosen to invest more during the post-GULAG period in locations that had received more enemies to exploit complementarities between human and physical capital. We find no evidence for this mechanism. We document that Soviet investment in railroads, factories of the defence industry, or universities was, if anything, lower in places with a large share of enemies.

Conclusion

We show that the massive and forced re-allocation of human capital that took place under Stalin had long-run effects on local development. Sixty years after the death of Stalin and the demise of the GULAG, areas around camps that had a higher share of enemies are richer today, as captured by firms’ wages and profits, as well as by night-lights per capita. We argue that the education transferred from forcedly displaced enemies of the people to their children and grandchildren partly explains variation in prosperity across localities of Russia. This can be seen as a historical natural experiment that identifies the long-run persistence of higher education and its effect on long-run prosperity. Sadly, it also highlights how atrocious acts by powerful individuals can shape the development path of localities over many generations.

Bibliography

  • Applebaum, A., Gulag: A History of the Soviet Camps, Penguin Books Limited, 2012.
  • Ertz, Simon. Making Sense of the Gulag: Analyzing and Interpreting the Function of the Stalinist Camp System. No. 50. PERSA Working Paper, 2008.
  • Khlevnyuk, Oleg, “The objectives of the Great Terror, 1937–1938.” In Soviet History, 1917–53, pp. 158-176. Palgrave Macmillan, London, 1995.
  • Khlevnyuk, Oleg, The History of the Gulag: From Collectivization to the Great Terror Annals of Communism, Yale University Press, 2004.
  • Mikhailova, Tatiana, “Gulag, WWII and the long-run patterns of Soviet city growth,” 2012.
  • Solzhenitsyn, Aleksandr, The Gulag Archipelago, 1918-56: An Experiment in Literary Investigation, New York: Harper Row, 1973.
  • Toews, Gerhard, and Pierre-Louis Vézina. “Enemies of the people.” (2021).

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.

Carbon Tax Regressivity and Income Inequality

Posted on May 17, 2021 | Policy Brief

20210517 Carbon Tax Regressivity FREE Network Image 01

A common presumption in economics is that a carbon tax is regressive – that the tax disproportionately burdens low-income households. However, this presumption originates from early research on carbon taxes that used US data, and little is known about the factors that determine the level of regressivity of carbon taxation across countries. In this policy brief, I explore how differences in income inequality may determine the distribution of carbon tax burden across households in Europe. The results indicate that carbon taxation will be regressive in high-income countries with relatively high levels of inequality, but closer to proportional in middle- and low-income countries and in countries with low levels of income inequality.

Introduction

Climate change is one of the main challenges facing us today. To reduce emissions of greenhouse gases, and thereby mitigate climate change, economists recommend the use of a carbon tax. The environmental and economic efficiency of carbon taxation is often highlighted, but the equity story is also of importance: who bears the burden of the tax?

How the burden from a carbon tax is shared across households is important since it affects the political acceptability of the tax. For instance, the “Yellow Vests” protests against the French carbon tax started due to concerns that the tax burden is disproportionately large on middle- and working-class households. Research in economics also shows that people prefer a progressive carbon tax (Brännlund and Persson, 2012).   

In this brief, I explore what we know about the distributional effects of carbon taxes and analyze the link between carbon tax regressivity and levels of income inequality in theory and in application to Sweden as well as other European countries.

Carbon Tax Burden Across Households

It is a common finding in the economics literature that carbon taxes are, or would be, regressive (Hassett et al., 2008; Grainger and Kolstad, 2010). However, most of the earlier literature is based on US data, and the US is unrepresentative of an average high-income country in terms of variables that are arguably important for carbon tax incidence. Compared to most countries in Europe, income in the US is high but unequally distributed, carbon dioxide emissions per capita are high, the gasoline tax rate is low, and the access to public transport is poor. If we want to understand the likely distributional effects of carbon taxes across Europe, we thus need to look beyond the US studies.

A recent study by Feindt et al. (2020) examines the consumer tax burden from a hypothetical EU-wide carbon tax. They find that the distributional effect at the EU-level is regressive, driven by the high carbon intensity of energy consumption in relatively low-income countries in Eastern Europe. At the national level, however, carbon taxation in Eastern European countries is slightly progressive due to car ownership and transport fuel being luxuries. Conversely, in high-income countries – where transport fuel is a necessity – carbon taxation is slightly regressive.

That the incidence of carbon and gasoline taxation varies across countries with different levels of income, has been found in numerous studies (Sterner, 2012; Sager, 2019). To understand the source of this variation, we need to identify the determinants of the incidence of carbon taxes.

The Role of Income Inequality

In a recent paper, I, together with Giles Atkinson at the London School of Economics, present a simple model where the variation in the carbon tax burden across countries and time can be determined by two parameters: the level of income inequality and the income elasticity of demand for the taxed goods (Andersson and Atkinson, 2020). The income elasticity specifies how the demand for a good, such as gasoline, responds to a change in income. If the budget share decreases as income increase, we refer to gasoline as a necessity. If the budget share increases with income, we refer to gasoline as a luxury good. Our model predicts that rising inequality increases the regressivity of a carbon tax on necessities. Similarly, we will see a more progressive incidence if inequality increases and the taxed good is a luxury.

To mitigate climate change, a carbon tax should be applied to goods responsible for the majority of greenhouse gas emissions: transport fuel, electricity, heating, and food. To estimate the distribution of carbon tax burden, we must then first establish if these goods are necessities or luxuries, respectively. Gasoline is typically found to be a luxury good in low-income countries but a necessity in high-income countries (Dahl, 2012). Food, in the aggregate, is consistently found to be a necessity. A carbon tax on food would, however, mainly increase the price of red meat – beef has a magnitude larger carbon footprint than all other food groups – and red meat is generally a luxury good, even in high-income countries (Gallet, 2010). Lastly, electricity and heating are necessities, with little variation across countries in the level of income elasticities.  A broad carbon tax would thus likely be regressive in high-income countries, but more proportional, maybe even progressive, in low-income countries. The overall effect in low-income countries depends on the relative budget shares of transport fuel and meat (luxuries) versus electricity and heating (necessities). A narrow carbon tax on transport fuel has a less ambiguous incidence: it will be regressive in high-income countries where the good is a necessity and proportional to progressive in low-income countries where the good is a luxury.  

The income elasticities of demand, however, only provide half of the picture. To understand the degree of regressivity from carbon taxation, we also need to take into account the level of income inequality in a country. Our model predicts that a carbon tax on necessities will be more regressive in countries with relatively high levels of inequality. And increases in inequality over time may turn a proportional tax incidence into a regressive one.

To test our model’s prediction, we analyze the distributional effects of the Swedish carbon tax on transport fuel and examine previous studies of gasoline tax incidence across high-income countries. 

Empirical Evidence from Sweden

The Swedish carbon tax was implemented in 1991 at $30 per ton of carbon dioxide and the rate was subsequently increased rather rapidly between 2000-2004. Today, in 2021, the rate is above $130 per ton; the world’s highest carbon tax rate imposed on households. The full tax rate is mainly applied to transport fuel, with around 90 percent of the revenue today coming from gasoline and diesel consumption.

 Figure 1. Carbon tax incidence and income inequality in Sweden

Sources: Andersson and Atkinson (2020). Gini coefficients are provided by Statistics Sweden.

Using household-level data on transport fuel expenditures and annual income between 1999-2012, we find that the Swedish carbon tax is increasingly regressive over time, which is highly correlated with an increase in income inequality. Figure 1 shows the strong linear correlation between the incidence of the tax and the level of inequality across our sample period. The progressivity of the tax is measured using the Suits index (Suits, 1977), a summary measure of tax incidence that spans from +1 to -1. Positive (negative) numbers indicate that the tax is overall progressive (regressive) and a proportional tax is given an index of zero. The level of income inequality, in turn, is summarized by the Gini coefficient (0-100), with higher numbers indicating higher levels of inequality.

In 1991, when the Swedish carbon tax was implemented, income inequality was relatively low, with a Gini of 20.8. If we extrapolate, the results presented in Figure 1 indicate that the tax incidence in 1991 was proportional to slightly progressive. Since the early 1990s, however, Sweden has experienced a rise in inequality. Today, the Gini is around 28 and the carbon tax incidence is rather regressive. This can be a potential concern if people start to perceive the distribution of the tax burden as unfair and call for reductions in the tax rate.

Empirical Evidence Across High-Income Countries

Figure 2 presents the results of our analysis of previous studies of gasoline tax incidence across high-income countries. Again, we find a strong correlation with inequality; the higher the level of inequality, the more regressive are gasoline taxes.  In the bottom-right corner, we locate the results from studies on gasoline tax incidence that have used US data. The level of inequality in the US has been persistently high, and the widespread assumption that gasoline and carbon taxation is regressive is thus based to a large part on studies of one highly unequal country. Looking across Europe we find that the tax incidence is more varied, with close to a proportional outcome in the (relatively equal) Nordic countries of Denmark and Sweden.

Figure 2. Gasoline tax incidence and income inequality in OECD countries

Sources: Andersson and Atkinson (2020). Gini coefficients are from the SWIID database (Solt, 2019).

Conclusion

A carbon tax is economists’ preferred instrument to tackle climate change, but its distributional effect may undermine the political acceptability of the tax. This brief shows that to understand the likely distributional effects of carbon taxation we need to take into account the type of goods that are taxed – necessities or luxuries – and the level and direction of income inequality. Carbon taxation will be closer to proportional in European countries with low levels of inequality, whereas in countries with relatively high levels of inequality the carbon tax incidence will be regressive on necessities and progressive for luxury goods.

This insight may explain why we first saw the introduction of carbon taxes in the Nordic countries. Finland, Sweden, Denmark, and Norway all implemented carbon taxes between 1990-1992, and income inequality was relatively, and historically, low in this region at the time. Policymakers in the Nordic countries thus didn’t need to worry about possibly regressive effects. Looking across Europe today, many of the countries that have relatively low levels of inequality have either already implemented carbon taxes or, due to the size of their economies, have a low share of global emissions. In countries that are responsible for a larger share of global emissions – such as, the UK, Germany, and France – inequality is relatively high, and they may find it to be politically more difficult to implement carbon pricing as the equity argument becomes more salient and provides opportunities for opponents to attack the tax.

To increase the political acceptability and perceived fairness of carbon pricing, policymakers in Europe should consider a policy design that offsets regressive effects by returning the revenue back to households, either by lump-sum transfers or by reducing tax rates on labor income.   

References

  • Andersson, Julius and Giles Atkinson. 2020. “The Distributional Effects of a Carbon Tax: The Role of Income Inequality.” Grantham Research Institute on Climate Change and the Environment Working Paper 349. London School of Economics.  
  • Brännlund, Runar and Lars Persson. 2012. “To tax, or not to tax: preferences for climate policy attributes.” Climate Policy 12 (6): 704-721.
  • Dahl, Carol A. 2012. “Measuring global gasoline and diesel price and income elasticities.” Energy Policy 41: 2-13.
  • Feindt, Simon, et al. 2020. “Understanding Regressivity: Challenges and Opportunities of European Carbon Pricing.” SSRN 3703833.
  • Gallet, Craig A. 2010. “The income elasticity of meat: a meta-analysis.” Australian Journal of Agricultural and Resource Economics 54(4): 477-490.
  • Grainger, Corbett A and Charles D Kolstad. 2010. “Who pays a price on carbon?” Environmental and Resource Economics 46(3): 359-376.  
  • Hassett, Kevin A, Aparna Mathur, and Gilbert E Metcalf. 2009. “The consumer burden of a carbon tax on gasoline.” American Enterprise Institute, Working Paper.
  • Sager, Lutz. 2019. “The global consumer incidence of carbon pricing: evidence from trade.” Grantham Research Institute on Climate Change and the Environment Working Paper 320. London School of Economics.  
  • Thomas, Sterner. 2012. Fuel taxes and the poor: the distributional effects of gasoline taxation and their implications for climate policy. Routledge.
  • Suits, Daniel B. 1977. “Measurement of tax progressivity.” American Economic Review 67(4): 747-752.

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.

Five Years in Operation: the Polish Universal Child Benefit

Posted on May 10, 2021 | Policy Brief

Family in the golden hour representing Child Benefits

Over the last five years, Polish families with children have been entitled to a relatively generous benefit of approximately €110 per month and child. Initially granted for every second and subsequent child in the family regardless of income and for the first child for low-income families, the benefit was made fully universal in 2019. With the total costs amounting to as much as 1.7% of Poland’s GDP, the benefit reaches the parents of 6.7 million children and significantly affects these families’ position in the income distribution. Its introduction has led to a substantial reduction in the number of children living in poverty. However, since families with children are more likely to be among households in the upper half of the income distribution, out of the total cost of the benefit, a proportionally greater share ends up in the wallets of high-income families. While the implementation of the benefit has significantly changed the scope of public support to families in Poland, there are many lessons to be learnt and some important revisions to be undertaken to achieve an effective and comprehensive support system.

Introduction

One of the principal commitments in the 2015 Polish parliamentary elections of the then-main opposition party – Law and Justice (Prawo i Sprawiedliwość, PiS), was introducing a generous child benefit. The purpose of this benefit was to support families and encourage higher fertility, which had been one of the lowest in the European Union for a long time. Following PiS’s electoral victory, the new government introduced a semi-universal child benefit of approximately €110 per month (exactly 500 PLN per month, thus the Polish nickname of “the 500+ benefit”) in April 2016. Initially, the benefit was granted for every second and subsequent child in the family regardless of income and for the first child in low-income families. Since July 2019 (nota bene three months before the next parliamentary elections), it was made universal – all parents with children under the age of 18 are entitled to 500PLN per month for every child.  The benefit is relatively generous (for comparison, it accounts for 17.9% of the minimum wage in Poland in 2021), and universal coverage implies substantial costs for the government budget, totalling about 41bn PLN per year (1.7% of the Polish GDP).

Over the last five years, a number of analyses of the consequences of the benefit’s introduction have been conducted. These have encompassed a variety of socio-economic outcomes for Polish families with children – from a comprehensive assessment of these consequences (Magda et al. 2019) to analyses focused on specific effects of the benefit, such as the impact on women’s economic activity (Magda et al. 2018, Myck 2016, Myck and Trzciński 2019) or poverty (Brzeziński and Najsztub 2017, Szarfenberg 2017). The fifth anniversary of the benefit’s implementation seems to be a good opportunity for a summary and update of previous evaluations of the distributional consequences and financial gains for households resulting from this policy (an overview of all the previous CenEA analyses of the child benefit can be found in CenEA 2021). The results presented in this brief are based on analyses conducted using the Polish microsimulation model SIMPL on data from the 2019 CSO Household Budget Survey (more details in Myck et al. 2021). It should be noted that the analyses do not account for the impact of the Covid-19 pandemic on the material situation of households, as the data was collected before the outbreak. As previous studies suggest, the consequences for households of the pandemic and the series of resulting lockdowns varied greatly depending on various factors, such as the sources of income, sector, and form of employment, thus making it impossible to estimate precisely (Myck et al. 2020a).

The Child Benefit on Household Incomes

Due to its universal character, the distributional consequences of the child benefit payments are directly related to the position of households with children aged 0-17 in the income distribution relative to those without. As households with children are more likely to be in the upper half of the distribution (taking into account the demographic structure of households through income equivalisation), out of the total budget expenditure on the benefit, a proportionally greater share goes to high-income families (Table 1). Families with children in the two highest income decile groups (i.e., belonging to the 20% of households with the highest income) currently receive almost 25% of the total annual expenditure on the child benefit. On the other hand, among the 20% of households with the lowest incomes, families with children receive only 11.7% of the total annual cost of the benefit.

Table 1. Household gains resulting from the child benefit by income decile groups

Source: Myck et al. 2021. Notes: Income decile groups – ten groups each covering 10% of the population, from households (HH) with the lowest disposable income to the most affluent households, calculated on the basis of equivalised incomes.

Compared to the poorest 10% of households, families with children in the highest income decile receive 2.5 times more of the total funds allocated to the benefit.

It is also worth noting that the proportion of benefit in the disposable income is relatively evenly distributed if one considers all households in a given decile (with and without children). The proportional benefits in the first nine income deciles are in the range of 3.4% and 5.3% and only fall to 1.9% in the highest income group. A significant differentiation of the benefit in proportional terms can only be seen when accounting solely for households with children within each income decile. The benefit amounts to as much as 26.9% of the disposable income of households with children in the first decile, and the effect falls in subsequent groups – from 18.9% and 16.4% in the second and third deciles, to only 4.1% in the top decile.

The Child Benefit and the Position of Families With Children in the Income Distribution

Taking into account the magnitude of the policy, the position of families with children in the income distribution relative to other households may, to some extent, be the result of receiving the benefit itself. It is, therefore, reasonable to ask what role the benefit plays in shaping this relative position in the income distribution. Figure 1 presents the number of children under 18 in households by income decile groups when the benefit is included in total household income (left panel) and in a hypothetical scenario when the child benefit payment is withdrawn (right panel). As we can see, the withdrawal of the benefit would cause a substantial change in the relative position of families with children in the income distribution, significantly increasing the number of children in the lowest income groups. While in the current system, the poorest 10% of households include 342 thousand children aged 0-17, this number would be 553 thousand in a system without the benefit. However, the benefit also changes the relative position of high-income households with children. In the current system, the richest 10% of households include 762 thousand children. Subtracting the benefit from their household income would reduce this number to 687 thousand.

Figure 1. The child benefit and its impact on the position of families with children in the income distribution

Source: Myck et al. 2021.

Thus, even when taking into account the income distribution without the benefit, the number of children among the richest 10% of households is almost 25% higher than the number of children in the poorest 10% of households. Looking at the income distribution after including the benefit, there are more than twice as many children in the richest 10% of households than among the poorest 10%. This, in turn, inevitably means that, out of the total cost of the benefit, over twice as much money is transferred to households belonging to the richest deciles as compared to the funds transferred to families belonging to the poorest 10% of households.

Discussion

With the total costs amounting to 1.7% of Poland’s GDP, the child benefit introduced in April 2016 substantially raised the level of direct financial support for families with children. As shown in this brief, the benefit reaches the parents of 6.7 million children aged 0-17 and significantly affects the position of these families in the income distribution. While, on the one hand, a large proportion of families with children have incomes high enough to be in the highest income groups even without this support , the lowest decile group would include over 200 thousand more children in the absence of the benefit. This confirms that the child benefit alone contributes to a significant improvement in the material conditions of families with children and to a significant reduction in poverty (cf. Brzezinski and Najsztub, 2017; Szarfenberg, 2017). However, the scale of this reduction is modest given the size of the resources involved. This is not surprising given that the bulk of the total costs of the benefit comes from the 2019 program extension to cover all children regardless of family incomes, which largely ended up in the wallets of higher-income families (Myck et al. 2020b). One of the key goals of the benefit upon introduction was to increase the number of births in Poland by easing the material conditions of families with children. Yet despite a radical increase in the level of support, the number of births in Poland over the period 2017-2020 has essentially remained the same as that forecasted by the Central Statistical Office in its long-term population projection of 2014 (Myck et al. 2021). It is thus difficult to consider the benefit a success in terms of this major objective. Moreover, the withdrawal of the income threshold has largely eliminated the negative disincentive effects of the benefit with regard to employment (Myck and Trzcinski 2019). However, it is unclear whether the post-pandemic economic situation will allow for an increase in female labour force participation, which declined following the introduction of the benefit in 2016 (Magda et al., 2018).

The effects of every socio-economic programme should be assessed by comparing cost-equivalent alternatives. Despite all gains the “500+” child benefit has brought to millions of families in Poland over the last five years, the flagship programme of the ruling Law and Justice party does not fare well in this perspective. The need for change seems much broader than the reform of the benefit alone. The benefit was introduced on top of two other financial support mechanisms focused on families with children, namely family allowances and child tax credits, and the three elements have been operating in parallel since 2016. A number of suggestions on creating a streamlined, comprehensive system have been made a long time ago (e.g., Myck et al. 2016). However, a major restructuring of the entire support system with clearly defined socio-economic policy goals in mind seems all the more justified now, when many families may require additional assistance due to the difficult financial situation related to the Covid-19 pandemic.

Acknowledgement:

This Policy Brief draws on the CenEA Commentary published on 31.03.2021 (Myck et al. 2021). It has been prepared under the FROGEE project, with financial support from the Swedish International Development Cooperation Agency (Sida). The views presented in the Policy Brief reflect the opinions of the Authors and do not necessarily overlap with the position of the FREE Network or Sida.

References

  • Brzeziński, M., Najsztub, M. 2017. The impact of „Family 500+” programme on household incomes, poverty and inequality”, Polityka Społeczna44(1): 16-25.
  • CenEA 2021. Childcare benefit 500+ in CenEA analyses. https://cenea.org.pl/2021/04/06/childcare-benefit-500-in-cenea-analyses/
  • Magda, I., Brzeziński, M., Chłoń-Domińczak, A., Kotowska, I.E., Myck, M., Najsztub, M., Tyrowicz, J. 2019. „Rodzina 500+– ocena programu i propozycje zmian”. (“Child benefit 500+: the evaluation of the programme and suggestions for changes”), IBS report.
  • Magda, I., Kiełczewska, A., Brandt, N. 2018. “The Effects of Large Universal Child Benefits on Female Labour Supply”, IZA Discussion Paper No. 11652.
  • Myck, M. 2016. “Estimating Labour Supply Response to the Introduction of the Family 500+ Programme”. CenEA Working Paper 1/2016.
  • Myck, M., Król, A., Oczkowska, M., Trzciński, K. 2021. “Świadczenie wychowawcze po pięciu latach: 500 plus ile?”(„The child benefit after 5 years – 500 plus what?”), CenEA Commentary 31/03/2021.
  • Myck, M., Kundera, M., Najsztub, M., Oczkowska, M. 2016. „25 miliardów złotych dla rodzin z dziećmi: projekt Rodzina 500+ i możliwości modyfikacji systemu wsparcia” („25 billion PLN to families with children: Family 500+ programme and possible modifications of the family support system”),  CenEA Commentary 18/01/2016.
  • Myck, M., Oczkowska, M., Trzciński, K. 2020a. “Household exposure to financial risks: the first wave of impact from COVID-19 on the economy”, CenEA Commentary 23/03/2020.
  • Myck, M., Oczkowska, M., Trzciński, K. 2020b. „Kwota wolna od podatku i świadczenie wychowawcze 500+ po pięciu latach od prezydenckich deklaracji” („Tax credit and child benefit 500+ after five years since electoral declarations”, in PL), CenEA Commentary 22/06/2020.
  • Myck, M., Trzciński, K. 2019. “From Partial to Full Universality: The Family 500+ Programme in Poland and its Labor Supply Implications”, ifo DICE Report 17(03), 36-44.
  • Szarfenberg, R. 2017. “Effect of Child Care Benefit (500+) on Poverty Based on Microsimulation”, Polityka Społeczna 44(1): 25-30.

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.