Location: Eastern Europe
Public Healthcare Expenditures in Transition Countries: Does Government Spending Respond to Public Preferences?
The transition from centrally planned to free-market economies in 1989 initiated a period of social and economic upheaval in post-communist countries, which affected healthcare quality, expenditures, and outcomes. We use data from the Life in Transition Survey (LiTS) to demonstrate that in spite of improvements across various measures of these facets of the healthcare system, it remains the first choice for additional government spending among the public in all countries of the region included in this study. Preferences in priorities for extra budget spending were similar among men and women in respective countries, but the preference for additional healthcare spending was stronger among women than men. The transition countries are compared with Germany and Italy – two Western European LiTs survey participants, countries with higher spending, and better healthcare outcomes.
Introduction
Across the globe, the outbreak of the COVID-19 pandemic has brought a new spotlight to the preparedness of healthcare systems for profound shocks (Anser et al, 2020). Critical care is a particularly costly element of healthcare provision, and thus, under-resourced systems are uniquely susceptible to spikes in mortality resulting from an oversaturation of intensive care units during an epidemiological crisis of this sort. (Fowler et al, 2008; Mannucci et al, 2020) Considering the widespread discussion surrounding health system capacity and the necessity for implementing economically painful lockdowns when those limits are reached, pressure from society to increase public spending may grow even further. With these developments in mind, in this policy paper, we confront past expressions of preferences regarding public expenditures with changes in government spending on healthcare between 2006 and 2017. The analysis draws on the one hand on the data from the Life in Transition Survey (LiTS), and on the other on publicly available data on government expenditures and outcomes.
In the context of preferences for additional public spending, we present a descriptive summary of trends in government expenditures on healthcare in Armenia, Belarus, Estonia, Georgia, Latvia, Lithuania, Moldova, Poland, Russia, and Ukraine. We include Italy and Germany as wealthier Western benchmarks, for which the data became available in the second wave of the survey in 2010. Data on public healthcare spending shows that despite a clear and strong public preference for increased investment in healthcare provision, additional spending as a proportion of total government expenditures between 2006 and 2017 has been moderate in most countries, and even negative in some. It must be underlined that expenditures are not always reflected in healthcare outcomes, quality, and coverage. Issues of efficiency, system design, and underlying health conditions of the population play a significant role in the returns on investment. For instance, the United States has spent drastically more per capita on healthcare than any other country and yet ranked lowest in the Healthcare Access and Quality (HAQ) Index among comparable countries (Fullman et al, 2016). However, due to the focus of the survey on government spending, we emphasize government expenditures on healthcare as a pertinent measure, especially in relation to overall GDP, per capita spending, and the public budget as a whole.
There is mounting evidence that one of the most important elements in the mitigation of COVID-19 mortality is the ability to expand system capacity and acquire the necessary equipment (e.g. respirators, ventilators) while ensuring that there is equitable access to measures for spread prevention (e.g. testing) (Khan et al, 2020; Ranney et al, 2020; Wang and Tang, 2020). The increasing pressure on healthcare systems, coupled with the additional fiscal strain resulting from the economic fallout of the pandemic, could lead to further divergence between public preferences and government spending on healthcare.
Healthcare Systems During the Transition
The ability of transition countries to absorb the risks and short-term economic shocks associated with pivoting from a centrally planned to a free-market economy has had dramatic implications for healthcare systems. Although countries in this region were divergent in terms of underlying health conditions, levels of expenditures, and health outcomes, most of them fell victims to deficient funding and additional health risks associated with the initial increases in poverty that were commonplace (Adeyi et al, 1997)
Compared to other transition countries, Georgia and Armenia faced a sharper economic collapse as well as armed conflicts, which caused scarcity in the availability of public healthcare providers and spikes in out-of-pocket expenses. Belarus was slower in the implementation of economic reforms and faced issues of fiscal sustainability further down the line (Balabanova et al, 2012). However, following this short tumultuous period, countries transitioning away from centrally planned economies have generally invested heavily in healthcare since the early 1990s. In many cases, these investments were facilitated by rapid GDP growth and accompanied by significant improvements in life expectancy. For example, between 1989 and 2012, Latvia, Lithuania, and Poland increased their per capita healthcare expenditures by more than 1,000 PPP per year, with an increase in life expectancy ranging from 1.7 years in Lithuania to 5.8 years in Poland (Jakovljevic et al, 2015). Despite heterogeneous and extensive reforms in many of these countries, as well as mixed results in measurements of efficiency and outcomes, healthcare expenditures consistently rank as the top priority for further government spending among both men and women in each country. This consistency lends itself to further policy considerations.
Preferences for Government Spending in Transition Countries
As is demonstrated by Figure 1, in 2016, healthcare was the most common answer to the question – “Which field should be the first priority for extra government spending?”- for all ten post-transition countries included in our analysis (the other options were: education, housing, pensions, assisting the poor, public infrastructure, the environment, and other). The survey was carried out on a representative sample that covers approximately 1,000+ respondents from each of the 29 countries in wave I and up to 1,500+ respondents from each of the 34 countries in wave III (EBRD: LiTS, 2020). Despite intercountry differences, in 2016 healthcare persisted as the top priority for both men and women in every transition country we studied apart from Belarus. While healthcare remained the top priority on average, men expressed a higher preference for additional investment in education. In the countries where preferences for health were particularly strong, healthcare was the first priority for as many as 53.5% of Latvians, 47.7% of Poles, and 43.9% of Moldovans (Figure 1a). Notwithstanding some fluctuations in scale, these preferences were not only common across countries but also across time, with people expressing very similar preferences in the first two waves of the survey in 2006 and 2010. (See Annex Figure A1 and Figure A2). While healthcare remained a popular choice in Germany and Italy, spending on healthcare as a percentage of GDP was nearly twice that of any transition country in Germany. There, education outweighed healthcare among men and women in both available waves (II and III), while pensions surpassed healthcare among men in the latter wave. In Italy, despite a more comparable level of healthcare spending relative to the transition countries, a drastic shift took place as healthcare fell from being the first priority by a large margin of 24.9 percentage points (pp) in 2010 to becoming the second priority after pensions in 2016. This can likely be attributed to the prominence of pensions as a major political campaign issue following the austerity-driven reforms of 2011 (Alfonso and Bulfone, 2019).
Figure 1: 1a (left) : Preferences for additional government spending, 2016. / 1b (right): Preferences for additional healthcare spending by gender, 2016
Moreover, it is evident that men and women within countries have rather similar preferences, as far as extra government spending is concerned. Not only is healthcare the first priority in all ten transition countries, but their second, third, and fourth choices are also very similar. When digging deeper into the differences that do exist, in every country except for Georgia women had a stronger preference for healthcare than men, and by as much as 8.8 pp, 8.4 pp, 7.8 pp, and 7.9 pp in Latvia, Germany, Belarus, and Russia respectively (Figure 1b). Conversely, in every case except for Georgia and Ukraine, men had a stronger preference for additional spending on education than women, most notably in Armenia – by 7.8 pp, Germany – by 5.7 pp, Lithuania – by 4.6 pp and Poland – by 3.9 pp. It is apparent that despite rapid investment in healthcare over the first two decades of the transition, there remains a widespread desire for further expansion of expenditures in this area.
Trends in Government Expenditures, 2006-2017
Considering the primacy of healthcare as the priority for additional government spending in all ten studied transition countries, we look at trends in aggregate statistics on government expenditures on healthcare over the surveyed period to explore the extent to which these preferences have been reflected in government spending. Taking the most basic measure into account in Figure 2a, i.e. public health expenditures as a percentage of GDP, among the transition countries only Georgia and Estonia have significantly increased their healthcare expenditures, by 1.6 pp and 1.2 pp, respectively. Lithuania, Poland, and Russia saw more moderate increases in the range of 0.6 pp and 0.2 pp. Other countries have remained essentially stagnant, apart from Moldova and Ukraine which saw a notable drop of 0.8 pp. Considering that this measure is sensitive to fluctuations in GDP growth, we also consider public health spending as a proportion of all government expenditures (see figure A3 in the Annex), which is a better indicator of government priorities for additional spending from 2006 until 2017. Georgia was the only transition country with a significant increase in healthcare spending proportional to total government expenditures, nearly doubling it from 5.2% to 9.5%. Belarus, Estonia, Lithuania, Poland have implemented a more moderate redirection of the budget towards healthcare, increasing proportional expenditures by a factor of 1.26, 1.15, 1.21, and 1.21 respectively. In spite of public preferences, Armenia decreased the proportional share of the budget dedicated to healthcare by as much as 2.6 pp, Moldova, Russia, and Ukraine by 1.3 pp, and Latvia by 0.8 pp. Regardless of the direction of the trend, notwithstanding some slight convergence, no transition country spent as much of its budget on healthcare as Italy and Germany. The latter spent nearly two to four times as much on healthcare as a proportion of total expenditures compared to the studied transition countries, and this gap has been widening relative to all of those included in the analysis, apart from Georgia.
Figure 2: Public healthcare expenditures (% of GDP)
While expenditures per capita are less indicative of government priorities in the budget, they are a better comparative measure for assessing the changes in healthcare provision, barring differences in efficiency. This comes with a huge caveat, namely that it is well established in the literature that additional healthcare expenditures often translate into “small to moderate” direct improvements in healthcare quality and outcomes due to inefficient spending or underlying factors (e.g. lifestyle choices, poverty) that are not addressed by investment in the healthcare system itself (Hussey et al, 2013; Self and Grabowski, 2003). Nevertheless, this measure is more likely to translate to an improvement in the quality of care each person receives, and the data paints a more positive picture considering the clear preference of both men and women for higher spending. In Figure 3 we present healthcare expenditures per capita in USD, and apart from Italy and Ukraine, all of the countries have significantly increased spending between 2006 and 2017. While expenditures per capita in transition countries are dwarfed by Germany and Italy, Estonia, Georgia, and Lithuania have more than doubled their expenditures, and Armenia has more than tripled. Belarus, Latvia, Poland, Moldova, and Russia have also significantly increased their per capita spending on healthcare, by factors in the range of 1.54 and 1.91. However, while expenditures per capita is one indicator of improving healthcare quality, it does not identify government priorities and is largely dependent on overall economic growth (Fuchs, 2013; Bedir, 2016).
Figure 3: Health care expenditure per capita, USD
In every country we include, increasing healthcare expenditure per capita is accompanied by advancements in many measures of healthcare outcomes for men and women. Between 2006-2017, life expectancy at birth increased across the board, with men in Russia experiencing the greatest improvement of 7.1 years (Figure 4a). These are promising trends – for women, life expectancy at birth improved by a larger margin in each transition country than in Germany or Italy, and the same can be said for men in every country apart from Armenia. Furthermore, the Healthcare Access and Quality (HAQ) index, which is composed of 32 indicators related to preventable causes of mortality, has improved across all 12 countries between 2005-2016. The change was most notable in Armenia, Belarus, Estonia, and Russia, constituting as much as 8.7, 10.2, 8.9, and 8.9 points out of a hundred, respectively (Figure 4b). These trends indicate convergence in the quality of healthcare as they significantly outpaced improvements in the HAQ index in Italy (3.1 points) and Germany (3.9 points). As of 2016, among the countries of interest, Georgia (67.1 points) and Moldova (67.4) had the lowest scores, while Germany (92.0) and Italy (94.9) scored highest, as could be expected based on healthcare spending measures presented in Figures 2 and 3.
Figure 4: 4a (left): Change in life expectancy, 2006-2017 / 4b (right): HAQ index
However, as presented in Figure 5, there is no clear relationship between the strength of the preference for additional healthcare spending and the scale of expansion in spending. Taking three of the four countries (Armenia, Belarus, and Russia) with the greatest improvement in the HAQ index as an example, there was virtually no change in healthcare spending as a percentage of GDP over the same period. These countries were also different in terms of how strong the preferences were for additional spending on healthcare as the first priority in 2006.
Figure 5: Public preferences and government healthcare spending (% of GDP)
Conclusion
As we have demonstrated in this brief, in the ten post-communist countries for which we have analyzed LiTS data, there was a consistent and common preference for healthcare as the first priority for extra government spending between 2006 and 2016. We also find that in each country except Georgia, on average, women had a stronger preference for additional public healthcare spending, supporting a wealth of literature that suggests that women utilize healthcare services more frequently and spend more out of pocket on healthcare than men (Owens, 2008; Cylus et al, 2011; Williams et al, 2017). However, over the period we study, these preferences have not translated directly into a reallocation of budgetary resources. The countries with the strongest preferences for additional healthcare spending in 2006 did not experience the highest increases in any of the discussed measures of public healthcare expenditures since then.
People living in Italy and Germany chose an increase in public spending on healthcare as their first priority less frequently than residents of post-transition countries. Better understanding these differences requires further research, but there is likely a combination of factors that play into this effect. For one, wealthier Western countries performed better when looking at simple measures of healthcare outcomes such as life expectancy and deaths from non-communicable diseases (WHO, 2020), and hence other priorities may have gained in salience. Furthermore, they allocated a greater proportion of the public budget towards healthcare. This in part stems from the significant challenges associated with the transition following 1989. Healthcare systems in post-communist countries experienced a fiscal shock when joining the global economy, with the loss of centrally controlled price mechanisms causing an increase in the relative prices of healthcare inputs such as medicines and equipment (Obrizan, 2017). This was exacerbated by a shrinking capability of governments to spend more on healthcare related to the general economic shocks at that time and led to the passing over of costs to patients in the form of out-of-pocket expenses (Balabanova, et al. 2012). Although access to healthcare and the quality of that care have improved after the transition (Romaniuk and Szromek, 2016), these have failed to converge towards Western European countries on a number of substantial measures up to this point. Before the commencement of the COVID-19 pandemic, government healthcare spending did not reflect the preferences of the public in any of the ten studied transition countries. The outbreak of the pandemic has not only intensified the pressure on the healthcare system but also brought about a number of negative economic consequences. This combination can be expected to simultaneously increase the strain on the public budget and necessitate difficult decisions of reallocation at a time when fiscal sustainability during a global recession is already being brought under question (Creel, 2020).
References
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- EBRD, 2020. Life in Transition Survey (LiTS). European Bank for Reconstruction and Development.
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- Hussey, P.S., Wertheimer, S. and Mehrotra, A., 2013. The association between health care quality and cost: a systematic review. Annals of internal medicine, 158(1), pp.27-34.
- Mannucci, E., Silverii, G.A. and Monami, M., 2020. Saturation of critical care capacity and mortality in patients with the novel coronavirus (COVID-19) in Italy. Trends in Anaesthesia and Critical Care.
- Jakovljevic, M.B., Vukovic, M. and Fontanesi, J., 2016. Life expectancy and health expenditure evolution in Eastern Europe—DiD and DEA analysis. Expert Review of Pharmacoeconomics & Outcomes Research, 16(4), pp.537-546.
- Obrizan, M., 2017. Does EU membership prevent crowding out of public health care? Evidence from 28 transition countries.
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- Romaniuk, P. and Szromek, A.R., 2016. The evolution of the health system outcomes in Central and Eastern Europe and their association with social, economic and political factors: an analysis of 25 years of transition. BMC health services research, 16(1), p.95.
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Note: Annex included in the attached 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.
The Covid-19 Pandemic and Its Implications for Gender Equality
The Forum for Research on Gender Economics (FROGEE) organizes an online workshop focused on the COVID-19 implications for gender equality, with the focus on the labor market and entrepreneurship. It is the next in the series of online events focused on the economic perspectives on gender equality.
Gender Inequality During a Pandemic
The COVID-19 pandemic affects gender equality in multiple ways, ranging from the implications of the lockdowns on household work distribution to the deeper economic damage to the sectors which disproportionately employ women. These effects might be especially damaging for the countries with limited resources to support their locked economies.
Keynote Speaker and Representatives of the FREE Network
The webinar will start with the keynote presentation of “COVID-19 Inequality Project” from Dr. Teodora Boneva (University of Zurich). To continue the discussion, the representatives of the FREE Network from Armenia, Belarus, Georgia, Latvia, Poland, Russia, Sweden and Ukraine will present the brief overviews of the situation in their respective countries.
The workshop will be organized as part of the Forum for Research on Gender Economics (FROGEE) supported by the Swedish International Development Cooperation Agency (Sida). It is the next in the series of online events focused on the economic perspective on gender inequality. The event is aimed at providing a platform for discussion among academics and policy makers on issues related to broad consequences of socio-economic inequalities.
Program
Register here
About Forum for Research on Gender Economics (FROGEE)
FROGEE initiative is part of the Stockholm Institute of Transition Economics (SITE) umbrella. The aim of the FROGEE initiative is to contribute to the discussion on gender inequality, with a specific focus on the region of Central and Eastern Europe. By highlighting different dimensions of gender inequality and its consequences for socio-economic development, FROGEE aims at bringing the issue of gender equality to the focus of both the general public and policy makers. The project is supported by the Swedish International Development Cooperation Agency (Sida).
Addressing the Covid-19 Pandemic: Policy Responses Across Eastern Europe
The world holds its breath as Covid-19 continues to spread and challenge local health care systems as well as local economies. The focus of international media has mostly been on China and then Western Europe and the US. However, countries around the Baltic Sea, Eastern Europe and the Caucasus differ from the West with respect to their socio-economic development, trade integration, and political systems. The webinar “Addressing the Covid-19 Pandemic in Eastern Europe: Policy Responses Across Eastern Europe” hosted by the the Forum for Research on Eastern Europe and Emerging Economies (FREE) Network on May 28, 2020 aimed to fill this gap in the current discourse and give voice to experts from Latvia, Russia, Georgia, Belarus, Poland, Ukraine as well as Sweden, in order to contextualize their countries’ policy choices and experiences in the crisis. Policy recommendations can only be of preliminary nature at this point of time. Yet, it becomes clear that even though transition countries have fared relatively well during the health crisis, they will not be spared from the ensuing economic crisis and will require policy tools which are adapted to the local context.
Introduction
Less than six months after the outbreak of the Covid-19 crisis in China, the pandemic has spread across the globe. The epicenter has moved from Asia to Europe and the US, and in late May 2020 some voices are warning that it is now shifting towards Latin America. While the world´s eyes have been on Milan and Paris, little has been said about how the new EU member states and countries to the East of the European Union cope with the pandemic. Some observers have claimed the emergence of a new “iron curtain” in the corona crisis; Eastern Europe, the Baltic States and the Caucasus having been relatively unscathed compared to the West. Persisting differences in trade and travel patterns, demographic and socio-economic differences, as well as differences in trust levels could account for such an observation.
Yet, the most recent statistics suggest that this may be a premature interpretation and the overall picture is much more heterogeneous. Infections in Russia seem to be rising quickly, Georgia by contrast has turned out to be one of the top students.
Figure 1: Total confirmed Covid-19 cases vs. deaths per million.
On May 28, 2020, the Forum for Research on Eastern Europe and Emerging Economies (FREE) Network hosted a webinar with its member institutes: BEROC in Belarus, BICEPS in Latvia, CEFIR@NES in Russia, CenEA in Poland, ISET-PI in Georgia, KSE in Ukraine, and SITE in Sweden to discuss how their countries have fared in the corona crisis so far. The webinar provided an opportunity to share experiences and to add some interpretations and insights to the crude statistics, which often become unintelligible in the current overflow of information.
Figure 2: FREE Network Countries.
The webinar started with Torbjörn Becker, director of SITE, introducing recent developments in terms of health statistics in the region and the research being done within the framework of the FREE Network.
SITE on Sweden
Jesper Roine, Professor at the Stockholm School of Economics and SITE, then presented the case of Sweden, the country which – with regards to death rates – has surpassed all other FREE Network countries by far. The Swedish case has been very controversially discussed in international media throughout the pandemic. Yet, the common claim that in Sweden everything was “business as usual” is not true, according to Roine. Compared to its direct neighboring countries Finland, Denmark and Norway, Sweden has chosen a relatively lenient approach to Covid-19, but high schools and universities have moved to distance learning since March and working from home is highly encouraged. Mobility reports show that Swedes have reduced their movement a lot, but less so than their Scandinavian neighbors. Roine confirmed that the Swedish health policy has been dominated by the public health agency, Folkhälsomyndigheten. Even though this is the default option in Swedish law, Roine stressed that this does not mean that the government’s hands are tied.
He presented two preliminary conclusions regarding the impact of the Swedish strategy: first, Sweden’s mitigation strategy has worked relatively well; the public health system is seriously strained but not overwhelmed. Yet, Roine said that the “lack of testing [remained] a mystery”, even for advocates of the current mitigation strategy. Second, in Roine’s opinion the attempt to protect the elderly has failed. The virus has spread to numerous nursing homes and excess death rates indicate that mortality has increased sharply for citizens above 65 years of age, much less for other age groups. Geographically, Stockholm has been the center of the epidemic. Other parts of the country have been affected to a much lesser degree.
BICEPS on Latvia
Sergejs Gubins, Research Fellow at the Baltic International Centre for Economic Policy Studies (BICEPS) presented the Latvian experience of the corona crisis. A small country of about 2 million inhabitants, Latvia currently presents the second lowest Covid-19 mortality rate within the EU. Gubins related this to the Latvian government’s quick and determined policy reaction. After the first cases were reported in early March, schools and universities were closed, public gatherings forbidden, international travel halted, and a two-meter social distance rule imposed. Given the success of this strategy, Latvia has started to loosen its restrictions. A “Baltic Schengen area” was announced very recently and travel among the Baltic states is now possible again. The economic support package announced by the government amounts to 45 percent of GDP and includes a large equity investment in the airline airBaltic as well as important investments in infrastructure. According to Gubins, the current policy discussion focuses on the accessibility and size of help funds, widely deemed insufficient. Furthermore, the economic outlook of the country in terms of unemployment rates and GDP growth is bleak despite its success in containing the virus.
CEFIR on Russia
According to Natalia Volchkova, Director of the Centre for Economic and Financial Research (CEFIR) at the New Economic School in Moscow, Russia has pursued a “standard European strategy” in its fight against Covid-19. Two new hospitals exclusively for Covid-19 patients were created in Moscow, the current epicenter of the pandemic, and nearby. Most money spent on health care went to these new facilities, less was transferred as bonuses to medical workers. Russia has emphasized testing: around 10 million tests were performed; close to 400,000 cases of Covid-19 were confirmed. On May 27, free antibody testing was started in Moscow and is to be extended to other parts of the country. State-financed testing will serve to measure the potential degree of immunization of the population. While cases have started to decline in Moscow, other regions of Russia lag behind and are still expected to peak.
Volchkova stressed the role of the Russian shadow economy, which has been severely hit by the crisis. The size of the informal sector makes it difficult for the Kremlin to pass efficient support packages for the economy. Another policy problem lies in the weakness of the social security net, particularly unemployment benefits are hard to obtain. Therefore, most policy measures have focused on companies. Family allowances are the government’s second heavily used tool, which to Volchkova’s mind is an efficient policy choice. She concluded that the current help measures may already amount to 3 percent of GDP.
ISET-PI on Georgia
As of May 28, 2020, Georgia had only reported 12 corona deaths. According to Yaroslava V. Babych, Lead Economist at ISET Policy Institute in Tbilisi, the key explanation for Georgia’s relative success in the corona crisis is that, as in Latvia, testing started very early. She explained that even before Georgia’s neighbor Iran confirmed an outbreak of Covid-19, passengers’ temperatures were taken at the border crossing. The government in Tbilisi then soon imposed harsh quarantine measures, local quarantines in regional hotspots, a shutdown of public transport, an evening curfew and very high fines. Compliance with the measures was very high. Orthodox Easter celebrations were allowed to take place under strict hygiene measures and did not result in a spike in infection rates.
The country, largely reliant on tourism and agriculture, is now focusing on the economic consequences of the crisis. According to Babych, Georgia holds the ambition to become the first European country to open up to international tourism again from July 1, 2020. The government is also determined to avoid another meltdown of the important construction sector, as happened in 2008 – 2009. However, similar to the Russian case, Babych identified two factors which crucially weaken the Georgian economy: the lack of automatic stabilizers in the form of unemployment benefits and the large informal sector. Policymakers have therefore resorted to monthly cash payments to those who stopped paying income tax around March and fixing prices for specific food products. While the effectiveness of these measures still has to be evaluated, the policy discourse in Georgia has moved on to the socio-economic consequences of the crisis.
BEROC on Belarus
Lev Lvovskiy, Senior Research Fellow at the Belarusian Economic Research and Outreach Center (BEROC), provided an overview of the Belarusian policy measures. According to Lvovskiy, Belarus has a high number of nurses and doctors and a relatively efficient “Soviet style of fighting pandemics”. There have been hardly any restrictions to public gatherings and events, both the Orthodox and the Catholic Easter festivities were maintained, as were soccer games and the national Victory parade. Initially, the official policy was to trace and isolate cases, but this did not prove to be very efficient, supposedly due to poor enforcement. Lvovskiy said that testing is rare which is why statistics on the spread of the virus and its effects remain of questionable quality.
While Belarus disposed of a solid health care system, it was not well prepared economically, which explains why the government has not been very proactive in Lvovskiy’s opinion. The Belarusian industrial production decreased by 7 percent in April 2020 compared to the same month the year before; unemployment has started to increase, yet, there are no significant unemployment benefits. Increasing the height of unemployment pay is the key policy issue under discussion in Minsk but in the absence of international loans, the government´s hands are tied. The issue is urgent: the most recent BEROC survey suggests that 46% of individuals living in urban areas have already seen their income decrease. Lvovskiy’s preliminary conclusion is that the Belarusian policy response to the Covid-19 crisis was not as bad as expected by many international observers: the health crisis has mostly been contained. But like in the Georgian case, the socio-economic implications of the crisis are becoming more pressing now.
CenEA on Poland
Michal Myck, Director of the Centre for Economic Analysis (CenEA) in Szczecin, explained that Poland also successfully avoided a spike in infection rates thanks to a quick policy response. Poland was one of the first countries to impose international travel restrictions and very harsh social distancing measures, yet, infection rates remain higher than in other FREE Network countries. Since the second half of April, most measures have been lifted and the spread of the virus seems under control and concentrated in the region of Silesia.
All limitations were implemented without invoking a state of emergency. Myck suggested that the government may have made this choice because the presidential elections would have been automatically postponed otherwise, an outcome the government wanted to avoid. The elections were eventually postponed, but doubts persist with regards to the constitutional validity of the way this decision was taken. Myck stressed the persisting political uncertainty. Economic policy in Poland has focused on protecting jobs and providing liquidity to enterprises. State loans have been primarily directed to SMEs and will be partly written off, conditional on continued activity and employment. In Myck’s opinion, the economic outcome for Poland will depend on whether investments from and exports to Western Europe quickly resume or not.
KSE on Ukraine
Tymofiy Mylovanov, President of the Kyiv School of Economics and former Minister of Economic Development, Trade and Agriculture, stressed that in the first few weeks of the pandemic, Ukraine enforced harsher policy measures than its neighbors. The lock down was almost complete, with only grocery stores and pharmacies allowed to open. Compliance was high during the first few weeks but then started to decline.
The government allocated 3 percent of GDP to a Covid-19 support fund, there has been a lot of deregulation on the labor market, but the central bank’s key interest rate remains at 8 percent. Pressure for a looser monetary policy increases according to Mylovanov, as GDP has fallen by 1.2 percent and unemployment is expected to reach up to 10 percent by the end of the year.
Mylovanov’s thoughts about Ukraine’s economic prospects are mixed: average salaries continue to grow during the crisis which may be explained by the fact that low-skilled employees get laid off first, suggesting a potentially long-lasting change of the composition of the workforce. At the same time, the political situation is volatile with local elections coming up in October 2020 and public pressure mounting. As Poland, Ukraine did not declare a state of emergency. While Mylovanov thinks that the policy response could have been better, he is optimistic that Ukraine was better prepared to Covid-19 than to previous crises and will not have to resort to international loans.
Preliminary Conclusions
It is too early to draw any definite conclusions, but undoubtedly, a lot can be learned from the very diverse experiences of the corona crisis in the region. The former Soviet countries have a different historical and political legacy than Western European countries and accordingly, have found different ways of handling the crisis. Some have been more successful than their Western neighbors. But even those countries which have not faced a large health crisis have been severely hit economically and are likely to suffer economic hardship in the future.
The lack of a strong tradition of unemployment benefits and automatic stabilizers renders countries like Georgia, Belarus and Russia particularly vulnerable to the economic crisis which will inevitably follow the Covid-19 outbreak. In some countries, the corona shock may also accelerate or trigger political changes. In the view of this, the FREE Network will organize a series of follow-up webinars and briefs on more specific corona-related topics, with the aim of contextualizing statistics and providing a wider picture of the socio-economic consequences and policy implications of the crisis.
Please find a full recording of the webinar below. Updates on further events will be posted on the FREE website and on social media channels (Facebook, Twitter).
List of Speakers
- Jesper Roine, Professor at the Stockholm Institute of Transition Economics (SITE / Sweden)
- Sergejs Gubins, Research Fellow at the Baltic International Centre for Economic Policy Studies (BICEPS / Latvia)
- Natalia Volchkova, Director of the Centre for Economic and Financial Research at New Economic School (CEFIR@NES / Russia)
- Yaroslava V. Babych, Lead Economist at ISET Policy Institute (ISET / Georgia)
- Tymofiy Mylovanov, President at the Kyiv School of Economics (KSE / Ukraine)
- Lev Lvovskiy, Senior Research Fellow at the Belarusian Economic Research and Outreach Center (BEROC / Belarus)
- Michal Myck, Director of the Centre for Economic Analysis (CenEA / Poland)
- Torbjörn Becker, Director of the Stockholm Institute of Transition Economics (SITE)
Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.
Addressing the Covid-19 Pandemic in Eastern Europe
The Covid-19 pandemic is affecting everyone around the globe and leaves none untouched. However, much of the focus in international media has been on the most affected countries and richer countries in East Asia, the European Union and the United States with less attention given to countries around the Baltic Sea, Eastern Europe and the Caucasus.
Since the FREE Network includes research and policy institutes in Belarus (BEROC), Latvia (BICEPS), Russia (CEFIR@NES), Poland (CenEA), Georgia (ISET), Ukraine (KSE) and Sweden (SITE), experts from the FREE Network institutes discuss the regional perspective on the pandemic with examples of very different strategies implemented in the countries concerned.
Speakers
- Jesper Roine, Professor at the Stockholm Institute of Transition Economics (SITE / Sweden)
- Sergejs Gubins, Research Fellow at the Baltic International Centre for Economic Policy Studies (BICEPS / Latvia)
- Natalia Volchkova, Director of the Centre for Economic and Financial Research at New Economic School (CEFIR@NES / Russia)
- Yaroslava V. Babych, Lead Economist at ISET Policy Institute (ISET / Georgia)
- Tymofiy Mylovanov, President at the Kyiv School of Economics (KSE / Ukraine)
- Lev Lvovskiy, Senior Research Fellow at the Belarusian Economic Research and Outreach Center (BEROC / Belarus)
- Michal Myck, Director of the Centre for Economic Analysis (CenEA / Poland)
Chair/Moderator
- Torbjörn Becker, Director of the Stockholm Institute of Transition Economics (SITE / Sweden)
Addressing the Covid-19 Pandemic in Eastern Europe: Policy Responses Across FREE Network Countries
The FREE Network is delighted to invite you to a webinar to share insights and knowledge about how countries around the Baltic Sea, in Eastern Europe and the Caucasus have fared in the Covid-19 pandemic.
The Covid-19 pandemic is affecting everyone around the globe and leaves none of us untouched. However, much of the focus in international media has been on the most affected countries and richer countries in East Asia, the European Union and the United States with less attention given to countries around the Baltic Sea, in Eastern Europe and the Caucasus. Since the FREE Network includes research and policy institutes in Belarus (BEROC), Latvia (BICEPS), Russia (CEFIR@NES), Poland (CenEA), Georgia (ISET), Ukraine (KSE) and Sweden (SITE), we are uniquely placed to provide a comprehensive regional perspective on the pandemic with examples of very different strategies implemented in the countries concerned.
Many of the countries in Eastern Europe and the Baltic region differ from Western Europe in terms of successfully limiting infections and deaths resulting from the pandemic so far. At the same time, the situation in Russia has worsened rapidly over the last few weeks, despite a lockdown having been imposed. The fatality rate and the number of infections have also been high in Sweden, where, in contrast to other Baltic countries, only relatively lenient restrictions have been imposed on the population. In the same vein, the Belarusian government has taken few, mild measures in response to the pandemic, but the mortality rate seems to have remained rather low.
This webinar will provide a first overview of how countries in the region have fared in the pandemic and allow for a better understanding of what governments have done, how people have responded, how other countries are being portrayed in the national media, and what the current discussions focus on.
Speakers
- 🇸🇪 Jesper Roine, Professor at the Stockholm Institute of Transition Economics (SITE / Sweden)
- 🇱🇻 Sergejs Gubins, Research Fellow at the Baltic International Centre for Economic Policy Studies (BICEPS / Latvia)
- 🇷🇺 Natalia Volchkova, Director of the Centre for Economic and Financial Research at New Economic School (CEFIR@NES / Russia)
- 🇬🇪 Yaroslava V. Babych, Lead Economist at ISET Policy Institute (ISET / Georgia)
- 🇺🇦 Tymofiy Mylovanov, President at the Kyiv School of Economics (KSE / Ukraine)
- 🇧🇾 Lev Lvovskiy, Senior Research Fellow at the Belarusian Economic Research and Outreach Center (BEROC / Belarus)
- 🇵🇱 Michal Myck, Director of the Centre for Economic Analysis (CenEA / Poland)
Chair/Moderator
- Torbjörn Becker, Director of the Stockholm Institute of Transition Economics (SITE / Sweden)
The webinar opens a series of online discussions aiming to provide a regional overview, updates as well as in-depth analysis of specific topics related to the Covid-19 pandemic. Follow-up webinars will focus on such topics as regional differentiation within countries, effects on the environment, the gender dimension of the pandemic, and the analytical aspects of Covid-19 statistics.
Date: Thursday, May 28th, 15.00-16:30 CET
Location: Zoom webinar, link to be provided for registered participants
RSVP: The number of participants for the webinar is limited, therefore we invite to register as soon as possible, but no later than May 25, 23:59 CET.
Registration link: Please click here to register.
Quota or not Quota? On Increasing Women’s Representation in Politics
All over the world, politics remains one of the most male-dominated spheres in society, in spite of the substantial progress made in achieving more gender balance in the last decades. A large number of countries worldwide have adopted some form of electoral gender quotas to accelerate this progress, but the empirical evidence on the effectiveness of such policy tools is mixed.
In this policy brief, we first discuss the potential impacts of gender quotas. Quotas may (a) increase women’s representation in political positions, or decrease it, if there are backlash effects; (b) improve or worsen the quality of selected politicians; and (c) bring about important policy changes, given the wealth of empirical evidence of gender differences in policy preferences, with, for instance, women appearing more concerned about health and the health system than men. We then provide an overview of the empirical evidence on quota impacts in the economics literature, and contextualize these findings with a special focus on the countries of the FREE (Forum for Eastern Europe and Emerging Economies) network. We end with policy advice on the design of gender quotas in the domain of politics.
Quotas in the World and in the FREE Network Region
According to the International Institute for Democracy and Electoral Assistance (IDEA), 127 countries worldwide currently use quotas with the goal of increasing the presence of women in governmental institutions. Broadly speaking electoral gender quotas can be classified into seat reservation and candidate lists quotas. The former limit the competition for a governmental seat to women, whereas the latter prescribe a minimum representation of women in electoral lists. Candidate quotas can be legislated, i.e. they constitute a legal requirement, or voluntary, whereby parties adopt quotas in their internal statute.
Table 1: Share of women in national parliaments (in %) FREE Network countries
The popularity of gender quotas is, however, not uniformly distributed across the globe. For example, while political gender representation is far from equal in most countries of FREE network region (see e.g. table 1), out of these countries only Armenia, Poland and Sweden dispose of electoral gender quotas (see figure 1).
Figure 1: Gender quotas in the FREE Network region
Since 2011, Armenia has had a legislated candidate quota of 40% for its National Assembly. This quota replaced a previous quota of 15%, passed in 2005 – one of the requirements to enter the Council of Europe (Itano 2007). Poland has also had a legislated candidate quota of 35% for the Lower House (the Sejm) as well as for subnational elections since 2011 (IDEA 2020; World Bank 2019). Sweden, the fourth most gender equal country worldwide according to the 2020 ranking of the World Economic Forum, and ninth in the women’s political empowerment sub-index, does not have legislated quotas. However, political parties themselves have decided to adopt voluntary quotas: the ruling Social Democrats use a zipper system in which the two sexes alternate on party lists; the Left Party has a minimum 50% quota for women, while the Green Party has a 50% gender quota (IDEA 2020). The Swedish Moderates, Liberals, Center parties and the extreme-right Swedish Democrats currently do not have gender quotas. The Swedish Democrats entered the parliamentary elections in 2018 with the highest share of male candidates observed among the Swedish parties – 70% (SVT 2020; SVT 2018).
In spite of their popularity among policy-makers worldwide, the merits of quotas are still largely debated. Opponents of gender quotas are often concerned about their effects on the meritocratic selection of politicians. Another common criticism is that nominating more female candidates may not automatically translate into more women in powerful positions. For instance, the shares of women in the Armenian and the Polish Parliament are 24 and 29% respectively (World Bank 2019), well below the national legislated candidate quota (it bears noting, however, that these shares have been growing over the last ten years, as shown in Figure 3). The respective shares of female ministers are 7% and 23% (Government of the Republic of Armenia 2020; OECD 2020,).
Figure 2: Share of women in national parliaments (in %)
Why is increasing women’s political participation considered a policy objective of utmost importance in many countries worldwide, and how can gender quotas help achieving it? In this brief we contribute to the ongoing debate on the merits of gender quotas, by offering an overview of their potential effects and by critically reviewing the empirical evidence from the most recent academic literature.
Which Effects Can We Expect From Quotas?
The primary objective of electoral quotas is to reduce gender gaps in representation in electoral lists and in the targeted representative institutions. Quotas can also activate trickle-up mechanisms, whereby gender gaps decrease in positions that are not directly targeted by the quota. The trickle up effect occurs, for instance, if women’s networks within parties or in governmental organizations help the promotion of female leaders. Furthermore, gender quotas may help to improve the quality of politicians. As noted by, among others, Bertrand (2018), a society likely improves the quality of its leaders when it enlarges the pool where those leaders are chosen from. A critical underlying assumption in this line of argument is that there are no major differences in the distribution of “political talent” between women and men. However, even with equal distribution of political talent, if the supply of women willing to enter politics is very limited and there are not enough qualified women to fill the quota positions, the average quality of a “quota” politician may end up being lower than that of her colleagues – and quotas may have the unintended consequence of reinforcing stereotypes against female politicians. This, in turn, may ultimately imply lower promotion rates of women to key positions and/or worse electoral support of female politicians, thereby undermining women’s political empowerment at various levels.
One of the most popular arguments in favor of the adoption of gender quotas is that women’s political preferences may not be adequately represented by male-dominated political bodies. Gender quotas, by increasing female representation among politicians (and possibly among voters), can thus help closing a potential gap in substantial representation. A large body of literature has documented gender differences in policy preferences, by considering, e.g. the size and composition of government spending after the expansion of suffrage to women (Kenny and Lott 1999), voting records in referenda (Funk and Gathmann 2015), survey data (see, e.g. Bagues and Campa 2020), or women’s contributions to legislative amendments (Lippmann 2020). In this historical moment when the world is plagued by a pandemic, the most important gender difference to emphasize seems to be in the area of health. Exploiting the federal referenda held between 1981 and 2001 in Switzerland, Funk and Gathmann (2015) show that Swiss women are more likely to be in favor of health, unemployment and social security spending than men, and less likely to be in favor of military spending. Similarly, based on survey data from a sample of nearly 60,000 Spanish residents, Bagues and Campa (2020) find that women are significantly more likely than men to report that the health system is one of the problems that affects them the most. Likewise, Lippmann (2020) analyzes the contribution of French legislators to amendments and finds that women are 25% more likely than men to initiate at least one amendment related to health issues. This gender difference regarding health policy is also visible in the European Social Survey (ESS), which covers a representative sample of the population of 19 European countries. When asked to give a general opinion on the current state of health services in their country, female respondents turn out to be significantly less satisfied than male respondents on average. The difference is statistically significant, albeit not particularly large (12% of a standard deviation) and holds in most of the countries included in the ESS. One potential reason behind this noticeable difference in satisfaction with health services is that women also report lower health status than men (10% of a standard deviation and statistically significant).
Figure 3: Self-reported satisfaction with the current state of national health services
A natural question to ask in spring 2020 is whether a world with more women among political leaders would have had health systems better equipped to face a pandemic. While we will never have a definite answer to this question, studies of the impacts of gender quotas can help assessing whether the gender of political leaders matters for policy decisions.
What is the Empirical Evidence on the Effects of Quotas?
Quotas increase women’s representation in electoral lists, but only when they are binding and appropriately enforced (i.e. the cost for parties of not complying with the quota must be high enough). Yet, when quotas are limited to the composition of electoral lists, the strategic positioning of female candidates in “not-winning” positions tends to undermine the quota effect on the election of women (see Esteve-Volart and Bagues, 2012, and Bagues and Campa, 2020). This seems to be the case of Poland: According to Gwiazda (2017), the lack of a placement mandate obliging parties to put women in the top positions of a party list, is indeed one reason why the Polish quota has not translated into a higher share of female representatives.
The evidence on the spill-over of quotas to higher positions is mixed. Two studies find that candidate quotas in Italy and Sweden increased the probability that women reach leadership positions, above and beyond the quota mandate (De Paola et al. 2010, O’Brien and Rickne, 2016). Bagues and Campa (2020), however, fail to establish similar evidence in Spain.
In studies of developing countries, Beaman et al. (2009) find that seat reservation in India improved male voters’ perception of female leaders, as well as women’s probability of being elected once the reservation was removed. Conversely, experimental evidence from Lesotho suggests that, if anything, a quota-mandated female representative reduces women’s self-reported engagement with local politics (see Clayton, 2015).
An increasing number of studies also examine the quota impact on the quality of the elected politicians, proxied by different measures. Baltrunaite et al. (2014) find that a gender quota improved the average education of elected politicians in Italy, and Besley et al. (2017) provide similar evidence looking at a measure of labor market performance in Sweden. Bagues and Campa (2020), studying candidate quotas in Spain, fail to find an improvement in the quality of politicians, measured by their education and electoral performance; however, their assessment is that the quota did not decrease quality either, contrary to the expectation of many quota opponents. However, Chattopadhyay and Duflo (2004) find that, in the context of seat reservations in rural India, quota candidates are less educated.
Finally, the evidence on whether gender quotas bring about policy change is scarce. Chattopadhyay and Duflo (2004) show that the reservation of the most important seat in Indian villages brought policy choices closer to women’s preferences. In Spanish municipalities, Bagues and Campa (2020) fail to find significant increases in the share of “female expenditures” (issues women have been found to care more about than men, based on surveys) over two legislatures when candidate quotas were used.
Conclusion
Gender quotas are a popular policy tool used to close existing gender gaps in political empowerment, which are large in many countries in the FREE Network. A growing economics literature on the impacts of gender quotas helps assessing what objectives policy-makers may be pursuing when they adopt them, and under which conditions these objectives can be achieved. There is a number of lessons to be learned from this literature.
First, the design of the quota is crucial for it to achieve its primary objective, which is to increase women’s presence in the targeted political positions. Placement mandates, for instance, are particularly important in the design of candidate quotas to avoid that women are strategically placed at the end of the ballot. Second, policy-makers need to take the local context into account. Whether a candidate quota can generate spill-overs to higher-level positions likely depends on the degree of centralization of political parties for instance; where party leaders are very powerful, we may be less likely to see an increase in the share of female leaders following the adoption of a candidate quota. Third, the question when gender quotas successfully bring about policy change needs additional investigation. Different factors likely play a role, such as: the type of position targeted by the quota (legislative or executive, local or national, etc.); the extent of the increase in representation achieved; the magnitude of the gender difference in preferences; the type of decision-making process prevailing (majority voting or unanimity); how the selection of politicians is affected by the quota; and how women’s influence on policy is measured. Studies that systematically vary some of these factors will improve our understanding of this area of research. Fourth, there is no overwhelming evidence of negative effects of gender quotas in a number of dimensions, at least over a medium-term horizon.
The case for adopting and testing different forms of gender quotas, perhaps in combination with additional measures, is therefore relatively strong. Overall, our assessment is that quotas will have to remain in policy-makers’ toolbox for some time if the worldwide effort to close the persisting gender gaps in political empowerment is to continue.
References
- Bagues, Manuel; and Pamela Campa, 2020. “Can Gender Quotas in Candidate Lists Empower Women? Evidence from a Regression Discontinuity Design.” CEPR Discussion Paper No. 12149.
- Bagues, Manuel; Mauro Sylos-Labini; and Natalia Zinovyeva, 2017. “Does the Gender Composition of Scientific Committees Matter?”. American Economic Review, 107(4), pp. 1207–1238.
- Baltrunaite, Audinga; Piera Bello, Alessandra Casarico; and Paola Profeta, 2014. “Gender Quotas and the Quality of Politicians”, Journal of Public Economics, 118, pp. 62-74.
- Beaman, Lori; Raghabendra Chattopadhyay; Esther Duflo; Rohini Pande; and Petia Topalova, 2009. “Powerful Women: Does Exposure Reduce Bias?”. Quarterly Journal of Economics, 124(4), pp. 1497–1540.
- Besley, Timothy; Olle Folke; Torsten Persson; and Johanna Rickne, 2017. “Gender Quotas and the Crisis of the Mediocre Man: Theory and Evidence from Sweden”, American Economic Association, 107(8), pp. 2204-2242.
- Bertrand, Marianne, 2018. “Coase Lecture – The Glass Ceiling”. Econometrica 85, pp. 205-231.
- Chattopadhyay, Raghabendra and Esther Duflo, 2004. “Women as Policy Makers: Evidence from a Randomized Experiment in India”. Econometrica, 72, pp. 1409-1443.
- Clayton, Amanda, 2015. “Women’s Political Engagement Under Quota-Mandated Female Representation: Evidence From a Randomized Policy Experiment“. Comparative Political Studies, 48(3), pp.333 –369.
- Dahlerup, Drude (Ed.), 2006. “Women, Quotas and Politics”. Routledge, Taylor & Francis Group.
- De Paola, Maria; Vincenzo Scoppa; and Rosetta Lombardo, 2010. “Can gender quotas break down negative stereotypes? Evidence from changes in electoral rules”. Journal of Public Economics 94 (5), pp.344-353.
- Esteve-Volart, Berta; and Manuel Bagues, 2015. “Politicians’ Luck of the Draw: Evidence from the Spanish Christmas Lottery”, Journal of Political Economy, 124(5), pp. 1269-1294.
- Funk, Patricia; and Christina Gathmann, 2015. “Gender gaps in policy making: evidence from direct democracy in Switzerland”. Economic Policy, 30 (81), pp. 141–181.
- Government of the Republic of Armenia, 2020. Structure.
- Gwiazda, Anna, 2017. “Women in parliament: assessing the effectiveness of gender quotas in Poland”. Journal of Legislative Studies, 23(3), pp. 326-347.
- IDEA (Institute for Democracy and Electoral Assistance), 2020. Gender Quota Database.
- Itano, Nicole, 2007. “Quota Law Puts More Women in Armenia’s Election“. Women’s eNews.
- Kenny, Lawrence W. and John R. Lott, 1999. “Did Women’s Suffrage Change the Size and Scope of Government?”. Journal of Political Economy, 207, pp. 1163- 1198.
- Lippmann, Quentin, 2020. “Gender and Lawmaking in Times of Quotas.”
- O’Brien, Diana and Johanna Rickne, 2016. “Gender Quotas and Women’s Political Leadership”. American Political Science Review. 110(1), pp. 112-126.
- OECD (2020), Women in politics (indicator).
- SVT, 2018. “Här är partierna som har högst och lägst andel kvinnor bland kandidaterna”.
- SVT, 2020. “Ny mätning: SD Sveriges största parti“.
- World Bank Data, 2019. “Proportion of seats held by women in national parliaments (%)”.
- World Economic Forum, 2019. “Global Gender Gap Report 2020”. ISBN-13: 978-2-940631-03-2.
Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.
The Shadow Economy in Russia: New Estimates and Comparisons with Nearby Countries
We apply a new method to measure the shadow economy in Russia during the period 2017-2018 and provide evidence on the main factors that influence involvement in the shadow economy. Drawing on a methodology developed by Putnins and Sauka (2015), we estimate that the size of the shadow economy in Russia is 44.7% of GDP in 2018. This is similar to the size of the shadow economy in countries such as Kyrgyzstan, Kosovo, Ukraine, and Romania, but higher than the level of the Baltic countries. Our findings are largely consistent with other less direct approaches for estimating the shadow economy. An advantage of our approach is that it can provide more detailed information on the components of the shadow economy.
Introduction and Approach to Measuring the Shadow Economy
The aim of the Shadow Economy Index, which has now been estimated in a number of countries, is to measure the size of shadow economies and explore the main factors that influence participation in the shadow economy. We use the term “shadow economy” to refer to all legal production of goods and services produced by registered firms that is deliberately concealed from public authorities (OECD, 2002; Schneider, Buehn and Montenegro, 2010).
The Shadow Economy Index draws on a survey-based methodology developed by Putnins and Sauka (2015). It combines estimates of business income that has been concealed from authorities, unregistered employees, and ‘envelope’ wages. The approach exploits the fact that entrepreneurs and business leaders are in a unique position in that they have knowledge about the amount of business income that is concealed from authorities, the number of employees that work for them unofficially, and the extent to which they pay wages informally to avoid taxes.
The challenge for such methods is to elicit maximally truthful responses about these sensitive issues, otherwise, the size of the shadow economy will be underestimated. To address this challenge, we use a number of survey and data collection techniques shown in previous studies to be effective in eliciting more truthful responses (e.g. Gerxhani, 2007; Kazemier and van Eck, 1992; Hanousek and Palda, 2004). While the full details can be found in Putnins and Sauka (2015), they include confidentiality with respect to the identities of respondents, framing the survey as a study of satisfaction with government policy, phrasing misreporting questions indirectly about “similar firms in the industry” rather than the respondent’s actual firm, gradually introducing the most sensitive questions after less sensitive questions, excluding inconsistent responses, and controlling for factors that correlate with a potential untruthful response such as tolerance towards misreporting.
The Index measures the size of the shadow economy as a percentage of GDP. Computing the Index involves three steps:
- (i) estimate the degree of underreporting of employee remuneration and underreporting of firms’ operating income using the survey responses;
- (ii) estimate each firm’s shadow production as a weighted average of its underreported employee remuneration and underreported operating income, with weights reflecting the proportions of employee remuneration and firms’ operating income in the composition of GDP; and
- (iii) calculate a production-weighted average of shadow production across firms.
The survey about shadow activity in Russia from 2017 to 2018 was conducted between February and March 2019. We use random stratified sampling to construct samples that are representative of the population of firms in Russia drawing on the official company register and covering all regions in Russia. In total, 500 phone interviews were conducted with owners, directors, and managers of companies in Russia. We use the same methodology to collect data in other countries, which we compare with Russia, conducting a minimum of 500 interviews in each country.
Size of the Shadow Economy in Russia and Nearby Countries
The estimated size of the shadow economy in Russia is 44.7% of GDP in 2018. Our estimates suggest that the year before, in 2017, the shadow economy was slightly larger with 45.8% of GDP, although the annual change is not statistically significant. For comparison with nearby countries, using the same approach, high levels of shadow economy are also found in Kyrgyzstan (44.5% of GDP in 2018), Kosovo (39.5% of GDP in 2018), Ukraine (38.2% of GDP in 2018), and Romania (33.35% of GDP in 2016), but considerably lower levels are found in the Baltic countries, especially Estonia (16.7% of GDP in 2018). See Table 1 for the full set of estimates.
The estimates using our direct micro-level approach to measuring the shadow economy are largely consistent with other less direct approaches for estimating the size of the shadow economies, such as Schneider (2019). An advantage of the direct micro-level approach is that it is able to provide more detailed information on the components of the shadow economy, which we turn to next.
Components and Determinants of the Shadow Economy in Russia
We find that envelope wages and underreporting of business profits stand out as the two largest components of the Russian shadow economy. Underreporting of salaries or so-called ‘envelope wages’ in Russia are approximately 38.7% of the true wage on average in 2018, whereas approximately 33.8% of business income (actual profits) are underreported. Unofficial employees in Russia as a percentage of the actual number of employees are estimated 28.2% in 2018.
Some companies in Russia, rather than simply concealing part of the income or employees, are completely unregistered and therefore also contribute to the shadow economy. We estimate that such companies make up 6.1% of all enterprises in Russia.
Our findings also suggest that there is a very high level of bribery in Russia: the magnitude of bribery (percentage of revenue spent on ‘getting things done’) is estimated to be 26.4%, whereas the percentage of the contract value that firms typically offer as a bribe to secure a contract with the government in Russia is 20.6% in 2018. We also find that more than one-third of companies in Russia pay more than 25% of the revenue or contract value in bribes.
We find that the size of the shadow economy in all sectors of the Russian economy is close to 40% with somewhat higher levels in the construction and wholesale sectors, controlling for other factors. Using regression analysis, we find that entrepreneurs that view tax evasion as a tolerated behaviour tend to engage in more informal activity, as do entrepreneurs that are more dissatisfied with the tax system and the government. This result offers some insights into why the size of the shadow economy in Russia is so large – it is at least in part due to relatively high dissatisfaction of entrepreneurs with the business legislation and the government’s tax policy. We also find some evidence that higher perceived detection probabilities and, in particular, more severe penalties for tax evasion reduce the level of tax evasion, suggesting increased penalties and better detection methods as possible policy tools for reducing the size of the shadow economy.
Finally, while firms of all sizes participate in the shadow economy, we find that younger firms tend to do so to a greater extent than older firms. The results support the notion that young firms use tax evasion as a means of being competitive against larger and more established competitors.
Acknowledgments
This research was supported by a Marie Curie Research and Innovation Staff Exchange scheme within the H2020 Programme (grant acronym: SHADOW, no: 778118).
References
- Gerxhani, K. (2007). “Did you pay your taxes?” How (not) to conduct tax evasion surveys in transition countries. Social Indicators Research 80, pp. 555-581.
- Hanousek, J. and Palda, F. (2004). Quality of government services and the civic duty to pay taxes in the Czech and Slovak Republics, and other transition countries. Kyklos 57, pp. 237-252.
- Kazemier, B. & van Eck, R. (1992). Survey investigations of the hidden economy. Journal of Economic Psychology 13, pp. 569-587.
- Lechmann, E. and D. Nikulin (2017). Shadow Economy Index in Poland. Gdansk University of Technology, Poland: Gdansk.
- Lysa, O. et al. (2019) Shadow Economy Index in Ukraine. SHADOW: an exploration of the nature of informal economies and shadow practices in the former USSR region. Kyiv International Institute of Sociology, Ukraine: Kyiv.
- Mustafa, I., Pula J.S., Krasniqi, B., Sauka, A., Berisha, G., Pula, L., Lajqui, S. and Jahja, S. (2019) Analysis of the Shadow Economy in Kosova. Kosova Academy of Sciences and Arts, Kosova: Pristina.
- OECD, 2002. Measuring the Non-Observed Economy: A Handbook. OECD, Paris, France.
- Putnins, T.J. and Sauka, A. (2019). Shadow Economy Index for the ‘Baltic Countries 2019-2018. SSE Riga: Riga, Latvia.
- Putnins, T.J., A. Sauka and A. Davidescu (2020, forthcoming). Shadow Economy Index for Moldova and Romania, 2015-2018. SSE Riga, National Scientific Research Institute for Labour and Social Protection.
- Putnins, T.J. and Sauka, A. (2015). Measuring the shadow economy using company managers. Journal of Comparative Economics 43, pp. 471-490.
- SIAR (2019). Shadow Economy Index for Kyrgyzstan. SHADOW: an exploration of the nature of informal economies and shadow practices in the former USSR region. SIAR research and consulting, Kyrgyzstan: Bishkek.
- Schneider, F. (2019) Calculation of the Size and Development of the Shadow Economy of 35 Mostly OECD Countries up to 2018. Unpublished manuscript.
- Schneider, F., Buehn, A. and Montenegro, C. (2010). New estimates for the shadow economies all over the world. International Economic Journal 24, pp. 443-461.
Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.
Women at the Top of the Income Distribution: Are Transition Countries Different?
This policy brief reviews recent research on women at the top of the income distribution. The overall trend across a number of countries is that, while women are still a minority (and more so the closer to the top one moves), their share in top income groups has steadily increased since the 1970s. Detailed data from Sweden suggests that most of this rise is due to women increasingly earning high labor incomes (rather than capital becoming more important). It also shows that there are important differences between top income men and women, especially with respect to family circumstances. Comparing preliminary results from former Soviet and Eastern European countries indicates that there are, on average, more women at the top of the income distribution in these countries. On the other hand, the average time trend indicates that the share of women in top groups is falling. The preliminary results also indicate considerable heterogeneity across countries. These preliminary results require more detailed study, as does the question to which extent the relatively strong representation of women at the top of the income distribution reflects the “economic power” of women in the region.
The Gender Aspect of Rising Top Shares
Rising inequality has received a lot of attention in the policy debate as well as in the academic literature over the past decade. A particular feature of this discussion has been the increased concentration of both wealth and income in top groups. The summary of the World Inequality Report 2018 starts by stating that “The top 1% has captured twice as much of global income growth as the bottom 50% since 1980”. Such facts have, in turn, brought a lot of attention to the characteristics of top groups. What is driving their income growth? What is their income composition? Why have top shares increased so much in recent decades? (see, e.g., Roine and Waldenström, 2015, for an extensive overview, or Roine, 2016, for a brief summary).
However, one aspect which has received relatively little attention is that of gender. This may seem a little surprising. In a time when gender dimensions are often acknowledged as being important, one would expect that questions about the gender composition of top groups would also be of interest. If we know that top income shares are increasing, what is the gender composition of these groups? How has this changed over time?
This brief outlines some recent results on these questions and also points to some preliminary findings about a potential contrast between Western countries and (former) transition countries.
Evidence from Sweden, 1971-2017
Sweden is one of the few countries having had independent taxation of all taxpayers for a long period of time, allowing for a thorough analysis of the gender composition of top income groups. After having had joint taxation for married couples for most of the 20th century, and a short period of the option to be taxed independently even if married, Sweden switched to fully independent taxation in 1971. In a recent paper Boschini et al. (2020) study developments of men and women in top income groups in Sweden using detailed registry data on the full population for the almost 50-year period since.
The study finds a number of interesting results. First, it is evident that the share of women in top income groups has increased significantly, yet women remain clearly underrepresented, and more so the higher up in the distribution one moves. Figure 1 below shows the basic development over time for three top groups: the top 10 (P90-100), the top 1 (P99-100), and the top 0.1 group (P99.9-100) in the total income distribution and the labor income distribution respectively.
Figure 1. Share women in top groups in Sweden.
Besides showing the general development comparing the two panels also reveals a subtler point: especially in the earlier decades and in the very top group (the top 0.1 group), there were substantially more women at the top of the total income distribution than at the top of the labor earnings distribution. In the 1970s and 1980s, the share of women in the top 0.1 group of the total income distribution is about two to three times as large as in the labor earnings distribution. Put differently, this means that in the past, to the extent that there were any women at the very top, they were mainly there thanks to capital incomes. Over time this changes and detailed analysis in the paper shows that the growth of the share of women in top groups is driven by an increasing share of high-income women in the labor income distribution.
While it seems that top income men and women have converged in terms of income composition and observable individual characteristics, the one area that still stands out as being markedly different is partner income. Figure 2 shows that top income women are much more likely to have partners who are also in the top of the income distribution. Even if the trend indicates convergence, large differences remain. Out of the top 1 women who are married, 70% have a partner who is at least in the top 10 (and about 30% are also in the top 1). For married top 1 men, only 30% have a partner who is in the top 10, and only a couple of percentage points are in the top 1. Part of this is, of course, a reflection of there being fewer women in top groups, but this is far from explaining all the difference (See Boschini et al., 2020 for more details).
Figure 2. Share of top income partners in Sweden.
This is of course far from conclusive, but it points in the direction of family circumstances being a potential factor for explaining the relative absence of women in top income groups. Having a partner with a top (income) career is likely to be more demanding (for both parties) and such couples are much more common among top income women than men.
Several strands of research connect to this: for example, Fisman et al. (2006) find, among other things, that men are significantly “less likely to accept a woman who is more ambitious than he”. Also, work by Bertrand et al. (2015), on the impact of gender identity suggest that there is a social norm prescribing that men should earn more than women, which creates a discontinuity in the distribution of women’s contribution to total household income at 50 % (although Hederos Eriksson and Stenberg (2015) and Zinovyeva and Tverdostup (2018) find alternative explanations for this observation). Folke and Rickne (2020) find that women who are elected to high political office in Sweden face a higher probability of divorce (while this is not the case for men). Furthermore, according to the World Values Survey, close to 40% of Americans as well as Europeans agree with the statement “(i)f a woman earns more money than her husband, it’s almost certain to cause problems”. Taken together, findings like these suggest that, even in relatively progressive countries, social norms may contribute to women shying away from entering career paths leading to top incomes.
What About Other Countries?
Even though the Swedish data is unusually detailed, it is certainly not the only country where individual tax data exist. Atkinson et al. (2018) calculate the share of women in top groups for eight countries over time periods when individual tax data exist. Figure 3 puts their results next to those from Sweden. The resulting picture shows a remarkably similar development across countries and over time. The share of women in the top 10 has approximately tripled since the 1970s, from around 10% to around 30%. For the top 1 group, the level is slightly lower, but the relative increase is similarly large, from slightly above 5% to around 20%.
Figure 3. International comparison.
Bobilev et al. (2019) explore the extent to which Luxemburg Income Study (LIS) data can be used to shed light on the presence of women at the top of the income distribution. Their findings point to a similar trend across a broader set of countries. Even though the main analysis has to be limited to the share of women at the top of the labor income distribution (since the possibilities to separate out individual capital incomes is limited), the picture in terms of the share of women in top groups is surprisingly similar across the 28 countries for which sufficient data exists from around 1980 until today. The overall finding is that the share of women in the top 10 group increases from about 10% around 1980 to just below 30% today.
To the extent that LIS data allows us to look at partners and family circumstances, the data shows a consistent pattern of asymmetries between top income men and women similar to that in Sweden found by Boschini et al. (2020). Having a partner and having children are positively associated with being in top income groups for men, but negatively associated for women (even though these differences have decreased over time). Also, top income men are likely to have partners who are not in the top of the income distribution, while this is not the case for top income women. Understanding patterns like these and the underlying channels is likely to contribute to our comprehension of the remaining differences in top income shares between men and women.
Are There Differences Between “East and West”?
A particularly interesting pattern in the LIS data is the difference that emerges when contrasting transition countries to Western countries.
As has often been pointed out, the Soviet Union and many of the countries in Eastern and Central Europe were, at least in some dimensions, forerunners in terms of promoting gender equality (e.g., Brainerd, 2000; Pollert, 2003; Campa and Serafinelli, 2019). This was mainly due to the high participation of women in the labor market as well as the (officially) universal access to basic health care and education.
However, some scholars have suggested that not all aspects of gender equality were as advanced in the countries in the Soviet Union and in Central and Eastern Europe (Einhorn, 1993; Wolchik and Meyer, 1985). Even though women were highly integrated in the labor market, they were still expected to take care of child rearing and housework at the same time (UNICEF, 1999). The gender pay gap and gender segregation in the labor market was also similar to levels found in OECD countries. In addition, despite the high number of women in representative positions in communist party politics, women were rarely found in positions of real power in the political sphere (Pollert, 2003).
Looking just at average values (in the labor income distributions), there are clear differences between East and West in top groups. The share of women among the top earning groups was considerably higher in some former Soviet countries during and after transition. However, the shares of women in top income groups have been converging in East and West.
Figure 4. Share of women in the top 10 / top 1 income groups, East vs. West.
An analysis of the situation at the country level, provides a more complex picture. Figure 5 clearly indicates that the total representation of women in the top 10 income group has been higher in Eastern European countries than in the West (the pattern is similar for the top 1). However, while the share of women in top income groups has consistently increased in Western countries, the developments for women are much less homogenous in Eastern Europe (being below the diagonal indicates a higher share of women in the top 10 in 2005-2020 as compared to 1990-2005).
In Estonia, Slovakia and Poland, women are less likely to be part of the top income group in the period from 2005 to 2020 than they were in the years directly following transition. Considering that the most recent family policies in Poland have been shown to discourage female labor supply (Myck, Trzciński, 2019), this is maybe not so surprising.
Figure 5: Share of women in top 10 income group by country.
The share of women in the top 10 income group in Estonia declined from an astonishingly high 53% in 2000 to about 31% in 2013, which, admittedly, is still high compared to the corresponding average rate for Western countries (28%). Women in Russia, Hungary, Slovenia and the Czech Republic, by contrast, are more likely to be among the top earners in the period from 2005 to 2020 than they were between 1990 and 2005. Moreover, among all the countries in our sample, more recently, Slovenia is the country with the highest share of women in the top 10 of income earners (44% in 2007); Slovenian women seem to have gained grounds even after transition.
How come the representation of women in top income groups remains high (or even increases) in some transition countries but decreases in others? What is the role played by policy and regulation and what role can be attributed to social norms, family circumstances and institutions such as childcare? May economic growth have led to women dropping out of the labor force or never entering it to do care work, even when they had been or potentially could have been part of top income groups? What would be the impact of adding capital incomes to the picture?
Conclusion
Looking across a large number of countries, women seem to have increased their presence in top income groups since the 1970s. This has mostly been driven by women increasingly having high paying jobs. A preliminary look at LIS data indicates that former Soviet and Eastern European countries on average had higher shares of women in top groups around 1990, probably reflecting high labor market participation as well as relatively high education levels for women. But it also indicates that in some Eastern European countries, the share of women in top groups has dropped since the 1990s. As noted by Campa, Demirel, and Roine (2018) there seems to be an overall convergence in some dimensions of gender equality in transition countries, but there is also considerable variation across countries. More detailed studies of how men and women fare in terms of reaching top positions in incomes – but also in other areas like politics – are much needed and likely to be an interesting research area for years to come.
References
- Atkinson, Anthony B., Alessandra Casarico and Sarah Voitchovsky (2018). “Top incomes and the gender divide”, The Journal of Economic Inequality. 16 (2), 225–256.
- Azmat, Ghazala and Barbara Petrongolo, (2014). “Gender and the labour market: what have we learned from field and lab experiments?” Labour Economics. 30, 32–40.
- Blau, Francine D., Lawrence M. Kahn (2017). “The gender wage gap: Extent, trends, and explanations”, Journal of Economic Literature 55(3), 789-865.
- Bertrand, Marianne, Jessica Pan and Emir Kamenic (2015). “Gender identity and relative income within households”. The Quarterly Journal of Economics, 130 (2), 571–614.
- Bertrand, Marianne, (2018). “Coase Lecture – The Glass Ceiling”. Economica 85: 205–231.
- Bobilev, Roman, Anne Boschini, Jesper Roine (2019). Women in the Top of the Income Distribution –What Can we Learn from LIS-Data?, Forthcoming Italian Economic Journal.
- Boschini, Anne, Kristin Gunnarsson, Jesper Roine (2020). “Women in top incomes – Evidence from Sweden 1971–2017”. Journal of Public Economics, 181, January 2020.
- Brainerd, Elizabeth (2000). “Women in Transition: Changes in Gender Wage Differentials in Eastern Europe and the Former Soviet Union”, ILR Review, 54(1): 138-162.
- Campa, Pamela, Merve Demirel, Jesper Roine (2018). “How Should Policy-Makers Use Gender Equality Indexes?”. FREE Policy Paper, November 2018.
- Campa, Pamela and Michel Serafinell (2019). “Politico-Economic Regimes and Attitudes: Female Workers under State-Socialism.” The Review of Economics and Statistics, 101 (2). 233 – 248.
- Einhorn, Barbara (1993). “Cinderella Goes to Market: Citizenship, Gender and Women’s Movements in East Central Europe”. London/ New York: Verso.
- Eriksson, Karin Hederos and Anders Stenberg (2015). “Gender Identity and Relative Income within Households: Evidence from Sweden”, IZA Discussion paper No. 9533.
- Fisman, Raymond, Sheena S. Iyengar, Emir Kamenica and Itamar Simonson (2006). “Gender Differences in Mate Selection: Evidence From a Speed Dating Experiment”. The Quarterly Journal of Economics, 121 (2), 673–697.
- Folke, Olle and Johanna Rickne (2020). “All the Single Ladies: Job Promotions and the Durability of Marriage”. forthcoming American Economic Journal: Applied Economics.
- Fortin, Nicole, Brian Bell, Michael Böhm (2017). “Top earnings inequality and the gender pay gap: Canada, Sweden, and the United Kingdom.” Labour Economics, 47, 107–123.
- ILO (N.D.). “Gender Equality”. Accessed February 2020.
- Myck, Michal and Kajetan Trzciński (2019). “From Partial to Full Universality: The Family 500+ Programme in Poland and Its Labour Supply Implications”, FREE Policy Brief, December 16, 2019.
- Pollert, Anne (2003). “Women, work and equal opportunities in post-Communist transition”, Work, Employment and Society, 17(2): 331-357.
- Roine, Jesper, and Daniel Waldenström (2015). “Long-Run Trends in the Distribution of Income and Wealth”, chapter in Atkinson, A.B., Bourguignon, F. (Eds.), Handbook of Income Distribution, vol. 2A, North-Holland, Amsterdam.
- Roine, Jesper (2016),“Global Inequality – What Do We Mean and What Do We Know?”, FREE Policy Brief, April 24, 2016.
- UNICEF (1999). “Women in Transition”, Regional monitoring Report 6. UNICEF ICDC.
- Wolchik, Sharon L. and Alfred G. Meyer, eds. (1985). “Women, State, and Party in Eastern Europe”. Durham, NC.
- Zinovyeva, Natalia and Marina Tverdostup (2018). “Gender Identity, Co-Working Spouses and Relative Income within Households”. IZA Discussion Paper No. 11757.
Removing Obstacles to Gender Equality and Women’s Economic Empowerment – What Can Policy Makers Learn from Global Research on Gender Economics?
On November 15-16, 2019, the FREE Network and the ISET Policy Institute organized and conducted an international gender economics conference in Tbilisi, Georgia. The conference was organized as part of the FROGEE initiative – the Forum for Research on Gender Economics – supported by the Swedish International Development Agency (SIDA) and coordinated by the Stockholm Institute of Transition Economics (SITE). The conference brought together researchers, policymakers, and the broader development community to discuss obstacles to gender equality and women’s economic empowerment, as well as policies to remove existing constraints, with a particular focus on Eastern Europe and Emerging Economies. This policy brief provides an overview of the main takeaways from the presentations, with a special focus on policy-relevant lessons.
Introduction
In November 2019, Tbilisi welcomed its first international academic conference on gender economics, “Removing Obstacles to Gender Equality and Women’s Economic Empowerment”. The conference focused on the state of economic policy and gender issues around the world and more specifically in the ECA (Europe and Central Asia) region. The opening remarks were offered by two prominent keynote speakers – Dr. Caren Grown, Senior Director for Gender at the World Bank Group, Washington D.C, and Dr. Shahra Razavi, Chief of Research and Data at UN Women HQ in New York. The key addresses offered a global perspective on the current state of gender equality and progress made during the last 20 years. The global overview was followed by a policy panel discussion featuring prominent members of the policy-making community in Georgia. The panel participants reflected on how various policies have impacted gender (in)equality in the South Caucasus and in Georgia in particular. Later in the day, plenary presentations offered a preview of the South Caucasus Gender Equality Index, which is being developed by the ISET Policy Institute, and new research in gender economics done by academics in Georgia, Armenia, Belarus and Sweden.
The second day of the conference showcased research conducted by academics from over 15 countries covering 4 continents. It presented a range of diverse topics in gender economics, including, most prominently the links between childcare policies and labor supply decisions of women, female labor force participation (LFP) and happiness, evolving family structure and gender-selection preferences, the impact of economic, financial and public policies on women’s empowerment, the male-female earnings gap and gender aspects of international trade.
Below, we summarize the results and policy lessons that emerge from the body of work presented at the conference.
Gender Equality Progress in the ECA Region and Worldwide: Key Takeaways
First, as recent global data shows, the progress in women’s access to resources, in particular their access to the labor market, has on average stalled worldwide in the last 20 years. The labor market participation rate of women in 2018 stood at 63% globally, which is largely the same as in 1998, with some notable progress observed only in Latin America and the Caribbean (increase from 57% to 67% between 1998 and 2018), Australia and New Zealand (70 to 79%), as well as Northern Africa and West Asia (29 to 33%). The labor force participation gap between men and women is most pronounced for women who are married or in unions (44% gap, as opposed to 20% for single/never married or 17.9% for divorced/separated women).
Second, the ratio of time spent on unpaid care work by females was about 3-4 times that of males in most countries in the world, with some notable outliers: 11 times in Pakistan, 10 times in Cambodia and 9 times in Egypt. Only in Australia and New Zealand, the ratio of female to male time spent on unpaid work was slightly below 2. Thus, around the world, family responsibilities and unpaid work at home have clearly disproportionately burdened women, potentially preventing them from having an independent source of labor income, and generally weakening their financial position and bargaining power within the family unit. The recent UN Women report on Families in the Changing World (2019) argues for implementing a comprehensive package of family and women-friendly policy measures, which would include, among others, universal childhood education and care, universal healthcare coverage, long-term care for the elderly, etc. Such a comprehensive package would cost between 2-4% of GDP for most countries covered by the study. At the same time, the report argues that it would generate jobs, new investments and be a sizeable source of new tax revenue to the economies. Hence, the costs of such a program would be partially offset by the economic and tax benefits of formalizing the informal care economy. The study also details the ways in which countries could mobilize resources to pay for such packages, including improving tax collection, eliminating illicit financial flows, and leveraging aid and transfers.
For the South Caucasus in particular, the state of gender equality has not systematically been tracked until now. While there exists a number of thematic studies, surveys and narratives, as well as a more general Gender Inequality Index (GII) compiled by UNDP for all countries, a deeper systematic approach has recently been pioneered by the ISET Policy Institute, which started the ambitious project of developing a Gender Equality Index for the South Caucasus and, going forward, for the broader region of transition economies. The methodology behind the index is similar to the one adopted by the European Institute for Gender Equality, which tracks the Gender Equality Index for 28 European countries across a number of dimensions. Obviously, issues of data availability make it more challenging to build such an index in the context of transition economies. Thus, ISET-PI is working to construct some of the measures for the transition economies, using country-level data and household-level databases.
Childcare Policies and Labor Supply
One of the key messages emerging from the academic research in the area of childcare policies and labor supply was that gender-focused social policies need to be crafted carefully, with a focus on the binding constraints of the specific country context. A paper by Vardan Baghdasaryan and Gayane Barseghyan looked at how child-care service availability (affordability) affected the female labor force participation on the intensive and extensive margins in Armenia. The stage for a natural experiment in economic policy was set at the time when the Municipality of Yerevan unexpectedly decided to abolish childcare services fees (roughly 15% of average wage). The researchers hypothesized that such an intervention would have resulted in increased female LFP, as was the case in other (mostly developed) regions and countries around the world (e.g. Quebec in Canada). In the context of Armenia, however, the authors observe that there was no significant effect on female LFP rate on the extensive margin, meaning there was no evidence of inactive women entering the labor force. One possible explanation is that in the context of a developing country such as Armenia, the limiting factor to female participation in the labor force is the lack of market demand for the skills profile of non-active mothers. In such an environment, as the authors conclude, the monetary incentives do not suffice to lift the binding constraint on female LFP.
Yolanda Pena-Boquete presented a study on the case of Australia which analyzed how the labor hours and LFP of both women and men in the family are affected when either the mother’s or the father’s wages increase or when the price of childcare changes. The study finds that the mothers’ working hours respond positively and much stronger to a change in hourly wage than the fathers’. The policy implication is that an increase in mothers’ hourly wage would potentially result in a significant increase in their working hours and labor force participation. The wage effect on women’s working hours and LFP is much more pronounced even compared to the scenario when childcare prices decline.
Overall, the studies in this area demonstrated the need for a careful, multi-faceted approach in designing effective and cost-efficient labor market policies aimed at increasing labor force participation by married women with children.
Labor Force Participation and Happiness: Evidence from the South Caucasus
The paper by Norberto Pignatti and Karine Torosyan looked at the differences in the reported happiness levels between women of different labor market status in the three South Caucasus countries. The intriguing finding of the study is that while in Georgia, there is no difference in the reported happiness level between working women and housewives, in Armenia and Azerbaijan, working women with similar characteristics are much less likely to report being “very happy” than housewives. The interesting finding is that the overall results for Georgia also apply to the Armenian and Azerbaijani minority women in the country, implying that “cultural factors” may play a minor role in the reported differences between countries.
Family Structure and Gender-Selection Preferences
Gender-biased sex selection (GBSS) has been on the forefront of gender policy issues in the South Caucasus, as Armenia, Azerbaijan and, until recently, Georgia struggled with skewed sex ratios at birth (SRB). Understanding the driving forces behind GBSS, and in particular son-preference as a socio-economic phenomenon, is especially important. One of the recent studies on the issue was presented by Davit Keshelava of the ISET Policy Institute. The study “Social Economic Policy Analysis with Regard to Son Preference and Gender-biased Sex Selection” looked at the factors underlying GBSS rise and fall in Georgia over the last 15 years. The study also gleaned facts about the changing attitudes towards GBSS and son-preferences in different regions of Georgia. One of the study’s main findings is that the fall in the sex ratio at birth has been statistically significantly correlated with real income growth in the regions, reduction in poverty, and female employment. Among other factors significantly affecting the reduction in sex ratio at birth, was, surprisingly, the level of male education, while female education was statistically insignificant. The study documented a persisting son preference in Georgia, but also high awareness and strong negative attitudes towards gender biased sex selection in those regions that showed the sharpest improvement in sex ratio at birth over time.
Looking at the issue of gender preferences in the context of transition economies in Europe, Izabela Wowczko presented joint work with Michał Myck and Monika Oczkowska which investigated how preferences for the gender composition of children in the family might have changed in Central and Eastern European (CEE) countries after the fall of communism. The results showed that gender-neutrality was observed in almost all CEE countries before the transition. After the transition of the 1990s, many of the same forces which operated in the South Caucasus have affected the countries of Central and Eastern Europe – namely, decline in incomes, decimated traditional social safety nets and better access to ultrasound and family planning technologies. However, in the post-transition CEE countries, the authors observe a clear preference for a mix (boy/girl) or possibly boys at parity three (i.e. having two boys or a boy and a girl in the family reduced the likelihood of having a third child significantly, as opposed to having two girls). It was also observed that in most CEE countries (except Romania), there was an increased likelihood of having a second child if the first child is a boy – thus demonstrating a girl preference at parity two.
Policy Impact on Women’s Empowerment
A study from India by Mridula Goel and Nidhi Ravishankar looked at the impact of policy interventions on the long-term indicators of women empowerment. It shows that public policies were responsible for improving the so-called “power enablers”, such as literacy rates, financial access, property rights, political voice, etc. However, there is some evidence that not all traditional power enablers, e.g. having a bank account or working for money, are correlated with higher indicators of empowerment, measured by a woman’s autonomy in decision-making within the family. For example, working for money (receiving cash compensation) or having a bank account was found to be negatively correlated with a woman’s ability to decide how her own money is spent – possibly pointing to the existence of prejudice or negative attitudes within the household in such cases.
Another interesting study on this topic by Maria Perrotta Berlin, Evelina Bonnier and Anders Olofsgård looked at whether foreign aid projects foster female empowerment in the surrounding community using data from Malawi. It finds support for a small positive impact of aid on men’s and women’s attitudes related to domestic violence and sexual rights. There is, however, little systematic difference in the impact of gender-targeted aid versus general aid – with exceptions being the impacts on women’s experience of violence and women’s participation in decision-making.
Male-Female Earnings Gap and Gender Aspects of International Trade
The male-female earnings gap is a recurring topic in gender economics. Whether the gap is driven by differences in education and skills of men and women, labor market discrimination, choices of working hours, the “glass ceiling” or “sticky floor” phenomena, the gap is evident and persistent in both developed and developing countries. One of the papers presented by Dagmara Nikulin looked at the impact of trade liberalization on the gender wage gap in Europe. Generally, the economic literature does not provide conclusive evidence in this regard, and the link remains ambiguous. The paper, examining evidence from Europe, finds in particular that participation in global value chains (GVC), which the authors measure by foreign value added in exports, is correlated with reduced wages overall, but the negative effect on wage is lower for men than for women.
Echoing the results of the previous study, the paper by Marie-France Paquet and Georgina Wainwright-Kemdirim, “Since the effects of trade liberalization are not gender neutral, how can we improve its gender outcome? – Crafting Canada’s Gender Responsive Trade Policy” focuses on the problem of identifying and addressing potentially negative impacts of trade on female jobs. The study details a diagnostic modelling approach, which is to use CGE modeling combined with sectoral employment data (a labour module within CGE). The proposed model uses an overlapping generation framework and includes an occupational matrix to allow movements between occupations. This approach allows for specific potential impacts of generic FTAs by gender, age group and occupation.
Conclusion
To sum up, the first international academic conference on gender economics issues in Tbilisi highlighted the diversity and complexity of gender issues around the world and in the South Caucasus region in particular. It also became a powerful catalyst for new research and collaboration ideas among participating institutions and individual researchers. Finally, it demonstrated how policy-oriented research can help inform the policy-making community about the areas where intervention is most needed, design the most effective policies, and calculate the associated costs and benefits of interventions.
References to Selected Presentations
- Shahra Razavi “Policies for Gender Equality in an Unequal World: Challenges and Opportunities”, keynote presentation.
- Vardan Baghdasaryan and Gayane Barseghyan “Child Care Policy, Maternal Labor Supply and Household Welfare: Evidence From a Natural Experiment”.
- Michal Myck and Kajetan Trzcinski “From Partial to Full Universality: the Family 500+ Programme in Poland and its Labour Supply Implications”.
- Karen Mumford, Antonia Parera-Nicolau, Yolanda Pena-Boquete “Labour Supply and Childcare: Allowing Both Parents to Choose”.
- Norberto Pignatti, Karine Torosyan “Employment vs. Homestay and Happiness of Women in the South Caucasus”.
- Davit Keshelava et al. ISET Policy Institute Report “Social Economic Policy Analysis with Regard to Son Preference and Gender-biased Sex Selection”.
- Izabela Wowczko, Michał Myck and Monika Oczkowska “Gender Preferences in Central and Eastern Europe as Reflected in Family Structure”.
- Mridula Goel, Nidhi Ravishankar “Has Public Policy Succeeded in Enhancing Women Autonomy and Empowerment in India Over the Last Decade?”.
- Maria Perrotta Berlin, Evelina Bonnier and Anders Olofsgård “The Donor Footprint and Female Empowerment”.
- Dagmara Nikulin & Joanna Wolszczak-Derlacz “Gender Wage Gap and the International Trade Involvement. Evidence for European workers”.
- Marie-France Paquet, Georgina Wainwright-Kemdirim, “Since the Effects of Trade Liberalization are not Gender Neutral, How can we Improve its Gender Outcome? – Crafting Canada’s Gender Responsive Trade Policy”.
Does Gender Diversity Actually Matter?
Measuring the effects of gender diversity on performance is important to understand the impact of gender quotas. However, the effects of gender diversity remain understudied. We need data with a reliable assessment of team member quality to disentangle the effects of diversity from compositional effects (when higher-quality women replace mediocre men). We use the unique database of the trivia game “What? Where? When?” which has information on both the performance and gender composition of the team and allows to track each player individually. We find that the gender diversity of the team has no statistically significant effect once we control for the quality of each player. In this particular environment, with little evidence of gender discrimination, instruments like gender quotas have no merit. This result does not apply to discriminatory environments where gender quotas could bring benefits through compositional effects.
Introduction
As gender quotas have been widely introduced in politics and in the corporate world, the effects of gender diversity have become the center of attention of many economists. Many observational studies find positive effects of gender diversity on corporate boards’ performance (Desvaux, Devillard, & Sancier-Sultan, 2010). Other studies, using the introduction of gender quotas in boards as a natural experiment, find negative effects on stock valuation, which disappear in the longer run (Ahern and Dittmar, 2012; Matsa and Miller, 2013; Eckbo et al., 2019).
The effect of gender diversity on team performance may run through two different mechanisms. One mechanism is compositional effects due to discrimination: if women face a glass ceiling, only the best women get into teams/boards, and they are on average of higher quality than men. Hence, boards with female representatives perform better. The discrimination mechanism has been shown to be at work in the political setting, for example: gender quotas in parties lead to higher-quality women replacing mediocre men (Besley et al., 2017). The other mechanism is the true effect of gender diversity through complementarity between men and women: if they differ substantially in some dimensions, these differences might become the source of better team decisions, or, on the contrary, inefficiencies in decision-making.
To separate between the two mechanisms – compositional effects and diversity effects – we need data with reliable quality measurement for each team member. Controlling for team member quality would take care of the compositional effect, and the gender composition would be significant only if there is a true gender diversity effect.
We use the What? Where? When? trivia game dataset to measure the effects of gender diversity on team performance with and without control for a player’s quality.
The What? Where? When? Game
What? Where? When? (WWW) is a team-played trivia game popular in post-Soviet countries. Teams of six players are asked questions and have one minute to come up with an answer. Typically, in order to find the correct answer, a team needs to combine both logical thinking and knowledge. A tournament usually consists of 36-90 questions. The team with the most correct answers wins the first place. In 2003, a unified database of the game was created. This database contains records of more than 218,000 individuals who have played in at least one of the 6,000 recorded tournaments.
The What? Where? When? Dataset
A unit of observation in our dataset is one game played by a team. It contains the unique ID of the team, the ID of each player, information about the number of games played by the team and by each player, the tournament date, the difficulty of the tournament and the number of teams. We identify the gender of the players through their names and patronymic names. Overall, we use 74,475 team-game observations which were played by 2,854 teams (23,000 single players) from 2013 to 2018.
Performance Measure
The measure of a team’s performance in a tournament is the percentage of correct answers normalized by the average percentage of correct answers in this tournament. We use player’s individual fixed effects as a measure of their quality in our regression analysis.
Gender Aspects in What? Where? When?
Only 31.5% of the players in the sample are female, however, other than that, we fail to find any significant evidence indicating gender discrimination or segregation. Table 1 presents the actual shares of team-game observations by gender composition as well as the predicted shares if assignment to teams was random. The difference between the actual shares and predicted shares does not appear to be economically significant.
Table 1: The actual distribution of women across teams is not different from random
Results
The basic model of our analysis, Model 1 examines the association between the performance of a team, normalized by tournament difficulty, with dummy variables for gender diversity (defined as the number of minority gender players in the team, i.e. diversity_1 is true if there is only one woman or only one man on the team). We also include the individual fixed effects of each player in the second specification (Model 2), to control for the quality of players and rule out possible composition effects.
Table 2. Effect of diversity on performance with and without the individual quality controls
The coefficients of Model 1 and 2 are shown in Table 2. While diversity is significant in the first specification, after accounting for the individual quality of players, we cannot reject the hypothesis of insignificance of gender diversity. These results hold under different specifications: with controls for player experience, with different player experience cutoffs, or including the neural network-generated predictions of performance.
Figure 1. The distribution of individual coefficients (proxy for player quality) for female and male players
Figure 1 presents the distributions of individual coefficients of female and male players. In our sample, the female distribution centers slightly to the left of the male one. It explains the negative diversity coefficients in the specification without the individual fixed effects – in this case, the diversity dummies capture the lower average quality of female players.
Conclusion
Our study aimed at disentangling compositional and pure effects of gender diversity by using a novel dataset of a team played trivia game. Our main finding is that after accounting for the individual quality of team members, the gender composition of a team does not appear to be significant for a team’s performance.
Although it is always dangerous to extrapolate findings obtained in specific settings, we believe that the positive gender diversity effects found in other studies are often manifestations of the change in the average quality of team/board members i.e. compositional effects rather than gender diversity effects per se. From a policy point of view, this means that while we need gender quotas in areas suffering from gender discrimination, once we reach equal opportunities such instruments may no longer have any positive effects.
References
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