Tag: gender inequality

Dimensions of Well-being

20210920 Dimensions of Well-being Image 01

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

Introduction

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

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

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

Well-being in a Pandemic

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

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

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

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

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

Gender and Wellbeing

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

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

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

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

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

Expectations About the Future and Parenthood

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

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

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

Measuring Inequality and Social Deprivation

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

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

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

Conclusion

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

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

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

List of Participants

Part 1 | Online Workshop on Dimensions of Well-being

Part 2 | Online Workshop on Dimensions of Well-being

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

Covid-19 and Gender Inequality in Russia

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

Introduction

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

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

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

Heterogeneous Cross-Sectoral Effects

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

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

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

The Effect of Childcare Facilities Closure

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

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

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

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

The Interaction of Both Effects

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

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

Other Hypotheses

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

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

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

References

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

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

How Are Gender-role Attitudes and Attitudes Toward Work Formed? Lesson from the Rise and Fall of the Iron Curtain

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Gender differences in attitudes toward work and gender-role attitudes are important determinants of gender inequality in the labor market. In this brief we show that these attitudes vary considerably across countries and can also change within the same country over a relatively short time period. We then present evidence that politico-economic regimes that make substantial effort to bring women into the labor market can shape these attitudes: gender differences in attitudes toward work decrease, and gender-role attitudes become less traditional. Cultural norms with long historical roots are not necessarily invariant to large shocks, and policies aimed at raising women’s presence in the labor market can activate virtuous cycles of increasing female employment. 

Gender inequality and cultural attitudes

Levels of gender inequality in the labor market differ considerably worldwide, even among countries at similar levels of economic development. Policies, technology, and economic conditions have long been shown to play an important role in explaining cross-country and regional differences in gender inequality. More recently, researchers have emphasized the role of cultural attitudes, such as women’s attitudes toward work and gender role attitudes (i.e. the beliefs that individuals hold regarding the appropriate roles of men and women in societies). Fortin (2008), for instance, finds that gender differences in attitudes towards work account for part of the existing gender wage gap in the US.  Further, Fernández et al. (2004) show that differences in gender-role attitudes partly explain existing variation in female labor force participation. Given that gender differences in attitudes toward work and gender-role attitudes contribute to explain gender inequality in the labor market, economists have recently started studying the origins of these attitudes and their sources of variation over time.

In this policy brief we first document variation across space and over time in gender differences in attitudes toward work and gender-role attitudes; then, we present evidence that politico-economic regimes that put emphasis on women’s inclusion in the labor market can shape these attitudes.

Gender-role attitudes and attitudes toward work across space and over time

The World Values Survey (Inglehart et al., 2014) asks questions, among others, about the importance of work in one’s life, and about one’s beliefs on the appropriate roles for women and men in society.

Based on these questions, we measure gender differences in the importance given to work, and levels of agreement with statements regarding gender roles.  Below we show that such measures vary considerably among a sample of countries in Europe and Central-Asia, as well as within countries over time.

Figure 1 shows gender differences in the percentage of survey respondents who reported that work was very important or rather important to them in the survey wave of 1995-1998. There is substantial cross-country variation in whether men or women give more importance to work, and in the magnitude of the gender difference. Moreover, the underlying variation across women is larger than across men (data not shown): the minimum and maximum values among men are 84% (in Georgia) and 97.5% (in Bosnia), whereas the respective values for women are 77% (in Georgia) and 96.6% (in Macedonia).

Figure 1. Gender differences in attitudes toward work

Source: Data are from the 1995-1998 wave of the World Values Survey. Individuals are asked the following question: Please say, for each of the following, how important is work in your life, and the options given are Very important, Rather important, Not very important, Not at all important. Countries selected are those in Europe and Central Asia where the question was asked in the 1995-1998 wave.

Figures 2 and 3 show variation across countries in gender role attitudes. The share of respondents who agree with the statement “A working mother can establish just as warm and secure a relationship with her children as a mother who does not work “varies from a minimum of 47% in Poland to a maximum of 93% in Finland. The share of respondents who agree with the statement “Both the husband and wife should contribute to household income” varies from a minimum of 78% in Armenia and Finland to a maximum of 98% in Albania.

Figure 2. Working mother: warm relationship with her children.

Source: Data are from the 1995-1998 wave of the World Values Survey. Individuals are asked the following question: People talk about the changing roles of men and women today. For each of the following statements I read out, can you tell me how much you agree with each?. Do you agree strongly, agree, disagree, or disagree strongly? A working mother can establish just as warm and secure a relationship with her children as a mother who does not work. Countries selected are those in Europe and Central Asia where the question was asked in the 1995-1998 wave.

Figure 3. Husband and wife should both contribute to income.

Source: Data are from the 1995-1998 wave of the World Values Survey. Individuals are asked the following question: People talk about the changing roles of men and women today. For each of the following statements I read out, can you tell me how much you agree with each. Do you agree strongly, agree, disagree, or disagree strongly? Both the husband and wife should contribute to household income. Countries selected are those in Europe and Central Asia where the question was asked in the 1995-1998 wave.

A recent strand of the economics literature analyzes the long-term determinants of attitudes and finds that they have very deep historical roots (see Giuliano, 2018). However, attitudes also evolve over time. Figures 4 and 5 show that while in some countries attitudes remain rather stable after 1998, in other countries they change substantially. In Russia, for instance, the gender difference in attitudes toward work has doubled over a period of ten years, with men becoming from 5 to 10 percentage points more likely than women to report that work is important to them. Turning to gender-role attitudes, the percent of respondents who think that a working mother can have a warm relationship with her children has increased the most in countries as different as Macedonia and Spain. The percent of individuals who think that both husband and wife should contribute to income has increased relatively sharply in Moldova, while declining rather substantially in Montenegro and especially in Serbia.

Figure 4. Gender differences in attitudes toward work over time.

Source: See Note to Figure 1.

Figure 5. Gender role attitudes over time.

Source: See Notes to Figures 2 and 3.

The graphs thus suggest that the attitudes considered here vary not only cross-sectionally but can also change over a relatively short time period. A natural question to ask is then: what type of shocks cause a change in gender differences in attitudes toward work and in gender role attitudes?

The role of politico-economic regimes in shaping attitudes

In recent work (Campa and Serafinelli, 2018), we show that politico-economic regimes that focus on women’s inclusion in the labor market can reduce gender differences in attitudes toward work and make gender-role attitudes less traditional. Studying the question of whether politico-economic regimes can change attitudes is difficult, because countries or regions exposed to different regimes are likely very different along many other dimensions, including their history, which is known to shape attitudes. To circumvent this problem, we exploit the imposition of state-socialist regimes across Central and Eastern Europe and their efforts to promote women’s economic inclusion (see Campa and Serafinelli, 2018). First we focus on the socialist regime that emerged in East-Germany in 1949. This regime favored women’s access to tertiary education and to qualified employment through massive childcare provision and other policies that were popular throughout the entire Central and Eastern European region. Conversely, in West-Germany, women were encouraged to either stay home after they had children or take part-time jobs after extended breaks (Trappe, 1996; Shaffer, 1961). Since East and West-Germany before 1949 were part of the same country and as such had a common history and shared institutions, we can compare attitudes in East- and West-Germany after the separation to isolate the impact of different politico-economic regimes on attitudes. In other words, the underlying hypothesis is that attitudes toward work and gender role attitudes in East- and West-Germany were the same before the separation. Such a hypothesis is arguably valid especially because we compare only individuals who, during the separated years, lived relatively close to the East-West border (e.g. within 50 km from the border), and are, thus, expected to have close enough (geography, culture and social norm-driven) preferences and attitudes before the separation.

The results of the comparison can be summarized as follows: (a) due to exposure to a different politico-economic regime, East-German women participated more in the labor market and became more educated than their West-German counterparts; (b) the importance given to work by East-German women increased, which led to a lower gender gap in attitudes toward work with respect to West-Germany; (c) both women and men in East-Germany developed less traditional attitudes than West Germans regarding the relationship of working mothers with their children and the gender division of roles in the household.

In the second part of the paper, we also extend the analysis to a number of transition countries in the Central and Eastern European region. We show that in Central and Eastern Europe between 1945 and 1990 gender-role attitudes became less traditional than in Western Europe.

Conclusion

In this brief we have documented that gender differences in attitudes toward work and gender role attitudes vary substantially across space and can change over a relatively short time period. Since these attitudes affect the level of gender inequality in the labor market, understanding their determinants is important and policy-relevant. In recent work (Campa and Serafinelli, 2018), we exploit the imposition of state-socialist regimes in Central and Eastern Europe and show that individuals exposed to different regimes develop different attitudes toward work and different gender-role attitudes.

Such a finding suggests that policies aimed at increasing women’s participation in the labor market can activate virtuous cycles; namely, such policies might improve the cultural acceptance of female work, thus potentially further raising women’s labor force participation. The evidence from the Central and Eastern European region also suggests that history is not necessarily an excuse for inaction regarding women’s participation in the labor market. While deeply rooted cultural norms can be an obstacle to women’s economic empowerment, these norms are not necessarily absolutely time-invariant, and can respond to important economic and policy shocks.

A caveat to such conclusions is that the evidence presented here is specific to women’s attitudes toward work and attitudes regarding the acceptability of female work. Other attitudes and norms are also important in defining the level of gender equality in a society, such as those involving the division of roles in a couple when both couple members work outside of the home, the acceptability of violence against women, the suitability of women and men to different fields of education. Little is known about these attitudes and more research is needed to understand which policies, if any, can change them.

References

  • Campa, P. and M. Serafinelli (2018), Politico-economic regimes and attitudes: Female workers under state-socialism, Review of Economics and Statistics, Forthcoming
  • Fernández, R., A. Fogli and C. Olivetti (2004), Mothers and sons: Preference formation and female labor force dynamics, Quarterly Journal of Economics 119(4): 1249–1299.
  • Giuliano (2018). Gender: A Historical Perspective, in Oxford Handbook on the Economics of Women, ed. Susan L. Averett, Laura M. Argys, and Saul D. Hoffman, New York: Oxford University Press, forthcoming.
  • Inglehart, R., C. Haerpfer, A. Moreno, C. Welzel, K. Kizilova, J. Diez-Medrano, M. Lagos, P. Norris, E. Ponarin & B. Puranen et al. (eds.). 2014. World Values Survey: Round Three – Country Pooled Datafile Version: www.worldvaluessurvey.org/WVSDocumentationWV3.jsp.
  • Shaffer, H (1981), “Women in the two Germanies: A comparison of a socialist and a non-socialist society.”
  • Trappe, H (1996), “Work and family in women’s lives in the German Democratic Republic”, Work and Occupations 23(4): 354–377.

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.

How Should Policymakers Use Gender Equality Indexes?

We look at the development of gender inequality in transition countries through the lens of the Gender Inequality Index (GII), which aims to capture overall gender inequality. Extending the measure back to 1990, we look at the development of the overall index as well as that of its components. We show that, even though gender inequality in transition countries for the most part has decreased since 1990, once overall development is taken in account these countries appear to fare better in 1990 than today. We also caution against relying exclusively on composite indexes to understand patterns of gender inequality. While the desire of policy makers to get one number that captures gender inequality development is understandable, weak correlations of the GII with other indexes (over years when multiple gender inequality indexes exist) as well as across sub-indexes suggests that such an approach has limitations. Finally, we emphasize the need to understand levels as well as trends and underlying mechanisms to better inform policy to improve gender equality.

On Measuring Progress

When studying economic development, or any issue really, one faces the challenge not only of finding the right way to identify and measure what are often complex changes, but also of communicating the bottom line efficiently. This naturally leads to the search for a single metric according to which we can rank progress and follow it over time. In the realm of economic development the standard measure is GDP growth. But, of course, focusing only on GDP leaves out many important dimensions of development, such as health and education.[1] In an attempt to capture these dimensions, while still arriving at a single number that measures development, the Human Development Index (HDI) was developed in the late 1980s. Since then, a number of alternative indexes capturing additional aspects of human wellbeing have been suggested; see the report by the “Commission on the Measurement of Economic Performance and Social Progress” (Stiglitz, Sen and Fitoussi, 2009).

Just as for overall development, there is great interest in single measures that capture the gender dimension of this development. Over the past decades a number of such “gender equality indexes” have been developed by international organizations such as the UNDP, the EIGE (European Institute for Gender Equality) and the WEF (World Economic Forum), to name a few.

These measures receive a lot of attention and in particular the reporting of country rankings tends to have an influence on political and policy discussions. The various indexes proposed differ in what dimensions they include (as will be explained below) and, much as a consequence of this, in the time periods they can cover. In some cases (as will also be shown below) it is possible to extend the time coverage of the indexes, but most of the times it is hard to recover the underlying data.

In this brief we summarize what the most popular indexes tell us about the development of gender equality in transition countries, contrasting these to Western European countries.[2] Whenever we have been able to find the underlying data, we also add to publicly available measures by extending indexes back to early 1990s. We then comment on the development of gender equality in transition countries and, perhaps most importantly, on why an indexes-based analysis should be interpreted with some care.

Gender Equality Before 1990

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, 2018). This was mainly due to the high participation of women in the labor market as well as the (official) 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 also still expected to take care of child rearing and house work (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).

Generally speaking, while the communist regimes succeeded in promoting women’s access to the labor market and tertiary education, they failed to eliminate patriarchy (LaFont, 2001). Such a dichotomy gives rise to a broad set of questions regarding gender equality in transition countries as well as the measurement of gender equality in this context. What happened to gender equality, in relation to economic growth, during the transition, when new governments often broke with the tradition of promoting women’s employment and education? Did gender equality enhanced by communism leave a legacy or did underlying patriarchic values characterizing many of the communist societies come to dominate? How should we regard developments of indexes that try to weight several components within a context, such as that of transition countries, where these components may move in different directions from each other, given the dichotomy characterizing gender relations?

The Different Indexes

There are several different indexes that are often quoted in policy discussions. Two important measures are the Gender Development Index (GDI) and the Gender Inequality Index (GII), both calculated by the UNDP and reported annually in the Human Development Report (HDR). A third, more recent index that has received increasing attention is the World Economic Forum’s global Gender Gap Index (GGI), which is published in the yearly Gender Gap Report. These three can serve as illustrations of what gender equality indexes typically try to capture.

The Gender Development Index (GDI) essentially measures gender differences in the Human Development Index (HDI). The HDI in turn aims to capture achievements in three basic dimensions of human development: health (measured by life expectancy), knowledge (measured by expected and mean years of schooling) and living standards (measured by GNI per capita). The GDI then basically tries to assess the relative performance in these three dimensions for men and women respectively. If health (or education, or income)  in the population on average goes up, this improves the HDI. But to the extent that the improvements are felt differently by men and women, this will show in the GDI. There are several potential problems with the measurement of this index, especially when it comes to dividing GNI per capita between men and women (see e.g. Dijkstra and Hanmer, 2000); on the other hand, the index offers a transparent way to connect gender inequality to the HDI measure.

The other UNDP measure, the Gender Inequality Index (GII), was reported for the first time in the 2010 Human Development Report. It was created to address some of the perceived shortcomings of its forerunner, the Gender Empowerment Index (GEM) which had been introduced together with the GDI in 1995 (see e.g., Klasen and Schuler, 2011 for problems with GDI as well as GEM). The GII measures gender inequalities in three dimensions of human development: 1) reproductive health, measured by maternal mortality and adolescent birth rates; 2) empowerment, measured by representation in parliament and secondary education among adults; and 3) economic status, measured by labor force participation. As with the GDI, the areas of health, education, and economic empowerment are present, but the index also considers some aspects of health that are more directly relevant for women, and includes a component trying to capture political participation. The economic measure of labor force participation is also somewhat easier to interpret (and measure) than GNI divided between men and women. As for the GDI, GII country-values from 1995 are available on the UNDP website.  Conveniently for our purpose, most of the underlying data that the index is based on are also made available from the UNDP for the years 1990, 1995, 2000, 2005, and every year between 2010 and 2015, with the only exception of female seat share in parliaments in 1990[3]. We downloaded the latter from the World Bank indicators database[4]. We also added information on the share of women in the 1990 Polish Parliament, from the Inter-Parliamentary Union[5], and on the share of women in the 1990 Georgian “Supreme Council,” from Beacháin Stefańczak and Connolly (2015).

A third more recently developed index is the Global Gender Gap Index. This covers areas of political empowerment, health and survival, economic participation and educational attainment, as measured using 14 different variables. An indicator is available for each of the sub areas covered, which are then weighted together in an overall indicator of the gender gap. The Global Gender Gap Index is clearly more detailed and provides a more nuanced picture of existing gender gaps compared to the GDI or the GII. But this amount of detail also comes with potential costs; it is more difficult to interpret the overall index as there are more underlying components that may change simultaneously, and it is also more difficult to reconstruct the index back in time.

What Does the GII Index Tell Us About Gender Equality in Transition Economies?

Among the above mentioned indexes, we focus on the GII here. Extending this measure when possible allows us to study gender inequality starting from 1990 for a limited set of countries (we expand the sample of countries when looking at different dimensions of the GII separately below)[6]. Figure 1 reports values for the index in box plots, which show the index median, maximum, minimum, 75th and 25th percentile for two groups of countries: transition countries and Western-European countries. When interpreting Figure 1, recall that higher GII values imply more inequality.

Figure 1. The Gender Inequality Index in transition countries and Western Europe, 1990-2015

Source: Own calculations based mainly on UNDP data.

Figure 1 shows that based on the GII, median gender inequality is larger in transition countries than in Western Europe and has been so throughout the entire period since 1990. In both regions the index shows a decreasing trend, after an initial increase in 1995 in the transition countries. Below we will show that this is mainly due to a drop in female representation in national parliaments. The variance of the index scores has declined over time in Western Europe, while it remained mostly unchanged in the transition countries[7].

This first piece of evidence from the data is somewhat at odds with the common notion that transition countries enjoy relatively low level of gender inequality. However, two qualifications are in order here. First, transition and Western European countries are generally at different levels of development. Figure 2 displays the country groups performance in relation to their level of human development. This is done by measuring the difference between their GII ranking and their HDI ranking among all the countries with non-missing GII values in the years considered. The larger the difference, the worse the group performance in terms of gender inequality in relation to its level of development.

Figure 2. Difference between Gender Inequality Index ranking and Human Development Index ranking in transition countries and Western Europe, 1990-2015

Source: Own calculations based mainly on UNDP data.

The trends of transition countries and Western Europe are now opposite. In the former group, in 1990 the median standing in terms of gender equality was better than that in human development; this difference appears to have narrowed over time, and it is close to zero in 2015. Western European countries have instead improved their gender equality in relation to their level of overall human development over the period studied. Put differently, the gains in human development made by former socialist countries since the transition have not translated into comparable gains in gender equality as measured by the GII index.

Second, it is also important to emphasize that, as noted above, according to several scholars the socialist push in favor of gender equality was directed only to certain spheres of women’s lives, namely their economic empowerment. This suggests that a composite index can mask important contrasting patterns among its components.

In Figures 3 to 5 we document that different variables indeed paint quite diverging pictures of gender inequality in transition countries.

Figure 3. Development of adolescent births and maternal mortality, 1990-2015

Figure 4. Development of secondary education and share of women in parliament, 1990-2015.

Figure 5. Labor force participation, 1990-2015

Source: Own calculations based mainly on UNDP data.

In each figure we display box-plots for the three areas covered by the GII: health (measured by teenage births and maternal mortality), empowerment (measured by secondary education and share of women in Parliament) and labor force participation. Looking at the different variables separately also allows us to increase the number of countries significantly, since for many countries only the seat share of women in parliaments is missing in 1990.

As the figures show transition countries in 1990 displayed relatively low levels of gender inequality in labor force participation and secondary education. Over the last 25 years, they have kept improving the latter, while the former has stalled, resulting in Western European countries displaying a higher median level of gender equality in labor force participation for the first time around 2010. Reproductive health, while improving since the transition, is still far from converging to Western European standards. Finally, political representation appears to be responsible for the increase in inequality immediately after the transition that we have noted in Figure 1. While it is hard to compare the meaning of representation in the context of 1990 totalitarianisms to that of the democratic regimes emerged later, during the regime change women de facto lost descriptive representation, which was sometime guaranteed in socialist times by gender quotas (Ostrovska, 1994).

In conclusion, breaking down the GII by its components shows that, while Western European countries have invariantly improved their levels of gender equality since 1990, the trend in transition countries depends on the measure one looks at: women maintained but did not improve their relative status in the labor force, they gained more equality in education and especially in terms of reproductive health, and lost descriptive political representation.

What Does the GII Index (And Other Indexes) Not Tell Us?

The conclusion in the previous paragraph raises the question of which other areas of progress, stagnation or deterioration in gender equality in transition countries that might be overlooked in the GII index. Above, we have summarized two more indexes, the GDI and the Gender Gap Index, which focus on additional dimensions of gender inequality but are more limited in terms of time availability. For the time over which there is overlap between the available indexes, the correlation between the GII index and the GDI and the Gender Gap Index respectively, is roughly 0.60. Interestingly, such correlation is higher in the sample of western European countries (0.64 and 0.68 respectively); when the sample is limited to transition countries, the correlations are down to 0.40 and 0.50 respectively.

Several factors might account for the differences across indexes. Unlike the GII, both the GDI and the Gender Gap Index, for instance, include measures of income inequality. On the other hand, the GDI, as pointed out above, does not account for issues related to reproductive health and political representation. The Gender Gap Index is the only one to include, among the health measures, sex-ratios (typically defined as the ratio of male live births for every 100 female births). This turns out to be especially important for some of the transition countries: in the most recent Gender Gap Report, Georgia, Armenia and Azerbaijan remain among the worst-performing countries globally on the Health and Survival sub-index, due to some of the highest male-to-female sex ratios at birth in the world, just below China’s. This goes hand in hand with very high scores in terms of gender equality in enrolment in tertiary education, for which each of these countries ranks first (at par with a few other countries), having completely closed the gender gap. In fact, women are more likely to be enrolled in tertiary education than men.

The relatively low correlation among the different indexes for the group of transition countries also deserves special attention, because it might be a direct consequence of the peculiar history of women’s rights and empowerment in the region. Since some dimensions of gender equality were fostered through a top-down approach, rather than as the result of demands and needs expressed by an organized society, it is more likely that over the last thirty years elements of modernization coexisted with more traditional forms of gender inequality.

Finally, it is worth pointing out that none of the above indexes accounts for important dimensions of gender inequality such as,: gender violence, division of chores in the household, political representation at the local level, and the presence of women in STEM’s professions (where the largest job creation might happen over the next couple of decades). Once more, some of these measures might be particularly relevant for transition countries. Just to mention one example, gender violence is an urgent issue in a few of the countries in the area[8]. A case in point in this respect is Moldova: in 2017, the country ranked 30th out of 144 countries in the Gender Gap Index. Its rank for the sub-index called “Economic Opportunity and Participation” was 11[9]. The country performs especially well in terms of economic opportunity and participation because women not only participate in the labor market in almost equal rates as men, but they are also relatively fairly represented in professions traditionally less feminized elsewhere, such as “professional and technical workers” and “legislators, senior officials and managers.” At the same time, gender violence appears quite prevailing in Moldova: according to the UN, in 2014 “lifetime prevalence of psychological violence” in Moldova was of 60%. Official country statistics also report that the percentage of ever-partnered women aged 15-65 years experiencing intimate partner physical or sexual violence at least once in their lifetime in 2011 was 46%[10].

While limited in scope, the example above illustrates how some of the available indexes might not capture some important drivers of gender inequality in the region.

Conclusion

In this policy brief, we have reviewed some of the available gender inequality indexes that are commonly used in policy discussion as well as in policy-making.

We have then discussed gender inequality in transition countries focusing on one of these indexes, the Gender Inequality Index, whose span we have extended to the beginning of the transition period. Our analysis has highlighted some points to be mindful of when using comprehensive indexes to discuss gender inequality, especially in transition countries:

  • It can be fruitful to analyze gender inequality indexes in relation to levels of development. Some issues related to gender inequality, such as maternal mortality, are potentially addressed with a comprehensive strategy aimed at overall development. Conversely, other drivers of gender inequality, such as women’s political empowerment or gender violence, might require more targeted policy interventions, since they do not necessary go hand in hand with overall development.
  • While comprehensive indexes can be useful in terms of effective communication, it is often difficult to compress all the potential forms that gender inequality can take into a single index, especially over time. This is due to both conceptual issues and data limitations. Moreover, even when this is done, a comprehensive index can overshadow important sources of gender inequality if it is composed of sub-indexes that move in opposite directions.
  • The previous point can be especially relevant in the context of transition countries, which historically experienced a top-down approach to gender equality, the results of which in the long-term appear to be major advancements in some dimensions of women’s empowerment and contemporary potential backlash in other dimensions. In the context of transition countries, for instance, it has been argued that low levels of female representation in political institutions can be the result of women’s large participation to the labor market while division of roles in the household remained traditional. In the words of anthropologist Suzanne LaFont, “Women have been and continue to be overworked, and their lives have been over-politicized, the combination of which has led to apathy and/or the unwillingness to enter the male dominated sphere of politics. Many post-communist women view participation in politics as just one more burden.”[11] In such a context, average values of an index on gender equality might mask high achievements in economic empowerment coexisting with lack of political representation.
  • Identifying policies to address gender inequality in transition countries might be especially difficult because, depending on the dimension that one focuses on, the challenge at hand is different: in terms of education and employment, the policy goal appears to be maintaining current levels of equality or increasing them from relatively high initial points; the type of policies to do so are likely different than those used in Western European countries in the last 30 years, where the challenge was rather how to increase equality from relatively much lower levels. Conversely, in other dimensions the challenge is how to make major leaps forward, which move transition countries closer to Western European standards: this is the case for sex-ratios, for instance, and reproductive health more in general. The importance of initial levels and trends for policy implications also showcases how crucial it is to acquire more historical knowledge of policies, institutions, and statistics.

Overall, policy discussions and policy-making should go beyond mere descriptions of what indexes and related international comparisons tell us about gender inequality. A better knowledge and understanding of all of the drivers of gender inequality, of their historical evolution, and of their connections both with overall development and among them, is crucial to give sound policy recommendations.

References

  • Beacháin Stefańczak, K.Ó. and Connolly, E.(2015),  ‘Gender and political representation in the de facto states of the Caucasus: women and parliamentary elections in Abkhazia’. Caucasus Survey, 3(3), pp.258-268.
  • Brainerd, E. (2000), ‘Women in Transition: Changes in Gender Wage Differentials in Eastern Europe and the Former Soviet Union’, Industrial and Labor Relations Review, 54 (1), pp. 138-162.
  • Campa, P. and Serafinelli, M. (2018), ’Politico-economic Regimes and Attitudes: Female Workers under State-socialism’, Review of Economics and Statistics, Forthcoming.
  • Dijkstra, A. and L. Hanmer (2000), ‘Measuring socio-economic gender inequality: towards an alternative for UNDP’s Gender-related Development Index’, Feminist Economics, Vol. 6, No. 2, pp. 41-75.
  • Einhorn, B. (1993), Cinderella goes to market: citizenship, gender, and women’s movements in East Central Europe, London: Verso.
  • Klasen, S. and Schuler, D. (2011) Reforming the Gender-Related Development Index and the Gender Empowerment Measure: Implementing Some Specific Proposals. Feminist Economics. (1) 1 – 30
  • LaFont, Suzanne (2001), ‘One step forward, two steps back: women in the post-communist states.’ Communist and post-communist studies 34(2), pp. 203-220.
  • Ostrovska, I. (1994). Women and politics in Latvia. Women’s Studies International Forum 2, 301–303.
  • Pollert, A. (2003), ‘Women, work and equal opportunities in post-Communist transition’, Work, Employment and Society, Volume 17(2), pp. 331-357.
  • Stiglitz, Joseph, Amartya Sen, and Jean-Paul Fitoussi (2009). `The measurement of economic performance and social progress revisited.’ Reflections and overview. Commission on the Measurement of Economic Performance and Social Progress, Paris.
  • Tur-Prats, Anna (2018). Unemployment and Intimate-Partner Violence:  Gender-Identity Approach. GSE Working Paper No. 1564
  • Unicef. Women in transition. 1999.
  • UN. The World’s Women 2015.
  • Wolchik, S. L. and Meyer, A.G. (1985), Women, State and Party in Eastern Europe, Durham, NC: Duke University Press.

Footnotes

  • [1] In contrast to a common perception, economists are generally well-aware of the limitations of GDP as a measure of welfare. In fact, the reference manual of national accounts, the SNA 2008, makes this explicit in stating that there is “no claim that GDP should be taken as a measure of welfare and indeed there are several conventions in the SNA that argue against the welfare interpretation of the accounts”.
  • [2] By “transition countries,” we refer to all countries that were part of the Soviet Union plus the Central and Eastern European countries that were heavily influenced by the Soviet Union before 1990 (not including Albania and former Yugoslavia). Starting from this, we – as will be made clear below – sometimes limit the set of countries further depending on data availability.
  • [3] http://hdr.undp.org/en/data
  • [4] https://data.worldbank.org/indicator/SG.GEN.PARL.ZS
  • [5] http://archive.ipu.org/parline-e/reports/2255_arc.ht
  • [6] For Western Europe these countries are: Austria, Belgium, Cyprus, Denmark, Finland, France, Greece, Iceland, Italy, Luxembourg, Malta, Netherlands, Norway, Portugal, Spain, Sweden, and Switzerland. The transition countries are: Armenia, Bulgaria, Georgia, Hungary, Poland, Romania, Russian Federation.
  • [7] The outlier among Western countries is Malta.
  • [8] While explaining the sources of gender violence in the region is beyond the scope of this report, incidentally we notice that, according to recent research, female economic empowerment in a context where patriarchal values are dominant might backfire against women in the form of increased gender violence. See Tur-Prats, 2018.
  • [9] http://reports.weforum.org/global-gender-gap-report-2017/dataexplorer/#economy=MDA
  • [10] UNFPA (2015). Combatting Violence against Women and Girls in Eastern Europe and Central Asia. https://eeca.unfpa.org/en/publications/combatting-violence-against-women-and-girls-eastern-europe-and-central-asia
  • [11] LaFont, Suzanne (2001). One Step Forward, Two Steps Back: Women in the Post-Communist States. Communist and Post-Communist Studies, Vol. 34, pp 208.

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.

Gender Gaps in Transition – What do we learn (and what do we not learn) from gender inequality indexes?

20181112 Gender Gaps in Transition Image 01

We look at the development of gender inequality in transition countries through the lens of the Gender Inequality Index (GII), which aims to capture overall gender inequality. By extending the measure back to 1990, we show that even though gender inequality in transition countries for the most part has decreased since the fall of the iron curtain, once overall development is taken into account, transition countries did better in relation to other countries in terms of rank differences before transition. We, however, caution against relying exclusively on composite indexes to understand patterns of gender inequality. While the desire of policy makers to get one number that captures gender inequality development is understandable, weak correlations across different overall indexes, as well as across different sub-indexes that make up each index, suggest that such an approach has limitations.

Indexes of gender inequality

In the public debate of socio-economic issues there is an understandable interest in single measures that summarize complex issues, describe historical developments and allow international comparisons. The use of GDP to measure economic development is the most immediate example of this way of proceeding. The same applies to gender inequality. Over the past decades a number of “gender equality indexes” have been developed by international organizations such as the UNDP, the EIGE (European Institute for Gender Equality) and the WEF (World Economic Forum), to name a few. These measures receive a lot of attention and in particular the reporting of country rankings tends to have an influence on political and policy discussions.

In this brief, we study the development of the Gender Inequality Index (GII) in transition countries, contrasting these to Western European countries.  By transition countries, we refer to all countries that were part of the Soviet Union plus the Central and Eastern European countries that were heavily influenced by the Soviet Union before 1990 (not including Albania and former Yugoslavia). Whenever we have been able to find the underlying data, we extend the GII measure back to the early 1990s. This extension allows us to measure the development of gender inequality through the lens of a single index since the beginning of the transition. We then discuss what the GII tells us about gender inequality in transition, but also – perhaps more importantly – what it does not tell us. Our analysis is discussed as well as shown in some more detail in our forthcoming companion FREE Policy Paper.

The Gender Inequality Index

The GII was reported for the first time in the 2010 Human Development Report. It measures gender inequalities in three dimensions of human development: 1) reproductive health, measured by maternal mortality and adolescent birth rates; 2) empowerment, measured by representation in parliament and secondary education among adults; and 3) economic status, measured by labor force participation.

GII country-values from 1995 are available on the UNDP website.  Conveniently for our purpose, most of the underlying data that the index is based on are also made available from the UNDP for the years 1990, 1995, 2000, 2005, and every year between 2010 and 2015, with the only exception of the female seat share in Parliament in 1990. Using the UNDP data, and data on the female seat share in Parliament in 1990 from additional sources (see the FREE Policy Paper for a list of sources), we obtain values for the GII from the beginning of the transition in 1990 until 2015.

What does the GII index tell us about gender equality in transition economies?

Figure 1 reports values for the GII index in box plots, which show the index 25th and 75th percentile (respectively bottom and top of the box), its median (horizontal line in the box), its maximum and minimum (whiskers), and outliers (dots) for two groups of countries: transition countries and Western-European countries. We have reconstructed the values of the GII index for a limited set of countries within these groups (see the note to Figure 1 for the list of countries). When interpreting Figure 1, recall that higher GII values imply more inequality.

Figure 1. The Gender Inequality Index in transition countries and Western Europe, 1990-2015

Nov122018_Figure1

Source: Own calculations based mainly on UNDP data. The transition countries are: Armenia, Bulgaria, Georgia, Hungary, Poland, Romania, and the Russian Federation. For Western Europe the countries are: Austria, Belgium, Cyprus, Denmark, Finland, France, Greece, Iceland, Italy, Luxembourg, Malta, the Netherlands, Norway, Portugal, Spain, Sweden, and Switzerland.

Figure 1 shows that based on the GII, median gender inequality is larger in transition countries than in Western Europe and has been so throughout the entire period since 1990. In both regions, the index shows a decreasing trend, after an initial increase in 1995 in the transition countries. As we show in the Policy Paper, this decrease is mainly due to a drop in female representation in national parliaments. The variance of the index scores has declined over time in Western Europe, while it remained mostly unchanged in the transition countries.

The evidence from the GII is somewhat at odds with the common notion that transition countries enjoy relatively low level of gender inequality. However, it is important to notice that transition and Western European countries are generally at different levels of development. Figure 2 displays the country groups’ performance in relation to their level of human development. This is done by measuring the difference between their GII ranking and their Human Development Index ranking (HDI) among all the countries with non-missing GII values in the years considered. The HDI is an UNDP-developed measure of overall human development. See the policy paper for details about its measurement. The larger the difference between GII- and HDI-ranking, the worse the group performance in terms of gender inequality in relation to its level of development.

Figure 2. Difference between Gender Inequality Index ranking and Human Development Index ranking in transition countries and Western Europe, 1990-2015

Nov122018_Figure2

Source: Own calculations based mainly on UNDP data.

The trends between transition countries and Western Europe are now opposite. In 1990, the median standing in terms of gender inequality was better than that in human development for transition countries, and the relative level of gender inequality was lower than in Western Europe. The (negative) difference between GII and HDI ranking however appears to have narrowed over time, and it is close to zero in 2015. Western European countries have instead improved their gender equality ranking in relation to their ranking in terms of human development over the period studied. Put differently, the ranking improvement in terms of human development in former socialist countries since the transition have not translated into comparable gains in gender equality ranking as measured by the GII index.

It is also important to emphasize that, according to several scholars, a dichotomy in terms of gender relations existed in transition countries during the socialist period. This is because on one hand the socialists put substantial into effort to empower women economically (see e.g. Brainerd, 2000; Pollert, 2003; Campa and Serafinelli, 2018), but on the other hand they failed to eliminate patriarchy (LaFont, 2001). This suggests that a composite index can mask important contrasting patterns among its components. In the Policy Paper we uncover such contrasting patterns. By looking separately at the different components of the GII index, we show that while Western European countries have invariantly improved their levels of gender equality since 1990, the trend in transition countries depends on the measure one looks at: Women maintained, but did not improve, their relative status in the labor force. They gained more equality in education and especially in terms of reproductive health, and lost descriptive political representation.

Conclusion

In this policy brief we have studied the development of gender inequality in transition countries through the lens of the Gender Inequality Index, whose span we have extended to the beginning of the transition period. We have shown that, based on this index, gender inequality has decreased since 1990 in transition countries, a trend which is common to that in Western Europe. However, once the changes in overall development during this period are taken into account, it appears that transition countries fared better in 1990 than today. Our analysis thus shows that analyzing gender inequality indexes in absolute terms and in relation to levels of development can deliver different conclusions. The factors that account for these differences should be kept in mind in policy discussions and policy-making. Some issues related to gender inequality, such as maternal mortality, are potentially addressed with a comprehensive strategy aimed at overall development. Conversely, other drivers of gender inequality, such as women’s political empowerment, do not necessary go hand in hand with overall development, and might therefore require more targeted policy interventions.

We have also cautioned the reader about the limitation of using comprehensive indexes to describe developments in gender inequality. A comprehensive index can overshadow important sources of gender inequality if it is composed of sub-indexes that move in opposite directions. This point can be especially relevant in the context of transition countries, which historically experienced a top-down approach to gender equality, the results of which in the long-term appear to be major advancements in some dimensions of women’s empowerment and contemporary potential backlash in other dimensions. It has been argued, for instance, that low levels of female representation in political institutions in transition countries can be the result of women’s large participation in the labor market while the division of roles in households remained traditional. In the words of anthropologist Suzanne LaFont (2001), “Women have been and continue to be overworked, and their lives have been over-politicized, the combination of which has led to apathy and/or the unwillingness to enter the male dominated sphere of politics. Many post-communist women view participation in politics as just one more burden”. In such a context, average values of an index of gender equality might mask high achievements in economic empowerment coexisting with lack of political representation.

References

  • Brainerd, E. (2000), ‘Women in Transition: Changes in Gender Wage Differentials in Eastern Europe and the Former Soviet Union’, Industrial and Labour Relations Review, 54 (1), pp. 138-162.
  • Campa, P. and Serafinelli, M. (2018), ’Politico-economic Regimes and Attitudes: Female Workers under State-socialism’, Review of Economics and Statistics, Forthcoming.
  • LaFont, Suzanne (2001), ‘One step forward, two steps back: women in the post-communist states.’ Communist and post-communist studies 34(2), pp. 203-220.
  • Pollert, A. (2003), ‘Women, work and equal opportunities in post-Communist transition’, Work, Employment and Society, Volume 17(2), pp. 331-357.

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