Tag: gender equality
Does Foreign Aid Foster Female Empowerment?
Over decades much attention has been devoted to the relationship between foreign aid and economic growth, while few studies have focused on the effects of foreign aid on female empowerment. This despite the fact that empowerment of girls and women is a key driver of development, and often an explicit objective of foreign aid. Using geo-coded data on aid project placement and household-level survey responses, Perrotta Berlin, Bonnier and Olofsgård (2023), show that foreign aid has a modest but robust effect on several dimensions of female empowerment. This is the case for both aid in general and gender-targeted aid, highlighting the potential of foreign aid to reduce gender inequalities. It is also found, though, that the impact is contingent on the context, and that there can even be a backlash in male attitudes towards female empowerment in more traditional communities.
The donor community has long been invested in the empowerment of women and girls, and the 2030 Agenda for Sustainable Development also includes gender equality as an explicit goal. Yet surprisingly little quantitative research has tried to make a broader assessment of the effect of foreign aid on gender equality measures.
This policy brief summarises a study by Perrotta Berlin, Bonnier and Olofsgård (2023) which addresses this question by matching the location of aid projects with geo-coded household surveys in Malawi between 2004 and 2010. Analysing the community-level impact on five different female empowerment indices, the study finds foreign aid to affect positively women’s empowerment across several dimensions. Furthermore, the authors find that gender-targeted aid has an additional impact on an index measuring women’s control over sexuality and fertility-related decisions and an index focusing on violence against women.
When considering areas with patrilineal land inheritance traditions, the results however partly shift, especially in relation to men’s attitudes. This implies that the success of foreign aid and gender-targeted aid in reducing gender inequalities may be conditional on the community context.
Gender Equality and Foreign Aid in Malawi
Malawi is highly dependent on foreign aid. Net official development assistance (ODA) has exceeded 10 percent of gross national income yearly since 1975, reaching as high as 23.5 percent in 2016 (World Bank, WDI database).
In recent years, reforms have been undertaken by the Malawian government to improve gender equality. The minimum legal age of marriage was raised from 15 to 18 through the 2015 Marriage, Divorce and Family Relations Bill, and the 2013 Gender Equality Act strengthened the legislation concerning gender-based violence and included a universal condemnation of all types of gender-based discrimination. Yet, in 2020, Malawi was ranked 116 out of 153 in the World Economic Forum Gender Gap Report and 172 out of 189 in UNDP’s Gender Inequality Index. An area of concern regards the high rates of child marriage, with 9 percent of girls already married at age 15 and 42 percent by the age of 18. Alongside these numbers, 31 percent of women report to have given birth by the age 18.
Another aspect potentially influencing gender equality is the prevalence of matrilinear land tenure systems, particularly in the southern and central parts of the country (as depicted in Figure 1). While previous research has shown that land ownership empowers women and suggested that property rights affect decision power over key decisions, fertility preferences, age of marriage etc., less research has been devoted to analysing the effects on women’s empowerment outcomes in a matrilinear kinship setting. Some recent literature however suggests women in matrilinear societies have greater say in household decisions – including financial ones – and are less accepting of, as well as exposed to, domestic violence (Lowes, 2021; Djurfeldt et al., 2018).
Figure 1. Intensity of matrilineal tenure in Malawi.
Notes: The figure plots the geographic distribution of the authors’ matrilineal indicator. They base their definition of matrilineal societies on the ethnic identification of individual respondents. The intensity at the cluster level varies between 0 and 1 representing the share of respondents that identify themselves as belonging to one of the ethnic groups classified as matrilineal.
Source: Perrotta Berlin, Bonnier, Olosgård (2023).
Methodology and Data
For the analysis, the authors make use of geo-coded data on aid projects from the Government of Malawi’s Aid Management Platform (AMP) and match it to household-level data from the Malawi Demographic and Health Survey (DHS). The country of Malawi and the period 2004-2010 were chosen in order to maximize data coverage on aid disbursement. Malawi’s AMP covers 80 percent of all aid entering the country during those years, which gives a much more complete picture compared to only focusing on one specific donor.
To identify causal impact, the authors apply a difference-in-differences specification on survey clusters in proximity to aid projects implemented between 2004 and 2010. Proximity was identified as within a 10-kilometer radius from an aid project. Among those, households interviewed in 2004, i.e., prior to the implementation date of any aid project, were considered the control group, and households interviewed in 2010 formed the treatment group. The underlying assumption of parallel pre-treatment trends was confirmed with the use of earlier DHS surveys. The model specification includes individual-level controls (age, ethnicity, household size, a Muslim dummy, years of education and literacy) and also a geographic fixed-effect based on a grid of coordinates.
The analysis distinguishes between the impact of aid in general, and the additional impact of gender-targeted aid. Gender-targeted projects are defined as projects that have any of the words woman, girl, bride, maternal, gender, genital or child, in the title, description or activity list. When estimating the effect of gender-targeted aid the authors control for overall aid intensity in the household’s vicinity. The estimated effect should therefore be interpreted as the additional effect from being exposed to a gender-targeted aid project while keeping the general number of aid projects in the area constant.
Figure 2. Map of aid projects and household clusters from 2004 and 2010 survey waves in Malawi.
Notes: The figure plots the geographic distribution of aid projects and of household clusters in the two DHS waves. The colour of the dots reflects whether the project has a gender component or not, while the shape of the household dot reflects the survey wave.
Source: Perrotta Berlin, Bonnier, Olofsgård (2023).
To capture female empowerment, the authors make use of thousands of responses to DHS survey waves from 2004 and 2010. From these responses, the authors construct four different indices. Two of these are modelled on indices used in different contexts by Haushofer and Shapiro (2016) and Jayachandran et al. (2023). The former captures experiences of violence together with men’s and women’s attitudes towards violence, and some measures of decision making and control over household resources. The more recent index by Jayachandran et al. (2023) focuses on female agency and includes questions on women’s participation in decisions on large household purchases and daily expenditures, decisions on family visits, and decisions concerning their own healthcare.
To also capture questions related to sexual and fertility preferences, often regarded as measures of female empowerment, the authors construct two additional indices. The women’s attitudes index is based on responses to questions about whether the respondent is able to refuse sexual intercourse with her husband and ask him to use a condom, age at first marriage, and age at first childbirth, among others. The men’s attitudes index is based on questions about whether the respondent thinks it is justified to use violence to force intercourse, if a woman is justified to refuse intercourse, as well as fertility and child spacing preferences. In addition, all four indices are weighted and combined into an aggregated general index.
Results
Considering all aid projects, the authors find that being exposed to an aid project in the 2004 to 2010 window has a significant positive impact on the agency index, the female attitude index and the combined general index (12, 11 and 31 percent of their respective means). When considering gender-targeted aid, the authors found the exposure to at least one such project to increase the women’s attitude index by 7 percent and the general index by 17 percent of their respective means. The impact is present for both a narrower and a wider exposure area, and quite persistent over time.
When breaking down the analysis for areas with matrilineal versus patrilineal land tenure systems the results diverge. In communities where the share of matrilineal ethnic groups exceeds the mean of 73 percent, the results are largely in line with those in the full sample. In patrilineal communities (< 73 percent matrilineal households), the results are however vastly different. Aid projects in general, and gender-targeted aid in particular, affect negatively the men’s attitudes index. In addition, gender-targeted aid seems to have no additional impact on the other indices.
Conclusion
In the paper underlying this brief, the authors study the effect of foreign aid on female empowerment, a frequent but understudied objective often set by donors. Looking at geo-coded aid projects in Malawi, the authors estimated such projects to positively impact girl’s and women’s empowerment across several indices. This is true for aid in general, and for some indices even more so when considering gender-targeted aid. Some of the positive results disappear or even change sign, though, in patrilineal communities, displaying the significance of pre-existing community norms for the effectiveness of development investments. Aid even generates a backlash when it comes to men’s attitudes towards women’s sexual and fertility preferences in these communities.
The takeaway from the study lies in foreign aid’s potential to empower women in targeted communities. This however hinges on pre-existing norms in recipient communities – something that aid donors should be aware of.
The authors emphasize the need for more research to better understand the role of pre-existing norms in the uptake of aid, to distinguish direct effects from aid from potential spillovers, and to understand what type of aid projects deliver the best outcomes in terms of female empowerment.
References
- Djurfeldt, A. A., E. Hillbom, W. O. Mulwafu, P. Mvula, and G. Djurfeldt. (2018). “The family farms together, the decisions, however are made by the man” -Matrilineal land tenure systems, welfare and decision making in rural Malawi. Land use policy 70, 601-610.
- Haushofer, J. and J. Shapiro. (2016). The short-term impact of unconditional cash transfers to the poor: experimental evidence from Kenya. The Quarterly Journal of Economics, 131(4), 1973-2042.
- Jayachandran, S., M. Biradavolu, and J. Cooper. (2023). Using machine learning and qualitative interviews to design a five-question survey module for women’s agency. World Development 161, 106076.
- Lowes, S. (2021). Kinship structure, stress, and the gender gap in competition. Journal of Economic Behavior & Organization 192, 36-57.
- Perrotta Berlin, M., Bonnier, E., and A. Olofsgård. (2023). Foreign Aid and Female Empowerment. SITE Working Paper Series, No. 62.
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.
Lessons From the FROGEE Conference “The Playing Field in Academia: Why Are Women Still Underrepresented?”
Despite an increase in women’s representation since the beginning of the 20th century, women remain underrepresented in academia and other high-skilled professions. Academia has been prone to gender disparities both within and across fields as well as across academic ranks. In an endeavour to examine and address the underrepresentation of women in the academic profession, the Centre of Economic Analysis (CenEA), together with the Stockholm Institute of Transition Economics (SITE) and other partners of the Forum for Research on Gender Economics (FROGEE) at the FREE Network, organized the two-day conference “The playing field in academia: Why are women still underrepresented?”, in Warsaw June 21-22, 2023. This brief offers insights from the presentations and panel discussions held at the conference.
To date, there are few, if any, high-skilled professions exhibiting gender balance, and academia is no exception. Consequently, this imbalance has been subject to increased multidisciplinary research attention, exploring its origins and potential remedies. However, attaining a comprehensive understanding of gender disparities remains a challenge. For instance, much remains to be learnt about their long-run dynamics, a subject addressed by Carlo Schwarz, in one of the conference’s keynote lectures.
A Century of Progress
Carlo Schwarz (in joint work with Alessandro Iaria and Fabian Waldinger, 2022) trace the evolution of gender gaps in academia across a variety of domains at the global level throughout the 20th Century. Facilitated by an unprecedentedly large database of nearly 500,000 academics, spanning 130 countries and supplemented by publication and citation data, the authors specifically examine gender imbalances in recruitment, publishing, citation patterns, and promotions.
They find that in 1900 women constituted roughly 1 percent of all hires in academia (226 women, with only 113 hired as full professors). By 1969 the share of female academics had risen to about 6.6 percent, and by the year 2000 it had grown to approximately 17 percent. These rates varied across disciplines, institutions, and countries. For instance, teaching-centric disciplines such as pedagogy and linguistics, exhibited higher representation relative to research-oriented ones.
The research subsequently reveals a hump-shaped evolution of the gender gap in academic output – starting small before peaking at 45 percentage points fewer publications by women in 1969, thereafter declining to 20 percentage points. These publication disparities were also found to share a U-shaped relationship with the share of women in academia, indicating the interconnectedness of gender gaps.
The authors also address gender gaps in citations, identified by the use of a novel machine learning approach, forecasting a paper’s citations had it been written by a man. The results indicate a progressive reduction in the citation gap during the 20th century, decreasing from 27 percentage points (pre-WW1) to 14 percentage points (interwar) and eventually to 8 percentage points (post-WW2) fewer citations of papers by female relative to male academics. These gender gaps in academic output reiterated current evidence from Mexico, presented at the conference by Diana Terrazas-Santamaria, showing that women are associated with lower citation rates. Terrazas-Santamaria attribute the low rates to gender differences in both the number of publications and duration of academic careers.
The work by Iaria, Schwarz and Waldinger (2022) further showcase the gender disparities in career advancement in academia, which similarly decreased over the years. At the point of the greatest gender disparity, women required an approximately 6 percentage points better publication record to have the same promotion probabilities as their male counterparts.
The Leaky, Dry Pipeline
In the conference’s second keynote, Sarah Smith highlighted how academia, much like other professional occupations, exhibits a leaky pipeline. This is a phenomenon characterized by a declining representation of women as they ascend through the academic hierarchy. When examining specific fields, Smith’s results indicate that the gender disparities in economics much more closely align with those observed in STEM fields (science, technology, engineering, and mathematics) than other social science disciplines. Furthermore, the economics’ field illustrate a significant lack of diversity among its new entrants. This phenomenon, referred to as the dry pipeline, generates future cohort implications, as they result in less demographically representative cohorts from which future professors can be recruited (see Stewart et al., 2009).
The cross-disciplinary comparison of the dry pipeline addressed in the keynote, contest the mathematical rigor of economics as a barrier to entry, as mathematics itself demonstrated higher women representation at A-level and undergraduate levels. In a following discussion panel, which focused on ensuring a fair start in academia (comprised of Yaroslava Babych, Alessandra Casarico, Federica Braccioli and Marta Gmurek, and moderated by Maria Perrotta Berlin), the panellists acknowledged that deeply engrained social expectations, gender trained behaviours and a lack of awareness constitute some of the persistent hindrances to the (early) involvement of women in specific fields, and the academic profession in general.
Additional factors influencing the gender balance in recruitment and promotion are gendered references, and the presence or absence of shared research interests between candidates and recruitment panels. These themes were extensively investigated in the work presented by Alessandra Casarico on the conference’s opening day. Specifically, results from collaborative work with Audinga Baltrunaite and Lucia Rizzica, highlight that grindstone words (e.g., “determined”, “hardworking”, etc.) are frequently used in recommendation letters to describe female candidates, while standout words (e.g., “excellent”, “strongest” etc.) typify male candidates’ references. Compared to their male counterparts, women are also shown to be more inclined to accentuate personality traits when serving as referees. This added to a broader literature demonstrating that female candidates’ recommendation letters frequently exhibit brevity, raise doubts, carry a weak tone, and emphasize candidates’ interpersonal skills and personality traits rather than their ability. Moreover, separate results from Casarico’s work (with Piera Bello and Debora Nozza) illustrate that research similarity between the recruiting committee and the candidate predict the likelihood of recruitment. The authors argue that the relationship is indicative of a bias against women if – as shown by the authors – women are less likely to be the candidates with the highest similarity.
In her presentation, Anne Sophie Lassen offered a different factor that may contribute to the attrition in the pipeline: the influence of parenthood on academic careers. Results from her work (with Ria Ivandić) indicate that while parenthood does not significantly influence graduation rates, it extends doctoral studies by an average of 7 months for women. Moreover, Lassen highlighted a declining trend of remaining in academia after becoming a parent, particularly pronounced among women.
More Areas of Imbalance
The remaining conference presentations and panel discussions explored additional domains of gender imbalances within academia. Iga Magda showcased evidence from her joint work with Jacek Bieliński, Marzena Feldy and Anna Knapińska of gender differences in remuneration during the early stages of an academic career, substantiating a gap within a year of graduation. These disparities endure throughout respondents’ careers and are contingent on the field of study – largest among engineering and technology graduates and lowest among those from the humanities and arts fields. Furthermore, it was observed that productivity plays a negligible role in the identified pay gaps, as its impact is similar for both genders.
The panel composed of Eleni Chatzichritou, Marta Łazarowicz-Kowalik, Jesper Roine and Joanna Wolszczak-Derlacz, and moderated by Michał Myck, deliberated on exposed disparities in the application for, and the success rates in attaining research funding in Poland and Europe – as seen in the National Science Centre (NCN) and the European Research Council research grants, respectively. The discussion highlighted how quantitative measures used in the allocation of research funding are riddled with subjective criteria that often benefit male academics. They also recognized how quests to allocate funds to the most successful candidate inadvertently penalize women with career breaks.
Another panel including Lev Lvovskiy, Carlo Schwarz, Sarah Smith, Marieke Bos and Joanna Tyrowicz, and moderated by Pamela Campa, lauded the growing objective data shedding light on gender inequalities in academia. The panellists discussed current challenges in identifying and quantifying aspects of gender disparities. For instance, currently used proxies do not allow to capture more subtle disparities, like microaggressions faced by female academics from students – emphasizing the need for more individual level survey data.
The panels were further enriched by personal anecdotes and filled with retrospective advice shared by both early career and established academics. To contextualize the above, a few cases from the FREE Network countries follow.
Evidence From Within the FREE Network
Yaroslava Babych shared insights concerning women in higher education in Georgia and other countries of the South Caucasus. Preliminary findings of her study confirm the presence of gender inequality in academia, evident in disparities in access to higher education as well as gender segregation across both fields and countries. Notably, women comprise a majority of the graduates in bachelor’s and master’s of art programs, whereas higher research-level programs such as doctors of science, and top echelons of the academic hierarchy remain predominantly male. Moreover, female academic output is found to be lower than that of male counterparts.
Lev Lvovskiy discussed the case of Belarus, highlighting the influence of the Soviet legacy. A significant factor linked to this legacy is exploiting university enrolment to circumvent compulsory conscription of men, allowing male university admissions to serve a secondary purpose beyond acquiring knowledge. This increases the perceived opportunity cost of enrolling a woman. Lvovskiy further documented the academic trajectories of Belarusians, revealing a majority of women at college and doctoral levels, but being underrepresented among doctoral graduates. The results further indicate significant cross-disciplinary gender disparities, with humanities having close to 80 percent women representation and engineering and information and technology (IT) fields having less than 30 percent women representation.
Monika Oczkowska provided evidence of gender disparities in Poland. Findings from the country reveal an overrepresentation of women graduates from bachelor through doctoral levels, and relative parity at post-doctoral level, but lower proportions at habilitation, associate professor, and professor levels. These general results confirm the higher detail findings presented by Karolina Goraus-Tanska on the first day of the conference. Results from Goraus-Tanska’s work (with Jacek Lewkowicz and Krzysztof Szczygielski) suggest that the drop-off among female academics from habilitation levels is not attributed to higher output expectations for women, but rather stems from the impact of parenthood.
Oczkowska further demonstrated that female academics in Poland are characterized by fewer international collaborations and lower levels of international output. Polish female academics were also showcased to engage in more international mobility during their doctoral studies relative to men, with the converse holding true after obtaining a doctoral degree. A potential explanation for this mobility decline among female academics, could be the increased burden of familial responsibilities at the post-doctoral and higher levels. Moreover, fewer women were reported to have applied for NCN grants and were underrepresented among the beneficiaries of these calls. Lastly, female academics in Poland record significantly lower total project costs relative to their male counterparts.
‘Plugging’ the Leak
In light of the aforementioned, what measures can be taken to address the gender imbalances in academia? As summarized by Sarah Smith, early initiatives have involved tracking women representation (e.g., in admissions, progression, hiring, etc.) within departments and/or institutions to identify where in the pipeline their progress is impeded. Attempted initiatives include formulation of seminar guidelines to overcome unfair experiences, as well as using gender-blind recruiting and objective hiring criteria to equalize hiring opportunities. Some other efforts, such as diverse recruitment panels have been unsuccessfully adopted, as they seem to embolden hostile male recruiters and load female panellists with unrewarded administration tasks. Conversely, mentoring has helped women build networks, publish more, and advance professionally. Awareness raising campaigns have reduced disparities in teaching evaluations and remain vital in addressing the dry pipeline and both transparent workload allocation and rewarding of administrative tasks have been shown to reduce promotion gaps in academia. In addition to the above, initiatives such as fostering gender-neutral networking opportunities, collaborations and a more diverse faculty were also deliberated during the conference.
Concluding Remarks
The conference advanced dialogue on societal and structural constraints to gender equality in academia and provided a platform to exchange ideas on how the shared objective of a more inclusive and equitable academic environment can be achieved. While the challenges remain abundant, and the costs associated not always negligible, it remains crucial to assess achievements, such as those resulting from mentoring and awareness intervention initiatives and recognize that further opportunities to enhance equity within the profession exist.
Additional Material
Seminar Participants – short bios
Conference Programme 22.06.2023
Conference Participants – short bios
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.
Potential Climate Change Impacts on Women’s Vulnerability in Georgia
Climate change can increase the vulnerability of women to various risks, including natural disasters, food insecurity, water scarcity, and health problems. Women may also face unique challenges in accessing resources and services, which can limit their ability to adapt to a changing climate. Developing countries, with their more traditional gender roles, are even more likely to experience disproportionate impacts of climate change on women, and Georgia is no exception. Thus, the country needs to address this problem through a comprehensive approach which accounts for the social, economic, and environmental factors that contribute to gender inequality.
Introduction
According to Georgia’s fourth national communication report to the United Nation’s Framework Convention on Climate Change, the negative impacts of climate change on ecosystems and the economy can hinder Georgia’s path toward sustainable development. Therefore, a key focus for the country should be to develop climate-resilient practices and reduce the vulnerability of communities exposed to these impacts.
The climate scenarios in the communication report present a worrying picture of warming trends in the country, mainly due to increased temperatures in the last summer and autumn seasons, as depicted in Figure 1 (MEPA, 2021). Such alterations in weather patterns often lead to glacier retreat, water scarcity, coastal erosion, and biodiversity loss in different regions of Georgia (ibid).
Figure 1. Average summer and winter temperatures in Georgia, 1900-2021.
Source: Climate Change Knowledge Portal (World Bank, 2021).
An increasing body of international research has demonstrated that climate change can have adverse effects on agricultural production, food security, water management, and public health. Furthermore, research has revealed that these effects are not gender-neutral, with women and children being among the most affected groups (World Bank Group, 2021).
Climate Change Impacts on Women – A Georgian Perspective
Women in developing countries like Georgia experience various impacts of climate change, which affect them differently than men. The effects might vary according to region or community, but some common signs can be identified. The main channels through which women are disproportionally affected by climate change are discussed in the following sub-sections.
Health Impacts
Climate change has a significant effect on human health, with women being more vulnerable due to various cultural, social, and economic factors (Sbiroli et al., 2022). In particular, women appear to be more susceptible to infectious diseases and undernutrition, especially in middle and low-income countries (ibid).
Springmann et al. (2016) found that, by 2050, Georgia could experience about 32.36 climate-related deaths per million due to malnutrition caused by a lack of fruits and vegetables in people’s diets and due to increased health complications associated with undernutrition. In Georgia, malnutrition is a significant gender equality concern. According to the Global Nutrition Report, women in Georgia disproportionally experience exposure to undernutrition translated into underweight. Similarly, women represent the majority of Georgians with obesity (26.8 percent, compared to 22.2 percent among men). Both these issues may be further exacerbated by climate change in the future.
Furthermore, in Georgia, women are more likely to care for sick family members. According to Geostat, 31 percent of women who have sick or dependent family members are involved in providing them care, compared to only 15 percent of the men. This puts women at greater risk of exposure to climate change-induced infectious diseases, given that research has demonstrated an increased risk of such diseases worldwide, including in areas in Europe that have climate profiles similar to Georgia (Mora et al., 2022; Gray et al., 2009).
Figure 2. Prevalence of underweight among adults (>18) in Georgia, 2000-2016.
Source: Global Nutrition Report (2023).
Water and Food Scarcity
Climate change is also known to affect food and water supply through changes in agricultural conditions, droughts, and floods. In developing countries as women are often responsible for food and water supply, they are disproportionally affected by water shortages resulting from climate change (Figueiredo & Perkins, 2013). Women in poor rural households in Georgia are likely to face similar challenges.
Women in Georgia also play a crucial role in agriculture (according to Geostat, 47 percent of workers in agricultural holdings were women in 2021). Fluctuations in temperature and precipitation patterns can reduce crop yields, leading to lower income and food insecurity. This may disproportionately affect female farmers, as access to agricultural technologies, land ownership and lack of necessary knowledge and skills are some of the significant barriers for women involved in agriculture in Georgia (Gamisonia, 2015).
Economic Impacts and Access to Resources
One of the main reasons to why women are disproportionally affected by climate change is that their underlying economic conditions are less favourable than men’s (Yadav & Lal, 2018). In 2021, the majority of people outside the labor force were women (65 percent), while men constituted 35 percent (Geostat). It is important to mention that in the same year, only 33 percent of women were employed in Georgia, compared to 49 percent of men. Additionally, the average salary for women was 1056 GEL (813 GEL in the agricultural sector), while men earned an average of 1538 GEL (1006 GEL in the agricultural sector). Finally, although poverty rates among women in Georgia are slightly lower than among men, 17.1 vs. 17.9 percent respectively (absolute poverty rates in 2021), the poverty data does not account for the gender-biased distribution of household resources. Women face larger barriers in obtaining financial resources (collaterals, loans, etc.) than men because they own less property. For different types of property, only 44 percent are owned by at least one woman, according to the National Agency of Public Registry of Georgia. The corresponding number for men is 56 percent. Geostat data further indicates that households headed by men make up 63 percent of the total number of households, whereas households headed by women account for only 37 percent. These unfavorable conditions hinder women’s access to vital information and resources required for climate change adaptation and mitigation.
The discussed impacts may be especially prominent for women in poor rural households. Climate change-induced natural disasters are typically more detrimental for households dependent on agriculture (Dagdeviren et al., 2021), especially subsistence farmers and poor agricultural workers (in particular those without access to technology or resources). In Georgia, women are in majority in both these categories.
Natural Disasters and Displacement
Climate-driven disasters are over 14 times more likely to cause fatalities among women and children than men, according to UNHCR (2022). Additionally, women in agrarian societies impacted by climate change are less likely to use adaptive measures, putting them at higher risk of displacement (Palacios, Sexsmith, Matheu & Gonzalez, 2023). Such risks are also likely to pertain to the rural areas of Georgia.
Georgia’s International Obligations and Policies
In previous decades Georgia has made significant progress when it comes to incorporating gender equality and climate change into the policy agenda. In particular, Georgia follows numerous international legislative initiatives regarding sustainable development, gender equality, and climate change.
Georgia is a party to the Paris Agreement and the Beijing platform (a comprehensive roadmap for women’s rights and empowerment, which lists the problems associated with gender inequality and different strategies to overcome them, signed by Georgia in 1995). It is also a signatory of the Gender Action Plan (GAP), adopted a year after the Paris Agreement to integrate gender into targets and increase effectiveness, fairness, and sustainability.
The updated Nationally Determined Contribution (NDC) of Georgia includes a dedicated section on gender and climate change. This section aims to promote gender mainstreaming, encourage equal participation, empower women, build capacity, and develop climate policies that are responsive to gender considerations. Furthermore, the Long-term Low-emission Development Strategies for Paris Agreement parties (including Georgia) has a communication and awareness-raising strategy that seeks to address gender, youth, and people with disabilities in its outreach efforts (United Nations, 2022).
Despite these commitments, Georgia is lagging when it comes to tackling the issues of climate change and gender in coordination. For example, even though Georgia has adopted a Gender Equality Law and Action Plan, it does not address climate change issues. Therefore, municipalities are not required to consider gender aspects of climate change impacts.
Identified Gaps and Policy Recommendations
Despite the number of policies and measures undertaken, unsolved problems hinder the country’s ambition to adhere to gender-mainstreamed climate change-addressed policymaking.
For example, there is a lack of gender-disaggregated data on the impacts of climate change in Georgia, which prevents policymakers from developing targeted strategies to address women’s needs. Therefore, collecting and analyzing disaggregated data with gender-specific impacts in mind is recommended. Additionally, involving women in decision-making and ensuring their participation in climate change efforts is crucial as their unique experiences and perspectives can inform more effective and equitable responses to climate change impacts.
As previously mentioned, climate change in Georgia is expected to exacerbate water and food scarcity, which can disproportionately affect women. Therefore, implementing climate-resilient water management strategies and increasing access to climate-resilient agricultural practices, such as crop diversification and improved irrigation systems, can help increase farm productivity and reduce the adverse impacts of climate change on women.
Furthermore, there is a need to provide women with access to financial resources and services and to address gender-based inequalities that may limit women’s ability to access information and resources necessary for climate change adaptation and mitigation.
Finally, addressing the impacts of climate change on women in Georgia will require a coordinated and sustained effort from a range of stakeholders, including governments, civil society organizations, and local communities, so that women are not left behind in the global effort to address the impacts of climate change.
Conclusion
To effectively address the impacts of climate change on women in Georgia, it is essential to recognize that various social, economic, and cultural factors shape women’s experiences. For example, women in rural areas may face different challenges than women in urban areas; women with few economic means may be disproportionately affected by climate change. Therefore, policies should not only integrate gender-mainstreaming, but also account for these heterogeneities, to ensure that different parties of the society are adequately addressed within the climate change policy agenda.
References
- Dagdeviren, H., Elangovan, A., & Parimalavalli, R. (2021). Climate change, monsoon failures and inequality of impacts in South India. Journal of Environmental Management.
- Figueiredo, P., & Perkins, P. E. (2013). Women and water management in times of climate change: participatory and inclusive processes. Journal of Cleaner Production, 188-194.
- Gamisonia, N. (2015). Climate Change and Women. Heinrich Böll Stiftung.
- Global Nutrition Report. (2023). Global Nutrition Report. https://globalnutritionreport.org/resources/nutrition-profiles/asia/western-asia/georgia/
- Geostat. https://www.geostat.ge/en
- Gray, J. S., Dautel, H., Estrada-Peña, A., Kahl, O., & Lindgren, E. (2009). Effects of Climate Change on Ticks and Tick-Borne Diseases in Europe. National Library of Medicine.
- MEPA. (2021). Fourth National Communication of Georgia to the UNFCCC. Tbilisi: UNDP.
- Mora, C., McKenzie, T., Gaw, I. M., Dean, J. M., Hammerstein, H. v., Knudson, T. A., Franklin, E. C. (2022). Over half of known human pathogenic diseases can be aggravated by climate change. Nature Climate Change.
- National Agency of Public Registry of Georgia. https://www.napr.gov.ge/
- Palacios, H. V., Sexsmith, K., Matheu, M., & Gonzalez, A. R. (2023). Gendered adaptations to climate change in the Honduran coffee sector. Women’s Studies International Forum.
- Sbiroli, E., Geynisman-Tan, J., Sood, N., Maines, B. A., Junn, J. H.-J., & Sorensen, C. (2022). Climate change and women’s health in the United States: Impacts and opportunities. The Journal of Climate Change and Health.
- Springmann, M., Mason-D’Croz, D., Robinson, S., Garnett, T., Godfray, H. C., Gollin, D., Scarborough, P. (2016). Global and regional health effects of future food production under climate change: a modelling study. National Library of Medicine.
- UNHCR. (2022). Gender, Displacement and Climate Change. Potsdam Institute for Climate Impact Research.
- United Nations. (2022). Long-term Low-emission Development Strategies. United Nations.
- World Bank Group. (2021). Climate Risk Country Profile: Georgia. World Bank Group.
- World Bank. (2021). Climate Change Knowledge Portal. https://climateknowledgeportal.worldbank.org/country/georgia
- Yadav, S. S., & Lal, R. (2018). Vulnerability of women to climate change in arid and semi-arid regions: The case of India and South Asia. Journal of Arid Environments, 4-17.
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.
Economic and Social Context of Domestic Violence: Research Shared at the 2022 FROGEE Conference
This brief summarizes the research papers presented at the 2022 FROGEE conference “Economic and Social Context of Domestic Violence”, which took place on May 11, 2022. It was organized by the Stockholm Institute of Transition Economics (SITE) together with the Centre for Economic Analysis (CenEA) and the FREE Network. Two additional briefs related to the conference are published on the FREE policy briefs website – a brief on gender-based violence in conflict based on the panel discussion, and another sharing preliminary results from the recent FROGEE survey.
While the concerns about domestic violence (DV) and intimate partner violence (IPV) have been gaining prominence since the start of the COVID-19 pandemic, they were further exacerbated by the devastating events happening in Ukraine. Times of crisis or conflict makes the issue more severe, however, gender-based violence is sadly prevalent at normal times too, and a major portion of it is DV and IPV. Limiting violence towards women requires understanding the determinants of DV and IPV and the channels through which they take effect. With this in mind, the Stockholm Institute of Transition Economics (SITE) together with the Centre for Economic Analysis (CenEA) and the FREE Network invited researchers to present their work relating to the economic and social context of domestic violence. This brief provides an account of what was shared at the conference.
Prevention of Domestic Violence: What Works and What Doesn’t?
Three presented studies geared toward evaluating policies aimed to limit violence against women.
Dick Durevall shared his findings on IPV and national policy programs in Colombia, focusing on the laws and policies implemented based on the UN campaign “UNiTE to End Violence Against Women” between 2010 and 2015. To evaluate the effect of these policies, he adopts a differences-in-differences design and compares provinces that had a gender policy before this renewed effort with those that did not. This builds on the idea that provinces that had an IPV policy strategy before UN recommendations were adopted are more efficient in implementing new such policies. It is found that self-reported physical violence falls from 20% to 16% between 2010 and 2015 in provinces that had IPV policies while this number remained at 18% in those that did not. While sexual violence decreased in both groups, provinces with IPV policies experienced a stronger reduction.
Accurate reporting is a key issue when it comes to IPV since it makes up the foundation for designing effective policy. Due to long-lasting and tiresome judicial procedures, threats, social barriers, or emotional costs, victims might choose not to report. Looking at the introduction of specialized IPV courts in Spain, Marta Martínez-Matute presented her paper on how institutions shape reporting. Bestowed with specialized staff, victim-oriented resources, and a swifter judicial process, these courts are specifically designed to deal with IPV cases. Martínez-Matute and co-author investigate if these resources make women more prone to report IPV by exploiting the sequential rollout of specialized courts. They use yearly court-level data on individual IPV cases between 2005 and 2018 in a staggered difference-in-differences framework with matched control districts. The results show that the introduction of an IPV court in a judicial district reduces the length of the judiciary process by 61% and increases the reported number of IPV cases by 22%. Ensuring that this increase is not fully driven by a rise in false reports, it is found that the share of dismissed IPV cases remains unchanged. Further, it is shown that the increase is driven by less severe IPV cases and not aggravated IPV offenses or homicides.
A distinctive feature of DV crimes is that there is a high degree of recidivism, with many women experiencing repeated violence from the same partner. However, little is known about how police should respond to such crimes to ensure safety to those victimized. From one perspective police arrests deter repeated DV crimes since they incapacitate perpetrators and allow police to investigate while offering safety to victims. However, some argue that this safety is merely temporary and that DV arrests might trigger offenders to retaliate against victims, leading to increased long-term DV. Against this reasoning, Victoria Endl-Geyer presented a study on the relationship between police arrests and DV dynamics in the UK. It uses highly granular administrative data on the population of DV incidents in the West Midlands which allows the researchers to observe the detailed information on the incidents’ timing and location as well as on police officers and their crime scene responses. It adopts an instrumental variables approach using the dispatch team’s previous propensity to arrest (measured as the weighted average arrest rate of officers in the team) as an instrument. The results provide evidence consistent with a deterrence effect. While regular OLS estimates show an insignificant impact, the IV results indicate that an on-scene arrest decreases repeat DV incidents by 25-26 percentage points. They find that the effect is the same when restricting the sample to incidents reported by a third party, supporting that this effect is not driven by a change in reporting behavior.
Factors of Domestic Violence and its Mechanisms
Other studies presented at the conference focused less on policy assessment and more on identifying the determinants of IPV and DV.
Losing or obtaining a job causes a shock in the intra-relationship dynamics and changes the economic power balance between spouses. Deniz Sanin presented her paper on the DV effect of women’s employment in the context of Rwanda. Following the government-initiated National Coffee Strategy in 2002, the number of coffee mills in Rwanda increased from 5 to 213 over the course of ten years. This natural experiment allows studying the effect of having a paid job as it captures the shift from unpaid labor on a family farm to paid work on a mill, keeping job-related skills constant. Using survey data on both DV and labor market outcomes along with administrative data on DV hospitalizations, the study adopts a staggered difference-in-differences strategy and compares women before and after mill opening as well as within and outside of the catchment area (a buffer zone surrounding the mill). The results show that upon mill opening, the probability of working for cash increases and that of self-reporting domestic violence in the past 12 months decreases by 26% (relative to the baseline of 0.35). During the harvest months, the only period of the year in which the mills operate, hospitals are significantly less likely to admit DV patients compared to the month before the harvest season, suggesting that the initial results are not driven by reporting bias. Looking at the mechanisms, she finds evidence supporting an increased bargaining power explanation – women in catchment areas who are exposed to mill opening are more likely to have a bigger say in household decisions such as larger household purchases and contraception usage. Increases in husbands’ earnings and decreased exposure are also ruled out as possible channels since a decline in DV is also found among spouses where the husband works in a different occupation with no change in earnings.
Rather than studying the impact of women’s employment status, Cristina Clerici shared a related paper that focuses on male unemployment. To investigate its effect on IPV, the study exploits the exogenous shock to employment caused by COVID-19 containment measures in Uganda. The authors collect individual-level data via phone surveys on the incidence of IPV among food vendors, including information on husbands’ sector of employment. To identify a causal DV effect of male employment exit, the authors distinguish between two groups of women with similar pre-lockdown experiences of abuse: those with spouses employed in sectors where operations were halted by COVID-19 lockdowns (construction workers, taxi drivers, etc.) and those with spouses who were unaffected (food vendors, farmers, etc.). The results show that male unemployment increases the probability of experiencing physical violence by 4.9 percentage points, corresponding to a 45% increase relative to the average likelihood. The effect cannot be explained by increased exposure (the man being more at home) – affected and unaffected women spend on average an equal number of nights in the market, which could be used as a coping mechanism. This suggests it is the change in unemployment status itself that drives the increase in DV.
While most of the literature on domestic abuse has documented that its drivers often come from changing life conditions of the victim or perpetrator, there is broad anecdotal evidence that exogenous events can lead to exacerbations in domestic violence as well. Ria Ivandic presented her paper that documents a causal link between major football games and domestic violence in England. The authors use a dataset on the universe of calls and crimes in the Greater Manchester area. The data provides a time series on the incidence of different types of domestic abuse with information on the timing, relationship to the accused, and individual characteristics of the victim and perpetrator, including whether the perpetrator was under the influence of alcohol at the time of the incident. They adopt an event study approach focusing on the hours surrounding a game and document a substitution effect in that the two-hour duration of a football game is associated with a 5% decline in DV incidents. However, following the game, the initial decrease is offset as DV incidents start increasing and culminate after 10-12 hours, eventually leading to an aggregate positive effect which constitutes a 2.8% hourly increase on days when games are played.
The authors argue that alcohol consumption, rather than emotions, is the main mechanism through which domestic violence is affected by sporting events. Supporting this hypothesis, they first find that the outcome of the game or the associated element of surprise (measured using the ex-ante probability of winning a game through betting markets) does not affect the probability of DV occurring. Second, they show that the increase in DV following a game is solely driven by an increase in alcohol-related DV incidents, while those committed by non-alcoholized men remain constant. Further strengthening this finding, it is shown that for games scheduled early in the day, when perpetrators can start drinking sooner and continue throughout the day, they find a significant increase in DV incidents committed by alcoholized perpetrators while this is not the case for late-scheduled games.
The Role of Women’s Empowerment
In the literature on gender-based violence, there is a common disposition to think about women’s empowerment as a central element of DV mitigation. However, theories point in opposite directions making the effect of women’s economic empowerment rather unclear. On one end of the spectrum, there are bargaining theories indicating that an increase in women’s employment opportunities or income should have a negative effect on DV by creating outside options or increasing the bargaining power in a relationship. At the other end, there are backslash theories arguing that enhancing women’s financial empowerment may further exacerbate violence by undermining the role of the breadwinner, triggering male partners to retaliate with the use of violence in order to restore the power balance. Going in the same direction, theories of instrumental violence point towards that the male partner might also use violence to extract resources.
In her keynote lecture, Bilge Erten outlined the evidence relating to DV and women’s empowerment and discussed to what extent and in which contexts these theories are supported.
The evidence of a positive or negative effect of empowerment may depend on which aspect of it is studied. Education is seen as an important one because it has the potential to raise women’s self-awareness of IPV, increase the likelihood of matching with a well-educated partner (which is negatively correlated to abusive behavior), and improve labor market outcomes. Although evidence is scarce in this area, Erten shared her own findings on the causal effect of education reform on IPV in Turkey. In line with instrumental violence theories, it is found that, while women in cohorts affected by the reform performed better in the labor market, they experienced more psychological violence and financial control behavior, and there was no sign of an effect on DV attitudes, partner-match quality or marriage decisions.
What we know about women’s empowerment and DV is also different across countries. When it comes to the effect of employment, findings from developed countries are generally consistent with bargaining theory explanations while what is found in the developing world is more mixed. This is also the case for studies on unilateral divorce laws – while a negative effect on IPV has been documented in the United States, a positive effect of these laws is found in Mexico.
Assessing the literature on the income effect leads to a somewhat ambiguous verdict too. Although generally, most studies confirm that overall violence declines with women’s income, there is often heterogeneity in the effect. It has for instance been found that the sign of the income effect from cash transfers on DV changes from negative to positive as the size of the transfer increases.
Finally, Erten provided some important policy considerations. There is evidently a widespread backlash problem that can arise after a policy intervention of the types discussed above. Policymakers need to think more about monitoring and protecting victims from more violence when implementing such a policy. Further research assessing post-intervention is also needed to identify interventions that are the most effective in minimizing domestic violence. In particular, a change in broad social norms around gender roles should be a desirable outcome, to the effect that a new, improved status of women in society and in the household becomes more culturally acceptable and needs not lead to backlash. In the case of expressive violence (that is not a rational, calculated response but rather a compulsion in the heat of the moment), mental health interventions should also be considered.
Concluding Remarks
As highlighted by the 2022 FROGEE conference, domestic violence not only has been put in the spotlight following the pandemic or the ongoing conflict in Ukraine, but is widespread across the globe in regular conditions too. The mixed findings shared at the conference suggested that policies limiting gender-based violence should be designed with respect to the cultural and social setting where they are to be implemented as the heterogeneity is very high across contexts. Although research has come a long way, the conference stressed that there is much more to be done, in terms of not only knowledge but also the political will and commitment to seriously address the issue of gender-based violence.
The presentations held at the conference can be viewed at this link and a separate policy brief based on the panel discussion on gender-based violence in times of conflict can be found here.
List of Speakers
- Cristina Clerici, Ph.D. Student in Economics at the Stockholm School of Economics.
- Dick Durevall, Professor at the Department of Economics, University of Gothenburg.
- Victoria Endl-Geyer, Doctoral Student at the IFO Institute.
- Bilge Erten, Associate Professor of Economics and International Affairs at the Institute for Health Equity and Social Justice Research at Northeastern University.
- Ria Ivandic, Associate Researcher at the London School of Economics.
- Marta Martínez-Matute, Assistant Professor at the Department of Economic Analysis at Universidad Autónoma de Madrid.
- Deniz Sanin, Ph.D. Candidate at Georgetown University.
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.
Understanding the Economic and Social Context of Gender-based and Domestic Violence in Central and Eastern Europe – Preliminary Survey Evidence
This brief presents preliminary findings from a cross-country survey on perceptions and prevalence of domestic and gender-based violence conducted in September 2021 in eight countries: Armenia, Belarus, Georgia, Latvia, Poland, Russia, Sweden and Ukraine. We discuss the design and content of the study and present initial information on selected topics that were covered in the survey. The collected data has been used in three studies presented at the FROGEE Conference on “Economic and Social Context of Domestic Violence” and offers a unique resource to study gender-based violence in the region.
While the COVID-19 pandemic has amplified the academic and policy interest in the causes and consequences of domestic violence, the Russian invasion of Ukraine has tragically reminded us about the gender dimension of war. There is no doubt that a gender lens is a necessary perspective to understand and appreciate the full consequences of these two ongoing crises.
The tragic reason behind the increased attention given to domestic violence during the COVID-19 lockdowns is the substantial evidence that gender-based violence has intensified to such an extent that the United Nations raised the alarm about a “shadow pandemic” of violence against women and girls (UN Women on-line link). Already before the pandemic, one in three women worldwide had experienced physical or sexual violence, usually at the hands of an intimate partner, and this number has only been increasing. The tragic reports from the military invasion of Ukraine concerning violence against women and children, as well as information on the heightened risks faced by war refugees from Ukraine, most of whom are women, should only intensify our efforts to better understand the background behind these processes and study the potential policy solutions to limit them to a minimum in the current and future crises.
The most direct consequences of gender-based and domestic violence – to the physical and mental health of the victims – are clearly of the highest concern and are the leading arguments in favour of interventions aimed at limiting the scale of violence. One should remember though, that the consequences and the related social costs of gender-based and domestic violence are far broader, and need not be caused by direct acts of physical violence. Gender-based and domestic violence can take the form of psychological pressure, limits on individual freedoms, or access to financial resources within households. As research in recent decades demonstrates, such forms of abuse also have significant consequences for the psychological well-being, social status, and professional development of its victims. All these outcomes are associated with not only high individual costs, but also with substantial social and economic costs to our societies.
This policy brief presents an outline of a survey conducted in eight countries aimed at better understanding the socio-economic context of gender-based violence. The survey, developed by the FREE Network of independent research institutes, has a regional focus on Central and Eastern Europe, with Sweden being an interesting benchmark country. The data was collected in September 2021 in Armenia, Belarus, Georgia, Latvia, Poland, Russia, Sweden and Ukraine. The socio-economic situation of all these countries irrevocably changed with the Russian invasion of Ukraine on 24 February 2022, the ongoing war, and its dramatic consequences. The world’s attention focused on the unspeakable violence committed by the Russian forces in Ukraine, the persecution in Belarus and Russia of their own citizens who were protesting against the invasion, and the challenges other neighbouring countries have faced as a result of an unprecedented wave of Ukrainian refugees. This change, on the one hand, calls for a certain distance with which we should judge the survey data and the derived results. On the other hand, the data may serve as a unique resource to support the analysis of the pre-war conditions in these countries with the aim to understand the background driving forces behind this dramatic crisis. In as much as the gender lens is necessary to comprehend the full scale of the consequences of both the COVID-19 pandemic and the war in Ukraine, it will be equally indispensable in the process of post-war development and reconciliation once peace is again restored.
Survey Design, Countries, and Samples
The survey was conducted in eight countries in September 2021 through as a telephone (CATI) survey using the list assisted random digit dialling (LA-RDD) method covering both cell phones and land-lines, and the sampling was carried out in such a way as to make the final sample representative of the respective populations by gender and three age group (18-39; 40-54; 55+). The collected samples varied from 925 to 1000 individuals. The same questionnaire initially prepared as a generic English version was fielded in all eight countries (in the respective national languages). The only deviations from the generic version were related to the education categories and to a set of final questions implemented in Latvia, Russia and Ukraine with a focus on the evaluation of national IPV legislation.
Table 1 presents some basic sample statistics, while Figure 1 shows the unweighted age and gender compositions in each country. The proportion of women in the sample varies between 49.4% in Sweden and 55.0% in Belarus, Russia and Ukraine. The average sample age is between 43 (Armenia) and 51 (Sweden), while the proportion of individuals with higher education is between 29.3% in Belarus and 55.4% in Georgia. The highest proportion of respondents living in rural areas could be found in Armenia at 62.9%, while the lowest was in Georgia at 24.1%. Figure 1 illustrates good coverage across age groups for both men and women.
Table 1. FROGEE Survey: samples and basic demographics
Source: FROGEE Survey on Domestic and Gender-Based Violence.
Figure 1. FROGEE Survey: gender and age distributions
Source: FROGEE Survey on Domestic and Gender-Based Violence.
Socio-economic Conditions and Other Background Characteristics
To be able to examine the relationship between different aspects of domestic and gender-based violence to the socio-economic characteristics of the respondents, an extensive set of questions concerning the demographic composition of their household and their material conditions were asked at the beginning of the interview. These questions included information about partnership history and family structure, the size of the household and living conditions, education and labour market status (of the respondent and his/her partner) and general questions concerning material wellbeing. In Figure 2 we show a summary of two of the latter set of questions – the proportion of men and women who find it difficult or very difficult to make ends meet (Figure 2A) and the proportion who declared that the financial situation of their household deteriorated in the last two years, i.e. since September 2019, which can be used as an indicator of the material consequences of the COVID-19 pandemic. We can see that the difficulties in making ends meet are by far lowest in Sweden, and slightly lower in the other EU countries (Latvia and Poland). The differences are less pronounced with regard to the implication of the pandemic, but also in this case respondents in Sweden seem to have been least affected.
Figure 2. Making ends meet and the consequences of COVID-19
a. Difficulties in making ends meet
b. Material conditions deteriorated since 2019
Source: FROGEE Survey on Domestic and Gender-Based Violence.
Perceptions and Incidence of Domestic and Gender-Based Violence and Abuse
Frequency of differential treatment and abuse
The set of questions concerning domestic and gender-based violence started with an initial module related to the different treatment of men and women, with respondents asked to identify how often they witnessed certain behaviours aimed toward women. The questions covered aspects such as women being treated “with less courtesy than men”, being “called names or insulted for being a woman” and women being “the target of jokes of sexual nature” or receiving “unwanted sexual advances from a man she doesn’t know”, and the respondents were to evaluate if in the last year they have witnessed such behaviours on a scale from never, through rarely, sometimes, often, to very often. We present the proportion of respondents answering “often” or “very often” to two of these questions in Figure 3A (“People have acted as if they think women are not smart”) and 3B (“A woman has been the target of jokes of a sexual nature”). We find significant variation across these two dimensions of differential treatment, and we generally find that women are more sensitive to perceiving such treatment. It is interesting to note that the proportion of women who declared witnessing differential treatment in Sweden is very high in comparison to for example Latvia or Belarus, which, as we shall see below, does not correspond to the proportion of women (and men) witnessing more violent types of behaviour against women.
Figure 3. Frequency of differential treatment (often or very often)
a. People have acted as if they think women are not smart
b. A woman has been the target of jokes of a sexual nature
Source: FROGEE Survey on Domestic and Gender-Based Violence.
Questions on the frequency of witnessing physical abuse were also asked in relation to the scale of witnessed behaviour. Here respondents were once again asked to say how often “in their day-to-day life” they have witnessed specific behaviours. These included such types of abuse as: a woman being “threatened by a man”, “slapped, hit or punched by a man”, or “sexually abused or assaulted by a man”. The proportion of respondents who say that they have witnessed such behaviour with respect to two of the questions from this section are presented in Figure 4. In Figure 4A we show the proportion of men and women who have witnessed a woman being “slapped, hit or punched” (sometimes, often or very often), while in Figure 4B being “touched inappropriately without her consent”. Relative to the perceptions of differential treatment the incidence of a woman being hit or punched (4A) declared by the respondents seems more intuitive when considered against the overall international statistics of gender equality. The proportions are lowest in Sweden and Poland, and highest in Armenia and Ukraine. However, the perception of inappropriate touching by men with respect to women (Figure 4B) shows a similar extent of such actions across all analysed countries.
Figure 4. Frequency of abuse (sometimes, often or very often)
a. A woman has been slapped, hit or punched by a man
b. A woman has been touched inappropriately, without her consent, by a man
Source: FROGEE Survey on Domestic and Gender-Based Violence.
Perceptions of abuse
The questions concerning the scale of witnessed behaviours were complemented by a module related to the evaluation of certain behaviours from the perspective of their classification as abuse and the degree to which certain types of gender-specific behaviours are acceptable. Thus, for example respondents were asked if they consider “beating (one’s partner) causing severe physical harm” to be an example of abuse within a couple (Figure 5A) or if “prohibition to dress as one likes” represents abuse (Figure 5B). This module included an extensive list of behaviours, such as “forced abortion”, “constant humiliation, criticism”, “restriction of access to financial resources”, etc. As we can see in Figure 6, with respect to the clearest types of abuse – such as physical violence – respondents in all countries were pretty much unanimous in declaring such behaviour to represent abuse. With respect to other behaviours the variation in their evaluation across countries is much greater – for example, while nearly all men and women in Sweden consider prohibiting a partner to dress as he/she likes to be abusive (Figure 5B), only about 57% of women and 36% of men in Armenia share this view.
The questionnaire also included questions specifically focused on the perception of intimate partner violence. These asked respondents if they knew about women who in the last three months were “beaten, slapped or threatened physically by their intimate partner”, and the evaluation of how often intimate partners act physically violent towards their wives.
Figure 5. Perceptions of abuse: are these examples of abuse within a couple?
a. Beating causing severe physical harm
b. Prohibition to dress as one likes
Source: FROGEE Survey on Domestic and Gender-Based Violence.
A further evaluation of attitudes towards violent behaviour was done with respect to the relationship between a husband and wife and his right to hit or beat the wife in reaction to certain behaviours. In Figure 6 we show the distribution of responses regarding the justification for beating one’s wife in reaction to her neglect of the children (6A) or burning food (6B). The questions also covered such behaviour as arguing with her husband, going out without telling him, or refusing to have sex. As we can see in Figure 6, once again we find substantial country variation in the proportion of the samples – both men and women – who justify such violent behaviour within couples. This was particularly the case when respondents were asked about justification of violent behaviour in the case of a woman neglecting the children. In Armenia as many as 30% of men and 22% of women agree that physical beating is justified in those cases. These proportions are manyfold greater than what can be observed in countries such as Latvia, where 3% of men and women agreed that abuse was justifiable under these circumstances, or Sweden, where only 1% of men and women agreed.
Figure 6. Perceptions of abuse: is a husband justified in hitting or beating his wife
a. If she neglects the children
b. If she burns the food
Source: FROGEE Survey on Domestic and Gender-Based Violence.
Seeking help and the legal framework
The final part of the questionnaire focused on the evaluation of different reactions to incidents of domestic and gender-based violence. Respondents were first asked if a woman should seek help from various people and institutions if she is beaten by her partner – respondents were asked if she should seek help from the police, relatives or friends, a psychologist, a legal service or if, in such situations, she does not need help. In Figure 7 we show the proportion of people who agreed with the last statement, i.e. claimed that it is only the couple’s business. The proportions of respondents who declare such an attitude is higher among men than women within each country, and is highest among men in Armenia (48%) and Georgia (25%). Again, these proportions are in stark contrast to men in Sweden, or even Poland, where only 4% and 8% of men agreed, respectively. Nevertheless, looking at the total survey sample, a vast majority believe that a woman who is a victim of domestic violence should seek help outside of her home, indicating that at least some forms of institutionalised support for women are popular measures with most people.
Figure 7. Proportions agreeing that domestic violence is only the couple’s business
Source: FROGEE Survey on Domestic and Gender-Based Violence.
The interview also included questions on the need for specific legislation aimed at punishing intimate partner violence and on the existence of such legislation in the respondents’ countries. The latter questions were extended in three countries – Latvia, Russia and Ukraine – to evaluate the specific sets of regulations implemented recently in these countries and to facilitate an analysis of the role IPV legislation can play in reducing violence within households. Legislation on domestic violence is relatively recent. During the last four decades, though, changes accelerated in this respect around the world. Legislative measures have been introduced in many countries, covering different aspects of preventing, protecting against and prosecuting various forms of violence and abuse that might happen within the marriage or the family. Research strives to offer evaluations on what legal provisions are most effective, in a setting in which statistics and information are still far from perfect, and as a consequence of the dearth of strong evidence the public debate on the matter is often lively. For legislation to have an effect on behaviour through shaping the cost of committing a crime, on the one hand, and the benefit of reporting it or seeking help, on the other, or more indirectly through changing norms in society, information and awareness are key. For how can deterrence be achieved if people do not know what the sanctions are? And how can reporting be encouraged if victims do not know their rights? The evidence on legislation awareness is unfortunately quite scarce. A survey of the criminology field (Nagin, 2013) concludes that this is a major knowledge gap.
Figure 8 shows the proportions of answers to questions concerning the need for and existence of legislation specifically targeted towards intimate partner violence. We can see that while support for such legislation is quite high (Figure 8A), it is generally lower among men (in particular in Armenia, Russia and Belarus). Awareness of existence of such laws, on the other hand, is much lower, and it is particularly low among women. It should be pointed out that all countries have in fact implemented provisions against domestic violence in their criminal code, but only around half of the population, sometimes much fewer, are aware of that.
Figure 8. Need for and awareness of IPV legislation
a. State should have specific legislation aimed at punishing IPV
b. Country has specific legislation aimed at punishing intimate partner violence
Source: FROGEE Survey on Domestic and Gender-Based Violence.
Recent reforms of DV legislation that were implemented in Russia in 2017, in Ukraine in 2019 and in Latvia just a few months ago (at the time of the survey, the changes were at the stage of a proposal) were the subject of the final survey questions in these countries. We find that awareness of these recent reforms is very low in all three countries, and knowledge about the reform content (gauged with the help of a multiple-choice question with three alternative statements) is even lower. Our analysis suggests that gender and family situation are the two factors that most robustly predict support for legislation, while education and age are associated with awareness and knowledge of the reforms. Minority Russian speakers are less aware of the reforms in both Ukraine and Latvia, in Ukraine are also less likely to answer correctly about the content of the reform, and in Latvia are less supportive of DV legislation in general.
Analyses of this type are useful for policy design, to better understand which groups lack relevant knowledge and should be targeted by, for example, information campaigns to combat DV, such as those many governments around the world implemented during the covid-19 pandemic.
Future Work Based on the Survey
The above is just a small sample of the rich source of information that has resulted from conducting the survey. Already from this simple overview we can see some interesting results. There are, for example, clear differences between men and women in perceptions of how common certain types of abusive behaviour are. However, for many questions differences between countries are larger than those between men and women within a country. Interestingly such differences are also different depending on the severity of the abuse or violence. In Sweden the perception of women being victims of less violent abuse is higher than in some other countries where instead some more violent types of abuse are reported as being more common. This could, of course, be due to actual differences in actual events but it is also possible that there are differences in what types of behaviour are considered to represent harassment and abuse in different societies. More careful data work is needed to try to answer questions like this and many others. Currently there are a number of ongoing research projects based on the survey results, three of which will be presented at the FREE-network conference on “Economic and Social Context of Domestic Violence” in Stockholm on May 11, 2022. Our hope is that this work will help in taking actions to prevent gender-based abuse and domestic violence based on a better understanding of underlying cross-country differences in social norms and attitudes and their relation to socio-economic factors.
About FROGEE Policy Briefs
FROGEE Policy Briefs is a special series aimed at providing overviews and the popularization of economic research related to gender equality issues. Debates around policies related to gender equality are often highly politicized. We believe that using arguments derived from the most up to date research-based knowledge would help us build a more fruitful discussion of policy proposals and in the end achieve better outcomes.
The aim of the briefs is to improve the understanding of research-based arguments and their implications, by covering the key theories and the most important findings in areas of special interest to the current debate. The briefs start with short general overviews of a given theme, which are followed by a presentation of country-specific contexts, specific policy challenges, implemented reforms and a discussion of other policy options.
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.
Female Representativeness and Covid-19 Policy Responses: Political Representation and Social Representativeness
There is anecdotal evidence that countries with female leadership in policymaking are more efficient in combating the Covid-19 pandemic. This paper studies whether countries with high female representativeness in political and social layers respond differently to the Covid-19 outbreak. We explore patterns at a cross-country level, which enables us to consider the variation of gender implicated institutions. Our findings indicate that it is women’s social representation, rather than female political leadership, that has the potential to capture cross-country variation in Covid-19 policy responses. Our study confirms that well-functioning and effective institutions are not established from the top-down but rather from the bottom-up.
Introduction
In light of the Covid-19 outbreak and the resulting actions developed and implemented by countries worldwide, questions have been raised about government policy responses and what can trigger them. The pandemic brought forward the need for measures that help mitigate the spread of the virus such as hand washing, reduced face touching, face mask policies, and physical distancing. In many countries, the implementation of lockdowns and social distancing measures had a large impact on employment, including reductions in working hours, furloughs, and work from home arrangements (Brodeur et al., 2020; Coibion et al., 2020; Gupta et al., 2020). There are notable concerns about the potential damage non-pharmaceutical interventions can inflict on economies and labor markets (Andersen et al., 2020; Kong and Prinz, 2020). Further, the implementation of these measures requires certain institutional and individual behavioral changes. While some countries were successful in developing and implementing policy responses that addressed the challenges of the pandemic, others have experienced considerable difficulties.
There is anecdotal evidence suggesting that countries with female leadership in governmental policies are more efficient in combating the Covid-19 pandemic. Several articles from prominent media outlets, such as CNN, The Conversation and Forbes, hypothesize that female leaders are systematically better at managing the pandemic and that this divergence can be attributed to gender differences in management style and risk-taking behavior.
This policy paper explores whether countries distinguished by higher female representation in government policies, both in development and implementation, responded differently to the Covid-19 outbreak, and if so, how the response differed from other countries. For this purpose, we identify two layers of female representation: political representation and social representativeness. The layer of political representation considers the role of women’s representation in public policy design and implementation at the top level of executive and legislative institutions. Social representativeness captures women’s representativeness in different layers of society and spheres of life. It reflects social norms, legal inequality between men and women in different spheres of private, economic, and business life, as well as realized gender inequality, e.g., in labor market participation, education, or local leadership.
With respect to political representation, we address the question of whether countries distinguished by a higher female representation at top executive and legislative levels differ in terms of policy responses to Covid-19. With respect to social representativeness, we aim to capture the variation in these responses that may originate from differences in the expected reaction of the public, which in turn is driven by women’s representativeness in different layers of society. We derive evidence-based conclusions capturing the role of female leadership at the country’s executive and legislative level, as well as the role of gender representativeness in other layers and institutions of society.
The motivation for this research stems from the extensive literature on differences in values and social attitudes between men and women. For example, women have been shown to be more trustworthy, public-spirited, and likely to exhibit ‘helping’ behavior (Eagly and Crowley, 1986), vote based on social issues (Goertzel, 1983), score better on ‘integrity tests’ (Ones and Viswesvaran, 1998), take stronger stances on ethical behavior (Glover et al., 1997; Reiss and Mitra, 1998) and behave more generously when faced with economic decisions (Eckel and Grossman, 1998). Thereby, one may ask to which extent these differences transmit to public policies in societies where women are better represented, either politically or socially. While our study primarily concerns Covid-19 policy responses, we discuss other related literature on the relationship between women’s representativeness and public policy in the next section.
Our analysis shows that it is the women’s social representativeness layer, which can explain government reactions to the Covid-19 pandemic. This goes in line with the institutionalist literature, suggesting that more a gender-balanced character of institutions translates into policy measures and related outcomes. With this finding, our study suggests further evidence on the central role of institutions. Consistent with the existing evidence, we claim that well-functioning and effective institutions are not established from the top-down, but rather from the bottom-up (Easterly, 2008; Dixit, 2011; Greif, 2006). In such institutions, women’s participation in labor markets, businesses, and other spheres is essential as these are factors that distinguish countries in their response to the pandemic. While the evidence provided is suggestive, it opens further avenues for studies to assess causal relationships.
Covid-19 Policy Measurements
To conduct our analysis, we collect data from a number of different sources. For data on the Covid-19 situation and government policy responses, we use the Our World in Data portal. This online platform compiles a number of data sources, most of them updated on a daily basis. Statistics on female participation and leadership is retrieved from the World Bank and UNDP. Summary statistics of the variables are reported in Table A1 of the Appendix.
The policy response variables are based on a number of different measures implemented by national governments. These are aggregated into three composite indices: Stringency, Containment & health, and Economic support. (The index methodology can be found here.) We present the components of the three indices in Table 1 and a detailed description of the policy measures and their scoring in Appendix C.
As seen in Table 1, the Stringency and Containment & health indices have some common dimensions; containment & closure policies (C1 – C8) and public information campaign (H1). Both are rescaled to a value from 0 to 100 (100 = strictest). The Economic support index records measures such as income support and debt/contract relief and does not share any common dimensions with the other two policy response indices. The scale of the index also ranges from 0 to 100 (100 = full support). The extent of heterogeneity in government policy responses across countries is illustrated in Figures 1 – 3. While containment and closure policies are stricter in many Asian and Latin American countries, economic support is more extensive in many European countries, Canada, New Zeeland, and few other countries.
Table 1. The structure of the Covid-19 policy measurements.
Note: Categories and assigned values of policy measurements are in Appendix C.
Figure 1. Stringency Index
Note: A choropleth map shows countries/territories by their Stringency index score, based on data collected from the portal of https://ourworldindata.org/policy-responses-covid. Countries are grouped into five groups (quantiles), from the lowest to the highest values of the index.
Figure 2. Economic support index.
Note: A choropleth map shows countries/territories by their economic support index score, based on data collected from the portal of https://ourworldindata.org/policy-responses-covid. Countries are grouped into five groups (quantiles), from the lowest to the highest values of the index.
Figure 3. Containment & health index.
Note: A choropleth map shows countries/territories by their Containment and health index scores, based on data collected from the portal of https://ourworldindata.org/policy-responses-covid. Countries are grouped into five groups (quantiles), from the lowest to the highest values of the index.
Female Representativeness: Layers and Indicators
Multiple studies in economics and political science suggest that the gender of public officials shapes policy outcomes (Chattopadhyay and Duflo, 2004; Iyer et al., 2012; Svaleryd, 2009). Evidence suggests that increasing the number of women in higher ranks of public administration (legislative bodies and ministries) has a substantial impact on the political office and policymaking (Borrelli, 2002; Davis, 1997; Reynolds, 1999). On the other hand, a number of studies demonstrate that gender has no association with policy outcomes (Besley et al., 2007; Besley and Case, 2003; Bagues and Campa, 2021). The role of the institutional setting and environment can, thus, be decisive in this regard. Women are also found to be more concerned about social policy issues and prefer higher social spending than men (Lott and Kenny, 1999; Abrams and Settle, 1999; Aidt and Dallal, 2008). Further, women are more likely to use a collective or consensual approach to problem and conflict resolution rather than an approach founded on unilateral imposition (Rosenthal, 2000; Gidengil, 1995).
In our study, the political representation layer is measured as female leadership at a country’s executive level (representation in government cabinets) and participation at the legislative institution (parliament) level. To assess this, we consider the following indicators: 1) the presence of a female president or prime minister and proportion of women in ministerial positions, and 2) women’s representativeness in legislative bodies measured as the proportion of seats held by women in national parliaments. The variation of these indicators across countries is illustrated in Figures B4 – B6 in the Appendix.
Our approach to social representativeness is in line with social role theory. This framework provides a theoretical explanation of a structural approach to gender differences (Eagly, 1987; Eagly and Karau, 2002; Wood and Eagly, 2009). It claims that men and women behave according to stereotypes associated with the social roles they occupy, and these differences can, in turn, influence the role of women in local governance and leadership. In line with other research on gender, the social role theory proposes a rigorous framework for analyzing the gendered aspect of government organizations. For instance, evidence shows that women tend to be more collaborative and democratic, hence demonstrating a more caring and community-oriented behavior (Eagly and Johannesen-Schmidt, 2001).
The gender aspect of local governance indicates that the personal preferences and opinions of leaders predominate and shape policymaking (Besley and Coate, 1997). Female leaders (including municipality heads) are more inclined to favor the inclusion of citizens in the decision-making process (Fox and Schuhmann, 1999; Rodriguez-Garcia, 2015), implying that the society is a more informed and engaged stakeholder in the public policymaking (Ball, 2009). Given that municipalities are taking on a greater and more interactive role in citizens’ well-being, they become a key channel in reinforcing trust in government. Furthermore, the literature finds an interrelationship between female voters and government outcomes, whereby women’s enfranchisement affects government size and spending (Lott and Kenny, 1999; Miller, 2008, Aidt and Dallal, 2008). As such, this can lead to improvements in government outcomes and policy effectiveness. The evidence from Bloomberg’s Covid-19 Resilience Ranking suggests that success in containing Covid-19 while minimizing disruption appears to rely more on governments fostering a high degree of trust and societal compliance.
Furthermore, the patterns of gender relations in societies reflect formal and informal institutional rules and policies. Gender equality enhances good governance and helps to further improve relationships between government and citizens (OECD 2014). Similarly, Elson (1999) argues that labor markets are structured by practices, norms, and networks that are “bearers of gender”. Societies with better legal frameworks for women have more balanced gender participation in labor markets, governance, and leadership, along with more equal gender roles and less gender-biased stereotypes. We anticipate that better representation of women in policymaking in such societies is also reflected in the choice and effectiveness of Covid-19 policy measures.
Building on the above theories explaining the relevance of women’s representativeness in diverse societal layers for policy development and implementation, we identify three indices that have the potential to capture the effect of social representativeness – Women, Business and the Law index (WBLI), Gender Development Index (GDI) and Gender Inequality Index (GII). The WBLI is composed of eight indicators, covering different areas of the law related to the decisions women make at various stages of their career and life. These indicators include mobility, workplace, salary, marriage, parenthood, entrepreneurship, assets, and pension. Hyland et al. (2020) show that, globally, the largest gender inequalities are observed in the areas of pay and parenthood. That is, women are most disadvantaged by the legal system when it comes to compensation and how they are treated once they have children. The index scales from 0 to 100 (100 = equal opportunities). The diagram in Figure 4 illustrates how the components of the WBLI index measure key activities of economic agents throughout their life.
Figure 4. The linkages of 8 indicators in Women, Business and the Law index (WBLI)
Source. Women, Business and Law, 2020. World Bank Group.
The second index, the GDI, measures gender inequality in the achievements in three basic dimensions of human development: Health, measured by life expectancy at birth; Education, measured by expected years of schooling for children and mean years of schooling for adults aged above 25; and Command over economic resources, measured by estimated earned income. The same dimensions are included in the Human Development Index (HDI), and the GDI is defined as the female-to-male HDI ratio (i.e. perfect gender equality corresponds to a GDI equal to one).
Turning to the third index measuring social representativeness, the GII reflects gender-based disadvantages in the following dimensions—reproductive health, empowerment, and the labor market. The index measures the loss in potential human development due to gender inequality in achievements across these dimensions. It ranges from zero, where women and men fare equally, to one, where one gender fares as poorly as possible in all measured dimensions. One of the dimensions of the GII, women’s empowerment, has a sub-dimension – “Female and male shares of parliamentary seats”, one of our indicators measuring political representation. Generally, we do not consider the two layers being as mutually exclusive, but intersections are expected to be minimal.
Central to our study, the three indices capturing social representativeness in a country encompass the institutional quality of its society from a gender development perspective. The distribution of each index across countries is shown in Figures B1 – B3 (See Appendix B).
Women’s Representativeness and Covid-19 Policy Responses: Partial Correlation Analysis
In this section, we explore the relationship between Covid-19 policy responses and the measures of political representation and social representativeness. For this purpose, we explore (i) correlations between the indicators and indices of the political and social representation layers and (ii) partial correlations between these measures and policy response indices.
We start with a correlation analysis of the different indicators in the layers. It shows that the WBLI is in high correlation with other representativeness variables. This index captures the legal equality between women and men which has been shown to be “associated with a range of better outcomes for women, such as more entrepreneurship, better access to finance, more abundant female labor supply, and reductions in the gender wage gap”. (WB, 2021). One can think of the GDI and GII indices, as well as the political representativeness indicators, as reflections of a broad policy framework in diverse areas of social, business, and legal activities. A legal environment that promotes gender equality, even if not sufficient by itself, is likely to lead to progress in these areas. Indeed, Hyland et al. (2020) show that greater legal equality between men and women is associated with a lower gender gap in opportunities and outcomes, fewer female workers in vulnerable positions, and greater political representation of women. This way, the WBLI may capture key predispositions for women’s representativeness in society. Further, Hyland et al. (2021) show that the WBLI index is in high (partial) correlation with country GDP per capita, polity score, legal origin, religion and geographic characteristics. This evidence suggests that the WBLI may have the capacity to reflect important country characteristics which ultimately shape cross-country institutional variation.
Table 2. Scatterplot table for GDI, GII and Women, Business and the Law Index, Proportion of seats in parliament held by women and Proportion of ministerial seats held by women.
Note: Scatterplots are constructed for 149 countries. Fitted lines are based on a quadratic function, shaded areas indicate 95 percent confidence intervals and country population is used for weights applied for fitted lines and bubbles. For each scatterplot, correlation coefficients and their significance are reported. *** p<0.01, ** p<0.05, * p<0.1.
Next, we explore partial correlations of these indicators with Covid-19 policy responses (Table 3). In this analysis, we control for a number of factors that potentially confound the relationship between a particular policy response and representation layer. Specifically, we control for (i) the number of infected cases per million inhabitants, (ii) the number of deaths per million, (iii) GDP per capita, and (iv) life expectancy. The number of infected cases and deaths enter the model in order to control for country differences in the spread and consequences of the virus. GDP per capita captures the stage of country development, accounting for cross-country differences in resource capacities and constraints. Both of these control variables are claimed to have an important role in Covid-19 related research (Coscieme et al., 2020; Aldrich and Lotito, 2020; Elgar, Stefaniak and Wohl, 2020; Gibson, 2020; Conyon and Thomsen, 2020). Life expectancy is an important proxy for country inhabitants’ resilience against the virus, conditioned by health and health infrastructures.
Significant correlations are observed between the WBLI and the three policy response indices. The correlation between the WBLI and Stringency (and Containment & health) index is negative, implying that lighter restrictions have been imposed in countries with better business and legal conditions for women. A positive correlation is observed between the WBLI and the economic support index, suggesting that countries with better conditions for women in diverse business and societal areas have provided more extensive economic support in the pandemic. This finding is in line with existing evidence showing that women are more concerned about social policy issues and prefer higher social spending than men (Lott and Kenny, 1999; Abrams and Settle, 1999; Aidt and Dallal, 2008). Also, lighter restrictions and more generous economic support do not presume any trade-off in terms of the allocation of financial resources constrained by a state budget.
Interestingly, we do not observe significant correlations between policy responses and other indicators of women’s representativeness. The only exception is a correlation between GDI and the Containment & health index, which is significant at the 10% level and hinges heavily on two outliers (if we drop the two outliers, the P-value of the correlation increases from 0.0931 to 0.2735).
Table 3. Scatterplots of policy responses and social representativeness and political representation variables.
Note: Scatterplots are constructed for 133 countries. Fitted lines are based on a quadratic function, shaded areas indicate 95 percent confidence intervals and country population is used for weights applied for fitted lines and bubbles. Correlation coefficients are reported with significance levels: *** p<0.01, ** p<0.05, * p<0.1.
In our partial correlation analysis, we do not control for the direct effects of the gender dimension of social norms and practices. Social norms, practices, as well as informal and formal rules can, however, explain a substantial part of the gender gap (Hawkesworth, 2003; Mackay, 2009; Franceschet, 2011; Elson, 1999; Froehlich et al., 2020) relevant for making decisions. Our measures of women’s political and social representativeness do not fully cover gender differences in norms and practices. As Hyland et al. (2020) point out, de-jure female empowerment does not necessarily translate into de-facto empowerment, especially in countries with social norms and informal rules that result in low representation of women in diverse societal spheres. The authors indicate that laws are actionable in a short period, while more time is needed to bring changes in social norms. In our paper (Grigoryan and Khachatryan, 2021), we attempt to address this issue by incorporating the Social Institutions and Gender Index (SIGI) into the model and evaluating the confounding effect on the covariates of the model. We show that the WBLI captures the effect of the gender gap owing to social norms and practices on Covid-19 policy responses as measured by SIGI. This result suggests that the endogeneity arising from the omission of a measure of such a gender gap is likely to be minimal.
Discussion and Conclusions
Our correlation analysis suggests that it is the layer of women’s social representativeness that can explain the policy reactions of governments in times of the Covid-19 pandemic. This result is in line with the institutionalist literature on gender inequality and social role theory, which suggests that a more gender-balanced character of institutions translates into policy measures and related outcomes. Among the three indices constituting the social representativeness layer, the WBLI is, by construction, more inclusive in terms of capturing women’s role in diversified societal areas. From Table 2, we observe that the WBLI is the only index that is in strong correlation with all other indicators. We also identify strong dominance of the WBLI in correlations with policy responses: it is the only indicator that is significantly correlated with all three policy response measurements (Table 3).
To conclude, our results establish an association between female social representativeness, as measured by the (legal) equality of opportunities between men and women, and Covid-19 related policies. One potential interpretation of these findings concerns the central role of the gender balance in different institutions and layers of society in understanding policy responses to the Covid-19 pandemic. While it was parliaments and governments that implemented policies, we find that the measures undertaken correlate more strongly with factors related to the social representativeness of women rather than those related to their political representation. This suggests a dominant role of gender-balanced institutions at the ‘grass root’ level in terms of the scale and scope of the crisis response. Naturally, these institutions may result (or be correlated) with more gender-balanced political representation, but the latter alone is not helpful in explaining the variation in the reaction to the pandemic. These results underline the importance of balanced gender representation in the labor market, business, and other spheres of social life. Further investment and development of ‘grass root’ institutions that improve women’s socioeconomic opportunities, could provide a fundamental foundation for policy development in a crisis situation.
There could also be alternative interpretations of our findings. There is rich evidence that the gender dimension is deeply implicated in institutions (Acker, 1992; Chappell and Waylen, 2013; Lovenduski, 2005). Gender norms and gender practices have been shown to have an influence on the operation and interaction between formal and informal institutions (see, for instance, Chappell, 2010; Krook and Mackay, 2011; Chappell and Waylen, 2013) and the gender dimension of political institutions is reflected in their practices and values, hence affecting their outcomes (such as laws and policies), formation, and implementation (for instance, Acker, 1992). In turn, governmental policies and rules shape societal norms and expectations. These considerations imply that our results could be driven by the overall values, culture, and institutions of respective societies. These factors would both result in a more gender-neutral legal environment and ‘grass-root’ institutions, and ultimately, distinguish countries in their response to the Covid-19 pandemic. In this way, our results open an avenue for future studies in this important domain to better understand the causality of observed relationships.
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(The Appendix can be found in the PDF version of the brief)
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Inequality in the Pandemic: Evidence from Sweden
Most reports on the labor-market effects of the first wave of COVID-19 have pointed to women, low-skilled workers and other vulnerable groups being more affected. Research on the topic shows a more mixed picture. We contribute to this discussion. Using monthly official unemployment data in Sweden we find that across wage levels, occupations with lower salaries display higher increases in unemployment, and low-wage occupations are also more difficult to do from home. The job loss probability is also higher in sectors with a higher concentration of workers born outside of the EU and those aged below 30. But we find no evidence of a gender unequal impact in Sweden. Overall, our results point to higher effects for low-wage groups but small gender differences overall.
Introduction
The ongoing Covid-19 pandemic has affected the health of millions of people worldwide. But it has also had an enormous impact on economic and living conditions through government policies aimed at containing the spread of the infection. While, at the onset of the pandemic, government officials, mainstream media, and even celebrities labeled COVID-19 “the great equalizer” (Mein, 2020), the reality has proven quite different, with the most vulnerable groups of the population appearing to be the most harmed by both the health and the economic crises (see, for instance, The World Economic Forum, Joseph Stiglitz in this IMF article, and The World Bank). In this brief, we focus on one specific economic impact of the pandemic, namely its effect on unemployment status, and we study the extent to which this impact has been unequal across different groups of the Swedish society. Our analysis uses administrative data and segments the population by wage, gender, age, and foreign-born status.
Covid-19 and Inequality in the Labor Market
An extensive review of the emerging literature on the effect of the pandemic on different kinds of inequality is beyond the scope of this brief. However, a number of studies are especially relevant to put our analysis in context, as they are focused on the unequal labor market impacts of the crisis and study real-time data. Based on these studies, a number of patterns emerge. First, the effect of the pandemic on the increased probability of job loss appears stronger for low-skilled workers, as proxied by education level (see e.g., Adam-Prassl et al., 2020, Gaudecker et al. 2020, Casarico and Lattanzio 2020). Gaudecker et al. (2020) also observe that in the Netherlands the negative education gradient has been mitigated by the government identifying some sectors of the economy as essential since some of these sectors are characterized by a high concentration of low-educated workers. Second, the evidence of unequal gender impacts on the probability of job loss is mixed. While survey information from the UK and the US reveals that labor market outcomes for women have more severely deteriorated during the crisis (Adams-Prassl et al., 2020), there is no evidence of unequal impacts by gender in Germany (Adams-Pras et al., 2020) and Italy (Casarico and Lattanzio, 2020). Other papers confirm that the effect on labor-market outcomes by gender varies across contexts (see, e.g., Hupkau and Petrongolo, 2020).
Analysis of Labor Market Data From Sweden
Our analysis of the Swedish labor market provides a valuable contribution to the existing findings for a number of reasons. First, despite rising inequality over the past decades, Sweden is characterized by relatively low income inequality (e.g. OECD, 2019), high participation of women in the labor market, and high level of society inclusiveness (e.g. Gottfries, 2019, OECD 2016) among OECD countries. Second, unlike the majority of countries worldwide, throughout the pandemic, Sweden has not adopted stay-at-home orders that would have separated sectors of the economy between “essential” and “non-essential”. As a result, sectors that were typically shut down in other countries, for instance, the hospitality industry, were not ordered to close during the first wave of the pandemic and have then only faced partial limitations during the second wave. Importantly, schools below the secondary level were never closed. Third, as we will describe in more detail below, the availability of administrative information on unemployment claims on a monthly basis allows studying the “real-time” development of unemployment throughout the pandemic for the universe of employees in the Swedish labor market.
Data
We use data from the registry of unemployed individuals kept by the Swedish Public Employment Service (Arbetsförmedlingen), the government agency responsible for the functioning of the Swedish labor market. The incentives for laid-off individuals to register with the Employment Service are high since the registration is directly connected to the right to claim various (relatively generous) unemployment benefits. As such, the data arguably includes a large share of employees who lost their job over the period studied. Based on the high incentives to register as unemployed, we also assume that the probability to register does not differ the segments of the population that we consider. The data does not include some self-employed who for various reasons choose not to register, but this group is not believed to be significant. Also, furloughed workers do not count as unemployed. This group was significant, especially in the very early stages of the pandemic, but still small relative to all unemployed. As of July 2020, they represented 13% of the total pool of unemployed individuals in Sweden (Swedish Agency for Economic and Regional Growth, 2021).
The population-wide coverage is the main advantage of our data vis-à-vis the survey information used in many recent studies of the labor market throughout the pandemic (other studies using administrative data are Casarico and Lattanzi, 2020, studying the Italian labor market, and Forsythe et al., 2020, who analyze the US case).
We consider everyone registered as unemployed/seeking employment each month from January 2019 to July 2020. The data is grouped by 4-digit occupational classification (there are about 440 occupations at this level) and each occupational group is further broken down by sex, age, and foreign-born status (specifically, Sweden born, foreign EU born, and foreign non-EU born.) We then merge this data with information on the average wage by occupational group and gender in 2019, as reported by Medlingsinstitutet and publicly available at Statistics Sweden. This measure, although not being at the individual level, allows us to develop a relatively precise proxy of wages by occupation that we use to rank unemployment by wage deciles.
Evidence
With the data described above, we build the following measure of the change in job-loss probability (JLP) between February and July 2020, adjusted for seasonality:
where u is the number of workers in 4-digit occupational sector who registered as unemployed in a month over the average number of employed in the same sector in 2017 and 2018 (data available at Statistics Sweden). Put it simply, ΔJLP is a sector-level indicator of the change in job loss probability due to the pandemic; it measures the change in chances of job loss between February and July 2020, i.e. between five months after the start of the pandemic and the month before its onset, as compared to the equivalent change the year before. We thus account for seasonal factors by differencing out the job loss probability during the same months of 2019, when the pandemic was neither occurring nor anticipated. Below we use ΔJLP to show differences in the impact of the pandemic on the chances of job loss for different groups of the Swedish society.
Job loss probability by wage deciles. We leverage information on sector-level average wages and the number of employees to partition occupational sectors into (approximate) wage deciles. The purpose of such a partition is to rank sectors as being typically “low-” or “high-” wage within the Swedish context. As we document in Figure 1, the pandemic has increased the probability of job loss across all sectors of the economy; however, this increase in percentage points is higher the lower is the average sector wage, with the category of least-paid workers being the most likely to lose their job. This category includes occupations such as home-based personal care and related workers, cleaners and helpers in offices, hotels and other establishments, or restaurant and kitchen helpers. Considering that the pre-pandemic probability of becoming unemployed was already largest for this group (19.7% compared to the average 6% in 2019), the existing inequality in the labor market has been exacerbated by the Covid-19 crisis. In our regression analysis that is available by request, we also find that accounting for an index of the share of tasks that can be performed from home, defined at 2-digit occupational level, does not explain away the negative and significant relationship between wages and job loss probability. Although, we confirm previous evidence that the probability of losing jobs is lower among occupations that can be performed from home. The substantial contraction in economic activity in some sectors of the economy seems to be the driver of the unequal distribution of job losses.
Figure 1. Change in job loss probability by wage decile between February and July
Source: Author’s own calculation, for data sources see Data Section.
Job loss probability by gender. Figure 1 also documents that, even though the change in job loss probability is higher in sectors dominated by women, the likelihood of men losing jobs has increased more in these sectors. As a result, in the regression analysis we find that there is no significant association between the share of women in a sector and the sector-level change in job loss probability.
Job loss probability by foreign status and age. We find that workers who are born outside of EU countries are significantly more likely to transition into unemployment during the pandemic (see Figure 2). The difference is striking. Based on our indicator, considering male workers the pandemic has raised the probability of job loss by roughly 7 p.p. more for non-EU citizens as compared to non-Swedish EU citizens, and by 9 p.p. more compared to Swedish citizens. These differences are only slightly smaller for women. Another group particularly affected is that of workers in the age group below 30 (result available upon request). Such patterns are due to foreign-born and younger workers being more concentrated in those low-wage sectors that also appear, based on our analysis, to be more impacted by the pandemic in terms of job loss probability
Figure 2. Change in job loss probability by foreign status between February and July 2020
Source: Author’s own calculation, for data sources see Data section
Conclusion
Our analysis of administrative monthly data on the number of workers who register as unemployed in Sweden confirms previous evidence that the Covid-19 crisis has not been “the great equalizer”. While the pandemic has increased the probability of losing jobs across all sectors, the most affected in Sweden are those workers in occupations where the lowest wages were paid before the pandemic. Considering other demographic characteristics, vulnerable groups that were most impacted by the crisis are workers born outside of the EU and workers aged below 30. However, we do not find evidence of a gender-unequal impact of the pandemic in terms of the probability of job loss. There may of course be many other aspects to the issue along gender lines. For example, on one hand, there might be gender-unequal effects that we cannot observe in our data, for instance in the number of hours worked, temporary unemployment, and level of stress due to increased childcare responsibility. On the other hand, since schools in Sweden stayed open throughout the pandemic, the concerns related to increased childcare responsibility, which have led to identifying mothers as most vulnerable in other countries, do not necessarily apply to the Swedish context.
Sweden has adopted a number of measures to shield workers from the worst effects of the pandemic. As the country plans the recovery, special attention should be devoted to the opportunities for re-employment for the most vulnerable groups. Absent such focus, the economy emerging from the crisis might be less inclusive and equal than it has been before the pandemic, with important consequences for many societal outcomes that are generally linked to labor market inclusiveness.
References
- Adams-Prassl, A., Boneva, T., Golin, M., & Rauh, C. (2020). ”Inequality in the Impact of the Coronavirus Shock: New Survey Evidence for the UK”. Journal of Public Economics, 189, 104245.
- Casarico, A., & Lattanzio, S. (2020). ”The heterogeneous effects of COVID-19 on labor market flows: Evidence from administrative data”. Covid Economics, 52, 152-174.
- Forsythe, E., Kahn, L. B., Lange, F., & Wiczer, D. (2020). ”Labor demand in the time of COVID-19: Evidence from vacancy postings and UI claims”. Journal of Public Economics, 189, 104238.
- Gottfries, N. (2019). “The labor market in Sweden since the 1990s”. IZA World of Labor 2018: 411.
- Hupkau, C., & Petrongolo, B. (2020). ”Work, care and gender during the Covid‐19 crisis”. Fiscal studies, 41(3), 623-651.
- OECD (2016). ”Promoting Well-being and Inclusiveness in Sweden”, Better Policies, OECD Publishing, Paris.
- OECD (2019). ”OECD Economic Surveys: Sweden 2019”, OECD Publishing, Paris.
- Swedish Agency for Economic and Regional Growth, Database, 10 Mar. 2021.
- Von Gaudecker, H. M., Holler, R., Janys, L., Siflinger, B., & Zimpelmann, C. (2020). ”Labour supply in the early stages of the CoViD-19 Pandemic: Empirical Evidence on hours, home office, and expectations”. IZA Discussion Paper No. 13158.
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.
Source: Boschini et al. (2020)
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.
Source: Boschini et al. (2020)
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.
Source: Atkinson et al. (2018) and Boschini et al (2020).
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.
Data source: Own calculations based on LIS data. West: unweighted average for Australia, Canada, Denmark, Italy, Norway, New Zealand, Spain, Great Britain. East: unweighted average for the Czech Republic, Estonia, Georgia, Hungary, Lithuania, Poland, Russia, Serbia, Slovenia and the Slovak Republic.
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.
Data source: Own calculations based on LIS data. Eastern and Western countries defined as if Figure 4.
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.
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?
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
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
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.
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.