Report No. March 3, 2014 Poverty Reduction and Economic Management Unit Europe and Central Asia Region Document of the World Bank CURRENCY AND EQUIVALENT UNITS Exchange Rate Effective as of December 24, 2013 Currency Unit = Belarusian Ruble US$1 = 9514.74 BYR FISCAL YEAR January 1 – December 31 ACRONYMS AND ABBREVIATIONS BEEPS Business Environment and Enterprise Performance Survey CEDAW Committee on the Elimination of Discrimination against Women EBRD European Bank for Reconstruction and Development ECA Europe and Central Asia EVS European Values Survey FINDEX Financial Inclusion Database GNI Gross National Income HLSS Household Living Standards Survey IFC International Finance Corporation IT Information technology LiTS Life in Transition Survey NSC National Statistical Committee OLS Ordinary Least Squares UNECE United Nations Economic Commission for Europe UNFPA United Nations Population Fund UNICEF United Nations Children's Fund US United States WDI World Development Indicators WHO World Health Organization Vice President: Laura Tuck Country Director: Qimiao Fan Sector Director: Roumeen Islam Sector Manager: Carolina Sanchez Task Team Leader: Sarosh Sattar ...................................................... 1 ............................................................................................ 3 ................................................................................................... 4 ............................................................................. 5 ......................................................................................................................... 9 ............................................................................................................................ 11 ............................................................................................................... 17 ............................................................... 21 ................................................................................. 25 .................................................................................................... 27 ................................................................................................................................. 35 List of Tables Table 0.1: Views on Gender Related Statements Across Men and Women and Age Groups, % of Agreement, 2008 .................................................................................................................................. 7 Table 2.1: Demographic Tendencies ......................................................................................................... 12 Table 3.1: Employment and Earnings Statistics, 2009 ............................................................................... 19 Table 3.2: Profile of working and nonworking women above 55............................................................... 21 Table 3.3: Oaxaca Decomposition of Monthly Wages, 2010 .................................................................... 27 List of Figures Figure 0.1: Share of Women Representation in Legislative and Public Administration Bodies, % ............. 4 Figure 0.2: Measures of Life Satisfaction ..................................................................................................... 5 Figure 0.3: Mean Value of Indicators Showing Agreement with the Views on Gender Related Statements Across Gender, 2008 ............................................................................................................................ 6 Figure 2.1: Enrollment in Primary, Secondary and Tertiary Education, % .................................................. 9 iii Figure 2.2: Enrollment Among Population Aged 17-24 by Consumption per capita Quartiles in 2010, % ............................................................................................................................................................ 10 Figure 2.3: Enrollment in Tertiary Education by Subjects in 2009-2010, % .............................................. 11 Figure 2.4: Share of Population by Age Groups and Gender in 2011, % ................................................... 11 Figure 2.5: Life Expectancy at Birth, years ................................................................................................ 13 Figure 2.6: Mortality and Death Rates ........................................................................................................ 14 Figure 2.7: Infant and Under-5 Child Mortality ......................................................................................... 15 Figure 3.1: Male and Female Labor Force Participation, % ....................................................................... 18 Figure 3.2: Manual and Salaried Employees in by Gender 2009, % .......................................................... 19 Figure 3.3: Employment Ratios across Gender, Age and Education in 2010, %........................................ 20 Figure 3.4: Attempts and Success in Starting Business Across Gender ..................................................... 22 Figure 3.5: Female Management and Ownership of Firms in 2008, % ...................................................... 23 Figure 3.6: Female Ownership and Management by Economic Sectors in 2008, % .................................. 23 Figure 3.7: Use of Bank Accounts in Belarus and the ECA Region, 2011 ................................................. 24 Figure 3.8: Purposes of Accounts and Sources of Loans in Belarus, 2011................................................. 24 Figure 3.9: Log Monthly Wage Across Gender, 2010 ................................................................................ 25 Figure 3.10: Returns to Education and Experience Based on Heckman Model, 2010 ............................... 26 Figure 3.11: Share of male and female headed households in population by age groups, % ..................... 27 Figure 3.12: Share of men and women headed households among single person and single parent families by age groups, % ................................................................................................................................ 27 Figure 3.13: Monthly income per capita by gender of head of household and the type of household ....... 28 Figure 3.14: Monthly income per capita by gender of head of household and age group among single person and single parent households .................................................................................................. 28 Figure 3.15: Income per capita in female headed single parent households by the number of children below 12 years, 2010......................................................................................................................... 29 iv ACKNOWLEDGMENTS This note was prepared by Aziz Atamanov under the leadership of Sarosh Sattar. The team would like to thank Qimiao Fan, Country Director, and Young Chul Kim, Country Manager, for their support for this report. We are thankful to Elizaveta Perova, a peer reviewer of this report, for her constructive comments and suggestions. The report benefited from the comments provided by Sammar Essmat, Elena Klochan, Peter Nicholas, Irina Oleinik, Yulia Snizhko, the staff of UNICEF and UNFPA offices in Belarus and all participants of the World Bank Group Country Partnership Strategy for Belarus consultations on gender inequality. Administrative support was provided by Helena Makarenko. . v 1. This assessment identifies and describes main gender disparities in Belarus in agency, education, health and access to economic opportunities. The report builds on the framework of the of the World Bank’s regional gender report, Europe and Central Asia: Opportunities for Men and Women, as well as the World Development Report on Gender and Development. The assessment takes a quantitative approach using a wide range of different international data sources including World Bank’s World Development Indicators, the Global Financial Inclusion Database, the Life in Transition Survey, EBRD- World Bank Business Environment and Enterprise Performance Survey as well as local Household Living Standards Survey. Key findings of the assessment can be summarized as follows: 2. Belarus has high level of female human development indicators and gender neutral legislation. In particular, Belarusian women are more educated than men, have a high level of labor force participation, high share of firms with female ownership, and are represented in politics. Significant progress was achieved in reducing maternal and infant mortality to the level observed in developed countries. Belarusian legislation does not discriminate against women, and different policy measures were enacted in the field of gender equality along with establishing the coordinating and advisory agency. 3. Nevertheless, this report identifies important gender disparities in various spheres starting from education. In particular, in spite of very high and increasing levels of tertiary enrollment, the gender gap in favor of women is much higher than in the Europe and Central Asia (ECA) region and increasing. The high concentration of women in tertiary education may be a result of barriers women face in the labor market and therefore stronger efforts to get better education and/or low prestige of higher education among men. This may also be because women are less likely to be in blue collar jobs. In addition, university female students tend to choose such majors as social protection, catering, social sciences, and pedagogy than bring them to low paying public sector jobs. This segregation may be driven by social stereotypes about “appropriate� jobs and flexible hours of work in the public sector which help to combine work with the family responsibilities—something women may put greater emphasis on. 4. In spite of their higher educational level, women are worse off than men in economic opportunities and earnings. In particular, women on average earn less than men, are less likely to be represented at the top levels in politics and public administration, and less likely to start their own business and manage firms. Moreover, only a very small share of large and expanding wage gap can be explained by observable differences between male and female workers. These findings may signal the existence of stereotypes and discriminatory practices in political and economic life which are indeed documented in qualitative studies. 5. Belarus is experiencing a population decline and a growing share of elderly women. Population has been declining in Belarus since 1990. Low fertility rates accompanied with declining marriage and high divorce rates were among the key factors behind this trend. Among the positive tendencies are the declining rates of abortions and low infant mortality, which seem to contribute positively to slowing down the negative trend during the last three years. The population decline results in the aging of the population. Overall, an aging and shrinking population will strain the pension and health care systems and will have an adverse impact on the labor market, especially during the transition to a smaller population. 6. Particular concern is related to very high level of male mortality. In spite of a positive trend in mortality during the last decade in Belarus, male adult mortality is three times higher than that of women. Males have much higher death rates from cardiovascular diseases than women. Some of the main factors vi explaining excessive male mortality are related to non-communicable diseases and injures. Men are also more prone to injures than women including traffic accidents, alcohol poisoning, suicides, homicides and other external causes of death. 7. Low retirement age and lower labor earnings make female single headed households particularly vulnerable. The current retirement age in Belarus is one of the lowest among countries of the ECA region. Women retire at age 55 years and men at age 60 years. The gap of five years is inconsistent with men and women’s life expectancy. The earlier a person retires, he or she is likely to retire at a lower wage—and hence—pension level than if they had continued to work and experienced an increase in wages. As a result of aging and higher male mortality, there are more female heads of households than male heads among the elderly population. Households headed by women have lower income per capita than households headed by men. Single parent household headed by women have the lowest income per capita across all types of households. 8. In spite of the Government’s efforts, domestic violence also remains an important problem for Belarus. Tolerance of domestic violence in the society is quite high and people are reluctant to report the violence to the police. Lack of specific legislation on domestic violence against women and problems with enforcement of existing laws are of particular concern. The scale of domestic violence is potentially widespread such that poor and uneducated women with children are at the highest risk. 9. Reducing gender inequality in Belarus may benefit from the proposed set of policy measures. Adoption of legislation on domestic violence and sex discrimination would be an important step in protecting women in Belarus. Implementation and enforcement of domestic violence legislation can benefit from the provision of trainings, public education campaigns and ensuring access to short-term and long-term housing for the victims of domestic violence. Gender discrimination can be addressed through multiple means of which legislation is only one component. In order to change social norms introducing gender studies in secondary schools and higher education institutions, developing special courses on gender equality for future journalists, and positive representation of women in mass media are just a few options available to help change gender stereotypes. Health campaigns against smoking and alcohol, promotion of healthy lifestyle (to address non-communicable diseases (the main cause of higher mortality among men), and greater enforcement of road safety laws may help to reduce male mortality. Finally, greater availability of gender disaggregated data is needed. Success of gender related policies depend on data availability used both for identification of gender issues and monitoring success of the implementation of gender policies. International agencies could closely work and support the National Statistical Committee (NSC) in order to ensure availability of relevant and regularly updated gender disaggregated statistics in Belarus. vii INTRODUCTION 1. Belarus is an upper-middle income country with income per capita of US$5,380 (GNI using Atlas method) in 2011. Belarus demonstrated strong economic growth 2000-2008 and this translated into fast poverty reduction. Belarus has already achieved almost all of the Millennium Development Goals (MDGs), but some efforts are needed to stimulate progress in combating /AIDS and tuberculosis, to ensure environmental sustainability, and to develop a global partnership for development. The role of the state is substantial and largely unreformed economic model is based on the dominance of public sector and a “social contract� of broad-based income redistribution and a high level of social equity. Economic growth slowed substantially during the economic crisis of 2008-09, and since then the country has gone through a period of recurring macroeconomic instability (World Bank, 2013). 2. Belarus invested a lot in the human capital of its population both in men and women. This continued and significant investment in health and education has helped to close gender gaps in key areas of primary and secondary schooling and women’s access to maternal and child care (World Bank, 2013). Nevertheless, several important issues with gender equality remain (CEDAW, 2011). This report aims to describe the status of men and women in Belarus in selected areas of development and suggest some critical gaps the Government may wish to address. Following the framework of the World Development Report on Gender and Development (World Bank, 2012a), we focus on gender disparities in endowments and economic opportunities along with discussing institutional framework, social norms and voice and representation as factors which shape women’s and men’s ability to make effective choices and to transform those choices into desired outcomes. The approach adopted in the report is largely quantitative and complements the more qualitative studies that already exist. 3. This note is best read along with two World Bank reports: the World Development Report on Gender and Development (World Bank, 2012a) and “Opportunities for Men and Women in Emerging Europe and Central Asia� (Sattar, 2012). A wide range of data sources are used to empirically capture the status and trends in gender disaggregated statistics across a wide range of indicators. For international comparison we mostly rely on the World Bank’s World Development Indicators. The Life in T ransition Survey 2010 (LiTS II) and the European Value Study 2008 (EVS) were used to measure gender perceptions, subjective wellbeing and entrepreneurship. The Global Financial Inclusion Database 2011 (FINDEX) was used to measure gender disparities in access to finance. EBRD-World Bank Business Environment and Enterprise Performance Survey 2008 (BEEPS) was used to analyze gender disparities in entrepreneurship. Finally, the Household Living Standards Survey (HLSS) 2010 was used to analyze employment and earnings across gender along with other information collected by the National Statistical Committee of the Republic of Belarus (NSC). 4. The remainder of the report is organized as follows. The next section discusses “agency� and describes factors which may shape the process how men and women use their endowments and utilize economic opportunities to achieve desired outcomes. The third section analyzes gender disparities in endowments, such as health and education. The fourth section focuses on gender gaps in the labor market, entrepreneurship and earnings, access to finance and poverty. The last section presents conclusions and policy recommendations. Key Findings 5 Belarus is an upper-middle income country with income per capita of US$6,530 (GNI using Atlas method) in 2012. The country is ranked high on the “gender index� in the 15th position out of 86 viii countries in the 2012. The OECD’s “gender index� measures discriminatory social institutions1. Belarus also avoided a deterioration of human development indicators observed in the Former Soviet Union countries during the transition because of the gradual approach to economic reforms. Existing reports document gender disparities in several spheres of social-economic life of the Belarusian society (Institute of Economic Research under the Ministry of Economy of Belarus, 2010; CEDAW, 2011). 6. The analysis of a wide variety of data yields a broad overview of the status of gender equality in Belarus and the issues facing men and women in the areas of health, education, and economic opportunities as well as women’s role in society and peoples’ att itudes. Though this analysis is by no means exhaustive, it does identify some areas where Belarus’s achievements are exemplary and other areas of challenge that remain. Some of our key findings are as follows: • Belarusian legislation is “gender blind� and treats men and women equally in many key respects. However, there are some key gaps which include the lack of domestic violence legislation and legislation that prohibits discrimination based on sex. • The Government’s investment in health and education has paid off giving men and women equal access to schooling and health facilities. Yet, despite this, there is a large gender gap at the tertiary school level with more women than men enrolled in universities. Furthermore, men’s life style choices have led to high male mortality among prime age adults significantly lowering male life expectancy. • Though women’s labor force participation rate in Belarus is above the average for the ECA countries, women are still at a disadvantage in the labor market. On average, women earn less than men, even when corrected for occupation and human capital. Also, despite women’s better educational qualifications, they are less likely to be represented at the top levels of private and public institutions. • Some social benefits for women are reasonable, but others are not. Specifically, maternity leave for (employed) women is adequate in length. But the child care leave benefit for three years is excessive and hurts women’s career opportunities. Furthermore, there is a gender gap in the retirement age which adversely impacts women’s incomes, pensions, and career progression. • There is a lack of regular, detailed gender disaggregated statistics in Belarus. Existing sources of gender statistics provided sporadic information and are not regularly updated. However, in 2013 the results from the Multiple Indicator Cluster Survey was presented and this increased the availability of gender disaggregated indicators. 1 Due to the lack of sufficient data, Belarus is not ranked in “Gender Inequality Index� and the World Economic Forum Global Gender Gap Index. ix 1.1 Agency or “the ability of a person to act independently and make his or her own free choices� is an essential component of leading a satisfying life. If a characteristic beyond one’s control—such as gender—determines or limits what a person can do or his or her decisions, this means that the person has limited agency and is unable to use their endowments (e.g., education or other assets) effectively. In order to ensure greater agency or control over one’s actions and life choices, there are three important factors: legislation, societal attitudes towards roles and responsibilities, and discriminatory actions.2 This section discusses these factors along with an overview of how satisfied men and women are with their lives. 1.2 The legal framework in Belarus follows general principles of equality and non-discrimination. Belarus has been a member of the Convention on the Elimination of All Forms of Discrimination against Women since 1981 and also ratified Optional Protocol to the Convention on the Elimination of All Forms of Discrimination against Women in 2004. The country is ranked high occupying the 15th place out of 86 countries in OECD’s 2012 gender index which measures discriminatory social institutions. 1.3 Nevertheless, the legislation lacks specific prohibition of discrimination against women in all areas of life. According to the latest observations of the Committee on the Elimination of Discrimination against Women (CEDAW) in 2011, Belarus’s legislation could be strengthened by including a specific prohibition of discrimination against women and comprehensive anti-discrimination legislation covering sex and gender-based discrimination in the national legislation. CEDAW also considers that Convention forms are not given “sufficient visibility as the legal basis for measures for the elimination of all forms of discrimination� (CEDAW, 2011:3). Nevertheless, CEDAW welcomed several amendments to the current legislation aimed at achieving de jure and de facto equality of women and men. For instance, the amendment related to equality of spouses in family relations was made to the Marriage and Family code in 2006. Women are entitled to 126 days’ paid maternity leave (70 days before and 56 after delivery) a nd the employer is obliged to keep their job open for them up to three years. Besides this, several important measures were adopted by the State to prevent and combat trafficking in human beings, in particular women and girls. 1.4 The law guarantees women equal access to property, courts and credit. According to the Civil Code of Belarus, men and women have equal rights over moveable and immovable property. There are no laws prohibiting women regardless their marital status to sign a contract, open a banking account, and register a business. Women carry the same evidentiary weights in court as men. Unmarried women do not need permission from a guardian to initiate legal proceedings in court, while married women do not need permission from their husbands (World Bank and IFC). 1.5 Lack of specific legislation on domestic violence against women and problems with enforcement of existing laws are of particular concern. There is an absence of separate criminal law provisions on domestic violence and marital rape in Belarus’s current legislation. The current versions of the Criminal 2 The list of factors is not exhaustive and may also include informal institutions, self-esteem, and peer-pressure. The choice of selected factors is mostly driven by information and data availability. 1 Code and the Criminal Procedure Code do not specifically criminalize domestic violence and marital rape. The situation may improve with the adoption of the draft Law on Prevention of Domestic Violence, including specific rights for victims to assistance, protection and compensation pending with the Parliament (CEDAW, 2011:5). Currently, the Ministry of Interior developed amendments to the Law on crime prevention which define domestic violence, preventing and protective measures. 1.6 The scale of domestic violence is potentially widespread such that poor and uneducated women with children are at the highest risk. There are no publicly available detailed government statistics on domestic violence in Belarus, but there is an indication that the problem is potentially widespread3. According to Center of Sociological and Political Research of the Belarusian State University (2008), four out of five women in Belarus experienced psychological violence in family, 11 percent experienced physical violence from an intimate partner, 22 percent economic violence, 13 percent sexual violence 4. The incidence of violence is higher in poor households. Thus, 32 percent of women from low-income families experienced physical violence in comparison to 5 percent in more affluent households. The most recent survey conducted in Brest oblast by the Center of Sociological and Political Research of the Belarusian State University in 2012 (cited in UNFPA, 2013) determined the portrait of a typical victim of domestic violence: a woman aged 40-49 years with children, without higher education and living in families with low income. 1.7 Domestic violence continues to be viewed as a private matter rather than criminal behavior. According to Petina et al. (2010) and Solomatina (2011), violence is socially acceptable in the society and many people are reluctant to talk about this issue. The Government is aware that domestic violence is a societal problem and supported the establishment of crisis centers for women and opening a free hot line. 5 In 2012 international donors started financing the program on enhancing capacity of the Government to eliminate domestic violence which aims at establishing of effective system for preventing and counteracting it. 1.8 Human trafficking is a criminal offence in Belarus, but still remains a significant problem. According to United States Department of State (2012), Belarus is a source, destination, and transit country for women, men, and children subjected to sex trafficking and forced labor6. The report states that the Government of Belarus does not fully comply with the minimum standards for the elimination of trafficking; however, it is making significant efforts to do so. The population group at greatest risk of being trafficked is Belarusian single, unemployed females between the ages of 16 and 30 years and without higher education. Belarusian children aged 16 and 17 are found in sex trafficking within Belarus and in Russia. Belarusian men seeking work abroad are increasingly subjected to forced labor. Traffickers often used informal social networks to approach potential victims. 3 Detailed analysis how the collection and processing of the statistics related to domestic violence is organized can be found in Karazei and Kashevskaya (2012). 4 The United Nations Declaration on the Elimination of Violence against Women (1993) defines violence against women as "any act of gender-based violence that results in, or is likely to result in, physical, sexual or psychological harm or suffering to women, including threats of such acts, coercion or arbitrary deprivation of liberty, whether occurring in public or in private life. Detailed description of domestic violence and its types can be found in INNOCENTI digest on domestic violence available from http://www.unicef-irc.org/publications/pdf/digest6e.pdf. 5 Nevertheless, according to Petina et al. (2010) there is no statutory requirement to set up such centres and, therefore, the existence of shelters depends totally on the interest and motivation of individual staff and on support from local authorities. 6 Belarusian women and children are subjected to sex trafficking in Russia, Germany, Poland, Cyprus, Italy, Egypt, the Czech Republic, Lithuania, Spain, Greece, Belgium, Turkey, Israel, Lebanon, the United Arab Emirates, and within Belarus. 2 1.9 Belarus has established a national process for gender equality. Four national gender action plans were enacted in the country since 1996. These plans were the main documents covering the state policy aimed at achieving gender equality. Two national plans were adopted for the period between 1996-2000 years and between 2001-2005 years. The last fourth national gender action place was adopted in 2011. The plan has the following goals: (i) achieve gender equality at all levels of the decision-making; (ii) introduce gender concept in education system; (iii) shift social norms towards gender equality in social life; (iv) improve reproductive health of men and women; (v) strengthen the family institute; (vi) achieve gender equality in economic opportunities. 1.10 The main institutional bodies developing, implementing and coordinating gender policies are the National Council on Gender Policy and the Department of Population, Gender and Family Policy of the Ministry of Labor and Social Protection. The National Council on Gender Policy, an interagency advisory and coordinating body, was established in 2000. It is composed of the heads of central government agencies, local executive and administrative authorities, National Assembly deputies and representatives of the Supreme Court and public and international organizations. 1.11 Research and educational work could benefit from regularly updated and publicly available gender disaggregated statistics. In order to inform policy makers about gender related issue, there is a need to have regular, extended gender disaggregated statistics in all social-economic spheres. Even though gender statistics have been collected in Belarus, these can be improved and expanded. Existing data on labor market indicators could also be expanded including more gender disaggregated statistics on entrepreneurship, informal employment and so forth. The situation with gender statistics may improve taking into account that results from the Multiple Indicator Cluster Survey supported by UNICEF was published in 20137. Nevertheless, collection and provision of adequate gender disaggregated statistics would greatly help to ensure evidence based policy making and monitor progress with the National Gender Action Plans. 1.12 Women’s political representation at the national and local levels is high in Belarus. Significant progress has been achieved in Belarus with regards to the proportion of seats in the national legislature during the last decade. Thus, the proportion of women in the Parliament increased from 10 percent in 2002 to 30 percent in 2012. This compares favorably to the global average of 17 percent. 1.13 There are more women in state administration bodies, but they are mostly concentrated in the middle level of the job hierarchy. Women constitute 67 percent of total employment in state administration and judiciary sector, but such a high level of representation is achieved by concentration of women in low and middle level positions (Figure 1.1b). For example, only 20 percent among heads and deputy heads of republican bodies of state administration were women, while equal promotion seems to start only at the level of structural divisions, departments and further down to chief and leading specialists. The situation slightly improves as we move by regional hierarchy. For example, 25 percent of heads and deputy heads were women in regional state administration bodies and 50 percent at district and town bodies. 7 Preliminary results from the Multiple Indicator Cluster Survey became publicly available in March 2013 when this report had been completed. The revision of the report “Women and Men in Belarus� was planned by UNICEF and UNFPA to add more gender disaggregated statistics. 3 Figure 1.1: Share of Women Representation in Legislative and Public Administration Bodies, % a) National level b) Across levels, 2009 80 2009 100 Head and d.heads of 70 Local area 90 organizations governing 80 60 bodies 70 60 Head and d.heads of 50 50 divisions 40 40 National % 30 Heads of deparments Parliament 20 30 10 0 20 Chief specialists 10 Government ministers 0 Leading specialists 2002 2006 2011 Source: UNECE. Source: NSC (2010). Note: There are no heads of divisions and departments in rural and village executive committees. 1.14 Life satisfaction declined in Belarus between 2006 and 2010 years with a gender gap in favor of men. We use two rounds of the Life in Transition Survey data (LiTS) to check who was more satisfied with life: men or women. Firstly, the data from LiTS shows that the level of satisfaction drops substantially between 2006 and 2010 (Figure 1.2a). Secondly, in both years men are more satisfied with life than women. The gender gap in life satisfaction does not seem to be related to differences in satisfaction with public services, but is associated with subjective wellbeing of households. Individuals from households with higher living standards are more satisfied with life than individuals from poor households. 1.15 Data from the European Values Survey also show that men are slightly more satisfied with life. We use the EVS data to check the levels of satisfaction with life (Figure 1.2b). There are more satisfied men (64 percent) than women (60 percent). Men also believe they have greater control over their lives than women: 62 versus 55 percent accordingly which can be potentially linked to lower life satisfaction among women. 1.16 The impact of gender on life satisfaction is no longer statistically significant if we control for other factors. However, there is a difference in what brings satisfaction to men and women—employment yields more satisfaction to men, while marriage brings greater satisfaction to women. The gender gap in life satisfaction can be related to differences in personal characteristics between men and women. One approach to correct for this is to use econometric analysis which allows controlling for basic characteristics such as age, number of children, marital status, and employment and education level. Regression analysis shows no difference in the impact of gender on life satisfaction, but shows that married and employed people tend to be more satisfied with life than either single or unemployed/inactive (Appendix Table A1). These effects tend to differ between men and women. Thus, married women are happier than married men, while employed men are happier than employed women. 4 Figure 1.2: Measures of Life Satisfaction a) Life satisfaction by LiTS, 2006 and 2010 b) Life satisfaction by EVS, 2008 70 80 60 male % of each group in population men % of respondents satisfied 75 female women 50 70 65 40 with life 60 30 55 20 50 45 10 40 0 2006 2010 dissatisfied neutral satisfied Source: LiTS I and II (EBRD and World Bank, 2008 and Source: EVS (2010). 2011). Notes: The question states: “How satisfied are you with your Notes: Percentage of satisfied people includes respondents life in scale from 1 to 10, 1 being dissatisfied and 10 being who strongly agree or agree with the statement “All things satisfied�. Data was weighted. We aggregated first four steps considered, I am satisfied with my life now.� Data was into “dissatisfied�, the fifth step into “neutral� and the last four weighted. Answers ‘do not know’ were excluded from into “satisfied� groups. calculation of shares. 1.17 In spite of relatively egalitarian views on gender roles in Belarus, traditional views are still common in the society with less agreement among men on gender equality statements. EVS presents views on statements discussing gender roles in society. An absolute majority of men and women in Belarus agree on equal sharing of responsibilities for home and children and contribution to household income. Nevertheless, some gender stereotypes remain. For instance, more than half of the population thinks that being at home with children is what women want most. Almost for all gender equality statements, men tend to demonstrate slightly less agreement than women. For example, 85 percent among men believe that men should take the same responsibility for home and children versus 94 percent among women. In line with this, 85 percent of men believe that fathers are as well suited to look after children as mothers versus 90 percent among women. Figure 1.3 presents mean values of the indicators which range from 1 (strong agreement) to 4 (strong disagreement). Values below 2 mean relative agreement, while values above 2 indicate relative disagreement with the statement. Statements labeled with asterisks indicate statistically significant difference in views between men and women. 1.18 There are small, but statistically significant gender differences between men’s and women’s views on the role of women in the labor market. Men tend to underestimate the role of women in the labor market. Fewer men than women believe that having job is the best way to secure independence of women (79 versus 88 percent respectively). In line with this, 58 percent of men consider being housewife as fulfilling as paid job versus 54 percent of women. About 34 percent of men also believe that men should have more rights to get a job than women during the crisis in comparison to 20 percent of women. 1.19 Gender views of women change across age groups, but without any clear direction. Table 1.1 shows percentage of men and women agreeing with gender related statements. There are no clear patterns of how gender views of women change over time. For example, more middle-aged women disagree with the statement ‘Being a housewife is as fulfilling as having a paid job’ or ‘Pre-school child suffers with 5 working mother’, but higher agreement is observed for the oldest cohort above 65. Interestingly, women agree least with the statement ‘If jobs are scarce, men should have priority’ is observed when women enter the labor market (25-34 years old) and when they leave it during retirement age. Figure 1.3: Mean Value of Indicators Showing Agreement with the Views on Gender Related Statements Across Gender, 2008 Children women men Labor market Sharing of duties Marriage 0.0 1.0 2.0 mean fully agree fully disagree Source: EVS (2010). World Bank staff calculations. Notes: *** gender difference significant at 1%, ** gender difference significant at 5%, * gender difference significant at 10%. Respondents have to (1) strongly agree, (2) agree, (3) disagree, or (4) strongly disagree with each of the statements above. T-test is conducted for the mean value of indicators. 1.20 Gender views of men related to women’s role in the labor market seem to progress over time. In contrast to women, more young men disagree with the statements ‘being a housewife is as fulfilling as having a paid job’, ‘if jobs are scarce, men should have a priority’, ‘what women really want is home and children’. 1.21 Women believe religion is more important in life compared to men. We employ the data from EVS which asks how important work, family, friends, leisure, politics and religion are in life of men and women. Regression analysis demonstrates that women tend to indicate religion as more important in their lives than men after we control for the number of children, marital and employment status as well as education8. 8 Confidence intervals and coefficients are presented in Figure A.1 in Appendix. 6 Table 1.1: Views on Gender Related Statements Across Men and Women and Age Groups, % of Agreement, 2008 Age groups (years) 15- 20- 25- 30- 35- 40- 45- 50- 55- 60- 65 19 24 29 34 39 44 49 54 59 64 + Women who agree with the statement: Working mother makes warm relationship 91 93 79 83 83 93 91 88 85 85 89 with children Pre-school child suffers with working 63 58 61 50 63 57 62 66 57 73 71 mother Women really want home and children 57 42 43 57 53 58 50 52 38 58 58 Being housewife as fulfilling as paid job 58 44 49 47 49 56 48 56 39 63 66 Job is the best way for women’s 84 91 89 87 90 83 93 94 79 94 86 independence Husband and wife should contribute to 94 93 90 88 86 90 91 95 88 88 96 household income Fathers as well suited to look after children 94 91 82 80 92 89 88 97 87 94 92 as mothers Men should take the same responsibility for 88 93 90 92 93 90 93 98 96 94 95 home and children Jobs are scarce: giving men priority 18 20 18 14 17 19 21 15 18 21 17 Men who agree with the statement: Working mother makes warm relationship 86 89 84 95 82 82 85 74 90 83 78 with children Pre-school child suffers with working 64 64 58 65 64 64 59 66 56 49 70 mother Women really want home and children 50 57 68 58 74 74 63 70 66 62 69 Being housewife as fulfilling as paid job 46 50 60 63 66 66 49 54 68 49 66 Job is the best way for women’s 70 83 81 81 78 78 74 85 82 73 85 independence Husband and wife should contribute to 83 90 91 89 88 88 93 86 98 95 86 household income Fathers as well suited to look after children 79 89 87 82 80 80 85 91 83 70 79 as mothers Men should take the same responsibility for 72 88 81 91 76 76 83 87 83 74 88 home and children Jobs are scarce: giving men priority 27 30 31 29 47 50 27 32 39 49 38 Source: EVS (2010). 7 2.1 There are no gender disparities in primary and secondary education in Belarus. International comparison of school enrollment is only possible for gross indicators, but it still shows firstly, very high and increasing levels of enrollment in comparison to ECA region and secondly, no gender disparities in primary and secondary education (Figure 2.1). 2.2 Women are more likely to enroll in tertiary education and less likely in vocational education than men. According to the WDI data, tertiary enrollment is higher in Belarus than in ECA region. The gender gap in favor of women is substantial and increasing (twice higher of the average for ECA region in 2011). The gender disparities are observed in vocational education as well. Thus, men account for 67 percent in vocational enrollment in 2009/2010 according to the National Statistical Committee (NSC, 2010). The high concentration of women in tertiary education may be a result of barriers women face in the labor market and therefore stronger efforts to get better education and/or low prestige of higher education among men. This may also be because women are less likely to be in blue collar jobs. Figure 2.1: Enrollment in Primary, Secondary and Tertiary Education, % 100 90 80 70 60 2000 50 40 2011 30 20 10 0 Belarus ECA Belarus ECA Belarus ECA Belarus ECA Belarus ECA Belarus ECA primary, primary, male secondary, secondary, tertiary, tertiary, male female (% (% gross) female (% male (% gross) female (% (% gross) gross) gross) gross) Source: WDI. Note: The data on tertiary enrollment rates for women in the WDI are not consistent with the HLSS—which indicate less than universal tertiary enrolment among women. Thus, these data should be verified with the Ministry of Education and the National Statistical Committee of the Republic of Belarus. 9 2.3 Children from poor households are less likely to continue education after graduating from secondary school. Though there are no official data on school enrollment across households with different wealth status, we use data on students from the HLSS and analyze enrollment ratios among the population aged 17-24 years old across consumption per capita quartiles (Figure 2.2). Firstly, as already discussed, women are more likely to continue studying after graduating from general secondary school. Secondly, enrollment rates are highest for both men and women from the top richest quartile. This may reflect both entry barriers the poor face in enrollment in tertiary education and the need to earn money because parents are poor. 2.4 As in other countries, women and men in Belarus choose different fields of study in higher education. According to the data from NSC (2010), university female students tend to choose such majors as social protection, catering, social sciences, pedagogy and health where share of women reached 81 percent in 2009/2010. These majors bring women to low paying public sector jobs, while young men account for 73 percent in construction, security, engineering and technology (Figure 2.3). 2.5 This segregation may be driven by social stereotypes about “appropriate� jobs and flexible hours of work in the public sector which help to combine work with the family responsibilities—something women may put greater emphasis on. Stereotypes about “appropriate� jobs seem to develop in childhood. Thus, according to the qualitative study of girls aged 7-10, the most popular future jobs for them were teacher and doctor (Yanchuk, 2011). According to Bodrug-Lungu, Plahotnik, Kuznecova and Shurko (2009), about 60 percent of school and 77 percent of university male students in Belarus believe that first of all women should be prepared to be mother and housewife. More than half of male school and university students believe that men are better leaders in all spheres. Regarding the sector of employment, both male and female students agree that construction and industry sectors are more appropriate for men, while trade and catering are more appropriate for women. Figure 2.2: Enrollment Among Population Aged 17-24 by Consumption per capita Quartiles in 2010, % 50 men women 45 40 35 % 30 25 20 Bottom II III Top conumption per capita quirtiles Source: HLSS, World Bank staff calculations. Note: A quartile is one of the three points that divide a range of data or population into four equal parts. The first (bottom) quartile contains the poorest individuals, while the last (top) the richest. 10 Figure 2.3: Enrollment in Tertiary Education by Subjects in 2009-2010, % women men 0 25 50 75 100 Source: NSC (2010). 2.6 Belarus is experiencing a population decline and a growing share of elderly women. Population has been declining in Belarus since 1990. Low fertility rates accompanied with declining marriage and high divorce rates were among the key factors behind this trend (Table 2.1). Among the positive tendencies are the declining rates of abortions and low infant mortality, which seem to contribute positively to slowing down the negative trend during the last three years. The population decline results in the aging of the population. As shown in Figure 2.4, the proportion of older adults (55+ years) was about 26 percent in 2011 with a pronounced difference across gender. Thus, there are more 55+ years adult women than men (30 versus 20 percent respectively) because of women’s higher life expectancy. Overall, an aging and shrinking population will strain the pension and health care systems and will have an adverse impact on the labor market, especially during the transition to a smaller population. Figure 2.4: Share of Population by Age Groups and Gender in 2011, % 70+ 59-64 49-54 39-44 Age 29-34 19-24 9-14 0-4 15 10 5 0 5 10 share in total population men women Source: NSC (2011a). 11 2.7 Declining population and aging are considered key risks to national demographic security and the Belarusian Government is making efforts to improve the situation. The National Program for Demographic Security for 2011-2015 has been recently enacted to stabilize the population by 2015 at the level of 9.5 million people and to stimulate a graduate transfer to population growth. The main goals of the program are to increase fertility, to increase social-economic support of young families, and to improve reproductive health, maternity and childhood. Table 2.1: Demographic Tendencies per 1000 population Population Marriages Divorces Abortions Fertility rate 1990 10189 9.7 3.4 na 1.91 1995 10210 7.6 4.1 74.9 1.41 2000 10003 6.3 4.4 46.1 1.32 2001 9957 6.9 4.1 38.2 1.29 2002 9900 6.8 3.8 33.7 1.24 2003 9831 7.1 3.2 30 1.23 2004 9763 6.2 3 26.8 1.23 2005 9698 7.6 3.2 24.3 1.25 2006 9630 8.2 3.3 22.1 1.34 2007 9580 9.5 3.8 17.7 1.43 2008 9542 8.1 3.8 16.3 1.49 2009 9514 8.3 3.7 14.1 1.51 2010 9500 8.1 3.9 na 1.49 Source: NSC (2011a). 2.8 Selected empirical analysis in Belarus indicates that fertility decisions appear to be sensitive to economic determinants such as income and wage, economic uncertainty, maternity and childcare benefits. Amialchuk et al. (2011) analyzed economic determinants of fertility using micro data from Belarusian Household Living Standards Survey. The study finds that fertility is negatively correlated with job and income stability in families with younger women. Since the authors’ findings indicate women’s fertility decisions are responsive to incentives, they suggest ways of strengthening their impact while scaling back leakage of benefits. For example, they suggest that economic incentives would be better targeted if they were given for the second or third child since almost all women have their first child by the age 25 years. Their analysis also suggests that higher maternity benefits increased the likelihood of second births among older women. 2.9 In spite of an overall increase in life expectancy during the last decade there is still very large and expanding gender gap in favor of women and regional disparities. Life expectancy increased in Belarus both for men and women since 2000. However, male life expectancy did not grow as fast as female one and as a result male life expectancy is considerably lower and the gap is increasing. Thus, the gap was 11 years in 2000 and increased to 12 years in 2010. This gap is far above the average of ECA region of 9 years (Figure 2.5a). This happens because male expectancy in Belarus is lower than the regional average, while female expectancy is higher. Regarding regional differences, life expectancy is higher in rich urbanized regions (Figure 2.5b). 2.10 The gap in life expectancy in Belarus is driven by the high gap in adult mortality rates. In spite of a positive trend in mortality during the last decade in Belarus, male adult mortality is three times higher than that of women (330 per one thousand adult men versus 113 per one thousand women in 2010). Male mortality in Belarus is 20 percent higher than the ECA regional average in 2010 even though this gap was negligible back in 2000 year (Figure 2.6a). The gap in 12 mortality is driven by twice higher male age-standardized death rates than female (1335 versus 605 per 100,000 people)9. An alarming fact is that male mortality is 4.8 and 4 times higher than female mortality in the reproductive age of 20-24 and 25-29 years (Kalinina, 2012). Figure 2.5: Life Expectancy at Birth, years a) by gender in 2000 and 2010 b) by regions and gender, 2010 90 2000 70 79 80 2010 68 78 70 years years 60 66 77 years 50 64 76 40 62 75 30 60 74 20 10 0 Belarus ECA Belarus ECA women (rhs) men (lhs) female male Source: WDI. Source: NSC (2010). 2.11 Males have much higher death rates from cardiovascular diseases than women. Some of the main factors explaining excessive male mortality are related to non-communicable diseases and injures. Non-communicable diseases (mostly cardio-vascular diseases and cancer) account for 83 percent of all causes of death in total and male death rates are twice higher than female (Figure 2.6b). High stress due to difficult economic conditions, unhealthy diets, and alcohol abuse and tobacco consumption can be some of the factors explaining high male death rates from cardiovascular diseases. For instance, according to WHO (2012), 28 percent of all death rates for men in 2004 could be attributed to tobacco, while for women this rate is almost zero. 2.12 Men are also more prone to injuries than women. Traffic accidents, alcohol poisoning, suicides, homicides and other external causes of death account for 14 percent of all death rates and male death rates are five times higher than for female. Much higher death rates from injuries can be explained by concentration of men in industries with higher risk of injuries and more risky behavior. For example, only 6 percent of women worked in transport and construction sectors in 2008 compared to 23 percent of men. As a result, men accounted for 74 percent of all employment injuries in 2009 (NSC, 2010). 2.13 There was a substantial progress with the reduction of maternal mortality in Belarus during the last decade. According to Save the Children (2012), Belarus occupies the 25th place among 43 developed countries in Mother’s Index which indicates high level of mothers’ well - being in Belarus. Belarus managed to curb modeled maternal mortality from 31 in 2000 to a low 4 per 100,000 live births in 2010 which is eight times lower than the average for ECA region (WDI). Maternal mortality (nationally defined) declined sharply as well (from 21 to 1 per 100,000 live births). The positive tendencies were observed both in urban and rural areas, but rural areas are lagging behind which may be related to the less access to the qualified medical 9 Age-standardized death rate is calculated using the direct method, i.e. represents what the crude rate would have been if the population had the same age distribution as the standard European population. 13 assistance. Overall, safe pregnancy and delivery interventions, as well as reduction in the number of abortions are considered to stimulate decline in maternal mortality during the last decade (Institute of Economic Research under the Ministry of Economy of Belarus, 2010).10 Figure 2.6: Mortality and Death Rates a) Mortality rate per 1000 adults by gender, 2000 and b) Age-standardized death rates per 100,000 by 2010 cause and sex in 2008 400 1,400 Total death rates per 100,000 350 2000 1,200 Male 2010 1,000 300 Female per 1000 adults 800 250 600 200 400 150 200 100 0 Cardiovascula All causes Injuries neoplasms Malignant 50 r diseases 0 women men women men Belarus Belarus ECA ECA Source: WDI and WHO. 2.14 Contraceptive prevalence is quite high in Belarus and increased steadily over the last two decades with the level similar to western countries. According to UNICEF, contraceptive prevalence in Belarus was about 73 percent between 2006-2010 years. This is comparable or even slightly higher than in some Western countries. For instance, contraceptive prevalence in the Netherlands was 69 percent and in Portugal 67 percent. According to Denisov, Sakevich and Jasilioniene (2012), among the most popular contraceptive methods are intrauterine devices and condoms. Between 1990 and 2010, the proportion of Belarusian women (aged 15–49 years) using hormonal contraceptives increased from 5 to 20 per cent, which is consistent with falling abortion rates. 2.15 Under-five and infant mortality rates in Belarus declined sharply during the last decade and now are at the levels of developed countries, but with pronounced regional differences. As shown in Figure 2.7a, Belarus progressed extremely well in reducing both infant and under-5 child mortality to the levels of developed industrialized countries (3.9 per 1000 live births and 5.6 per 1000 children respectively). Nevertheless, there are still disparities between places of residence, with higher mortality rates observed in rural areas (Figure 2.7b). 2.16 More concerns are related to high rates of morbidity among children along with poor health of pregnant women. In spite of low infant and child mortality, there is no progress in the rates of morbidity among children. According to NSC (2011b), percentage of newborns with developed illnesses fluctuated around 19-20 percent in 2000-2010 years. According to the Institute for Economic Research under the Ministry of Economy of Belarus (2010), each year about 2.5 thousand cases of infant congenital diseases are recorded and often lead to child 10 Low adolescent fertility rates may also contribute to low maternal mortality in Belarus. Thus, there were only 21 births per 1,000 women ages 15-19 in Belarus in 2011 compared to 32 births in ECA region and 31 in upper-middle income countries (WDI). 14 disability. Taking into account that about 70 percent of women are ill during pregnancy (NSC, 2011b) the quality of existing health services and their accessibility still should be on the Government agenda. Figure 2.7: Infant and Under-5 Child Mortality a) Infant and under-5 child mortality in Belarus and b) Infant and under-5 child mortality in Belarus ECA region in 2000 and 2011 across residence in 2000 and 2009 2000 per 1000 children/live births 50 per 1000 children/live births 2000 20 2009 40 2011 16 30 12 20 8 10 4 0 0 U-5 female U-5 male U-5 female U-5 male Infant Infant U-5 rural U-5 urban Infant mortality Infant mortality urban rural Belarus ECA Source: WDI and NSC (2010). 15 3.1 Overall, there is minimum discrimination in the legislation related to getting a job. The Constitution guarantees equal rights to get jobs, to choose occupations and to get equal remuneration. Relationships in the labor market are regulated by the Labor Code which forbids any discrimination based on race, gender, language, and religion. Nevertheless, according to Women, Business and the Law (World Bank and IFC), women cannot work in some hazardous and dangerous sectors. The list of these sectors is prepared and approved by the Government (252 professions). 3.2 Women have specific benefits related to motherhood and parenting. Mandatory minimum length of paid maternity leave is 126 days in Belarus. The length of mandatory paid parental leave is 969 days. Full wage is paid by the government during the maternity leave. Paternity leave is not mandated in the law. The Labor Code obliges the employer to give the employee the same job when she returns from maternity leave. Employees with minor children have additional legal rights to a flexible or a part-time work schedule, while dismissal of pregnant women is penalized by the law (World Bank and IFC). Though on the one hand, benefits directly assist women with their responsibilities as mothers in taking care of children, on the other hand, these very same benefits can hurt women’s employment opportunities, wages, and career progression in the medium term. Employers, especially, private sector employers, may think twice about hiring young women as a result. Whether this is true in the case of Belarus, more analysis on better data would be required. 3.3 Nevertheless, there is a gender gap in labor force participation in Belarus, but it is not high due to low male participation. Female labor force participation rate almost has not changed in Belarus since 2000 being around 52-50 percent of female population above 15 years old (Figure 3.1)11. This is similar to the average for ECA countries. Male labor force participation has decreased slightly from 65 percent in 2000 to 62 percent in 2010. This is much lower than the average for ECA region (70 percent in 2010). As a result of low male labor force participation, the gender gap in Belarus was smaller than the average for the ECA region in 2010 (12 versus 20 percent respectively). 3.4 Women are more likely to be officially registered as unemployed, while there is higher unemployment among men based on the Household Living Standards Survey. Due to gradual reforms and the high level of state ownership in enterprise sector, registered unemployment was extremely low in Belarus during the last decade: 2.1 percent in 2000 and 0.6 percent in 2011 (NSC, 2012a). There are more women among official registered unemployed even though by 2011 the difference becomes negligible (women accounted for 52 percent of total registered 11 The picture does not change if we use labor force participation in population between 15-64 years. 17 unemployment in 2011). Official statistics on registered unemployed are lower than total unemployment. For example, according to Census, unemployment was about 6 percent in 2009 versus 0.9 percent of officially registered unemployment. The small size of benefits and cumbersome legislation could negatively affect incentives for registration. Interestingly, data from the HLSS also show a different profile of unemployed. Namely, men are more likely to be unemployed than women (World Bank, 2012b). Figure 3.1: Male and Female Labor Force Participation, % 100 80 Belarus 70 ECA Female labor force participation, 80 60 50 40 % 60 30 EC % 20 40 Belarus 10 0 20 2000 2010 2000 2010 20 40 60 80 100 Male labor force participation, % Male labor force Female labor force participation participation Source: WDI. Note: Only upper-middle income countries from ECA region are used in figure on the left. 3.5 Women are more likely to be salaried employees and less likely to be managers than men. Manual workers accounted for 60 percent and salaried employees for 40 percent of total wage employment in Belarus in 200812. Men are more likely to be manual workers than women. Manual workers accounted for 73 percent of total employment among men and 51 percent of employment among women in 2009 (Figure 3.2a). If only salaried employees are considered, men have 2.5 time higher chances to have managerial position in than women. Thus, managers account for 41 percent of total salaried employees among men and only for 17 percent among women (`Figure 3.2b). 3.6 There is a very high level of structural segregation in the labor market in Belarus. As shown in Table 3.1, women are concentrated in traditional female dominated sectors, such as education (81 percent), health and social security (83 percent), personal services (77 percent), trade and public catering (74 percent), culture and art (72 percent), communications (64 percent). Women are more likely to occupy managerial positions in these sectors, but averages salaries there are lower than the county average. In contrast, men account for higher shares in such sectors as construction, transport, industry where they occupy managerial positions and which offer salaries higher than average in the country. However, men are not better educated than women even in sectors where they hold more managerial positions. For example, women account for 24 percent of managerial positions in construction even though there are more women with higher education than men in this sector (34.7 of women in construction have higher education versus 13 percent of men in the same sector). 12 Manual workers are equivalent to blue-collar and salaried worker to white-collar workers. Total wage employment does not include self-employment, employment on personal subsidiary plots and employment on small enterprises (less than 15 workers). 18 Figure 3.2: Manual and Salaried Employees in by Gender 2009, % a) Share of manual and salaried workers across b) Structure of salaried employment across gender gender 80 100 70 60 80 50 60 % 40 % 40 30 20 20 10 0 0 women men women men total Other salaried workers Professionals Manual workers Salaried employees Managers Source: NSC (2010). Table 3.1: Employment and Earnings Statistics, 2009 % of % of Ratio of women Ratio of women women in wage to mean men wage to in sector managerial wage mean wage positions Total 53 46 85 113 Forestry 16 16 72 77 Construction 21 24 134 136 Transport 29 25 100 119 Housing and communal services 36 29 79 96 Agriculture 41 43 60 64 Material supply and sales 43 36 127 146 Industry 45 35 88 125 Science and science services 51 37 143 182 Real estate 55 45 109 129 Commercial activities to support market functioning 58 47 172 248 Communications 64 73 97 126 Culture and art 72 74 68 86 Trade and public catering 74 69 85 110 Non-productive personal services 77 60 60 95 Education 81 73 66 83 Health and social security 83 66 73 117 Source: NSC (2010). Notes: Gross nominal monthly wages are for December, 2009. 3.7 Employment gap in favor of men is observed among young, old people and less educated. There is no significant employment gap between men and women aged 24-54 years. However, young women aged 20-24 years are less likely to be employed than men. Taking into account that the average age at birth of first child was 25 years, this is rather a reflection of higher women enrollment in tertiary education. A large employment gap is observed when women reach retirement at the age of 55. The gap is 45 percent for 55-59 years age cohort (Figure 3.3a). Education seems to be an important determinant of employment which helps women to keep 19 employment at comparable levels with men. Thus, gender employment gap is not observed among individuals with higher education, but widens to 14 percent among individuals with general secondary education (Figure 3.3b). Figure 3.3: Employment Ratios across Gender, Age and Education in 2010, % a) Employment ratios across age groups and b) Employment ratios across education categories gender, % and gender, % men 100 men 100 women 80 women 80 60 % 60 40 % 40 20 20 0 Basic Primary specialized secondary Vocational Secondary Higher General 0 50-54 20-24 25-29 30-34 35-39 40-44 45-49 55-59 60-65 age groups Source: HLSS, World Bank staff calculations. Notes: population aged 17-65. 3.8 The retirement age is low with a large gender gap in favor of women. The current retirement age in Belarus is one of the lowest among countries of the ECA region13. Women retire at age 55 years and men at age 60 years. The gap in five years is inconsistent with men and women’s life expectancy. The earlier a person retires, he or she i s likely to retire at a lower wage—and hence—pension level than if they had continued to work and experienced an increase in wages. Moreover, since pensions do not fully replace wage earnings, retirees are likely to have lower income than employed persons. Moreover, since employers know that women will retire early, the incentive to groom women for positions with higher responsibilities (and wages) is lower and consequently, women’s careers are adversely impacted. About half of the newly retired men and women continue to work while receiving generous pension benefits indicating that many persons eligible for pensions are able and interested to keep working (World Bank, 2011a). 3.9 Women working after retirement age are more educated and more likely to live in the capital. Profile of women after retirement age (55 years) is presented in table 3.2. As can be seen, working women are more likely to live in the capital than women who do not work: 30 percent versus 15 percent respectively. Working women are better educated than women who do not work: 25 percent had higher education compared to 13 percent respectively in 2010. Finally, there is no statistically significant difference in the size of average pension between working and nonworking women. 13 Many developed countries, such as the United States, the United Kingdom, Germany are raising their retirement age because of changing demographic conditions and growing life expectancy (World Bank, 2011a). 20 Table 3.2: Profile of working and nonworking women above 55 not employed employed Region Minsk city 15 30 Large city 27 32 Small city 24 19 Rural 34 19 Education Higher education 13 25 Secondary specialized education 27 38 Vocational school 7 12 General secondary education 18 23 General basic education 17 2 General primary education 15 0 Incomplete primary education 1 0 Doesn't know, refuses to answer 1 0 total 100 100 Pension size Monthly average pension, rubles 514774 513747 Source: HLSS, World Bank staff calculations. Notes: female population older 55. 3.10 Childcare availability and affordability are important for female employment. Even though we cannot explore employment patterns of women with and without children due to data limitations, there is some evidence that women with children are more likely to engage in housework and caring of children. Thus, among families with female heads of household, around one percent of heads engage in housework compared to 26 percent among heads with more than two kids under five years old (HLSS). This may indicate importance of childcare and preschool service for women. According to NSC (2012b), 75 percent of children aged 1-5 years were covered by preschool education in Belarus in 2011 with a pronounced difference across urban and rural areas: 81 versus 56 percent respectively. Yet, it should be noted that lack of information on how working women with young children cope in terms of child care is a gap that needs to be filled, especially as it may adversely affect women employment opportunities or career development as a result. Entrepreneurship 3.11 Women are less likely to start their own business and are less successful given the decision to start. The LiTS database collects data for attempts and success in business for men and women. As shown in Figure 3.4a, men are more likely to start their own business (12 versus 6 percent respectively). Men also are more likely to be successful among those who tried (49 versus 45 percent respectively). The reasons for failure are difficult to analyze because 12 percent of men refused to answer. Nevertheless, nobody among men indicated such reasons as “competitors threatened me� and “could not afford protection measures’ which were mentioned by 15 percent of women. 21 3.12 Unwillingness to take personal or financial risks can be one of the factors affecting fewer attempts to start business among women, but business women seem to be less risk averse than men. The role of risk in entrepreneurship can be assessed by using data from the LiTS, where men and women rate their willingness to take risks. Firstly, regardless of gender, those who attempted to start a business seem to be less risk averse which is rather expected (Figure 3.4b). Secondly, among those who did not attempt to start business, women are less willing to take risks than men. Thirdly, among those who started business and who managed to do this, women are willing to take more risks than men. This may imply that more willingness to take risks among female entrepreneurs is needed to succeed in starting business. Figure 3.4: Attempts and Success in Starting Business Across Gender a) Attempts and success in starting business, 2008 b) Willingness to take risks by attempts and success in starting business, 2008 60 8 Men Women 50 Men Women 7 40 6 30 20 5 10 4 0 ***No ***Yes ***No ***Yes ***Attempted ***Succeed Attempted Succeed Source: LiTS II (EBRD and World Bank, 2011). World Bank staff calculations. Notes: *** gender difference significant at 1%, ** gender difference significant at 5%, * gender difference significant at 10%. Willingness to take risks ranges from 1 to 10 where 1 means not willing to take risks at all and where 10 means very much willing to take risks. 3.13 Women have equal chances with men in participating in firms’ ownership, but are less likely to manage them. According to BEEPS (2012) data, the share of firms with female ownership is not different from men and is much higher than the average for ECA region (53 versus 36 percent, respectively (Figure 3.5a).14 Participation of women in management is also higher in Belarus than in ECA, but women still are less likely to manage firms – (25 percent of all firms had women in top management in 2009). As observed in many other countries, women are more likely to manage small firms (firms with 5-19 employees), but female ownership is higher among the largest firms (Figure 3.5b). This can be partly because women ownership is high in manufacturing and state firms which employ significantly more workers than other firms. Finally, firms with a female top manager employ a higher percentage of full-time female workers: 76 percent versus 40 percent in firms with a male top manager. 14 In some post-Soviet countries firms are registered under relative’s names for tax evasion purposes , but the data do not allow us checking this. 22 Figure 3.5: Female Management and Ownership of Firms in 2008, % a) Female ownership and management in b) Female ownership and management by firm Belarus and ECA size (employees) 60 70 50 60 40 50 40 30 % % 30 20 20 10 10 0 0 Belarus ECA 5-19 20-99 100+ Female ownership Female ownership Female management Female management Source: BEEPS (EBRD and World Bank, 2009). Note: Results are taken from http://www.enterprisesurveys.org/. Figure 3.6: Female Ownership and Management by Economic Sectors in 2008, % Hotel&restaurants Garments Retail Textiles Machinery and equipment Other services Food Non metallic mineral products Wholesale Construction Other manufacturing Plastics & rubber Female ownership Metal products Transport Chemicals Female management IT Basic metals 0 20 40 60 80 100 Source: BEEPS (EBRD and World Bank, 2009). World Bank staff calculations. Note: Strict weight is used. 3.14 Similar to employment patterns, women are concentrated in garment, hotels and restaurants, retail and textiles. One can distinguish three main types of sectors based on female management and ownership: high female ownership and management, high ownership and low management, low ownership and management. High women ownership and management is observed in such traditional feminized sectors as hotels and restaurants, garment and retail. High female ownership is observed in food, transport, other manufacturing, but with much lower female management. Finally, male dominated sectors include basic metals, IT, chemicals, plastic and rubber (Figure 3.6). Access to Finance 3.15 Financial inclusion, measured as access to financial accounts and loans, is deeper in Belarus than the average for ECA countries. According to the available statistics on access to 23 finance and usage of financial resources in 2011 (Demirguc-Kunt & Klapper, 2012), the population in Belarus has higher access to banking system than the average for ECA region, measured by percentage of population having accounts or obtaining loans from formal financial institutions. Thus, 59 percent of the population has banking accounts and 53 percent obtained the loan in past year in Belarus in comparison to 45 percent of having accounts and 40 percent receiving loans in ECA region. Moreover, people in Belarus use available accounts more frequently. Thus, 95 percent of those having accounts put deposits once or twice per month in comparison to 81 percent in ECA regions. The same picture is observed with regards to money withdrawals, which are more frequent in Belarus (Figure 3.7). Figure 3.7: Use of Bank Accounts in Belarus and the ECA Region, 2011 a) Belarus b) ECA 100 100 80 80 60 60 % % 40 40 20 20 0 0 men women men women men women men women Deposits Withdrawls Deposits Withdrawls 0 1-2 3* No asnwer 0 1-2 3* No asnwer Source: FINDEX (Demirguc-Kunt & Klapper, 2012). 3.16 There are no gender disparities in access to financial resources in Belarus, but there seem be some gender differences in purposes of accounts and sources of loans. Access to financial accounts and loans from formal financial institutions is not different across gender in Belarus (Figure 3.8). No differences are observed in the use of bank accounts as well. Women in Belarus make deposits and withdraw money from accounts with the same frequency as men. Nevertheless, some gender differences can be observed from detailed data on purposes of accounts. Thus, women are more likely to have accounts to receive government transfers than men, while men are more likely to open accounts for business purposes (Figure 3.8b). Regarding sources of loans, men obtain more loans from family and friends in comparison to women (Figure 3.8a). Figure 3.8: Purposes of Accounts and Sources of Loans in Belarus, 2011 a) Source of loans, % b) Purpose of banking account , % loan account employer men men remittances **private lender women women **business financial institution purposes ***gov-t store credit payments *family and friends wages 0 20 40 60 % 0 20 40 60 % Source: FINDEX (Demirguc-Kunt & Klapper, 2012). 24 3.17 The raw gender wage gap in Belarus was increasing during the last decade in spite of relatively stable participation rates among women and improving education. The raw monthly wage gap increased from 19 to 26 percent in Belarus from 2001 to 2011. Since hourly wage gap is not available international comparison is limited. Analysis of gross monthly wages from main jobs based on the HLSS also shows that women tend to earn less than men (Figure 3.9). Increasing wage gap was accompanied by relatively stable female labor force participation and improving education. According to Pastore and Verashchagina (2007), sharp increase of gender wage gap between 1996 and 2004 in Belarus is associated with the fact that efforts of women to increase their qualification were offset by concentration of women into low wage occupations in the public and social services. Figure 3.9: Log Monthly Wage Across Gender, 2010 .8 .6 Density .4 .2 0 6 8 10 12 14 16 log_monthly_wage Men Women Source: HLSS, World Bank staff calculations. Source: Kernel density distributions. The gender difference in the distributions is statistically significant at 5% level based on Kolmogorov-Smirnov equality-of-distributions test. 3.18 Returns to education and experience are very high in Belarus with education paying equal across gender and experience paying more for women (Figure 3.8). Running basic Mincer equation shows no difference in returns to education between men and women15. One additional year of schooling brings 10 percent increase in wages both for men and women. Experience, measured by age and its squared term, is also very important and generates very high returns especially for women. Obtained results similar to what have been obtained by Pastore and Verashchagina (2006) who explain high returns to education and experience by a strong role of state in controlling wage rates and seniority rules16. 15 Years of education were imputed using the following scheme. 2 years for those without education, 4 for primary, 9 for basic, 11 for general secondary education, 12 for vocational education, 13 for secondary specialized education, and 16 for higher education. 16 Running similar regressions for quarterly data (4th quarter selected) where we can control for the sector of employment yields slightly different results. Firstly, returns to experience are lower about 6 percent, while returns to education are slightly higher for women than men (10 versus 8 percent respectively). Results from OLS regressions are presented in Table A4 in Appendix. 25 Figure 3.10: Returns to Education and Experience Based on Heckman Model, 2010 0.12 log of monthly wage 0.10 0.08 0.06 0.04 0.02 0.00 Men Women Heckman Age Years of education Source: HLSS, World Bank staff calculations. Note: Full results available in the annex (Table A2). Other controls include regional dummies and dummy for rural/urban areas. Heckman selection equation is identified by using dummy for head of household and size of the household. All presented coefficients are significant at 1 percent level. Conditional marginal effects are presented from Heckman regression (for working women and men). 3.19 Oaxaca decomposition reveals that observed characteristics explain only a tiny share of the monthly wage gap observed in Belarus. We estimate regressions explaining men’ s and women’s mean monthly wages for the 4th quarter of 2010 to obtain threefold and twofold Oaxaca-Blinder decomposition of gender wage gap17. As shown in Table 3.3a, estimated wage gap is 31 percent. Absolute majority of this gap stems from differences in returns to observed characteristics (unexplained part), while observed characteristics explain only 3 percent of wage gap. Large unexplained part of wage gap may be a result of omitted variables, such as occupation type, but could also signal about discrimination of women in the labor market of Belarus. 3.20 Occupational segregation is the key observed factor explaining the wage gap and contributing to the unexplained part as well. As shown in Table 3.3b, education and experience tend to narrow both the explained and unexplained parts of the wage gap, but concentration of women in low paid sectors overweighs this effect and increases the explained part of gender gap. Sectoral segregation contributed to the unexplained part of gender gap as well, but mostly it stems from intercept and this requires further analysis based on more detailed data. 17 Detailed description of the Oaxaca-Blinder methodology and empirical application can be found in Jann (2008). We had to use quarterly data for Oaxaca-Blinder decomposition because yearly data do not contain information about the sector of employment. 26 Table 3.3: Oaxaca Decomposition of Monthly Wages, 2010 a) Threefold and twofold decomposition of wage b) Detailed twofold decomposition of wage gap, log gap, original scale of monthly wage Threefold decomposition Gap 100 Gender gap, % 31*** Explained 3 Endowments 1.3 Experience -7 Coefficients 27*** Education -15 Interaction 2 Region -2 Twofold decomposition Sector 27 Explained 1 Unexplained 97 Unexplained 29*** Experience -6 Education -65 Region 9 Sector 9 Constant 150 Source: HLSS, World Bank staff estimation. Note: *** significant at 1%, ** significant at 5%, * significant at 10%. Explanatory variables include age, age squared, years of education, regional and sectoral dummies. Positive sign of components indicates increase of wage gap. 3.21 As a result of aging and higher male mortality, there are more female heads of households than male heads among elderly. As shown in Figure 3.11, the share of male heads of household drops from 46 percent in the population group aged 19-24 to 32 percent among population above 65. Women are more likely to be heads of households in single person and single parent households across all age groups. Thus, about 90 percent of all single person and single parent households were headed by women for age groups from 25 years (Figure 3.12). Figure 3.11: Share of male and female Figure 3.12: Share of men and women headed households in population by age headed households among single person and groups, % single parent families by age groups, % 80 men 100 % of single person and one 70 women 80 parent households, % 60 60 50 40 40 % 30 20 20 0 10 19-24 25-39 40-55 56-65 65+ 0 19-24 25-39 40-55 56-65 65+ women is head of household men is head of household Source: HLSS. World Bank staff calculations. 3.22 Households headed by women have lower income per capita than households headed by men. As have been discussed in the previous section, women tend to earn less than men. Moreover, they retire five years earlier than men. As a result, single person households, one 27 parent households and households headed by pensioners have lower monthly income per capita if the head is a woman. Single parent household headed by women have the lowest income per capita across all types of households (Figure 3.13). Women heads of single person households are poorer than men across all age groups, but those older than 65 are particularly vulnerable (Figure 3.14). Figure 3.13: Monthly income per capita by Figure 3.14: Monthly income per capita by gender of head of household and the type of gender of head of household and age group household among single person and single parent households 1,100 Male head 1,700 thousand belarusian rubles men women thousand belarusian rubles 1,000 1,500 Female head 900 1,300 800 1,100 700 900 600 700 500 500 ***19-24 ***25-39 ***40-55 ***56-65 ***+65 ***Single ***Single ***Head of person parent household household household is pensioner age group Source: HLSS. World Bank staff calculations. Note: Income per capita includes average monthly cash and in-kind income. *** gender difference significant at 1%. 3.23 The risk of poverty increases with the number of children. Analysis of single parent households headed by women shows that their income per capita drops sharply with an increasing number of children (Figure 3.15). Thus, single parent female headed household without children below 12 years have income per capita around 672 thousand of Belarusian rubles per month, while a household with 3 children has almost halve less income per capita: 353 thousand Belarusian rubles per month. This presentation of the income data does not take into account that there may be economies of scale so that for each additional household member, less money is needed to meet their consumption needs18. 3.24 Relative poverty is highest for single person households predominantly headed by women. Absolute rates of poverty are quite low in Belarus. Thus, the share of population below the national poverty line declined from 41.9 percent in 2001 to 7.3 percent in 2011 (NSC, 2012c). Nevertheless, relative poverty shows higher number of the poor- 11.4 percent of the population in 2011 (Research Center of the Institute for Privatization and Management, 2012)19. The relative poverty was the highest (35 percent) in single person households with the head older than 65 years in 2011. Taking into account that women form the majority of single person households, relative poverty is higher among them than among men. The gender gap in relative poverty 18 Nevertheless, the robustness of results in Figure 3.15 were checked by calculating income per capita using the following equivalence scale: 1 to the first adult, 0.6 to each additional adult, 0.5 to children between 6 and 18 years and 0.4 to children below 6 years (Research Center of the Institute for Privatization and Management, 2012). The results do not change and the risk of poverty still increases with the number of children. 19 The relative poverty line is established below 60 percent of the median total disposable resources which is measured as a sum of cash income, subsidies, privileges, and net income from personal subsidiary plot. 28 increased in Belarus during the economic crisis in 2011 in comparison to 2010 and this is related to a higher share of women among old age population and their concentration in low paid public sectors (Research Center of the Institute for Privatization and Management, 2012). Figure 3.15: Income per capita in female headed single parent households by the number of children below 12 years, 2010 700 600 thousand belarusian rubles 500 400 300 200 100 0 no children one child two children four children three children number of children 0-12 years Source: HLSS, World Bank staff calculations. 3.25 Targeting performance and efficiency of the current social protection system can be improved since privileges often flow to non-poor beneficiaries. Social assistance system in Belarus is one of the most extensive in the region covering about half of the population. Among the largest programs measured by spending are child birth and child care related benefits. These benefits, except for the childcare benefit for children above three years old, use the categorical approach and do not require income testing for eligibility. Even though child care benefits demonstrate good targeting performance, overall targeting performance and efficiency of social assistance system could be improved by rationalization of poorly targeted privileges which still absorb about 20 percent of social spending (World Bank, 2011ab). 29 4.1 Belarus has an advantage over other ECA countries in its high level of female human development indicators. Belarusian women are more educated than men, have a high level of labor force participation and are represented in politics. Significant progress was achieved in reducing maternal and infant mortality to the level observed in developed countries. Belarusian legislation does not discriminate against women, and different policy measures were enacted in the field of gender equality along with establishing the coordinating and advisory agency. 4.2 Nevertheless, this report identifies important gender disparities in various spheres. Thus, in spite of their higher educational level, women on average earn less than men, are less likely to be represented at the top levels in politics and public administration, and less likely to start their own business and manage firms. Moreover, preliminary analysis shows that only a very small share of large and expanding wage gap can be explained by observable differences between male and female workers. These findings may signal the existence of stereotypes and discriminatory practices in political and economic life which are indeed documented in qualitative studies. 4.3 Besides the gap in economic and political opportunities, the population in Belarus is shrinking as a result of low fertility rates and high male mortality rates. Men have higher mortality rates than women due to their unhealthy and risky behavior leading to increasing gap in life expectancy far above the average for ECA region. In spite of the Governme nt’s efforts, domestic violence also remains an important problem for Belarus. Tolerance of domestic violence in the society is quite high and people are reluctant to report the violence to the police. 4.4 Women are more likely to be heads of single person and single parent households which have the lowest income per capita among the population groups in Belarus. Single parent households with young children are particularly vulnerable. The gender gap in relative poverty in favor of men in Belarus was one of the largest in Europe in 2011. This could be related to higher share of women among old age population and their concentration in low paid public sectors. 4.5 Policy Recommendations 4.6 Promotion of gender equality in human development, economic opportunities, voice and representation is a complex task. Based on the existing situation, the following policy measures for the Government and the donor community may be beneficial for strengthening gender policy- making in Belarus: • In order to take a first step at combating violence and discrimination against women, adoption of legislation on domestic violence and sex discrimination would be an important step in protecting women in Belarus. Support and guidance on this legislation is available through CEDAW. Implementation and enforcement of proposed domestic violence legislation can benefit: (i) from the provision of training for the judges, prosecutors, the police and staff of the crisis centers, (ii) from public education campaigns and raising public awareness that gender based 30 violence is human rights violation, (iii) from ensuring access to short-term and long-term housing for the victims of domestic violence. • The government may wish to convene a task force that looks at gender disparities in vocational training and higher education. Relatively fewer women attend vocational schools and fewer men attend universities. The reasons for the disparities are not clear and greater knowledge is necessary. Factors may range from the lack of courses in vocational training that attract women to the societal pressures on young adult males to earn income (at the expense of going to college). • Men’s health in Belarus is in crisis and greater efforts need to be made to reduce mortality of prime age men. The Government may wish to first identify the leading causes of male mortality and do a thorough analysis of demographic and regional trends. This could be followed by a menu of options to reduce prime age adult mortality through such means as health campaigns against smoking and alcohol, promotion of healthy lifestyle to address non-communicable diseases (the main cause of higher mortality among men), and greater enforcement of road safety laws. • More research is needed to identify the factors affecting the large gender gap in earnings and occupational segregation by sex. There is little information on why women earn less than men. Though qualitative studies demonstrate the presence of discrimination, other factors may be possible as well. For example, women may work fewer hours than men or have less experience (due to taking extended child care leave) or women may be less willing to take jobs that require long commutes or are not compatible with household responsibilities. • The Government may wish to revisit the lower retirement age for women and family benefits. The analysis of benefits should be viewed from the perspective of women’s total lifespan including their retirement years. Thus, some benefits may seem appealing (e.g., being able to take extensive time off to care for young children), but these may have an adverse long- term impact on women’s welfare by reducing wage progression and pensions in old age. • Gender discrimination can be addressed through multiple means of which legislation is only one component. In order to change social norms, some options are available: introducing gender studies in secondary schools and higher education institutions, developing special courses on gender equality for future journalists, and positive representation of women in the mass media are just a few options available to help change gender stereotypes. Women’s achievements in business and success stories could also be highlighted in the media to be a positive example for women aspiring for the entrepreneurship. • Greater availability of gender disaggregated data is needed. Success of gender related policies depend on data availability used both for identification of gender issues and monitoring the implementation of gender policies. International agencies could closely work and support the National Statistical Committee (NSC) in order to ensure availability of relevant and regularly updated gender disaggregated statistics in Belarus. 31 Amialchuk, A., Lisenkova, K., Salnykov, M., & Yemelyanau, M. 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Ordered probability model for life satisfaction, 2008 1 2 3 4 Female 0.00671 0.0129 0.2 0.213 [0.0958] [0.0962] [0.204] [0.204] Age -0.0368* -0.0275 -0.0344 -0.0252 [0.0209] [0.0211] [0.0209] [0.0212] Age squared 0.000235 0.00011 0.000219 9.57E-05 [0.000224] [0.000228] [0.000225] [0.000229] Married 0.464*** 0.447*** 0.171 0.147 [0.111] [0.111] [0.181] [0.179] Married Female 0.491** 0.500** [0.232] [0.229] Number of children 0.0236 0.0221 0.0667 0.063 [0.0654] [0.0657] [0.0953] [0.0941] Children Female -0.0694 -0.0646 [0.107] [0.106] Employed 0.0277 -0.0339 0.347* 0.295 [0.132] [0.135] [0.185] [0.188] Employed Female -0.530** -0.556** [0.223] [0.221] Education level dummies No Yes No Yes Source: EVS (2010). Notes: *** significant at 1%, ** significant at 5%, significant at 10%. The dependent variables is measured by question: “How satisfied are you with your life in scale from 1 to 10, 1 being dissatisfied and 10 being satisfied. Figure A1. Coefficients and Confidence Intervals for Gender Dummy from Ordered Probit, 2008 a) Ordered probit regression without controls b) Ordered probit regression with controls 1 0.5 0 -0.5 -1 -1.5 Lower Upper coef Source: EVS (2010). World Bank staff calculations. Source: EVS (2010). World Bank staff calculations. Notes: Dummy takes one for female and zero for men. Note: Controls include number of children, marital status, The question states: “How important in your life the employment dummy, and education. All variables except following with your life in scale from 1 to 4, 1 being very education categories are interacted with female dummy. important and 4 being least important. Positive coefficient Positive coefficient indicates less importance of a indicates less importance of a particular category for particular category for women than men. women than men. 35 Table A2. Returns to education and experience, 2010 Dependent variable log of monthly average wage from the main job in 2010 Men Women Variables OLS Heckman OLS Heckman Age 0.127*** 0.0840*** 0.151*** 0.116*** [0.00865] [0.00951] [0.00843] [0.00923] Age squared -0.00149*** -0.000973*** -0.00169*** -0.00131*** [0.000101] [0.000114] [9.85e-05] [0.000113] Year of education 0.106*** 0.0967*** 0.101*** 0.103*** [0.00742] [0.00838] [0.00764] [0.00672] Brest -0.136*** -0.245*** -0.250*** -0.267*** [0.0491] [0.0456] [0.0441] [0.0408] Vitebsk -0.125** -0.216*** -0.286*** -0.279*** [0.0487] [0.0436] [0.0458] [0.0442] Gomel -0.287*** -0.367*** -0.430*** -0.396*** [0.0520] [0.0445] [0.0508] [0.0413] Grodno -0.163*** -0.277*** -0.272*** -0.273*** [0.0506] [0.0459] [0.0469] [0.0428] Minsk region -0.124** -0.171*** -0.191*** -0.192*** [0.0499] [0.0445] [0.0478] [0.0424] Mogilev -0.252*** -0.307*** -0.276*** -0.277*** [0.0523] [0.0484] [0.0461] [0.0412] Rural -0.245*** -0.232*** -0.0771*** -0.0665*** [0.0283] [0.0257] [0.0287] [0.0255] Constant 9.884*** 8.976*** [0.212] [0.205] R-squared 0.22 0.21 N 3303 3,837 Source: HLSS. Notes: *** significant at 1%, ** significant at 5%, significant at 10%. Marginal conditional effects are presented for the Heckman model. Table A3. Oaxaca Blinder decomposition, log of monthly wage, 2010 Explained Unexplained Experience -0.0185*** -0.0171 [0.00346] [0.176] Years of education -0.0390*** -0.175 [0.00501] [0.107] Region -0.00551 0.0254*** [0.00360] [0.00891] Sector 0.0717*** 0.0239 [0.00694] [0.0197] Constant 0.403** [0.203] N=6517 Source: HLSS. Notes: *** significant at 1%, ** significant at 5%, significant at 10%. Twofold decomposition. Age and age squared are included in experience category. Region category includes oblast and residence dummies. Sector category includes 16 economic sectors. 36 Table A4. Returns to education and experience, 4th quarter 2010 Dependent variable log of monthly average wage from 4th quarter of 2010 Men Women Men Women Age 0.0692*** 0.0677*** 0.0656*** 0.0636*** [0.00614] [0.00639] [0.00619] [0.00635] Age squared -0.000860*** -0.000803*** -0.000815*** -0.000761*** [7.27e-05] [7.65e-05] [7.35e-05] [7.57e-05] Year of education 0.0795*** 0.100*** 0.0875*** 0.105*** [0.00582] [0.00557] [0.00602] [0.00569] Brest -0.192*** -0.248*** -0.218*** -0.250*** [0.0372] [0.0342] [0.0375] [0.0335] Vitebsk -0.181*** -0.233*** -0.193*** -0.239*** [0.0384] [0.0354] [0.0383] [0.0348] Gomel -0.298*** -0.328*** -0.307*** -0.334*** [0.0367] [0.0360] [0.0364] [0.0356] Grodno -0.224*** -0.219*** -0.236*** -0.220*** [0.0394] [0.0340] [0.0397] [0.0332] Minsk region -0.155*** -0.161*** -0.176*** -0.176*** [0.0374] [0.0364] [0.0371] [0.0351] Mogilev -0.315*** -0.289*** -0.333*** -0.286*** [0.0403] [0.0358] [0.0399] [0.0352] Rural -0.255*** -0.114*** -0.236*** -0.103*** [0.0230] [0.0214] [0.0235] [0.0223] Dummies for sectors included yes yes Constant 11.78*** 11.11*** 11.74*** 11.22*** [0.145] [0.142] [0.150] [0.144] R-squared 0.22 0.201 2,997 3,520 N 2,997 3,520 0.263 0.234 Source: HLSS. Notes: *** significant at 1%, ** significant at 5%, significant at 10%. 37