Gender segregation in the labour market of Central Asian countries: Kazakhstan, Kyrgyzstan and Uzbekistan

Gender segregation in the labour market of Central Asian countries: Kazakhstan, Kyrgyzstan and Uzbekistan | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Gender segregation in the labour market of Central Asian countries: Kazakhstan, Kyrgyzstan and Uzbekistan Yeldar Mubarakov, Ilona Bordiyanu, Ayazhan Seriktayeva This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5709710/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Central Asian countries face serious socio-economic problems related to gender segregation in the labour market. This study examines the extent and scope of gender segregation in Kazakhstan, Kyrgyzstan and Uzbekistan between 2014 and 2023, which includes significant changes caused by economic development, political reforms and the global crisis. The study used data from each country’s national statistical agencies, which examined gender differences in employment levels, sectoral distribution and access to decision-making positions. The persistence of gender inequality in the labour market can be identified through comparative analysis, which examines trends and identifies factors that contribute to it. The study shows that two ideas are true by using the Gini Index, Duncan Index, Glass Ceiling Index, and analyzing data over time. Despite some progress, women still face limitations in certain occupational areas and at higher levels, as the results show. The analysis shows that gender segregation and its characteristics in each of the three countries are influenced by socioeconomic and cultural factors. The final result of the study is a set of recommendations to reduce gender imbalance and achieve an equitable distribution of opportunities for both genders, which can ultimately contribute to the sustainable development and economic efficiency of the region. JEL Classification J16, J21, J31 gender segregation labour market Central Asia employment types of economic activity economic development gender equality Figures Figure 1 Figure 2 Figure 3 Figure 4 1 Introduction Gender inequality is exacerbated by many social and economic factors affecting the labour market in Central Asian countries such as Kazakhstan, Kyrgyzstan and Uzbekistan. Equality of opportunity and economic development are hampered by gender segregation, which remains a pressing problem manifested in the differences in employment and income between men and women. According to international studies, gender segregation in the labour market can have a detrimental impact on social and economic progress, which will lead to limited access to jobs and reduced development potential for women (International monitoring mission on labour rights in Central Asia, 2024). The importance of this topic is compounded by socio-economic and demographic changes in Central Asian countries where women, especially in rural areas, lack access to quality jobs and resources due to limited access to quality resources. Agreeing with the OECD ponder, it appears that in Central Asia the normal level of segregation against ladies and young ladies in social education by the Social Educate and Sex List (SIGI) is 37.2, medium or near to much higher than the worldwide normal (24), and for OECD nations (15.3) (OECD, 2024). This truth looks indeed more regrettable after you consider the 33 a long time went through on accomplishing correspondence. In the meantime, according to ILO, ladies still win almost 20% less than men (Papuc, 2024). This information recommends that there's a decently tall degree of isolation within the Asian locale. The 2023 gender inequality report shows that in Kazakhstan, there is a clear separation between men and women in politics. Women occupy just 19.4% of the seats in parliament and 14.8% of the government leadership positions. Also, there is still a gap in pay between men and women; women hold 41.1% of the management jobs at work (Bureau of National Statistics of Kazakhstan, 2023). At the same time, research shows that there aren't many women in management jobs in companies in Uzbekistan. Companies also hire women based on different factors, like good looks, being single, or living in dorms. The National Statistical Committee (NSC) of Kyrgyzstan says that women earn 26. 6% less than men. It's important to mention that the unpaid work that women do at home is not respected and is seen as an easy task in our society. The NSC says that women spend around 4. 5 hours each day doing housework, while men spend about 1 hour. Women also earn 29% less than men for doing the same job (Facrieva, 2017). Also, past research has found that in Kyrgyzstan, men and women are more separate in cities than in the countryside. In countryside areas, separation in education is the biggest reason for gender separation. In cities, job separation is a more important factor (National strategy of the Kyrgyz Republic for achieving gender equality by 2030, 2020). Social beliefs, traditions, and cultural habits greatly affect the lower status of women, taking away chances that are often seen as powerful for men in Central Asian countries. Another research done by Blackburn and others in 2002, it was noted that there are two types of segregation. Vertical segregation leads to unfairness or inequality, while horizontal segregation shows differences among groups but doesn't create inequality. Together, these two types of segregation contribute to overall segregation. (Blackburn et al., 2002). At the same time, studies have found that there is a need for fair chance rules that are connected to the job markets in different European countries (Rubery & Fagan, 1995). Past studies have mainly looked at how gender separation happens. This study will look at how things are done in Kazakhstan, Kyrgyzstan, and Uzbekistan, and it will also examine research related to gender separation in these three countries. The information shows that there are big issues with separating men and women in these three countries. This study looks at how men and women are treated differently in the job market in Central Asia, especially in Kazakhstan, Kyrgyzstan, and Uzbekistan. 2 The Gender Pay Gaps The World Economic Forum thinks it will take 202 years to completely fix the difference in pay between men and women. This is based on what has happened over the last 12 years (WEF, 2018). The pay gap between women and men has usually been linked to the small number of women in higher-paying jobs in companies (Alkadry & Tower, 2006). Economists have usually focused on things related to gender, like women lacking education or skills, or companies maltreating women, to understand the difference in pay between men and women and how it has changed over time (Blau & Kahn, 2003). Jobs that need to be done quickly are often filled by men, and they tend to make more money than women. The International Labour Organization (ILO) explains that there are many reasons for differences in pay between men and women. The first group should have more leaders. The second thing is the hours you work. Men and women workers usually engage in their jobs differently, especially when it comes to how much time they spend working. In the Global Wage Report 2018/2019, the ILO points out that in almost all of the 73 countries with data, women are more likely to work part-time than men, except in five countries (ILO, 2019). Women often take part-time jobs because they have family duties, like being a wife and mother. The third point is about education. Women are still behind men in certain job areas like Science, Technology, Engineering, and Mathematics (STEM). Most people in this job are men. Job opportunities in science, technology, engineering, and math (STEM) will make it harder for women to join these areas. The fourth is jobs typically done by women. 3 Conceptual framework and hypothesis When there are limits on how much money people can spend, they can pick how long they want to work each day by trying to get the most satisfaction from their choices, according to neoclassical theory. The number of hours employees work is determined by their bosses and unions, how easily workers can move to different jobs, and the overall economic conditions (Simic 2002). So, the hours people want to work and the hours they actually work might not match. This means some people might work too many hours (over-employment), while others might work fewer hours than they want (part-time employment). Some workers may want to work longer hours but can’t because they have limited time. This research focuses on how men and women are treated differently in jobs, especially in terms of the types of jobs they have and their positions in those jobs (Silber, 1989; Hutchens, 1991, 2004; and Mora and Ruiz-Castillo, 2003, 2004, among others). Anker (1998) gives five reasons why researchers and decision-makers in rich countries should pay attention to job and status differences between men and women, besides just fairness issues. First, when women mostly work in low-paying or less important jobs, it affects how men view women and how women view themselves. This supports gender stereotypes and increases poverty among women, which greatly affects households led by women. Second, not allowing women to work in some jobs means wasting talent, and this leads to very poor outcomes when women are skilled workers. Third, separating jobs by gender creates strict rules that make it harder for the job market to adjust to changes in the workforce. Of course, we can't overlook these things in a world economy that cares about being efficient and competitive. Fourth, having different jobs and levels for men and women can harm the education of future generations, especially in what boys and girls decide to study. About one-third of the pay gap between men and women worldwide is because of jobs that are divided by gender and the way jobs are organized in higher positions. When looking at segregation, whether with two groups or more, most tools measure how mixed or separate the groups are overall. They do this by looking at how different all the population groups are within different areas, rather than focusing on just one specific group. So, when looking at the way jobs are divided by gender, we often compare how many women work in different jobs to how many men do. People may want to study not just overall segregation, but also how specific groups are separated from others. The way a certain demographic group is spread out across jobs can be very different from how other groups are. Measuring how separated a group is doesn't mean you can understand that separation without considering other groups in the population. Segregation is something we need to look at based on how people are positioned compared to one another, similar to how we measure poverty by comparing people's situations to each other. Actually, both things are more alike than you might think at first. To measure how poor a country is, we usually look at the income of everyone in the country. Find the poverty line, which means the income level used as a standard. Sure, if one group's income changes, it can affect the poverty level of other groups because the limit for what counts as poverty has changed. But the way groups depend on each other doesn’t stop us from discovering how poor a specific group is. (by using, for example, the decomposability property of the popular family of indexes proposed by Foster, Greer, and Thorbecke, 1984). Similarly, if the way a certain demographic group is spread out in different parts of an organization changes, this can impact not just how separate that group is, but also how separate other groups are. This is because the overall population distribution might have changed too. Like with relative poverty, we believe that we can measure how separated a particular group is, and this is a useful way to study segregation more thoroughly. Measuring how women are separated in jobs has been studied for a long time. Fifty-five years ago, Moir and Selby Smith (1979) created a new way to measure how separated female workers were in different jobs in Australia. As far as we know, only Alonso-Villar and Del Río (2008) have looked at this topic using clear rules and have suggested new ways to measure it that meet basic requirements. Previous research has looked at the social and job-related reasons that cause men and women to work in different jobs (Bettio 2009; Fuchs 2016; England 2005; Cohen and Huffman 2003; Kjellstad and Nymmoen 2012a, 2012b; Wilkins 2006; Acosta-Ballesteros et al. 2018). There are big differences in the number of men and women working in different types of jobs (Kjeldstad and Nimuen 2012a, 2012b; Valletta et al. 2016). There have been only a few studies that connect gender segregation in jobs and industries to these differences in the labour market. Kjellstadt and Nimuan (2012a and 2012b) found that women often face underemployment in jobs and fields mostly held by men. However, their study doesn't include important factors that show job segregation. In jobs where women are the majority, they are more likely to have less work than they want, according to Kameradé and Richardson (2018). However, this isn't seen in all types of industries. Khitarishvili (2016) says that in Central Asia, there is a big difference between men and women in the workplace. This is caused by traditional beliefs about gender and social and economic issues, like women having less education and many family duties. Even though there have been big shifts in the number of men and women working in this area since the Soviet Union ended, there are still issues of unfairness between genders. Many women in Kazakhstan have jobs in government and public services, especially in health and education. But they are mostly missing in high-paying private sectors like energy and technology, where salaries and job opportunities are better. There are clear differences between men and women when it comes to jobs and pay. In fields where women usually work, like education and health, they often have shorter hours and earn less money than men who work in industries like manufacturing and construction. This leads to many women not having enough work or jobs that match their skills, which makes it harder for them to grow in their careers and increases the difference in earnings between men and women. Women in Central Asia usually have to take temporary, low-paying jobs, which makes it hard for them to be financially independent. Factors that lead to gender separation include not having enough money and fewer chances for women to hold management jobs. In countries like Kazakhstan, Kyrgyzstan, and Uzbekistan, women seldom have top jobs or leadership positions in private companies. This makes it harder for them to be financially independent and earn more money, which limits their chances for growth and development in both economic and social areas. Even though good reasons are showing that job and industry separation affects the chances of underemployment, the study mentioned above did not provide clear measurements of its effect. In this article, we tackle this problem by using a better way to assess it. We suggest and check this idea: Hypothesis 1. In Kazakhstan, Kyrgyzstan, and Uzbekistan, many women work in low-paying jobs and have less respected roles compared to men. Women are more likely than men to be underemployed, which creates a gap between the two genders. This problem is made worse by the way jobs are divided into different fields and types. Also, this effect might be partly because men and women work in different jobs in unequal numbers, according to Barrett and Doyon (2001). We should also consider the pay differences between jobs that are mainly held by men and those mainly held by women. In a straightforward example, the authors mention how women are spread across different jobs and industries compared to men. They find that the main reason women often have to work part-time against their wishes is that they are in different types of jobs and fields. Studies have found that men can gain advantages from being in jobs mostly held by women in a few different ways (Simpson 2004). According to Lupton (2006), men advance in their careers more quickly than women because women face challenges, like the «glass ceiling», that prevent them from rising to higher positions. Men might be given certain jobs that are seen as better suited for them. The benefit is that men who work in jobs mainly held by women earn more money than women do in those jobs (Torre 2018). In fact, women might experience bad outcomes in jobs that are mostly held by men. For example, Martin and Barnard (2013) discovered that rules and behaviors in organizations that treat women unfairly are big problems for them. These points might also relate to part-time jobs, which means women might have a higher chance of working part-time than men, whether in jobs mostly held by women or those mostly held by men. But, as far as we know, in 2013, Vuluk and others tried to find out why there is a difference between men and women in jobs. However, they didn't look at how jobs are separated by gender, and they used simple models that might give inaccurate results. We address this missing information by using a new method that helps us suggest and check the following ideas: Hypothesis 2. Workplaces and areas that are mainly employed women are the service sectors. This makes women more financially unstable. 4 Data and methods The aim of this study is to identify the characteristics of gender segregation in the labour market in Central Asian countries (Kazakhstan, Kyrgyzstan, Uzbekistan), determine the extent of gender barriers to employment, The distribution of roles by industry and women’s leadership, as well as an assessment of national change dynamics. The study was conducted in the framework of comparative analysis. It can be said that it consists of three stages: Descriptive analysis: the main indicators of employment, such as the share of women in various sectors of the economy, achievement of leadership positions, and wage level were studied. Quantitative analysis: the segregation indices (horizontal and vertical), Dunkan dissimulation index, Gini index, and Glass ceiling index are calculated. Qualitative analysis: Government programmes, legal frameworks, and policies for gender equality have been reviewed. The study is about the labour market in Central Asian countries. Different approaches were used to assess gender segregation in the labour market of countries in Central Asia, including Kazakhstan, Kyrgyzstan and Uzbekistan. The earliest statistics were analyzed. Sources of data include statistical data from state bodies: the National Bureau of Statistics of Kazakhstan, the Agency for Statistics under the President of the Republic of Uzbekistan, the National Statistical Committee of the Kyrgyz Republic and others. The analysis period is 2014–2023. The methodology is oriented towards a combined approach combining statistical analysis methods and contextual interpretation. The analysis of gender segregation in the labour market in Central Asia countries has been done using a mixed approach, including quantitative and qualitative methods. Gender segregation has been quantified. Two interrelated aspects need to be considered for the quantification of economic phenomena: measurement methodology and research information base. There is now a wide range of methods and tools in world practice to analyze and define gender segregation. To better measure gender segregation in the labour market, a number of statistical indicators called segregation indices are used. Gender segregation can be divided into several types: horizontal and vertical. Horizontal segregation is the division of women and men into occupational groups (Rogacheva, 2011). Horizontal segregation also includes the problem of restrictions on women’s employment in difficult and harmful conditions. Vertical segregation - unequal distribution of gender groups in formal hierarchy. Discrimination against women means that they have limited access to management and the most prestigious professions. Such discrimination is often seen at the structural level of an organization and includes «glass» barriers. These barriers are artificial because they do not depend on the employee’s qualifications or experience; they depend not only on personal behavior or business relationships but also on the organization’s structure. The quantification of any economic phenomenon is intended to take into account two interrelated aspects of statistics: computational methodology and research information base. Microsoft Excel was used for statistical analysis. The data visualization was done in Python language with the help of Jupyter Notebook and Mathcad programs. The oldest and most commonly used is the Duncan dissimilarity index (ID). The general formula for calculating the Duncan Dissimilarity (ID) index is as follows (1): $$\:ID=\frac{1}{2}\sum\:_{i}\left|\frac{{F}_{i}}{F}-\frac{{M}_{i}}{M}\right|$$ 1 Where, F i and M i respectively i - number of women and men working in occupations, F and M - total number of women and men working in the economy; i - ranges from 1 to number corresponding to the number of professions (branches) in the economy. The most common explanation for the dissimilarity index is that it reflects the proportion of men or women who need to change their profession or industry in order to achieve an even distribution of women and men between occupation. The following occupational sectors were considered for the calculation of the index (Table 1 ). Table 1 Types of economic activity № Economic activity 1 Agriculture, forestry and fisheries 2 Manufacturing 3 Construction 4 Wholesale and retail trade; repair of motor vehicles and motorcycles 5 Transportation and storage 6 Accommodation and catering services 7 Information and communication 8 Financial and insurance activities 9 Real estate transactions 10 Professional, scientific and technical activities 11 Management and provision of ancillary services 12 Public administration and defense; compulsory social security 13 Education 14 Health care and social services 15 Art, entertainment, and recreation 16 Provision of other types of services For Kazakhstan, Kyrgyzstan and Uzbekistan, the dissimilarity index will be the same as in the table below (Table 2 ) between 2014 and 2023 according to formula (1). Table 2 Index of dissimilarity Year Index of dissimilarity (Kazakhstan) Index of dissimilarity (Kyrgyzstan) Index of dissimilarity (Uzbekistan) 2014 0,2965 0.2859 0.3223 2015 0,3188 0.2934 0.3043 2016 0,3041 0.2926 0.3070 2017 0,2992 0.2952 0.2953 2018 0,3046 0.3023 0.2637 2019 0,298 0.3063 0.2719 2020 0,2907 0.3398 0.2612 2021 0,3013 0.3232 0.2585 2022 0,30997 0.3349 0.2641 2023 0,32195 0.3347 0.3104 Sometimes the traditional income inequality indicator is used to estimate the gender distribution by industry and occupation, the Gini coefficient (G) (Mehta, 2010). The Gini coefficient is a statistical indicator of the degree of stratification of a particular country or region by a given subject. Used to estimate economic inequality. The Gini coefficient can vary from 0 to 1. The more its value deviates from zero and approaches one, the more income is concentrated in the hands of individual population groups (Farris, 2010). The Gini index is a percentage representation of the coefficient. Often used to measure income inequality, but can also be used to estimate gender inequalities in occupations. The Gini coefficient can be viewed graphically or algebraically. Find the Gini coefficient in algebraic ways. Gini coefficient (2): $$\:G=1-2\sum\:_{i=1}^{n}{x}_{i}cum\:{y}_{i}+\sum\:_{i=1}^{n}{x}_{i}{y}_{i}$$ 2 Where, x i - the proportion of the i-th group in the population ( \(\:i=\stackrel{-}{1,n};\:\sum\:_{i=1}^{n}{x}_{i}=1\) )); y i - the proportion of the i-th group in the income volume (the proportion of the i-th group in the population) ( \(\:\sum\:_{i=1}^{n}{y}_{i}=1\) )); cum y i - the total proportion of the income (i-th and preceding population). If G is close to zero, the division between citizens of a state is almost even. The study used data provided by national statistical offices for each year. The analysis includes the creation of a time series: The Gini index was presented as a time series for each of the three states. For three states, the Gini coefficient for 2014–2023 was calculated using formula (2). Here, the population was divided into two groups: men and women. The Gini indices can be seen in the table below (Table 3 ). Table 3 Gini coefficient Year Gini (Kazakhstan) Gini (Kyrgyzstan) Gini (Uzbekistan) 2014 11,59 9,02 24,41 2015 11,95 7,53 24,33 2016 10,95 7,45 24,19 2017 11,17 8,42 24,11 2018 11,84 8,98 21,94 2019 11,12 7,27 23,14 2020 8,62 7,41 22,45 2021 7,53 7,51 23,46 2022 8,4 6,08 24,32 2023 8,53 8,33 19,12 Vertical gender segregation is a type of inequality in which men and women occupy different positions in the hierarchical structure of professions, organizations or industries. This type of segregation is manifested in women’s limited access to higher and more prestigious positions than men, despite equal educational and professional qualifications. Main characteristics: gap in achieving leadership positions, glass ceiling. The quantitative analysis of vertical gender segregation involves the use of statistical and analytical methods to measure the level of inequality between men and women in occupational hierarchies. This analysis helps to determine the extent of women’s limited access to leadership positions, regardless of their number or competencies. The main method of quantitative analysis is the glass ceiling index. This index is calculated as the proportion of women in senior management positions to the overall share of women in employment. The index is studied in Kazakhstan, Kyrgyzstan, and Uzbekistan. What is the «Glass ceiling» index for: Assessment of the level of gender inequality in the labour market. Comparison between industries, regions. Proposals to eliminate barriers in women’s career growth. The calculation formula will be as in (3). $$\:\text{G}\text{C}\text{I}=\frac{\text{P}\text{r}\text{o}\text{p}\text{o}\text{r}\text{t}\text{i}\text{o}\text{n}\:\text{o}\text{f}\:\text{w}\text{o}\text{m}\text{e}\text{n}\:\text{i}\text{n}\:\text{s}\text{e}\text{n}\text{i}\text{o}\text{r}\:\text{p}\text{o}\text{s}\text{i}\text{t}\text{i}\text{o}\text{n}\text{s}\text{і}}{\text{S}\text{h}\text{a}\text{r}\text{e}\:\text{o}\text{f}\:\text{e}\text{m}\text{p}\text{l}\text{o}\text{y}\text{e}\text{d}\:\text{w}\text{o}\text{m}\text{e}\text{n}}$$ 3 Where GCI is the Glass Ceiling Index. GCI 1 women are more likely to take up leadership positions. If GCI < 1, that is a «Glass ceiling». The lower the index, the stronger the resistance. The following statistics were used to calculate the Glass ceiling index. The statistical data of Kazakhstan, Kyrgyzstan, and Uzbekistan can be found in Table 4 respectively. Table 4 Statistical indicators for the calculation of the index «Glass ceiling» Year Percentage of employed women, % Percentage of women in management positions, % Kazakhstan Kyrgyzstan Uzbekistan Kazakhstan Kyrgyzstan Uzbekistan 2014 48,42 39,97 46,72 34,8 33,4 27,7 2015 48,17 40,90 46,14 37,0 36,1 27,7 2016 48,09 40,41 45,67 37,3 35,2 27,1 2017 48,06 39,53 45,78 37,0 34,2 27,0 2018 48,54 38,25 41,62 41,2 37,8 27,0 2019 48,35 38,19 41,49 43,0 40,9 26,6 2020 48,24 38,35 41,44 41,1 47,4 26,5 2021 48,28 38,81 41,33 39,0 42,9 27,7 2022 47,91 37,99 41,20 40,8 46,2 28,2 2023 48,12 38,57 39,46 41,2 47,9 29,2 Using these data, the Index for three states was calculated by the formula (3) of the glass ceiling (Table 5 ). Table 5 Glass ceiling index Year GCI (Kazakhstan) GCI (Kyrgyzstan) GCI (Uzbekistan) 2014 0,72 0,84 0,59 2015 0,77 0,88 0,60 2016 0,78 0,87 0,59 2017 0,77 0,87 0,59 2018 0,85 0,99 0,65 2019 0,89 1,07 0,64 2020 0,85 1,24 0,64 2021 0,81 1,11 0,67 2022 0,85 1,22 0,68 2023 0,86 1,24 0,74 The panel regression was used to check the validity of hypothesis 2 . Panel regression for 3 states (Kazakhstan, Uzbekistan, and Kyrgyzstan) for 2014–2023 gross domestic product per capita, Percentage of women in management positions, Ratio of employed men to employed women (in the area of services for living and food), Gender level in the communication used labour data. Based on this data, the panel data (Table 6 ) was created. Table 6 Panel data Country Year Gross domestic product per capita, US dollars Percentage of women in management positions, % Unemployment rate of women Ratio of men’s to women’s employment Gender pay gap, (%) Kazakhstan 2014 12 807,4 34,80 5,8 36,35 33,04 2015 10 510,7 37,00 5,9 39,59 34,14 2016 7 714,8 37,30 5,5 37,18 31,44 2017 9 247,6 37,00 5,4 44,50 32,18 2018 9 812,5 41,20 5,4 43,38 34,15 2019 9 812,5 43,00 5,3 46,42 32,24 2020 9 121,7 41,10 5,4 57,49 24,99 2021 10 370,8 39,00 5,5 52,79 21,72 2022 11 476,6 40,80 5,5 54,00 25,20 2023 13 193,7 41,20 5,3 50,91 25,69 Kyrgyzstan 2014 1 100,0 34,80 9,5 52,00 28,90 2015 1 120,0 37,00 9,0 55,84 24,60 2016 1 150,0 37,30 8,7 68,30 24,70 2017 1 180,0 37,00 8,9 71,32 27,50 2018 1 200,0 41,20 6,9 80,34 28,40 2019 1 230,0 43,00 6,2 105,03 23,00 2020 1 120,0 41,10 6,7 104,22 24,60 2021 1 150,0 39,00 6,3 106,29 24,90 2022 1 210,0 40,80 6,1 115,51 20,40 2023 1 260,0 41,20 6,0 106,74 26,60 Uzbekistan 2014 2610 34,80 4,9 86,96 65,60 2015 2750 37,00 5,0 88,31 65,50 2016 2870 37,30 5,0 90,71 65,50 2017 2940 37,00 5,6 89,53 65,40 2018 3060 41,20 11,6 93,90 61,40 2019 3190 43,00 12,8 91,79 63,80 2020 3190 41,10 14,1 93,48 62,50 2021 3360 39,00 13,3 96,03 64,40 2022 3470 40,80 13,4 97,17 66,00 2023 3600 41,20 13,5 97,76 70,40 A few concluding comments approximately our strategy are vital. To begin with, the standard mistakes of the Duncan contrast list, glass ceiling list, and Gini coefficient were calculated utilizing introductory stacking strategies, which permitted us to evaluate the measurable importance of watched patterns and contrasts in three nations. These strategies give dependable gauges in spite of the complexity of the information structure. Although the Duncan, Gini, and glass ceiling indexes are primarily used to measure gender segregation, the methodology is flexible and can be adapted to other indexes such as the Carmichael and McLachlan indexes. This flexibility ensures reliable future validation and cross-validation using alternative measures of segregation in the labor market. At long last, our counterfactual demonstration was created to think about how changes in labour approaches can influence gender segregation. This reenactment appeared that closing the holes in instruction and preparation can essentially decrease isolation, and this conclusion is steady with past considerations in comparative settings. The methodological rigor applied in this study ensures that the results will serve as a reliable basis for policy recommendations aimed at reducing gender segregation in Central Asian labor markets. 5 Results A comparison of the Duncan dissimilation index statistics of the three countries to identify general trends and differences can be seen in the figure below (Fig. 1 ). The chart shows data on the dissimilation index (ID) in the employment sector of Kazakhstan, Kyrgyzstan and Uzbekistan for the analyzed period. This table measures the level of sexual isolation by measuring the proportion of workers who are forced to change professions or departments in order to achieve a real spread of sexual orientation. In Kazakhstan, at the beginning of the period, it was about 35 people and by the middle of the period it was slowly decreasing to 25. This indicates certain positive changes in the business structure related to sexual orientation, which are probably related to the increased access of women to various specialized fields. Be that as it may, from the center of the period, the record started to develop once more, coming to the level of 30 by the conclusion, which demonstrates the tirelessness of basic boundaries within the labour showcase that require further consideration. For Kyrgyzstan, the Duncan index is characterized by a relentless decrease: beginning from 25, it gradually reduced to 15, and after that got stabilized. This drift shows a critical decrease in gender segregation, likely due to dynamic work changes, counting measures to extend women's cooperation in already customarily male-dominated businesses. This progress highlights the adequacy of the approach, in spite of the fact that it is vital to proceed observing its long-term maintainability. In Uzbekistan, there are sharp changes within the data. At the introductory stage, it was approximately 40, at that point dropped strongly to 20 by the middle of the period, likely due to short-term financial or social changes. However, by the conclusion of the period, the record had expanded once more to 35, demonstrating an increment in sex isolation, conceivably due to the rebuilding of conventional parts or insufficient viability of changes. In this way, the flow of the dissimilation index within the three nations reflects multidirectional patterns. Whereas Kyrgyzstan has seen consistent reductions in isolation, the advance has been less relentless in Kazakhstan and Uzbekistan. This highlights the got to create and actualize more focused on and long-term measures to decrease sex imbalance within the labour showcase within the locale. Highest and lowest values of the index for the period (Table 7 ): Table 7 Highest and lowest index values Сountry Minimum load-capacity index Year Maximum load-capacity index Year Kazakhstan 0.2907 2020 0.32195 2023 Kyrgyzstan 0.2859 2014 0.3398 2020 Uzbekistan 0.2585 2021 0.3223 2014 The table shows distribution of jobs among men and women in Kazakhstan, Kyrgyzstan, and Uzbekistan. In Kazakhstan, the lowest score was 0. 2907 in 2020, and the highest score was 0. 32195 in 2023 This shows that the imbalance has gotten bigger. In Kyrgyzstan, the lowest value was 0. 2859 in 2014, and the highest value was 0. 3398 in 2020 This change might be because of economic problems. In Uzbekistan, the lowest score was 0. 2585 in 2021, and the highest score was 0. 3223 in 2014 This shows that the situation got better by the end of that period. The data shows different patterns that need to be looked at based on local factors. To compare the Gini indices of three states, their dynamics were built on one coordinate axis (Fig. 2 ). The problems with using this coefficient are that it only looks at cash income. Some workers might be paid with food, products, or receive other incentives. Over time, Uzbekistan has the biggest gap between rich and poor. The Gini index is much higher than in Kazakhstan and Kyrgyzstan. Kyrgyzstan has the most equal income spread among its people. The three countries have all experienced a drop in the Gini index, suggesting they are making progress in reducing inequality. Kazakhstan and Kyrgyzstan have steady and smooth changes, while Uzbekistan's changes are more abrupt. The highest and lowest Gini index values for this period (Table 8 ): Table 8 Highest and lowest index values Сountry Minimum load-capacity index Year Maximum load-capacity index Year Kazakhstan 7,53 2021 11,95 2015 Kyrgyzstan 6,08 2022 9,02 2014 Uzbekistan 19,12 2023 24,41 2014 In Kazakhstan, the index dropped a lot over 10 years by 26. 4% In Uzbekistan, it went down by 21. 7% compared to last year. The index of Kyrgyzstan went down by 7.6% over the whole period. In 2023, Kyrgyzstan has the lowest Gini index (8. 33), which means income is shared more fairly among people. Kazakhstan is the second place with a score of 8.53, and it has improved a lot since 2014. Uzbekistan still has the highest level of inequality (19. 12), even though things have improved. In all three Central Asian countries, the Gini index went down from 2014 to 2023, which means that income inequality is getting smaller. Kazakhstan and Kyrgyzstan have a fair share of money among their people. Uzbekistan is making progress, but there are still big gaps in equality. Uzbekistan needs to understand why there are sudden changes in the index, especially in 2023, and keep working on its social programs. The evolution of the «glass ceiling» index for the three states is shown in Fig. 3 . The information about the glass ceiling in Kazakhstan, Kyrgyzstan, and Uzbekistan shows some important trends in gender equality in jobs in these countries. In Kazakhstan, the Gender Community Index (GCI) ranges from 0.72 to 089 from 2014 to 2023. This shows that there are major obstacles preventing women from getting leadership roles. Although there are some small changes in the numbers, overall, it is clear that gender equality in management is still a problem in Kazakhstan. Kyrgyzstan is a country that has done a great job breaking down barriers for women. Since 2019, its score on the GCI index has been above 1, reaching 1.24 in 2023This means that women in Kyrgyzstan now have the same or even better chances to get higher jobs than men. In Uzbekistan, the GCI is below 1 all the time we looked at, but it has been going up slowly from 0.59 in 2014 to 0.74 in 2023This shows that things are getting better, but women in Uzbekistan still face challenges that make it hard for them to achieve the same career success as men. Highest and lowest index numbers for the period (Table 8 ): Table 8 Highest and lowest index values Сountry Minimum load-capacity index Year Maximum load-capacity index Year Kazakhstan 0,72 2014 0,89 2019 Kyrgyzstan 0,84 2014 1,24 2020,2023 Uzbekistan 0,59 2014, 2016, 2017 0,74 2023 Kazakhstan and Uzbekistan are both showing slow progress in improving gender equality in their governments. Both states have the issue of a «glass ceiling», even though they are trying to help women do better in their jobs. Kyrgyzstan, on the other hand, has made more noticeable progress in breaking down gender barriers, as shown by the GCI score, which has gone above 1 in recent years. The information about the glass ceiling index shows that there are big differences in gender equality in Central Asia. Kyrgyzstan has made the most improvements in helping women take on leadership roles. Meanwhile, Kazakhstan and Uzbekistan still have challenges for women who want to lead. Women are less likely than men to get promoted to the top jobs, even when everything else is the same. As a result of the panel analysis, the panel regression equation is (5): The regression results have led to such conclusions. The role of independent variables in explaining gender differences can be seen below (Fig. 4 ). The positive coefficient (β1 > 0) shows that as the economy develops more, the difference in pay between men and women increases. In wealthy countries, men often work in jobs that pay more money, while women are more likely to have jobs that pay less, like in areas such as food and hospitality services. A negative and important factor (β2 0) means that when more women are unemployed, the difference between men’s and women’s employment grows larger. This shows that women have a harder time finding jobs, which makes their money situation less stable. The positive factor (β4 > 0) means that when more men are employed than women, the pay gap between them gets bigger. This might mean that even when women have jobs in this area, they still earn less money than men. The results from the calculations (Duncan index, Gini coefficient, Index glass ceilings) support the first hypothesis. Women earn less money than men. They are also less likely to hold leadership roles than men. The panel results support the second idea. The information shows that places where more women work, like in housing and food services, make it harder for women to have stable finances. This is proven by the importance of the female unemployment rate and women in leadership. The difference in pay between men and women might happen because there are fewer job chances for women, like not having many women in leadership roles, and there are higher rates of joblessness among women. The results of the analysis show clear evidence that women's money problems in living and food services are connected to job conditions, the number of women in leadership positions, and the way jobs are organized. 6 Conclusion This study points out the ongoing problem of men and women working in different jobs in Kazakhstan, Kyrgyzstan, and Uzbekistan, and explains how this affects the economy and society. Even with different laws and economic situations in each country, jobs are still often divided by gender. This makes it harder for women to find good opportunities and increases inequality in the area. Kazakhstan has made good progress in promoting gender equality with new politics. However, Kyrgyzstan and Uzbekistan still face problems because their institutions and cultural beliefs support traditional gender roles. The results show that dividing jobs by gender keeps women from getting high-paying jobs and leadership roles. This limits their job opportunities and keeps pay differences between men and women. Also, there are strong obstacles, like job stereotypes, limited access to education, and unfair practices at work, that contribute to this inequality. The calculated measures of gender inequality support these findings by showing clear differences in job participation and types of jobs for men and women in all three countries. From a policy viewpoint, the study highlights the important need for specific actions to tackle gender separation. First, we need to make rules that break down gender stereotypes in schools and job training. This will help women get into high-paying jobs that are usually held by men. Next, changes in the job market should focus on gender equality by ensuring men and women are paid equally, improving safety and fairness at work, and making sure laws against discrimination are followed. Third, working together with neighboring Central Asian countries could help improve gender equality by sharing ideas and creating programs together. This study provides important information, but more research is needed to look at how gender separation changes over time and how it affects the economy in Central Asia. Future studies should look at how new industries, technology changes, and flexible jobs might help reduce or increase gender inequality in the area. By tackling these problems, leaders can build job markets that support both gender equality and steady economic growth in Kazakhstan, Kyrgyzstan, and Uzbekistan. Declarations Acknowledgments First and foremost, I thank the Editors and Reviewers for their valuable feedback and suggestions, which significantly improved this manuscript. I thank my supervisor Ilona Bordiyanu. Conceptualization and theory YM; research design: YM, IB, and AS; data collection: YM, IB, and AS; analysis and interpretation: YM, IB, and AS; writing draft preparation: YM, IB, and AS; supervision: YM; correction of article: YM, IB, and AS; proofread and final approval of article: YM, IB, and AS. All authors have read and agreed to the published version of the manuscript. Financial support The study was not sponsored (own resources) Data and materials are available. The author can share the data if you ask for it. The author has copies of the computer programs that created the results in the article, and they can be obtained from them. Approval for ethics and permission to take part. Not relevant Permission to publish Not relevant. Conflicting interests The authors say they do not have any conflicts of interest. Information about the authors Yeldar Y. Mubarakov – PhD candidate, Kazakh-American Free University, Ust-Kamenogorsk, Kazakhstan, e-mail: [email protected] , ORCID ID: https://orcid.org/0009-0001-3619-9088 *Ilona V. Bordiyanu – PhD, professor, Kazakh-American Free University, Ust-Kamenogorsk, Kazakhstan, e-mail: [email protected] , ORCID ID: https://orcid.org/ 0000-0002-7175-9829 Ayazhan S. Seriktayeva – Lecturer, D. Serikbayev East Kazakhstan technical university, Ust-Kamenogorsk, Kazakhstan, e-mail: [email protected] , ORCID ID: https://orcid.org/0009-0004-4445-612X References Acosta-Ballesteros, J., Osorno-del Rosal, M.P., Rodríguez-Rodríguez, O.M.: Underemployment and employment among young workers and the business cycle in Spain: the importance of education level and specialisa‑ tion. J. Educ. Work 31(1), 28–46 (2018) Ahmed, A.M. & Hyder, A. Sticky floors and occupational segregation: evidence from Pakistan. Pak Dev Rev 47(4), 837–849 (2008) Alkadry, M. G., & Tower, L. E: Unequal Pay: The Role of Gender. Public Administration Review. 66 (6) (2006). https://doi.org/https://doi.org/10.1111/j.1540-6210.2006.00656.x Anker, R.: Gender and jobs: sex segregation of occupations in the world. International Labour Ofce, Geneva (1998) Barrett, G.F., Doiron, D.J.: Working part time: by choice or by constraint. Can. J. 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The American Mathematical Monthly. 117(10), 851–864. https://doi.org/10.4169/000298910X523344 (2010) ILO: Global Wage Report 2018-2019. https://www.ilo.org/publications/global-wage-report-201819-what-lies-behind-gender-pay-gaps (2019). Accessed 20 Nov 2024 International monitoring misson on labour rights in Central Asia: Reports. https://labourcentralasia.org/en/publications/reports/ (2024). Accessed 12 Dec 2024 Kamerāde, D., Richardson, H.: Gender segregation, underemployment and subjective well-being in the UK labour market. Hum. Relat. 71 (2), 285–309 (2018) Khitarishvili, T. Gender pay gaps in the former Soviet Union: A review of the evidence. Working Paper, No. 899, Levy Economics Institute of Bard College, Annandale-onHudson, NY (2018) Kjeldstad, R., Nymoen, E.H.: Part-time work and gender: worker versus job explanations. Int. Labour Rev. 151 (1–2), 85–107 (2012a) Kjeldstad, R., Nymoen, E.H.: Underemployment in a gender-segregated labour market. Econ. Ind. Democracy 33 (2), 207–224 (2012b) Lupton, B.: Explaining men’s entry into female-concentrated occupations: issues of masculinity and social class. Gender Work Organ. 13 (2), 103–128 (2006) Martin, P., Barnard, A.: The experience of women in male-dominated occupations: A constructivist grounded theory inquiry. Journal of Industrial Psychology 39 (2), 1–12 (2013) Mehta, C. & Strough, J. Gender Segregation and Gender-Typing in Adolescence. Sex Roles. 63 , 251-263. 10.1007/s11199-010-9780-8 (2010) Moir, H. & Smith, J. S. Industrial segregation in the Australian labour market. J Ind Relat 21 (3), 281–291 (1979) OECD: Social Institutions & Gender Index Dashboard. https://www.oecd.org/en/data/dashboards/social-institutions-gender-index.html?oecdcontrol-18ae15c5e9-var1=TUR (2024). Accessed 27 Nov 2024 Papuc, A: Asia is fighting off the diversity backlash. The Japan Times. https://www.japantimes.co.jp/co mmentary/2024/02/07/world/asi a-diversity-backlash/ (2024). Accessed 29 Nov 2024 Rogacheva M.V.: Occupational segregation based on gender. http://sisupr.mrsu.ru/2011-4/PDF/1/Rogacheva.pdf (2011). Accessed 20 Nov 2024 Rubery, J., & Fagan, C: Gender Segregation in Societal Context. Work, Employment and Society. 9 (2), 213–240 (1995). https://doi.org/10.1177/0950017 09592001 Silber, J.G. On the measurement of employment segregation. Econ Let 30. 237–243 (1989) Simic, M.: Underemployment and overemployment in the UK. Labour Market Trends 110(8), 399–414 (2002) Simpson, R.: Masculinity at work: the experiences of men in female-dominated occupations. Work Employ. Soc. 18 (2), 349–368 (2004) The Central Commission for Elections and Referendums of the Kyrgyz Republic: National strategy of the Kyrgyz Republic for achieving gender equality by 2030. https://www.shailoo.gov.kg/media/azamat/2023/01/27/2030.pdf (2020). Accessed 21 Nov 2024 Torre, M.: Stopgappers? The occupational trajectories of men in female-dominated occupations. Work Occup. 45 (3), 283–312 (2018) Valletta, R.G., Bengali, L., Van der List, C.: Cyclical and Market Determinants of Involuntary Part-time Employment. IZA Discussion Paper No.9738, Bonn (2016) Vuluku, G., Wambugu, A., Moyi, E.: Unemployment and underemployment in Kenya: a gender gap analysis. Economics 2 (2), 7–16 (2013) WEF: The Global Gender Gap Report. https://www.weforum.org/publications/global-gender-gap-report-2023/economy-profiles-5932ef6d39/ (2023). Accessed 20 Nov 2024 Wilkins, R.: Personal and job characteristics associated with underemployment. Aust. J. Labour Econ. 9 (4), 371–393 (2006) Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5709710","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":394270784,"identity":"bdf1f846-7557-4611-a8ec-f0e6df14e708","order_by":0,"name":"Yeldar Mubarakov","email":"","orcid":"https://orcid.org/0009-0001-3619-9088","institution":"Kazakh-American Free University","correspondingAuthor":false,"prefix":"","firstName":"Yeldar","middleName":"","lastName":"Mubarakov","suffix":""},{"id":394270785,"identity":"f854f68c-b4e4-47a5-af24-ab2960f3e265","order_by":1,"name":"Ilona 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Kazakhstan, Kyrgyzstan and Uzbekistan\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5709710/v1/f9bcd354adb9bdfc0d9d3857.png"},{"id":72576377,"identity":"f769fc81-f6b7-4dea-ab75-030fcefa23dc","added_by":"auto","created_at":"2024-12-30 04:17:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":23940,"visible":true,"origin":"","legend":"\u003cp\u003eThe Gini Index of Central Asian countries\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5709710/v1/525fb8430617580d9267ab39.png"},{"id":72576379,"identity":"5f2aa55a-917f-400f-8005-959823feea02","added_by":"auto","created_at":"2024-12-30 04:17:52","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":91940,"visible":true,"origin":"","legend":"\u003cp\u003e«Glass ceiling» dynamics\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5709710/v1/e3dbed87951f42b9b3febc7f.png"},{"id":72576381,"identity":"fdf600bb-6a1b-42c0-8911-960adcfbc73b","added_by":"auto","created_at":"2024-12-30 04:17:52","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":47849,"visible":true,"origin":"","legend":"\u003cp\u003eBar-chart model coefficients\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5709710/v1/e8326e755a5e8b396a3ed4fe.png"},{"id":72578324,"identity":"45069ce3-cf06-4db5-b193-42bfa4397da5","added_by":"auto","created_at":"2024-12-30 04:41:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1212424,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5709710/v1/085a1d11-09dd-4fb7-9b4a-8affb54469df.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eGender segregation in the labour market of Central Asian countries: Kazakhstan, Kyrgyzstan and Uzbekistan\u003c/p\u003e","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eGender inequality is exacerbated by many social and economic factors affecting the labour market in Central Asian countries such as Kazakhstan, Kyrgyzstan and Uzbekistan. Equality of opportunity and economic development are hampered by gender segregation, which remains a pressing problem manifested in the differences in employment and income between men and women. According to international studies, gender segregation in the labour market can have a detrimental impact on social and economic progress, which will lead to limited access to jobs and reduced development potential for women (International monitoring mission on labour rights in Central Asia, 2024). The importance of this topic is compounded by socio-economic and demographic changes in Central Asian countries where women, especially in rural areas, lack access to quality jobs and resources due to limited access to quality resources.\u003c/p\u003e \u003cp\u003eAgreeing with the OECD ponder, it appears that in Central Asia the normal level of segregation against ladies and young ladies in social education by the Social Educate and Sex List (SIGI) is 37.2, medium or near to much higher than the worldwide normal (24), and for OECD nations (15.3) (OECD, 2024). This truth looks indeed more regrettable after you consider the 33 a long time went through on accomplishing correspondence. In the meantime, according to ILO, ladies still win almost 20% less than men (Papuc, 2024). This information recommends that there's a decently tall degree of isolation within the Asian locale.\u003c/p\u003e \u003cp\u003eThe 2023 gender inequality report shows that in Kazakhstan, there is a clear separation between men and women in politics. Women occupy just 19.4% of the seats in parliament and 14.8% of the government leadership positions. Also, there is still a gap in pay between men and women; women hold 41.1% of the management jobs at work (Bureau of National Statistics of Kazakhstan, 2023). At the same time, research shows that there aren't many women in management jobs in companies in Uzbekistan. Companies also hire women based on different factors, like good looks, being single, or living in dorms. The National Statistical Committee (NSC) of Kyrgyzstan says that women earn 26. 6% less than men. It's important to mention that the unpaid work that women do at home is not respected and is seen as an easy task in our society. The NSC says that women spend around 4. 5 hours each day doing housework, while men spend about 1 hour. Women also earn 29% less than men for doing the same job (Facrieva, 2017).\u003c/p\u003e \u003cp\u003eAlso, past research has found that in Kyrgyzstan, men and women are more separate in cities than in the countryside. In countryside areas, separation in education is the biggest reason for gender separation. In cities, job separation is a more important factor (National strategy of the Kyrgyz Republic for achieving gender equality by 2030, 2020). Social beliefs, traditions, and cultural habits greatly affect the lower status of women, taking away chances that are often seen as powerful for men in Central Asian countries. Another research done by Blackburn and others in 2002, it was noted that there are two types of segregation. Vertical segregation leads to unfairness or inequality, while horizontal segregation shows differences among groups but doesn't create inequality. Together, these two types of segregation contribute to overall segregation. (Blackburn et al., 2002).\u003c/p\u003e \u003cp\u003eAt the same time, studies have found that there is a need for fair chance rules that are connected to the job markets in different European countries (Rubery \u0026amp; Fagan, 1995). Past studies have mainly looked at how gender separation happens. This study will look at how things are done in Kazakhstan, Kyrgyzstan, and Uzbekistan, and it will also examine research related to gender separation in these three countries.\u003c/p\u003e \u003cp\u003eThe information shows that there are big issues with separating men and women in these three countries. This study looks at how men and women are treated differently in the job market in Central Asia, especially in Kazakhstan, Kyrgyzstan, and Uzbekistan.\u003c/p\u003e"},{"header":"2 The Gender Pay Gaps","content":"\u003cp\u003eThe World Economic Forum thinks it will take 202 years to completely fix the difference in pay between men and women. This is based on what has happened over the last 12 years (WEF, 2018). The pay gap between women and men has usually been linked to the small number of women in higher-paying jobs in companies (Alkadry \u0026amp; Tower, 2006). Economists have usually focused on things related to gender, like women lacking education or skills, or companies maltreating women, to understand the difference in pay between men and women and how it has changed over time (Blau \u0026amp; Kahn, 2003). Jobs that need to be done quickly are often filled by men, and they tend to make more money than women. The International Labour Organization (ILO) explains that there are many reasons for differences in pay between men and women. The first group should have more leaders.\u003c/p\u003e \u003cp\u003eThe second thing is the hours you work. Men and women workers usually engage in their jobs differently, especially when it comes to how much time they spend working. In the Global Wage Report 2018/2019, the ILO points out that in almost all of the 73 countries with data, women are more likely to work part-time than men, except in five countries (ILO, 2019). Women often take part-time jobs because they have family duties, like being a wife and mother. The third point is about education. Women are still behind men in certain job areas like Science, Technology, Engineering, and Mathematics (STEM). Most people in this job are men. Job opportunities in science, technology, engineering, and math (STEM) will make it harder for women to join these areas. The fourth is jobs typically done by women.\u003c/p\u003e"},{"header":"3 Conceptual framework and hypothesis","content":"\u003cp\u003eWhen there are limits on how much money people can spend, they can pick how long they want to work each day by trying to get the most satisfaction from their choices, according to neoclassical theory. The number of hours employees work is determined by their bosses and unions, how easily workers can move to different jobs, and the overall economic conditions (Simic 2002). So, the hours people want to work and the hours they actually work might not match. This means some people might work too many hours (over-employment), while others might work fewer hours than they want (part-time employment). Some workers may want to work longer hours but can\u0026rsquo;t because they have limited time.\u003c/p\u003e\n\u003cp\u003eThis research focuses on how men and women are treated differently in jobs, especially in terms of the types of jobs they have and their positions in those jobs (Silber, 1989; Hutchens, 1991, 2004; and Mora and Ruiz-Castillo, 2003, 2004, among others). Anker (1998) gives five reasons why researchers and decision-makers in rich countries should pay attention to job and status differences between men and women, besides just fairness issues. First, when women mostly work in low-paying or less important jobs, it affects how men view women and how women view themselves. This supports gender stereotypes and increases poverty among women, which greatly affects households led by women. Second, not allowing women to work in some jobs means wasting talent, and this leads to very poor outcomes when women are skilled workers. Third, separating jobs by gender creates strict rules that make it harder for the job market to adjust to changes in the workforce. Of course, we can't overlook these things in a world economy that cares about being efficient and competitive. Fourth, having different jobs and levels for men and women can harm the education of future generations, especially in what boys and girls decide to study.\u003c/p\u003e\n\u003cp\u003eAbout one-third of the pay gap between men and women worldwide is because of jobs that are divided by gender and the way jobs are organized in higher positions.\u003c/p\u003e\n\u003cp\u003eWhen looking at segregation, whether with two groups or more, most tools measure how mixed or separate the groups are overall. They do this by looking at how different all the population groups are within different areas, rather than focusing on just one specific group. So, when looking at the way jobs are divided by gender, we often compare how many women work in different jobs to how many men do. People may want to study not just overall segregation, but also how specific groups are separated from others.\u003c/p\u003e\n\u003cp\u003eThe way a certain demographic group is spread out across jobs can be very different from how other groups are.\u003c/p\u003e\n\u003cp\u003eMeasuring how separated a group is doesn't mean you can understand that separation without considering other groups in the population. Segregation is something we need to look at based on how people are positioned compared to one another, similar to how we measure poverty by comparing people's situations to each other. Actually, both things are more alike than you might think at first. To measure how poor a country is, we usually look at the income of everyone in the country.\u003c/p\u003e\n\u003cp\u003eFind the poverty line, which means the income level used as a standard. Sure, if one group's income changes, it can affect the poverty level of other groups because the limit for what counts as poverty has changed. But the way groups depend on each other doesn\u0026rsquo;t stop us from discovering how poor a specific group is. (by using, for example, the decomposability property of the popular family of indexes proposed by Foster, Greer, and Thorbecke, 1984). Similarly, if the way a certain demographic group is spread out in different parts of an organization changes, this can impact not just how separate that group is, but also how separate other groups are. This is because the overall population distribution might have changed too. Like with relative poverty, we believe that we can measure how separated a particular group is, and this is a useful way to study segregation more thoroughly. Measuring how women are separated in jobs has been studied for a long time. Fifty-five years ago, Moir and Selby Smith (1979) created a new way to measure how separated female workers were in different jobs in Australia. As far as we know, only Alonso-Villar and Del R\u0026iacute;o (2008) have looked at this topic using clear rules and have suggested new ways to measure it that meet basic requirements.\u003c/p\u003e\n\u003cp\u003ePrevious research has looked at the social and job-related reasons that cause men and women to work in different jobs (Bettio 2009; Fuchs 2016; England 2005; Cohen and Huffman 2003; Kjellstad and Nymmoen 2012a, 2012b; Wilkins 2006; Acosta-Ballesteros et al. 2018). There are big differences in the number of men and women working in different types of jobs (Kjeldstad and Nimuen 2012a, 2012b; Valletta et al. 2016). There have been only a few studies that connect gender segregation in jobs and industries to these differences in the labour market. Kjellstadt and Nimuan (2012a and 2012b) found that women often face underemployment in jobs and fields mostly held by men. However, their study doesn't include important factors that show job segregation. In jobs where women are the majority, they are more likely to have less work than they want, according to Kamerad\u0026eacute; and Richardson (2018). However, this isn't seen in all types of industries. Khitarishvili (2016) says that in Central Asia, there is a big difference between men and women in the workplace. This is caused by traditional beliefs about gender and social and economic issues, like women having less education and many family duties. Even though there have been big shifts in the number of men and women working in this area since the Soviet Union ended, there are still issues of unfairness between genders. Many women in Kazakhstan have jobs in government and public services, especially in health and education. But they are mostly missing in high-paying private sectors like energy and technology, where salaries and job opportunities are better. There are clear differences between men and women when it comes to jobs and pay. In fields where women usually work, like education and health, they often have shorter hours and earn less money than men who work in industries like manufacturing and construction. This leads to many women not having enough work or jobs that match their skills, which makes it harder for them to grow in their careers and increases the difference in earnings between men and women. Women in Central Asia usually have to take temporary, low-paying jobs, which makes it hard for them to be financially independent. Factors that lead to gender separation include not having enough money and fewer chances for women to hold management jobs. In countries like Kazakhstan, Kyrgyzstan, and Uzbekistan, women seldom have top jobs or leadership positions in private companies. This makes it harder for them to be financially independent and earn more money, which limits their chances for growth and development in both economic and social areas.\u003c/p\u003e\n\u003cp\u003eEven though good reasons are showing that job and industry separation affects the chances of underemployment, the study mentioned above did not provide clear measurements of its effect. In this article, we tackle this problem by using a better way to assess it. We suggest and check this idea:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHypothesis 1.\u0026nbsp;\u003c/strong\u003e\u003cem\u003eIn Kazakhstan, Kyrgyzstan, and Uzbekistan, many women work in low-paying jobs and have less respected roles compared to men.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWomen are more likely than men to be underemployed, which creates a gap between the two genders. This problem is made worse by the way jobs are divided into different fields and types. Also, this effect might be partly because men and women work in different jobs in unequal numbers, according to Barrett and Doyon (2001). We should also consider the pay differences between jobs that are mainly held by men and those mainly held by women. In a straightforward example, the authors mention how women are spread across different jobs and industries compared to men. They find that the main reason women often have to work part-time against their wishes is that they are in different types of jobs and fields.\u003c/p\u003e\n\u003cp\u003eStudies have found that men can gain advantages from being in jobs mostly held by women in a few different ways (Simpson 2004). According to Lupton (2006), men advance in their careers more quickly than women because women face challenges, like the \u0026laquo;glass ceiling\u0026raquo;, that prevent them from rising to higher positions. Men might be given certain jobs that are seen as better suited for them. The benefit is that men who work in jobs mainly held by women earn more money than women do in those jobs (Torre 2018). In fact, women might experience bad outcomes in jobs that are mostly held by men. For example, Martin and Barnard (2013) discovered that rules and behaviors in organizations that treat women unfairly are big problems for them. These points might also relate to part-time jobs, which means women might have a higher chance of working part-time than men, whether in jobs mostly held by women or those mostly held by men.\u003c/p\u003e\n\u003cp\u003eBut, as far as we know, in 2013, Vuluk and others tried to find out why there is a difference between men and women in jobs. However, they didn't look at how jobs are separated by gender, and they used simple models that might give inaccurate results. We address this missing information by using a new method that helps us suggest and check the following ideas:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHypothesis 2.\u0026nbsp;\u003c/strong\u003e\u003cem\u003eWorkplaces and areas that are mainly employed women are the service sectors. This makes women more financially unstable.\u003c/em\u003e\u003c/p\u003e"},{"header":"4 Data and methods","content":"\u003cp\u003eThe aim of this study is to identify the characteristics of gender segregation in the labour market in Central Asian countries (Kazakhstan, Kyrgyzstan, Uzbekistan), determine the extent of gender barriers to employment, The distribution of roles by industry and women\u0026rsquo;s leadership, as well as an assessment of national change dynamics.\u003c/p\u003e\n\u003cp\u003eThe study was conducted in the framework of comparative analysis. It can be said that it consists of three stages:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eDescriptive analysis: the main indicators of employment, such as the share of women in various sectors of the economy, achievement of leadership positions, and wage level were studied.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eQuantitative analysis: the segregation indices (horizontal and vertical), Dunkan dissimulation index, Gini index, and Glass ceiling index are calculated.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eQualitative analysis: Government programmes, legal frameworks, and policies for gender equality have been reviewed.\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe study is about the labour market in Central Asian countries. Different approaches were used to assess gender segregation in the labour market of countries in Central Asia, including Kazakhstan, Kyrgyzstan and Uzbekistan. The earliest statistics were analyzed. Sources of data include statistical data from state bodies: the National Bureau of Statistics of Kazakhstan, the Agency for Statistics under the President of the Republic of Uzbekistan, the National Statistical Committee of the Kyrgyz Republic and others. The analysis period is 2014\u0026ndash;2023.\u003c/p\u003e\n\u003cp\u003eThe methodology is oriented towards a combined approach combining statistical analysis methods and contextual interpretation. The analysis of gender segregation in the labour market in Central Asia countries has been done using a mixed approach, including quantitative and qualitative methods.\u003c/p\u003e\n\u003cp\u003eGender segregation has been quantified. Two interrelated aspects need to be considered for the quantification of economic phenomena: measurement methodology and research information base. There is now a wide range of methods and tools in world practice to analyze and define gender segregation.\u003c/p\u003e\n\u003cp\u003eTo better measure gender segregation in the labour market, a number of statistical indicators called segregation indices are used.\u003c/p\u003e\n\u003cp\u003eGender segregation can be divided into several types: horizontal and vertical. Horizontal segregation is the division of women and men into occupational groups (Rogacheva, 2011). Horizontal segregation also includes the problem of restrictions on women\u0026rsquo;s employment in difficult and harmful conditions.\u003c/p\u003e\n\u003cp\u003eVertical segregation - unequal distribution of gender groups in formal hierarchy. Discrimination against women means that they have limited access to management and the most prestigious professions. Such discrimination is often seen at the structural level of an organization and includes \u0026laquo;glass\u0026raquo; barriers. These barriers are artificial because they do not depend on the employee\u0026rsquo;s qualifications or experience; they depend not only on personal behavior or business relationships but also on the organization\u0026rsquo;s structure.\u003c/p\u003e\n\u003cp\u003eThe quantification of any economic phenomenon is intended to take into account two interrelated aspects of statistics: computational methodology and research information base.\u003c/p\u003e\n\u003cp\u003eMicrosoft Excel was used for statistical analysis. The data visualization was done in Python language with the help of Jupyter Notebook and Mathcad programs.\u003c/p\u003e\n\u003cp\u003eThe oldest and most commonly used is the Duncan dissimilarity index (ID). The general formula for calculating the Duncan Dissimilarity (ID) index is as follows (1):\u003c/p\u003e\n\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\n \u003cdiv id=\"FileID_Equ1\" class=\"mathdisplay\"\u003e$$\\:ID=\\frac{1}{2}\\sum\\:_{i}\\left|\\frac{{F}_{i}}{F}-\\frac{{M}_{i}}{M}\\right|$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eWhere, F\u003csub\u003ei\u003c/sub\u003e and M\u003csub\u003ei\u003c/sub\u003e respectively i - number of women and men working in occupations, F and M - total number of women and men working in the economy; i - ranges from 1 to number corresponding to the number of professions (branches) in the economy.\u003c/p\u003e\n\u003cp\u003eThe most common explanation for the dissimilarity index is that it reflects the proportion of men or women who need to change their profession or industry in order to achieve an even distribution of women and men between occupation. The following occupational sectors were considered for the calculation of the index (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTypes of economic activity\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e№\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEconomic activity\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAgriculture, forestry and fisheries\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eManufacturing\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConstruction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWholesale and retail trade; repair of motor vehicles and motorcycles\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTransportation and storage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAccommodation and catering services\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInformation and communication\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFinancial and insurance activities\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReal estate transactions\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProfessional, scientific and technical activities\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eManagement and provision of ancillary services\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePublic administration and defense; compulsory social security\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHealth care and social services\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArt, entertainment, and recreation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProvision of other types of services\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eFor Kazakhstan, Kyrgyzstan and Uzbekistan, the dissimilarity index will be the same as in the table below (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) between 2014 and 2023 according to formula (1).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eIndex of dissimilarity\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIndex of dissimilarity (Kazakhstan)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIndex of dissimilarity (Kyrgyzstan)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIndex of dissimilarity (Uzbekistan)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,2965\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2859\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3223\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,3188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2934\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3043\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,3041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2926\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3070\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,2992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2953\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,3046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2637\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2719\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,2907\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2612\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,3013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2585\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,30997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2641\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,32195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3104\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eSometimes the traditional income inequality indicator is used to estimate the gender distribution by industry and occupation, the Gini coefficient (G) (Mehta, 2010).\u003c/p\u003e\n\u003cp\u003eThe Gini coefficient is a statistical indicator of the degree of stratification of a particular country or region by a given subject. Used to estimate economic inequality. The Gini coefficient can vary from 0 to 1. The more its value deviates from zero and approaches one, the more income is concentrated in the hands of individual population groups (Farris, 2010).\u003c/p\u003e\n\u003cp\u003eThe Gini index is a percentage representation of the coefficient.\u003c/p\u003e\n\u003cp\u003eOften used to measure income inequality, but can also be used to estimate gender inequalities in occupations. The Gini coefficient can be viewed graphically or algebraically.\u003c/p\u003e\n\u003cp\u003eFind the Gini coefficient in algebraic ways. Gini coefficient (2):\u003c/p\u003e\n\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\n \u003cdiv id=\"FileID_Equ2\" class=\"mathdisplay\"\u003e$$\\:G=1-2\\sum\\:_{i=1}^{n}{x}_{i}cum\\:{y}_{i}+\\sum\\:_{i=1}^{n}{x}_{i}{y}_{i}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eWhere, x\u003csub\u003ei\u003c/sub\u003e - the proportion of the i-th group in the population (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i=\\stackrel{-}{1,n};\\:\\sum\\:_{i=1}^{n}{x}_{i}=1\\)\u003c/span\u003e\u003c/span\u003e)); y\u003csub\u003ei\u003c/sub\u003e - the proportion of the i-th group in the income volume (the proportion of the i-th group in the population) (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sum\\:_{i=1}^{n}{y}_{i}=1\\)\u003c/span\u003e\u003c/span\u003e)); cum y\u003csub\u003ei\u003c/sub\u003e - the total proportion of the income (i-th and preceding population).\u003c/p\u003e\n\u003cp\u003eIf G is close to zero, the division between citizens of a state is almost even.\u003c/p\u003e\n\u003cp\u003eThe study used data provided by national statistical offices for each year. The analysis includes the creation of a time series: The Gini index was presented as a time series for each of the three states.\u003c/p\u003e\n\u003cp\u003eFor three states, the Gini coefficient for 2014\u0026ndash;2023 was calculated using formula (2). Here, the population was divided into two groups: men and women. The Gini indices can be seen in the table below (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eGini coefficient\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGini (Kazakhstan)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGini (Kyrgyzstan)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGini (Uzbekistan)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11,59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9,02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24,41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11,95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7,53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24,33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10,95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7,45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24,19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11,17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8,42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24,11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11,84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8,98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21,94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11,12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7,27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23,14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8,62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7,41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22,45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7,53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7,51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23,46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6,08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24,32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8,53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8,33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19,12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eVertical gender segregation is a type of inequality in which men and women occupy different positions in the hierarchical structure of professions, organizations or industries. This type of segregation is manifested in women\u0026rsquo;s limited access to higher and more prestigious positions than men, despite equal educational and professional qualifications.\u003c/p\u003e\n\u003cp\u003eMain characteristics: gap in achieving leadership positions, glass ceiling. The quantitative analysis of vertical gender segregation involves the use of statistical and analytical methods to measure the level of inequality between men and women in occupational hierarchies. This analysis helps to determine the extent of women\u0026rsquo;s limited access to leadership positions, regardless of their number or competencies.\u003c/p\u003e\n\u003cp\u003eThe main method of quantitative analysis is the glass ceiling index. This index is calculated as the proportion of women in senior management positions to the overall share of women in employment.\u003c/p\u003e\n\u003cp\u003eThe index is studied in Kazakhstan, Kyrgyzstan, and Uzbekistan.\u003c/p\u003e\n\u003cp\u003eWhat is the \u0026laquo;Glass ceiling\u0026raquo; index for:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eAssessment of the level of gender inequality in the labour market.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eComparison between industries, regions.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eProposals to eliminate barriers in women\u0026rsquo;s career growth.\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe calculation formula will be as in (3).\u003c/p\u003e\n\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\n \u003cdiv id=\"FileID_Equ3\" class=\"mathdisplay\"\u003e$$\\:\\text{G}\\text{C}\\text{I}=\\frac{\\text{P}\\text{r}\\text{o}\\text{p}\\text{o}\\text{r}\\text{t}\\text{i}\\text{o}\\text{n}\\:\\text{o}\\text{f}\\:\\text{w}\\text{o}\\text{m}\\text{e}\\text{n}\\:\\text{i}\\text{n}\\:\\text{s}\\text{e}\\text{n}\\text{i}\\text{o}\\text{r}\\:\\text{p}\\text{o}\\text{s}\\text{i}\\text{t}\\text{i}\\text{o}\\text{n}\\text{s}\\text{і}}{\\text{S}\\text{h}\\text{a}\\text{r}\\text{e}\\:\\text{o}\\text{f}\\:\\text{e}\\text{m}\\text{p}\\text{l}\\text{o}\\text{y}\\text{e}\\text{d}\\:\\text{w}\\text{o}\\text{m}\\text{e}\\text{n}}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eWhere GCI is the Glass Ceiling Index.\u003c/p\u003e\n\u003cp\u003eGCI 1 women are more likely to take up leadership positions.\u003c/p\u003e\n\u003cp\u003eIf GCI\u0026thinsp;\u0026lt;\u0026thinsp;1, that is a \u0026laquo;Glass ceiling\u0026raquo;. The lower the index, the stronger the resistance.\u003c/p\u003e\n\u003cp\u003eThe following statistics were used to calculate the Glass ceiling index. The statistical data of Kazakhstan, Kyrgyzstan, and Uzbekistan can be found in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e respectively.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eStatistical indicators for the calculation of the index \u0026laquo;Glass ceiling\u0026raquo;\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003ePercentage of employed women, %\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003ePercentage of women in management positions, %\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKazakhstan\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKyrgyzstan\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUzbekistan\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKazakhstan\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKyrgyzstan\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUzbekistan\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48,42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39,97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46,72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27,7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48,17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40,90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46,14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27,7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48,09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40,41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45,67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27,1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48,06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39,53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45,78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27,0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48,54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38,25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41,62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27,0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48,35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38,19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41,49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26,6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48,24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38,35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41,44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26,5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48,28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38,81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41,33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27,7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47,91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37,99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41,20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28,2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48,12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38,57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39,46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29,2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eUsing these data, the Index for three states was calculated by the formula (3) of the glass ceiling (Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eGlass ceiling index\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGCI (Kazakhstan)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGCI (Kyrgyzstan)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGCI (Uzbekistan)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe panel regression was used to check the validity of hypothesis \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. Panel regression for 3 states (Kazakhstan, Uzbekistan, and Kyrgyzstan) for 2014\u0026ndash;2023 gross domestic product per capita, Percentage of women in management positions, Ratio of employed men to employed women (in the area of services for living and food), Gender level in the communication used labour data. Based on this data, the panel data (Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e) was created.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePanel data\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCountry\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGross domestic product per capita, US dollars\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePercentage of women in management positions, %\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUnemployment rate of women\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRatio of men\u0026rsquo;s to women\u0026rsquo;s employment\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGender pay gap, (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"10\" align=\"left\"\u003e\n \u003cp\u003eKazakhstan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 807,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34,80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36,35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33,04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 510,7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37,00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39,59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34,14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 714,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37,30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37,18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31,44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 247,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37,00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44,50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32,18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 812,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41,20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43,38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34,15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 812,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43,00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46,42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32,24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 121,7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41,10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57,49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24,99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 370,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39,00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52,79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21,72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 476,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40,80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54,00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25,20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 193,7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41,20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50,91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25,69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"10\" align=\"left\"\u003e\n \u003cp\u003eKyrgyzstan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 100,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34,80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52,00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28,90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 120,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37,00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55,84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24,60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 150,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37,30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8,7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e68,30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24,70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 180,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37,00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e71,32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27,50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 200,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41,20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e80,34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28,40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 230,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43,00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e105,03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23,00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 120,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41,10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6,7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e104,22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24,60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 150,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39,00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e106,29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24,90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 210,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40,80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e115,51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20,40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 260,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41,20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e106,74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26,60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"10\" align=\"left\"\u003e\n \u003cp\u003eUzbekistan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34,80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86,96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65,60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37,00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e88,31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65,50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2870\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37,30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e90,71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65,50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2940\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37,00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e89,53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65,40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41,20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e93,90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61,40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43,00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e91,79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63,80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41,10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e93,48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62,50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39,00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e96,03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64,40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3470\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40,80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97,17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e66,00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41,20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97,76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e70,40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eA few concluding comments approximately our strategy are vital. To begin with, the standard mistakes of the Duncan contrast list, glass ceiling list, and Gini coefficient were calculated utilizing introductory stacking strategies, which permitted us to evaluate the measurable importance of watched patterns and contrasts in three nations. These strategies give dependable gauges in spite of the complexity of the information structure.\u003c/p\u003e\n\u003cp\u003eAlthough the Duncan, Gini, and glass ceiling indexes are primarily used to measure gender segregation, the methodology is flexible and can be adapted to other indexes such as the Carmichael and McLachlan indexes. This flexibility ensures reliable future validation and cross-validation using alternative measures of segregation in the labor market.\u003c/p\u003e\n\u003cp\u003eAt long last, our counterfactual demonstration was created to think about how changes in labour approaches can influence gender segregation. This reenactment appeared that closing the holes in instruction and preparation can essentially decrease isolation, and this conclusion is steady with past considerations in comparative settings.\u003c/p\u003e\n\u003cp\u003eThe methodological rigor applied in this study ensures that the results will serve as a reliable basis for policy recommendations aimed at reducing gender segregation in Central Asian labor markets.\u003c/p\u003e"},{"header":"5 Results","content":"\u003cp\u003eA comparison of the Duncan dissimilation index statistics of the three countries to identify general trends and differences can be seen in the figure below (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe chart shows data on the dissimilation index (ID) in the employment sector of Kazakhstan, Kyrgyzstan and Uzbekistan for the analyzed period. This table measures the level of sexual isolation by measuring the proportion of workers who are forced to change professions or departments in order to achieve a real spread of sexual orientation.\u003c/p\u003e\n\u003cp\u003eIn Kazakhstan, at the beginning of the period, it was about 35 people and by the middle of the period it was slowly decreasing to 25. This indicates certain positive changes in the business structure related to sexual orientation, which are probably related to the increased access of women to various specialized fields. Be that as it may, from the center of the period, the record started to develop once more, coming to the level of 30 by the conclusion, which demonstrates the tirelessness of basic boundaries within the labour showcase that require further consideration.\u003c/p\u003e\n\u003cp\u003eFor Kyrgyzstan, the Duncan index is characterized by a relentless decrease: beginning from 25, it gradually reduced to 15, and after that got stabilized. This drift shows a critical decrease in gender segregation, likely due to dynamic work changes, counting measures to extend women\u0026apos;s cooperation in already customarily male-dominated businesses. This progress highlights the adequacy of the approach, in spite of the fact that it is vital to proceed observing its long-term maintainability.\u003c/p\u003e\n\u003cp\u003eIn Uzbekistan, there are sharp changes within the data. At the introductory stage, it was approximately 40, at that point dropped strongly to 20 by the middle of the period, likely due to short-term financial or social changes. However, by the conclusion of the period, the record had expanded once more to 35, demonstrating an increment in sex isolation, conceivably due to the rebuilding of conventional parts or insufficient viability of changes.\u003c/p\u003e\n\u003cp\u003eIn this way, the flow of the dissimilation index within the three nations reflects multidirectional patterns. Whereas Kyrgyzstan has seen consistent reductions in isolation, the advance has been less relentless in Kazakhstan and Uzbekistan. This highlights the got to create and actualize more focused on and long-term measures to decrease sex imbalance within the labour showcase within the locale.\u003c/p\u003e\n\u003cp\u003eHighest and lowest values of the index for the period (Table \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e):\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eHighest and lowest index values\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eСountry\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMinimum load-capacity index\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMaximum load-capacity index\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKazakhstan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2907\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.32195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKyrgyzstan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2859\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUzbekistan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2585\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eThe table shows distribution of jobs among men and women in Kazakhstan, Kyrgyzstan, and Uzbekistan. In Kazakhstan, the lowest score was 0. 2907 in 2020, and the highest score was 0. 32195 in 2023 This shows that the imbalance has gotten bigger. In Kyrgyzstan, the lowest value was 0. 2859 in 2014, and the highest value was 0. 3398 in 2020 This change might be because of economic problems. In Uzbekistan, the lowest score was 0. 2585 in 2021, and the highest score was 0. 3223 in 2014 This shows that the situation got better by the end of that period. The data shows different patterns that need to be looked at based on local factors.\u003c/p\u003e\n\u003cp\u003eTo compare the Gini indices of three states, their dynamics were built on one coordinate axis (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe problems with using this coefficient are that it only looks at cash income. Some workers might be paid with food, products, or receive other incentives.\u003c/p\u003e\n\u003cp\u003eOver time, Uzbekistan has the biggest gap between rich and poor. The Gini index is much higher than in Kazakhstan and Kyrgyzstan. Kyrgyzstan has the most equal income spread among its people. The three countries have all experienced a drop in the Gini index, suggesting they are making progress in reducing inequality. Kazakhstan and Kyrgyzstan have steady and smooth changes, while Uzbekistan\u0026apos;s changes are more abrupt.\u003c/p\u003e\n\u003cp\u003eThe highest and lowest Gini index values for this period (Table \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e):\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable id=\"Tab8\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eHighest and lowest index values\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eСountry\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMinimum load-capacity index\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMaximum load-capacity index\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKazakhstan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7,53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11,95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKyrgyzstan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6,08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9,02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUzbekistan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19,12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24,41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eIn Kazakhstan, the index dropped a lot over 10 years by 26. 4% In Uzbekistan, it went down by 21. 7% compared to last year. The index of Kyrgyzstan went down by 7.6% over the whole period.\u003c/p\u003e\n\u003cp\u003eIn 2023, Kyrgyzstan has the lowest Gini index (8. 33), which means income is shared more fairly among people. Kazakhstan is the second place with a score of 8.53, and it has improved a lot since 2014. Uzbekistan still has the highest level of inequality (19. 12), even though things have improved.\u003c/p\u003e\n\u003cp\u003eIn all three Central Asian countries, the Gini index went down from 2014 to 2023, which means that income inequality is getting smaller. Kazakhstan and Kyrgyzstan have a fair share of money among their people. Uzbekistan is making progress, but there are still big gaps in equality. Uzbekistan needs to understand why there are sudden changes in the index, especially in 2023, and keep working on its social programs.\u003c/p\u003e\n\u003cp\u003eThe evolution of the \u0026laquo;glass ceiling\u0026raquo; index for the three states is shown in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eThe information about the glass ceiling in Kazakhstan, Kyrgyzstan, and Uzbekistan shows some important trends in gender equality in jobs in these countries. In Kazakhstan, the Gender Community Index (GCI) ranges from 0.72 to 089 from 2014 to 2023. This shows that there are major obstacles preventing women from getting leadership roles. Although there are some small changes in the numbers, overall, it is clear that gender equality in management is still a problem in Kazakhstan. Kyrgyzstan is a country that has done a great job breaking down barriers for women. Since 2019, its score on the GCI index has been above 1, reaching 1.24 in 2023This means that women in Kyrgyzstan now have the same or even better chances to get higher jobs than men. In Uzbekistan, the GCI is below 1 all the time we looked at, but it has been going up slowly from 0.59 in 2014 to 0.74 in 2023This shows that things are getting better, but women in Uzbekistan still face challenges that make it hard for them to achieve the same career success as men.\u003c/p\u003e\n\u003cp\u003eHighest and lowest index numbers for the period (Table \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e):\u003c/p\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e Highest and lowest index values\u0026nbsp;\u003c/p\u003e\n\u003ctable id=\"Taba\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eСountry\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMinimum load-capacity index\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMaximum load-capacity index\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKazakhstan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKyrgyzstan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2020,2023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUzbekistan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2014, 2016, 2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eKazakhstan and Uzbekistan are both showing slow progress in improving gender equality in their governments. Both states have the issue of a \u0026laquo;glass ceiling\u0026raquo;, even though they are trying to help women do better in their jobs. Kyrgyzstan, on the other hand, has made more noticeable progress in breaking down gender barriers, as shown by the GCI score, which has gone above 1 in recent years.\u003c/p\u003e\n\u003cp\u003eThe information about the glass ceiling index shows that there are big differences in gender equality in Central Asia. Kyrgyzstan has made the most improvements in helping women take on leadership roles. Meanwhile, Kazakhstan and Uzbekistan still have challenges for women who want to lead.\u003c/p\u003e\n\u003cp\u003eWomen are less likely than men to get promoted to the top jobs, even when everything else is the same.\u003c/p\u003e\n\u003cp\u003eAs a result of the panel analysis, the panel regression equation is (5):\u003c/p\u003e\n\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\u003cimg 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\" style=\"width: 671px;\"\u003e\u003cbr\u003e\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eThe regression results have led to such conclusions. The role of independent variables in explaining gender differences can be seen below (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe positive coefficient (\u0026beta;1\u0026thinsp;\u0026gt;\u0026thinsp;0) shows that as the economy develops more, the difference in pay between men and women increases. In wealthy countries, men often work in jobs that pay more money, while women are more likely to have jobs that pay less, like in areas such as food and hospitality services.\u003c/p\u003e\n\u003cp\u003eA negative and important factor (\u0026beta;2\u0026thinsp;\u0026lt;\u0026thinsp;0) means that having more women in leadership helps to lessen the gender gap. This shows that putting women in leadership roles can help make their finances more stable.\u003c/p\u003e\n\u003cp\u003eThe positive number (\u0026beta;3\u0026thinsp;\u0026gt;\u0026thinsp;0) means that when more women are unemployed, the difference between men\u0026rsquo;s and women\u0026rsquo;s employment grows larger. This shows that women have a harder time finding jobs, which makes their money situation less stable.\u003c/p\u003e\n\u003cp\u003eThe positive factor (\u0026beta;4\u0026thinsp;\u0026gt;\u0026thinsp;0) means that when more men are employed than women, the pay gap between them gets bigger. This might mean that even when women have jobs in this area, they still earn less money than men.\u003c/p\u003e\n\u003cp\u003eThe results from the calculations (Duncan index, Gini coefficient, Index glass ceilings) support the first hypothesis. Women earn less money than men. They are also less likely to hold leadership roles than men.\u003c/p\u003e\n\u003cp\u003eThe panel results support the second idea.\u003c/p\u003e\n\u003cp\u003eThe information shows that places where more women work, like in housing and food services, make it harder for women to have stable finances. This is proven by the importance of the female unemployment rate and women in leadership.\u003c/p\u003e\n\u003cp\u003eThe difference in pay between men and women might happen because there are fewer job chances for women, like not having many women in leadership roles, and there are higher rates of joblessness among women.\u003c/p\u003e\n\u003cp\u003eThe results of the analysis show clear evidence that women\u0026apos;s money problems in living and food services are connected to job conditions, the number of women in leadership positions, and the way jobs are organized.\u003c/p\u003e"},{"header":"6 Conclusion","content":"\u003cp\u003eThis study points out the ongoing problem of men and women working in different jobs in Kazakhstan, Kyrgyzstan, and Uzbekistan, and explains how this affects the economy and society. Even with different laws and economic situations in each country, jobs are still often divided by gender. This makes it harder for women to find good opportunities and increases inequality in the area. Kazakhstan has made good progress in promoting gender equality with new politics. However, Kyrgyzstan and Uzbekistan still face problems because their institutions and cultural beliefs support traditional gender roles.\u003c/p\u003e \u003cp\u003eThe results show that dividing jobs by gender keeps women from getting high-paying jobs and leadership roles. This limits their job opportunities and keeps pay differences between men and women. Also, there are strong obstacles, like job stereotypes, limited access to education, and unfair practices at work, that contribute to this inequality. The calculated measures of gender inequality support these findings by showing clear differences in job participation and types of jobs for men and women in all three countries.\u003c/p\u003e \u003cp\u003eFrom a policy viewpoint, the study highlights the important need for specific actions to tackle gender separation. First, we need to make rules that break down gender stereotypes in schools and job training. This will help women get into high-paying jobs that are usually held by men. Next, changes in the job market should focus on gender equality by ensuring men and women are paid equally, improving safety and fairness at work, and making sure laws against discrimination are followed. Third, working together with neighboring Central Asian countries could help improve gender equality by sharing ideas and creating programs together.\u003c/p\u003e \u003cp\u003eThis study provides important information, but more research is needed to look at how gender separation changes over time and how it affects the economy in Central Asia. Future studies should look at how new industries, technology changes, and flexible jobs might help reduce or increase gender inequality in the area.\u003c/p\u003e \u003cp\u003eBy tackling these problems, leaders can build job markets that support both gender equality and steady economic growth in Kazakhstan, Kyrgyzstan, and Uzbekistan.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFirst and foremost, I thank the Editors and Reviewers for their valuable feedback and suggestions, which significantly improved this manuscript. I thank my supervisor Ilona Bordiyanu.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConceptualization and theory\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYM; research design: YM, IB, and AS; data collection: YM, IB, and AS; analysis and interpretation: YM, IB, and AS; writing draft preparation: YM, IB, and AS; supervision: YM; correction of article: YM, IB, and AS; proofread and final approval of article: YM, IB, and AS. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFinancial support\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was not sponsored (own resources)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData and materials are available.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author can share the data if you ask for it. The author has copies of the computer programs that created the results in the article, and they can be obtained from them.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eApproval for ethics and permission to take part.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot relevant\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePermission to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot relevant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors say they do not have any conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformation about the authors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYeldar Y. Mubarakov \u0026ndash; PhD candidate, Kazakh-American Free University, Ust-Kamenogorsk, Kazakhstan, e-mail: [email protected], ORCID ID: https://orcid.org/0009-0001-3619-9088\u003c/p\u003e\n\u003cp\u003e*Ilona V. Bordiyanu \u0026ndash; PhD, professor, Kazakh-American Free University, Ust-Kamenogorsk, Kazakhstan, e-mail: [email protected] , ORCID ID: https://orcid.org/ 0000-0002-7175-9829\u003c/p\u003e\n\u003cp\u003eAyazhan S. Seriktayeva \u0026ndash; Lecturer, D. Serikbayev East Kazakhstan technical university, Ust-Kamenogorsk, Kazakhstan, e-mail: [email protected], ORCID ID: https://orcid.org/0009-0004-4445-612X\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAcosta-Ballesteros, J., Osorno-del Rosal, M.P., Rodr\u0026iacute;guez-Rodr\u0026iacute;guez, O.M.: Underemployment and employment among young workers and the business cycle in Spain: the importance of education level and specialisa‑ tion. J. Educ. Work 31(1), 28\u0026ndash;46 (2018)\u003c/li\u003e\n\u003cli\u003eAhmed, A.M. \u0026amp; Hyder, A. Sticky floors and occupational segregation: evidence from Pakistan. Pak Dev Rev 47(4), 837\u0026ndash;849 (2008)\u003c/li\u003e\n\u003cli\u003eAlkadry, M. 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Economics \u003cstrong\u003e2\u003c/strong\u003e(2), 7\u0026ndash;16 (2013)\u003c/li\u003e\n\u003cli\u003eWEF: The Global Gender Gap Report. https://www.weforum.org/publications/global-gender-gap-report-2023/economy-profiles-5932ef6d39/ (2023). Accessed 20 Nov 2024\u003c/li\u003e\n\u003cli\u003eWilkins, R.: Personal and job characteristics associated with underemployment. Aust. J. Labour Econ. \u003cstrong\u003e9\u003c/strong\u003e(4), 371\u0026ndash;393 (2006)\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"gender segregation, labour market, Central Asia, employment, types of economic activity, economic development, gender equality","lastPublishedDoi":"10.21203/rs.3.rs-5709710/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5709710/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCentral Asian countries face serious socio-economic problems related to gender segregation in the labour market. This study examines the extent and scope of gender segregation in Kazakhstan, Kyrgyzstan and Uzbekistan between 2014 and 2023, which includes significant changes caused by economic development, political reforms and the global crisis. The study used data from each country’s national statistical agencies, which examined gender differences in employment levels, sectoral distribution and access to decision-making positions. The persistence of gender inequality in the labour market can be identified through comparative analysis, which examines trends and identifies factors that contribute to it. The study shows that two ideas are true by using the Gini Index, Duncan Index, Glass Ceiling Index, and analyzing data over time. Despite some progress, women still face limitations in certain occupational areas and at higher levels, as the results show. The analysis shows that gender segregation and its characteristics in each of the three countries are influenced by socioeconomic and cultural factors. The final result of the study is a set of recommendations to reduce gender imbalance and achieve an equitable distribution of opportunities for both genders, which can ultimately contribute to the sustainable development and economic efficiency of the region.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJEL Classification \u003c/strong\u003eJ16, J21, J31\u003c/p\u003e","manuscriptTitle":"Gender segregation in the labour market of Central Asian countries: Kazakhstan, Kyrgyzstan and Uzbekistan","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-30 04:17:48","doi":"10.21203/rs.3.rs-5709710/v1","editorialEvents":[{"type":"communityComments","content":1}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8b9364b6-f48e-4cf8-86fb-a1027f7fe603","owner":[],"postedDate":"December 30th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-30T04:17:48+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-30 04:17:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5709710","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5709710","identity":"rs-5709710","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}