Recent Evidence on the Evolution of Women’s Empowerment Across Dimensions and Countries: A Multidimensional Index of Women’s Empowerment Across Countries

Advances in Women’s Empowerment: Critical Insight from Asia, Africa and Latin America

ISBN: 978-1-83982-473-9, eISBN: 978-1-83982-472-2

ISSN: 1529-2126

Publication date: 21 September 2020

Abstract

Women’s empowerment is a multidimensional concept that encompasses different aspects such as access to education, freedom to make vital decisions, labor market access, wages, and political participation, among others. In this research, the authors construct a multidimensional index of women’s empowerment that takes into account individual resources and achievements and analyze its evolution across countries using data from the United Nations Development Programme and the United Nations for 17 gender indicators across 96 countries over the period 1995–2015. By means of exploratory and confirmatory factor analysis, the authors identify three dimensions of women’s empowerment: reproductive health, economic participation, and basic education. In addition, the authors use cluster techniques to classify countries into four groups with similar behavior patterns in the different domains of women’s empowerment: a group of countries with high levels in the domains of reproductive health and basic education but with low levels in economic participation; a group of countries with high levels in the domains of reproductive health and economic participation that should pay attention to education; a group of countries with medium levels across the three dimensions of women’s empowerment, especially in reproductive health and economic participation; and a group of countries with low levels in all the dimensions of women’s empowerment, especially in reproductive health and basic education. The comparison of these different patterns serves to highlight the aspects in which improvements have been made or, on the contrary, to highlight the obstacles that are hindering the improvement of gender equality. Finally, the results suggest that advancements in women’s empowerment improve the countries’ level of development.

Keywords

Citation

Medina, E. and Herrarte, A. (2020), "Recent Evidence on the Evolution of Women’s Empowerment Across Dimensions and Countries: A Multidimensional Index of Women’s Empowerment Across Countries", Ochman, M. and Ortega-Díaz, A. (Ed.) Advances in Women’s Empowerment: Critical Insight from Asia, Africa and Latin America (Advances in Gender Research, Vol. 29), Emerald Publishing Limited, Bingley, pp. 13-37. https://doi.org/10.1108/S1529-212620200000029001

Publisher

:

Emerald Publishing Limited

Copyright © 2020 Emerald Publishing Limited


1. Introduction

Achieving gender equality through women’s empowerment is currently one of the major objectives included in the 2030 Agenda for Sustainable Development (United Nations (UN), 2015). Similarly, there is a fast-growing body of research that examines women’s empowerment from a variety of perspectives. However, the main difficulty researchers must contend with is the availability of reliable and sufficient data to carry out rigorous research.

Data are necessary to better understand and overcome the challenges that women face in society, as well as to influence policy changes and measure their impact (Sell & Minot, 2018). However, in developing countries which face the greatest challenges to gender equality and women’s empowerment, there is an especially pressing need for data on this issue as well as reliable statistics. In this line, an increasing number of institutions have made significant efforts in recent years to measure different aspects of women’s position in the world, which has substantially improved the availability of data.

Nonetheless, there are still certain shortcomings in analyzing the current situation of women. For example, data are not homogeneous for all countries, since in many cases they are available only for a small number of countries, while in others they refer to different time periods, such that making any attempt at comparisons between countries is quite difficult. Some international organizations have prioritized measurements of gender equality and women’s empowerment in their statistics. In particular, the UN has taken notable steps in this direction through the United Nations Development Programme (UNDP), which introduced composite indexes of gender development (GDI) and gender inequality (GII) in 1995. Although both indexes cover broad geographic areas and time periods, they offer aggregated information, and hence do not allow studying the different dimensions behind the concept of women’s empowerment. Therefore, much research is still needed to fully understand the concept of women’s empowerment and ascertain the progress made in its different domains.

This study tries to fill this gap and contribute to the research on gender issues and social indicators by constructing composite indexes of women’s empowerment for a wide sample of countries that enable studying the different dimensions of this concept in greater depth. Specifically, we start by constructing a database which aims to standardize, organize, and synthesize the existing information on gender issues. Based on factor analysis techniques, this information allows us to identify the different dimensions of women’s empowerment and to construct individual composite indexes for each of them. The analysis of these composite indexes across time and across countries provides a more complete picture of the current reality, as not all the dimensions of women’s empowerment could be progressing at the same pace. A disaggregated study, such as the one proposed in this research, will permit us to gain a better understanding of the obstacles that each country faces in gender-related matters. In addition, we seek to establish comparisons between countries in order to offer a framework that could serve for future research to study, in greater depth, successful actions in those countries that have made the greatest advances, as well as the barriers that hinder this process in countries where progress has not been achieved at the desired pace or has yet to occur.

For the purpose of this research, the first step consists in identifying current data sources on the subject of gender. Once the gender indicators are identified, we group them by subject according to the level of temporal and geographical availability. The aim is to synthesize all these indicators in composite indexes that can be used to analyze the evolution of women’s empowerment in recent decades. To that end, we perform a factor analysis in two stages. In the first stage, we identify the specific dimensions of women’s empowerment, while in the second stage, we construct a multidimensional index of women’s empowerment (MIWE) composed of individual indexes for each domain of empowerment.

Finally, we conduct a cluster analysis that allows us to construct groups of countries according to their levels in the different dimensions of women’s empowerment. Although an in-depth study of the specific economic and social features behind women’s empowerment in each group of countries is beyond the scope of this chapter, this research aims to identify similar behavior patterns across countries. The findings could provide a working basis for policymakers to better define future policies targeted at improving women’s role in the economy and society and hence promote the progress and development of the least developed countries.

2. Background

Women’s equality and empowerment is addressed in Goal 5 of the 17 Sustainable Development Goals. According to the UN (2017):

Women and girls, everywhere, must have equal rights and opportunity, and be able to live free of violence and discrimination. […] Gender equality is not only a fundamental human right, but a necessary foundation for a peaceful, prosperous and sustainable world. Providing women and girls with equal access to education, health care, decent work, and representation in political and economic decision-making processes will fuel sustainable economies and benefit societies and humanity at large.

In order to determine the degree to which this objective has been achieved, the first step is to be able to measure women’s empowerment. However, women’s empowerment is difficult to measure because of its abstract nature (Bishop & Bowman, 2014; Mishra & Tripathi, 2011). The seminal work of Kabeer (1999) defines women’s empowerment as “the process by which those who have been denied the ability to make strategic life choices acquire such an ability.” The author states that empowerment encompasses three elements: individual resources (access to material and human and social resources), agency (the ability to make own life choices, such as the decision to marry and who to marry, the decision to have children, etc.), and achievements (well-being outcomes such as life expectancy, access to education, labor market participation, political representation, etc.). The World Bank’s (2014) definition of empowerment focuses on agency as “the ability to make decisions about one’s own life and act on them to achieve a desired outcome, free of violence, retribution, or fear.” It should be noted that while agency would provide direct evidence of empowerment, resources and achievements are proxy measures usually used as evidence of women’s agency (Richardson, 2018). However, other definitions of women’s empowerment are more related to the perspective of achievements (Duflo, 2012), since women’s empowerment is conceived of as improving the ability of women to access the components of development, namely health, education, earning opportunities, rights, and political participation.

At present, the Gender Inequality Index (GII) developed by the UNDP allows comparisons across a wide sample of countries since 1995 which focuses on the measurement of achievements. Nevertheless, the kind of achievements measured in the GII encompasses a wide range of features, and although there is broad consensus that empowerment is a multidimensional factor expressed at multiple levels (Bayissa, Smits, & Ruben, 2018; Huis, Hansen, Otten, & Lensink, 2017; Mahmud, Shah, & Becker, 2012; Taylor & Pereznieto, 2014; Tsikata & Darkwah, 2014; Sharaunga, Mudhara, & Bogale, 2019, among many other), the debate continues on the dimensions that should be considered and how to measure them across countries. The GII aims to measure the human development costs of gender inequality. In particular, it measures gender inequalities in three important aspects of human development: reproductive health, empowerment, and economic status. Nevertheless, as we mentioned previously, the GII offers aggregated information and does not allow us to analyze the different dimensions behind the concept of women’s empowerment. In this same line, the World Economic Forum (WEF) (2018) developed the Gender Gap Index to measure gender differences across a wide sample of countries based on gender indicators that are classified into four dimensions: economic participation and opportunity, educational attainment, health and survival, and political empowerment. Nonetheless, it should be noted that, as the authors remark, the index ranks countries according to gender equality rather than women’s empowerment.

Due to the need for available data that enable in-depth research on gender issues, several new measures of women’s empowerment use survey data at the individual and household level across countries. Nevertheless, most of these indexes are based on a specific dimension of women’s empowerment such as reproductive health (Afifi, 2009; Tadesse, Teklie, Yazew, & Gebreselassie, 2013; Upadhyay & Karasek, 2012), partner violence (Msuya, Adinan, & Mosha, 2014), or the inclusion of women in the agricultural sector (Alkire et al., 2013). Indeed, these indexes are constructed for specific countries (Ethiopia in the case of Tadesse et al., 2013; Tanzania in Msuya et al., 2014; or Egypt in the research of Afifi, 2009) or for a small group of countries, thus complicating comparisons across countries. Based on the data from 170 countries over the period 1900–2012, Sundström, Paxton, Wang, and Lindberg (2017) constructed an index of women’s political empowerment. While the index covers a wide sample of countries and consumes a long period of time, it measures only the political dimension of women’s empowerment.

However, the literature that seeks a multidimensional analysis, taking into account several domains of empowerment, is scarcer. In this regard, Miedema, Haardörfer, Girard, and Yount (2018) exploited the Demographic and Health Surveys (DHS) from Ethiopia, Kenya, Rwanda, Tanzania, and Uganda. The authors used factor analysis techniques to capture three dimensions of women’s empowerment: women’s human/social assets, gender attitudes related to wife abuse, and women’s participation in household decisions. Ewerling et al. (2017) also exploited the DHS by extending the sample to 34 African countries and used factor analysis techniques to capture three dimensions: attitude to violence, social independence, and decision-making.

Although multidimensional analysis enhanced the results in both cases, the DHS allows only analyzing a part of the broader concept of women’s empowerment. Moreover, while the DHS is a rich source of data, it also has some limitations. In particular, the intervals between the DHS waves are irregular and vary largely across countries, thus limiting the ability to construct indexes that could be used to make comparisons between countries and to study their evolution over time.

Besides the difficulties mentioned above, as Richardson (2018) highlighted based on a review of the research measuring women’s empowerment, the use of specific analytical methods could lead to imprecise or biased measurement models. In particular, Richardson (2018) argued that, despite being very popular in the literature, methods that measure empowerment by summary scores that add together responses to each indicator should be avoided, as these kinds of scores rely on the untested assumption that each indicator contributes to empowerment equally. In contrast, other analytical methods, such as confirmatory factor analysis (CFA), that do not weight indicators equally lead to more accurate measures of women’s empowerment than summary scores. The author remarks that despite the advantages of using CFA, this method is rarely used in empirical studies.

In this line, as we mentioned earlier, the contribution of this research is to develop a methodology that allows constructing a MIWE by obtaining indicators for the different dimensions of gender inequality and women’s empowerment, which could be easily updated for a wide number of countries. For that aim, we exploit data on individual resources and achievements from the UN and the UNDP covering 96 countries. As the changes in empowerment must be assessed over time (Sundström et al., 2017), we analyze data for the period 1995–2015. The methodology is based on exploratory factor analysis (EFA) and CFA and cluster techniques. The analysis of these indexes will allow us to determine whether the evolution has been similar across all dimensions and to identify countries where progress has been made. Furthermore, the comparison between countries will also allow us to identify groups of countries with similar behavior patterns in the different dimensions of women’s empowerment. The comparison of these different patterns will serve us to highlight those aspects in which improvements have been made or, on the contrary, to highlight the obstacles which are hindering the improvement of gender equality.

3. Data and Methodology

We have selected several economic and social indicators related to gender inequality and women’s empowerment from the UNDP (2018) and the UN (2018). The first step in the selection of indicators was to take into account the relevance of the indicators for the study of women’s empowerment. Nevertheless, as one of the objectives of this research is to make cross-country comparisons, the final selection of indicators was made based on the availability of homogenous data over time for the widest possible sample of countries. Our database finally included 17 indicators for 96 countries for the period 1995–2015, which are shown in Table 1. Some of the indicators are related to reproductive health, access to education, income, labor market participation,1 political participation, and access to own financial resources. Although we consider a wide set of indicators, it should be noted that our database does not provide information on agency, and hence the MIWE presented here does not reflect the aspects related to freedom to make vital decisions. Hence, the interpretation of the MIWE is restricted to the specific domains under study.

Table 1.

Indicators Selected for the Construction of the MIWE.

Indicator Unit
Adjusted net enrollment rate in primary education (female) % of total enrollment
Adjusted net enrollment rate in primary education (male − female) % of total enrollment
Adolescent birth rate Births per 1,000 women ages 15–19
Antenatal care coverage (at least one visit) % of total women aged 15 -49 with a live birth
Contraceptive prevalence (any method) % of married or in-union women of reproductive age 15-49 years
Estimated gross national income per capita (male minus female) 2011 PPP$
Female share of employment in senior and middle management % of total employment
Gross enrollment ratio in secondary education (female) % of secondary school-age population
Gross enrollment ratio in secondary education (male − female) % of secondary school-age population
Maternal mortality ratio Deaths per 100,000 live births
Proportion of births attended by skilled health personnel % of total births
Female share of non-agriculture employment % of total non-agriculture employment
Female share of seats in parliament % held by women
Total unemployment rate Female to male ratio
Youth unemployment rate Female to male ratio
Unmet need for family planning % of married or in-union women of reproductive age 15–49 years
Women with account at financial institution or with mobile-money-service provider % of female population ages 15 and older

It should be noted that there are many indicators which measure women’s empowerment as a global concept. Nevertheless, there could be different domains or dimensions behind the overall concept of women’s empowerment, each of which may be related to a specific group of highly correlated indicators. To identify the dimensions of women’s empowerment, as well as the indicators measuring the precise dimension, we condense all the indicators into a reduced number of factors, each of which represents a specific domain of women’s empowerment. In order to obtain these factors, we perform a factor analysis in two stages. In the first stage, we conduct an EFA to identify the unobservable underlying structure of the data and to obtain a reduced number of factors. Each factor encompasses indicators with the maximum intracorrelation, while the correlation between factors is minimum. Specifically, we perform a factor analysis on the 17 indicators using principal components to define the number of retained factors and apply orthogonal varimax rotation to the retained components.2 The 17 indicators refer to the year 2015, as this is the year for which data are available for the largest number of countries. We were able to retain six factors with a meaningful interpretation, each of which measures a specific dimension of women’s empowerment. These factors explain 92% of the total variance and the communalities vary between 0.97 and 0.78. Table 2 provides a description of the six dimensions of women’s empowerment as follows: (1) reproductive health; (2) labor market; (3) participation in production activities; (4) basic education; (5) access to financial resources; and (6) public life. In order to confirm the robustness of the results, the analysis was replicated individually for the rest of the years and pooled for the whole period, obtaining similar dimensions in all cases.

Table 2.

Dimensions of Women’s Empowerment for 2015.

Factors (Women’s Empowerment Dimensions) Indicator Highest Correlation
1) Reproductive health Proportion of births attended by skilled health personnel 0.78
Contraceptive prevalence (any method) 0.89
Maternal mortality ratio −0.86
Unmet need for family planning −0.91
2) Labor market Adolescent birth rate 0.77
Total unemployment rate (female to male ratio) 0.84
Youth unemployment rate (female to male ratio) 0.83
Gross enrollment ratio in secondary education (female) −0.69
3) Participation in production activities Female share of non-agriculture employment 0.84
Female share of employment in senior and middle management 0.81
Estimated gross national income per capita (male minus female) −0.75
Gross enrollment ratio in secondary education (male minus female) −0.68
4) Basic education Antenatal care coverage (at least one visit) 0.67
Adjusted net enrollment rate in primary education (female) 0.72
Adjusted net enrollment rate in primary education (male minus female) −0.86
5) Access to financial resources Women with account at financial institution or with mobile-money-service provider 0.74
6) Public life Female share of seats in parliament 0.85

Note: Total explained variance: 92%.

Varimax rotation. See rotation matrix in Table A2 (Appendix A).

With regard to the composition of each dimension shown in Table 2, some aspects deserve mention. Improvements in girls’ basic education are associated with greater antenatal care, and the greater presence of women in secondary education increases women’s participation in relevant production activities, which in turn serves to reduce the gender wage gap. In addition, secondary education for girls, jointly with a reduction in the adolescent birth rate, also reduces the gender unemployment gap, both at the time of entry into the labor market and during the rest of their working lives. Finally, a greater proportion of births attended by skilled health personnel along with a greater use of contraceptive methods is associated with a lower maternal mortality ratio and makes it possible to improve family planning.

In the second stage, we perform CFA, taking as a starting point at which the dimensions are identified in the EFA. The underlying idea is to extrapolate the results from the EFA for all time periods. Due to the limited data availability for many of the years, and with the aim of obtaining comparable results over time and across countries, it is necessary to reduce both the number of indicators and the number of dimensions analyzed. Hence, in this second stage, we focus on nine indicators and three dimensions of women’s empowerment. The three dimensions are: (1) Reproductive health; (2) Economic participation; and (3) Basic education, where the dimension of Economic participation is the result of merging the information (indicators) contained in the previously mentioned dimensions of Labor market and Participation in production activities. In this stage, the dimension of Reproductive health is associated with three indicators, Economic participation is associated with four indicators, and Basic education is related to two indicators. For each of the three dimensions, we perform an individual factor analysis, retaining one factor for each dimension (see Table A3). This procedure is replicated for all the five-year time periods (e.g., 2000, 2005, 2010, and 2015). Table 3 presents the indicators included in the individual factor analysis for each dimension, as well as the percentage of explained variance in the corresponding individual factor analysis for each five-year period.

Table 3.

Dimensions of Women’s Empowerment.

Dimensions in Exploratory Factor Analysis Dimensions in Confirmatory Factor Analysis Variables in Each Dimension Used in CFA % of Explained Variance for Each Five-year Period
1) Reproductive health A) Reproductive health
  • Adolescent birth rate

  • Proportion of births attended by skilled health personnel

  • Maternal mortality ratio

2000: 80%
2005: 78%
2010: 81%
2015: 82%
2) Labor market
3) Participation in production activities
B) Economic participation
  • Female share of non-agriculture employment

  • Total unemployment rate (female to male ratio)

  • Youth unemployment rate (female to male ratio)

  • Estimated gross national income per capita (male minus female)

2000: 60%
2005: 56%
2010: 62%
2015: 61%
4) Basic education C) Basic education
  • Adjusted net enrollment rate in primary education (female)

  • • Adjusted net enrollment rate in primary education (male minus female)

2000: 81%
2005: 79%
2010: 80%
2015: 77%
5) Access to financial resources
6) Public life

Source: Own calculations.

The data for each dimension have been standardized in such a way that each index varies from 0 to 1, where 0 is the best position and 1 the worst. The results allow us to study the evolution of the different dimensions of women’s empowerment over time and make comparisons for a large number of countries.

In order to perform a cross-section analysis, we use the statistical technique of cluster analysis. The aim is to identify behavior patterns associated with different levels of progress in gender empowerment. The results will provide evidence regarding the priorities of the countries’ government agendas, since they make it possible to determine the obstacles in countries that have made the least progress as well as to identify successful countries whose policies could serve as guidelines for the rest. In particular, the cluster analysis groups the countries according to the values of the three dimensions of women’s empowerment identified in the CFA. The clustering is performed in such a way that countries belonging to the same cluster or group are the most similar in terms of the analyzed dimensions while fulfilling the criterion of maximum differences between countries belonging to different clusters (Hair, Anderson, Tatham, & Black, 2014). We use Schwarz’s Bayesian information criterion (BIC)3 as our grouping criteria and the log-likelihood as the distance measure. The results allow us to identify four behavior patterns across the world regarding the evolution of women’s empowerment: (1) a group of countries with high levels in the domains of reproductive health and economic participation that should pay attention to education; (2) a group of countries with medium levels across the three identified dimensions of women’s empowerment, especially in reproductive health and economic participation; (3) countries with good positions as regards the domains of reproductive health and basic education but poor positions in economic participation; and (4) countries with the lowest levels in all the dimensions of women’s empowerment, especially in reproductive health and basic education. Table 4 presents the countries included in each group, and in Section 5, we discuss the results of the cross-section analysis.

Table 4.

Clusters of Countries According to the Dimensions of Women’s Empowerment.

Group 1: Countries with high levels in reproductive health and economic participation that should pay attention to education
Armenia, Belarus, Bulgaria, Croatia, Cuba, Cyprus, El Salvador, Estonia, Georgia, Latvia, Lithuania, Republic of Moldova, Mongolia, Montenegro, Portugal, Romania, Russian Federation, Serbia, Thailand, North Macedonia, Ukraine, Uzbekistan
Group 2: Countries with medium levels in all the dimensions of women’s empowerment, especially in reproductive health and economic participation
Argentina, Azerbaijan, Bhutan, Botswana, Brazil, Cabo Verde, Chile, Colombia, Congo, Costa Rica, Dominican Republic, Ecuador, Guatemala, Guyana, Honduras, India, Indonesia, Kenya, Kyrgyzstan, Maldives, Mexico, Morocco, Namibia, Panama, Paraguay, Peru, Philippines, Senegal, Suriname, Tajikistan, Turkey, Uganda, Uruguay, Venezuela, Zambia
Group 3: Countries with high levels in reproductive health and basic education, but low levels in economic participation
Bahrain, Brunei, Egypt, Italy, Kazakhstan, Kuwait, Malaysia, Malta, Mauritius, Oman, Qatar, Saudi Arabia, United Arab Emirates
Group 4: Countries with low levels in all the dimensions of women’s empowerment, especially in reproductive health and basic education
Benin, Bolivia, Burkina Faso, Cambodia, Cameroon, Chad, Comoros, Côte d’Ivoire, Equatorial Guinea, Ethiopia, Gambia, Ghana, Guinea, Lao People’s Democratic Republic, Lesotho, Liberia, Mali, Mauritania, Mozambique, Nepal, Niger, Pakistan, Sierra Leone, Sudan, Togo, Zimbabwe

4. Results: Time Trend Analysis

At the global level, we can conclude that gender equality and women’s empowerment have improved in the past 20 years. However, the rate of progress has not been the same in all geographical areas. To illustrate this, Fig. 1 shows the GII4 calculated by the UNDP for 1995 and 2017, which takes values from 0 to 1, where 1 indicates the worst position. According to these data, we can observe that the worst values are recorded in Sub-Saharan Africa and Latin America, which are the areas where the rate of progress has also been slower. Asia shows some heterogeneity, with Central and Eastern Asia being the areas that show the best positions. As for Western Asia, the area has recorded strong progress and although this region started from a worse position than Latin America in 1995, it has now achieved better levels of gender equality and women’s empowerment. Something similar has also occurred in Southern Asia, although the rate of progress has not been so high. The best positions can be found in developed countries and Eastern Asia, which are also the regions where the progress has been most rapid, especially in Eastern Asia.

Fig. 1. 
Evolution of the UNDP Gender Inequality Index by Geographical Areas.
Source: UNDP.
Note: The data are expressed on a scale from 0 (best level of GII) to 1 (worst level of GII).

Fig. 1.

Evolution of the UNDP Gender Inequality Index by Geographical Areas.

Source: UNDP.

Note: The data are expressed on a scale from 0 (best level of GII) to 1 (worst level of GII).

However, the evolution of the GII shows an aggregated perspective of gender inequality, and more detail can be obtained when focusing on the different domains of women’s empowerment. At this point, it is interesting to observe whether the evolution described at aggregate level coincides or not with the evolution in the different dimensions of women’s empowerment identified in this research (reproductive health, economic participation, and basic education).

Fig. 2 shows the values of the three dimensions of women’s empowerment under study, as well as their recent evolution for the different geographical areas. As shown, the results present a certain amount of heterogeneity across dimensions. Developed countries continue to occupy the highest positions in the three dimensions. However, African countries, and particularly Sub-Saharan countries, despite still occupying the worst positions in reproductive health and basic education, stand out for their favorable position in the dimension of economic participation due to the high participation of women in African labor markets in relation to the levels recorded in other developing areas. Moreover, it is the only geographical area that has made significant improvements in this dimension.

Fig. 2. 
Evolution of the Three Dimensions of Women’s Empowerment by Geographical Area.
Source: Own calculations.
Note: The data are expressed on a scale from 0 (highest women’s empowerment) to 1 (lowest women’s empowerment).

Fig. 2.

Evolution of the Three Dimensions of Women’s Empowerment by Geographical Area.

Source: Own calculations.

Note: The data are expressed on a scale from 0 (highest women’s empowerment) to 1 (lowest women’s empowerment).

Focusing on Latin America, the main weakness of these countries is related to reproductive health, followed by economic participation. As regards the dimension of basic education, it is worth noting that, despite having one of the best positions, there has been a sharp deterioration in recent years. Asia occupies the worst positions in the dimension of economic participation, especially Western and Southern Asia. However, Southern Asian countries occupy similar positions to developed countries in terms of reproductive health and basic education, which are the two dimensions that have experienced the greatest progress in all geographical areas. In the case of Central and Eastern Asia countries, although they occupy good positions in all dimensions of women’s empowerment, it is important to note the sharp deterioration in recent years in terms of women’s economic participation.

Regarding the specific dimensions, our results suggest that the greatest progress has occurred in reproductive health, since all the geographical areas have made improvements, although African countries have done so at a slower pace. The dimension of basic education also shows a favorable evolution, except in Latin America and Central and Eastern Asia, where the situation has significantly worsened in recent years. However, there have been slight changes in women’s economic participation in all areas except Africa, where there has been an improvement, and Central and Eastern Asia, where there has been a sharp deterioration.

While comparisons with other indicators of women’s empowerment and gender equality have limitations due to the different variables and methodologies used to construct global indicators and thus measure different dimensions, some similarities should be highlighted. For example, like our results, the Gender Equality Index (GEI) elaborated by the European Institute for Gender Equality for the 28 EU Member States shows that the dimension of work, which is somewhat similar to our dimension of economic participation across developed regions, has remained quite stable. As regards the domain of reproductive health, our results for developed countries indicate improvements, which is in line with the evolution of the GEI for the domain of health. In our case, however, the evolution seems to be of a lower intensity. In contrast, the results of the MIWE regarding the domain of basic education across developed countries remains stable, while the domain of knowledge in the GEI presents an upward path.

Other indicators of gender equality such as the one proposed by the WEF indicate, similar to the MIWE, that education has improved across countries. Although the results of the WEF indicator indicate that levels of health have remained quite stable, the MIWE shows a clear positive trend.

Appendices B and C present the top 10 and worst 10 positions for each dimension of the MIWE by countries for both the index levels and the corresponding percent changes. Countries that occupy a good position and have made significant progress in recent years are shaded in green. In contrast, countries with the most gender problems are shaded in red, since they occupy low positions and have non-existent or poor progress.

5. Results: Cross-section Analysis

The cluster analysis explained in Section 3 allowed us to identify four groups of countries according to the levels achieved in the different dimensions of women’s empowerment (see Table 4). Two of the groups (groups 1 and 3) include countries with good positions in terms of reproductive health. In particular, group 3 displays the best levels in reproductive health and basic education, but presents very unfavorable assessments in terms of women’s economic participation. This group includes countries mainly from Western Asia and Southeastern Asia, although it also includes a country from Sub-Saharan Africa (Mauritius). Basic education is the weak point of the other group of countries that have made notable progress in the dimensions of reproductive health and economic participation (group 1). This group is comprised mainly of developed countries and countries with outstanding positions in economic participation and reproductive health but intermediate values in the dimension of basic education.

On the other hand, the other two groups of countries (groups 2 and 4 in Table 4) could be classified as being less advanced in women’s empowerment, as they have worse positions in practically all dimensions. However, each of the two groups has even greater weaknesses in some specific dimension. Specifically, the countries in group 4, which is made up mostly of Sub-Saharan African countries, need to improve mainly in reproductive health and basic education, while group 2, which mainly comprises Latin American and some Sub-Saharan countries, includes countries with poorer achievements in reproductive health and economic participation.

Finally, Fig. 3 displays the relationship between the different dimensions of women’s empowerment and the Human Development Index (HDI). Our aim here is to delve deeper into the idea that women’s and girl’s empowerment is a goal in itself that promotes development, including economic growth and poverty reduction (Alsop, Bertelsen, & Holland, 2006). Additionally, Table 5 shows the correlations between the three dimensions of women’s empowerment and different indicators of development and poverty (HDI, GDP per capita, Gini Index, and Multidimensional Poverty Index) for each of the four clusters of countries previously identified. As our indexes of women’s empowerment take the value of 0 for the highest levels of empowerment, we would expect to find a negative correlation with the HDI and GDP per capita, and a positive correlation with the Gini Index and the Multidimensional Poverty Index, which would indicate that progress in women’s empowerment improves development and reduces poverty.

Table 5.

Pearson’s Correlation Coefficient Between the Three Dimensions of Women’s Empowerment and Different Indicators of Development and Poverty.

HDI GDP Per Capita Gini Index Multidimensional Poverty Index
A. Reproductive health
Group 1 Correlation −0.642*** −0.633*** 0.297 0.490
Prob. 0.001 0.001 0.191 0.151
Group 2 Correlation −0.700*** −0.631*** 0.330* 0.737***
Prob. 0.000 0.000 0.057 0.000
Group 3 Correlation −0.784* ** 0.542* 0.180
Prob. 0.001 0.056 0.732
Group 4 Correlation −0.705*** 0.189 0.160 0.659***
Prob. 0.000 0.344 0.446 0.000
B. Economic participation
Group 1 Correlation 0.090 0.122 0.018 −0.261
Prob. 0.682 0.580 0.937 0.467
Group 2 Correlation 0.117 0.187 0.174 −0.142
Prob. 0.496 0.274 0.324 0.489
Group 3 Correlation 0.114 0.422 0.006
Prob. 0.711 0.151 0.991
Group 4 Correlation 0.017 0.219 0.126 0.149
Prob. 0.932 0.272 0.547 0.477
C. Basic education
Group 1 Correlation −0.544*** 0.399* 0.080 0.206
Prob. 0.007 0.059 0.731 0.568
Group 2 Correlation 0.108 0.129 0.084 −0.092
Prob. 0.529 0.453 0.638 0.656
Group 3 Correlation 0.190 0.056 0.056
Prob. 0.535 0.856 0.916
Group 4 Correlation 0.338 0.080 0.137 0.514***
Prob. 0.084 0.691 0.515 0.009

Source: Own calculations.

*** Significance at 1% level; ** Significance at 5% level; *Significance at 10% level.

Notes: Data in bold represent statistically significant results.

Fig. 3. 
Relation Between the HDI and the Three Dimensions of Women’s Empowerment.
Source: UNDP, UN, and own calculations.

Fig. 3.

Relation Between the HDI and the Three Dimensions of Women’s Empowerment.

Source: UNDP, UN, and own calculations.

Our results are in line with the results by Duflo (2012). In particular, they suggest that advancements in women’s empowerment improve countries’ level of development, but the relation remains too weak. According to Duflo (2012) and Menon and van der Meulen Rodgers (2018), the women’s empowerment-development nexus is bidirectional and development could play a relevant role as a driving force of women’s empowerment. However, the weakness of this bidirectional relation suggests the need for additional policy actions that could bring equality between men and women. In addition, its impact does not seem to be the same for all dimensions of women’s empowerment. Particularly, the dimension of reproductive health exerts the highest effect for all the development and poverty indicators considered, followed, but not significant in many cases, by basic education. However, the correlation between the dimension of economic participation and the poverty and development indicators is not statistically significant for any of the groups of countries.

A more detailed analysis attending to the groups of countries shows that countries which present the least progress in terms of the three dimensions of women’s empowerment (groups 2 and 4) are those that could benefit most in terms of poverty reduction if women’s empowerment were improved, as these are the only groups of countries that exhibit significant correlations with the multidimensional poverty index. At the same time, we observe that progress in the dimension of reproductive health is associated with higher levels of development and GDP per capita for all groups of countries, although in this latter case the group of countries that has advanced least in the different dimensions of women’s empowerment, especially as regards education and reproductive health (group 4), does not benefit from this advantage. Finally, it is worth noting that improvement in the domain of basic education is the only that appears to be correlated with the HDI for the group of countries with the highest recorded levels across all dimensions of women’s empowerment (group 1), which are mostly developed countries. In this regard, the social and economic structures of the other countries may impede taking advantage (in terms of development) of the achievements made in gender equality in education, and in these cases, it is also necessary to develop institutional policies targeted at redesigning the organizational structures of these countries.

6. Main Conclusions

In this study, we have proposed a methodology to construct an index of women’s empowerment that takes into account the multidimensional features of this concept. For this purpose, we have exploited data from the UNDP and the UN for 17 gender indicators across 96 countries for the period 1995–2015 and used EFA and CFA. We have identified three dimensions of women’s empowerment: reproductive health, economic participation, and basic education. It should be noted that our index of women’s empowerment is based on data referring to individual resources and achievements, while agency (the ability to make own life choices) are not considered in this study, and hence it cannot be used to draw conclusions in this regard.

While at a global level we can conclude that women’s empowerment have improved in the past 20 years throughout the world, our results present some heterogeneity across dimensions and geographical areas. The proposed MIWE shows that the greatest progress has been achieved in reproductive health, since all geographical areas have made improvements in that dimension. The dimension of basic education also shows a favorable evolution, except in Latin America and Central and Eastern Asia, where the situation has significantly worsened. In contrast, women’s economic participation has changed little in all areas except in Africa, where there has been some improvement, and Central and Eastern Asia, where there has been a sharp deterioration.

Using a cross-country perspective, our results show that developed countries continue to rank first in the three dimensions. However, despite the fact that African countries remain in the worst positions in terms of reproductive health and basic education, they stand out for their favorable position in the dimension of economic participation. The main weakness of Latin America is related to reproductive health and, despite having one of the best positions in basic education, it is worth mentioning that there has been a sharp deterioration in recent years. Asia occupies the worst positions in the dimension of economic participation, especially Western and Southern Asia. However, Southern Asian countries occupy similar positions to developed countries regarding reproductive health and basic education. Moreover, although Central and Eastern Asia countries occupy good positions in all dimensions of women’s empowerment, women’s economic participation has significantly deteriorated in recent years.

As regards the evolution of the three dimensions across countries based on cluster analysis, we have identified four groups of countries with similar behavior patterns concerning the specific dimension of women’s empowerment: first, a group of countries with high levels in the dimensions of reproductive health and basic education but low levels in economic participation; second, a group of countries with high levels in the dimensions of reproductive health and economic participation that should pay attention to education; third, a group of countries with medium levels in the three dimensions of women’s empowerment, especially in terms of reproductive health and economic participation; and fourth, a group of countries with low levels across all dimensions, especially with regard to reproductive health and basic education.

Finally, our results suggest that advancements in women’s empowerment improve countries’ level of development. In particular, the dimension of reproductive health exerts the highest impact on development and poverty reduction, followed by the dimension of basic education. In addition, improving women’s empowerment in countries that exhibit the lowest levels of empowerment would exert a significant effect on their human development and poverty levels, but does not seem to have an impact on per capita income or inequality.

Notes

1

The selected indicators related to the employment of women refer to women’s employment in the labor market. Hence, unpaid work (mainly domestic work and care activities) is not considered, resulting in the undervaluation of women’s participation in the economy (see Durán Heras, 2012, 2018; García Sáinz, 2002; Lobera & García Sáinz, 2014).

2

Principal components and Varimax rotation are the most common procedures in factor analysis. See Hair et al. (2014) for a detailed description of the statistical procedure.

3

The BIC method is a widely used grouping criteria in cluster analysis. The BIC has been applied successfully to the problem of determining the number of components in a model and for deciding which among two or more partitions most closely matches the data for a given model (see Fraley & Raftery, 1998).

4

See the technical notes of the GII calculation for a more detailed explanation: http://hdr.undp.org/sites/default/files/hdr2018_technical_notes.pdf.

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Appendix A

Table A1.

EFA for 2015.

2015
Factor Eigenvalues Explained Variance (%) Cumulative Explained Variance (%)
1 6.56 38.58 38.58
2 3.99 23.50 62.08
3 1.95 11.46 73.54
4 1.47 8.64 82.18
5 0.98 5.74 87.92
6 0.62 3.66 91.59
7 0.59 3.45 95.04
8 0.48 2.80 97.83
9 0.17 1.01 98.85
10 0.10 0.61 99.46
11 0.06 0.35 99.81
12 0.02 0.14 99.96
13 0.01 0.03 99.99
14 0.00 0.01 100.00
15 0.00 0.00 100.00
16 0.00 0.00 100.00
17 0.00 0.00 100.00

Note: Principal component factors method.

Table A2.

Rotated Factor Loadings (Varimax Rotation) for 2015.

Variables Reproductive Health Labor Market Participation in Production Activities Basic Education Access to Financial Resources Public Life
Adolescent birth rate −0.30 0.77 0.20 0.40 −0.25 0.07
Antenatal care coverage (at least one visit) 0.61 −0.27 0.01 0.67 −0.08 0.01
Proportion of births attended by skilled health personnel 0.78 −0.34 0.02 0.44 0.11 0.03
Contraceptive prevalence (any method) 0.89 0.02 0.08 0.23 0.23 −0.17
Female share of non-agriculture employment −0.08 0.12 0.84 0.14 0.40 −0.06
Female share of employment in senior and middle management 0.30 0.08 0.81 0.38 −0.10 0.20
Women with account at financial institution or with mobile-money-service provider 0.39 −0.01 0.03 0.25 0.74 0.11
Estimated gross national income per capita (male minus female) 0.52 0.00 0.75 0.06 0.11 0.11
Maternal mortality ratio 0.86 0.44 0.02 −0.15 −0.11 0.03
Female share of seats in parliament −0.31 0.02 0.03 0.23 0.10 0.85
Total unemployment rate (Female to male ratio) −0.25 0.84 0.08 −0.36 0.10 0.14
Youth unemployment rate (Female to male ratio) −0.03 0.83 0.46 −0.21 0.10 −0.05
Unmet need for family planning 0.91 0.19 −0.01 0.04 −0.14 0.24
Adjusted net enrollment rate in primary education (female) 0.28 −0.17 0.14 0.72 0.46 0.27
Gross enrollment ratio in secondary education (female) 0.42 0.69 0.40 0.00 0.10 0.29
Adjusted net enrollment rate in primary education (male minus female) −0.11 0.00 −0.31 0.86 −0.20 −0.16
Gross enrollment ratio in secondary education (male minus female) −0.42 −0.42 0.68 −0.23 0.16 −0.13

Note: Shaded cells indicate highest correlations.

Table A3.

CFA.

2000 2005 2010 2015
Eigenvalue Explained Variance (%) Eigenvalue Explained Variance (%) Eigenvalue Explained Variance (%) Eigenvalue Explained Variance (%)
Factor analysis for reproductive health
1 2.39 79.63 2.34 77.88 2.43 81.12 2.45 81.77
2 0.36 11.90 0.42 13.93 0.33 11.08 0.34 11.49
3 0.25 8.46 0.25 8.19 0.23 7.79 0.20 6.74
Factor analysis for economic participation
1 2.39 59.66 2.23 55.72 2.47 61.75 2.45 61.17
2 0.88 21.90 0.91 22.67 0.78 19.56 0.78 19.41
3 0.65 16.14 0.72 17.94 0.65 16.34 0.65 16.25
4 0.09 2.29 0.15 3.66 0.09 2.35 0.13 3.16
Factor analysis for basic education
1 1.62 80.97 1.58 78.92 1.60 79.80 1.54 76.76
2 0.38 19.03 0.42 21.08 0.40 20.20 0.46 23.24

Note: Principal component factors method.

Appendix B. Top 10 Positions According to Country Values in the MIWE by Dimensions (Reproductive Health, Economic Participation, and Basic Education)

Table B1.

Reproductive Health.

Position Level Percent Change
Country Region Country Region
1 Singapore Southeast Asia Qatar Western Asia
2 China Eastern Asia Saudi Arabia Western Asia
3 Kuwait Western Asia Kuwait Western Asia
4 Qatar Western Asia Iran (Islamic Republic of) Southern Asia
5 Oman Western Asia Armenia Central Asia
6 Saudi Arabia Western Asia Oman Western Asia
7 Bahrain Western Asia United Arab Emirates Western Asia
8 Brunei Southeast Asia Mauritius Sub-Saharan Africa
9 Malaysia Southeast Asia Kazakhstan Central Asia
10 Uzbekistan Central Asia Bhutan Southern Asia

Note: Shaded cells represent those countries that obtanied the top positions, in both levels and percent change, according to values in the MIWE

Table B2.

Economic Participation.

Position Level Percent Change
Country Region Country Region
1 Sierra Leone Sub-Saharan Africa Zimbabwe Sub-Saharan Africa
2 Togo Sub-Saharan Africa Benin Sub-Saharan Africa
3 Guinea Sub-Saharan Africa Mali Sub-Saharan Africa
4 Cambodia Southeast Asia El Salvador Latin America and the Caribbean
5 Zimbabwe Sub-Saharan Africa Nepal Southern Asia
6 Lao People’s Democratic Republic Southeast Asia Madagascar Sub-Saharan Africa
7 South Sudan Sub-Saharan Africa Nigeria Sub-Saharan Africa
8 Madagascar Sub-Saharan Africa Armenia Central Asia
9 Burundi Sub-Saharan Africa Guinea Sub-Saharan Africa
10 Nepal Southern Asia South Sudan Sub-Saharan Africa

Note: Shaded cells represent those countries that obtanied the top positions, in both levels and percent change, according to values in the MIWE

Table B3.

Basic Education.

Position Level Percent Change
Country Region Country Region
1 Kuwait Western Asia Kuwait Western Asia
2 Suriname Latin America and the Caribbean Suriname Latin America and the Caribbean
3 Mexico Latin America and the Caribbean Mexico Latin America and the Caribbean
4 Ecuador Latin America and the Caribbean Philippines Southeast Asia
5 Qatar Western Asia Oman Western Asia
6 Philippines Southeast Asia Guyana Latin America and the Caribbean
7 Mauritius Sub-Saharan Africa Indonesia Southeast Asia
8 India Southern Asia United Arab Emirates Western Asia
9 Guyana Latin America and the Caribbean Saudi Arabia Western Asia
10 Kazakhstan Central Asia Kenya Sub-Saharan Africa

Note: Shaded cells represent those countries that obtanied the top positions, in both levels and percent change, according to values in the MIWE

Appendix C. Worst 10 Positions According to Country Values in the MIWE by Dimensions (Reproductive Health, Economic Participation, and Basic Education)

Table C1.

Reproductive Health.

Position Level Percent Change
Country Region Country Region
1 Chad Sub-Saharan Africa Singapore Southeast Asia
2 Lao People’s Democratic Republic Southeast Asia Lao People’s Democratic Republic Southeast Asia
3 Bangladesh Southern Asia Malawi Sub-Saharan Africa
4 Togo Sub-Saharan Africa Guinea-Bissau Sub-Saharan Africa
5 Afghanistan Southern Asia Congo (Democratic Republic of the) Sub-Saharan Africa
6 Malawi Sub-Saharan Africa Suriname Latin America and the Caribbean
7 Congo (Democratic Republic of the) Sub-Saharan Africa Togo Sub-Saharan Africa
8 Guinea-Bissau Sub-Saharan Africa Chad Sub-Saharan Africa
9 Gambia Sub-Saharan Africa Sierra Leone Sub-Saharan Africa
10 Sierra Leone Sub-Saharan Africa Gambia Sub-Saharan Africa

Note: Shaded cells represent those countries that obtained the worst positions, in both levels and percent change, according to values in the MIWE

Table C2.

Economic Participation.

Position Level Percent Change
Country Region Country Region
1 Qatar Western Asia Mongolia Eastern Asia
2 Suriname Latin America and the Caribbean Tajikistan Central Asia
3 Egypt Northern Africa Uganda Sub-Saharan Africa
4 Iraq Western Asia Kazakhstan Central Asia
5 Syrian Arab Republic Western Asia Cuba Latin America and the Caribbean
6 Kuwait Western Asia Togo Sub-Saharan Africa
7 Oman Western Asia Guatemala Latin America and the Caribbean
8 United Arab Emirates Western Asia Turkmenistan Central Asia
9 Saudi Arabia Western Asia Nicaragua Latin America and the Caribbean
10 Bahrain Western Asia Azerbaijan Central Asia
Table C3.

Basic Education.

Position Level Percent Change
Country Region Country Region
1 Angola Sub-Saharan Africa Bahrain Western Asia
2 Equatorial Guinea Sub-Saharan Africa Honduras Latin America and the Caribbean
3 Côte d’Ivoire Sub-Saharan Africa Syrian Arab Republic Western Asia
4 Guinea Sub-Saharan Africa Mongolia Eastern Asia
5 Mali Sub-Saharan Africa Bolivia (Plurinational State of) Latin America and the Caribbean
6 Pakistan Southern Asia Qatar Western Asia
7 Liberia Sub-Saharan Africa Namibia Sub-Saharan Africa
8 Eritrea Sub-Saharan Africa El Salvador Latin America and the Caribbean
9 Niger Sub-Saharan Africa Armenia Central Asia
10 Central African Republic Sub-Saharan Africa Peru Latin America and the Caribbean