literature review on gender inequality

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Research Article

Twenty years of gender equality research: A scoping review based on a new semantic indicator

Contributed equally to this work with: Paola Belingheri, Filippo Chiarello, Andrea Fronzetti Colladon, Paola Rovelli

Roles Conceptualization, Formal analysis, Funding acquisition, Visualization, Writing – original draft, Writing – review & editing

Affiliation Dipartimento di Ingegneria dell’Energia, dei Sistemi, del Territorio e delle Costruzioni, Università degli Studi di Pisa, Largo L. Lazzarino, Pisa, Italy

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Methodology, Visualization, Writing – original draft, Writing – review & editing

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliations Department of Engineering, University of Perugia, Perugia, Italy, Department of Management, Kozminski University, Warsaw, Poland

Roles Conceptualization, Formal analysis, Funding acquisition, Writing – original draft, Writing – review & editing

Affiliation Faculty of Economics and Management, Centre for Family Business Management, Free University of Bozen-Bolzano, Bozen-Bolzano, Italy

• Paola Belingheri, 
• Filippo Chiarello, 
• Andrea Fronzetti Colladon, 
• Paola Rovelli

• Published: September 21, 2021
• https://doi.org/10.1371/journal.pone.0256474
• Reader Comments

9 Nov 2021: The PLOS ONE Staff (2021) Correction: Twenty years of gender equality research: A scoping review based on a new semantic indicator. PLOS ONE 16(11): e0259930. https://doi.org/10.1371/journal.pone.0259930 View correction

Gender equality is a major problem that places women at a disadvantage thereby stymieing economic growth and societal advancement. In the last two decades, extensive research has been conducted on gender related issues, studying both their antecedents and consequences. However, existing literature reviews fail to provide a comprehensive and clear picture of what has been studied so far, which could guide scholars in their future research. Our paper offers a scoping review of a large portion of the research that has been published over the last 22 years, on gender equality and related issues, with a specific focus on business and economics studies. Combining innovative methods drawn from both network analysis and text mining, we provide a synthesis of 15,465 scientific articles. We identify 27 main research topics, we measure their relevance from a semantic point of view and the relationships among them, highlighting the importance of each topic in the overall gender discourse. We find that prominent research topics mostly relate to women in the workforce–e.g., concerning compensation, role, education, decision-making and career progression. However, some of them are losing momentum, and some other research trends–for example related to female entrepreneurship, leadership and participation in the board of directors–are on the rise. Besides introducing a novel methodology to review broad literature streams, our paper offers a map of the main gender-research trends and presents the most popular and the emerging themes, as well as their intersections, outlining important avenues for future research.

Citation: Belingheri P, Chiarello F, Fronzetti Colladon A, Rovelli P (2021) Twenty years of gender equality research: A scoping review based on a new semantic indicator. PLoS ONE 16(9): e0256474. https://doi.org/10.1371/journal.pone.0256474

Editor: Elisa Ughetto, Politecnico di Torino, ITALY

Received: June 25, 2021; Accepted: August 6, 2021; Published: September 21, 2021

Copyright: © 2021 Belingheri et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its supporting information files. The only exception is the text of the abstracts (over 15,000) that we have downloaded from Scopus. These abstracts can be retrieved from Scopus, but we do not have permission to redistribute them.

Funding: P.B and F.C.: Grant of the Department of Energy, Systems, Territory and Construction of the University of Pisa (DESTEC) for the project “Measuring Gender Bias with Semantic Analysis: The Development of an Assessment Tool and its Application in the European Space Industry. P.B., F.C., A.F.C., P.R.: Grant of the Italian Association of Management Engineering (AiIG), “Misure di sostegno ai soci giovani AiIG” 2020, for the project “Gender Equality Through Data Intelligence (GEDI)”. F.C.: EU project ASSETs+ Project (Alliance for Strategic Skills addressing Emerging Technologies in Defence) EAC/A03/2018 - Erasmus+ programme, Sector Skills Alliances, Lot 3: Sector Skills Alliance for implementing a new strategic approach (Blueprint) to sectoral cooperation on skills G.A. NUMBER: 612678-EPP-1-2019-1-IT-EPPKA2-SSA-B.

Competing interests: The authors have declared that no competing interests exist.

Introduction

The persistent gender inequalities that currently exist across the developed and developing world are receiving increasing attention from economists, policymakers, and the general public [e.g., 1 – 3 ]. Economic studies have indicated that women’s education and entry into the workforce contributes to social and economic well-being [e.g., 4 , 5 ], while their exclusion from the labor market and from managerial positions has an impact on overall labor productivity and income per capita [ 6 , 7 ]. The United Nations selected gender equality, with an emphasis on female education, as part of the Millennium Development Goals [ 8 ], and gender equality at-large as one of the 17 Sustainable Development Goals (SDGs) to be achieved by 2030 [ 9 ]. These latter objectives involve not only developing nations, but rather all countries, to achieve economic, social and environmental well-being.

As is the case with many SDGs, gender equality is still far from being achieved and persists across education, access to opportunities, or presence in decision-making positions [ 7 , 10 , 11 ]. As we enter the last decade for the SDGs’ implementation, and while we are battling a global health pandemic, effective and efficient action becomes paramount to reach this ambitious goal.

Scholars have dedicated a massive effort towards understanding gender equality, its determinants, its consequences for women and society, and the appropriate actions and policies to advance women’s equality. Many topics have been covered, ranging from women’s education and human capital [ 12 , 13 ] and their role in society [e.g., 14 , 15 ], to their appointment in firms’ top ranked positions [e.g., 16 , 17 ] and performance implications [e.g., 18 , 19 ]. Despite some attempts, extant literature reviews provide a narrow view on these issues, restricted to specific topics–e.g., female students’ presence in STEM fields [ 20 ], educational gender inequality [ 5 ], the gender pay gap [ 21 ], the glass ceiling effect [ 22 ], leadership [ 23 ], entrepreneurship [ 24 ], women’s presence on the board of directors [ 25 , 26 ], diversity management [ 27 ], gender stereotypes in advertisement [ 28 ], or specific professions [ 29 ]. A comprehensive view on gender-related research, taking stock of key findings and under-studied topics is thus lacking.

Extant literature has also highlighted that gender issues, and their economic and social ramifications, are complex topics that involve a large number of possible antecedents and outcomes [ 7 ]. Indeed, gender equality actions are most effective when implemented in unison with other SDGs (e.g., with SDG 8, see [ 30 ]) in a synergetic perspective [ 10 ]. Many bodies of literature (e.g., business, economics, development studies, sociology and psychology) approach the problem of achieving gender equality from different perspectives–often addressing specific and narrow aspects. This sometimes leads to a lack of clarity about how different issues, circumstances, and solutions may be related in precipitating or mitigating gender inequality or its effects. As the number of papers grows at an increasing pace, this issue is exacerbated and there is a need to step back and survey the body of gender equality literature as a whole. There is also a need to examine synergies between different topics and approaches, as well as gaps in our understanding of how different problems and solutions work together. Considering the important topic of women’s economic and social empowerment, this paper aims to fill this gap by answering the following research question: what are the most relevant findings in the literature on gender equality and how do they relate to each other ?

To do so, we conduct a scoping review [ 31 ], providing a synthesis of 15,465 articles dealing with gender equity related issues published in the last twenty-two years, covering both the periods of the MDGs and the SDGs (i.e., 2000 to mid 2021) in all the journals indexed in the Academic Journal Guide’s 2018 ranking of business and economics journals. Given the huge amount of research conducted on the topic, we adopt an innovative methodology, which relies on social network analysis and text mining. These techniques are increasingly adopted when surveying large bodies of text. Recently, they were applied to perform analysis of online gender communication differences [ 32 ] and gender behaviors in online technology communities [ 33 ], to identify and classify sexual harassment instances in academia [ 34 ], and to evaluate the gender inclusivity of disaster management policies [ 35 ].

Applied to the title, abstracts and keywords of the articles in our sample, this methodology allows us to identify a set of 27 recurrent topics within which we automatically classify the papers. Introducing additional novelty, by means of the Semantic Brand Score (SBS) indicator [ 36 ] and the SBS BI app [ 37 ], we assess the importance of each topic in the overall gender equality discourse and its relationships with the other topics, as well as trends over time, with a more accurate description than that offered by traditional literature reviews relying solely on the number of papers presented in each topic.

This methodology, applied to gender equality research spanning the past twenty-two years, enables two key contributions. First, we extract the main message that each document is conveying and how this is connected to other themes in literature, providing a rich picture of the topics that are at the center of the discourse, as well as of the emerging topics. Second, by examining the semantic relationship between topics and how tightly their discourses are linked, we can identify the key relationships and connections between different topics. This semi-automatic methodology is also highly reproducible with minimum effort.

This literature review is organized as follows. In the next section, we present how we selected relevant papers and how we analyzed them through text mining and social network analysis. We then illustrate the importance of 27 selected research topics, measured by means of the SBS indicator. In the results section, we present an overview of the literature based on the SBS results–followed by an in-depth narrative analysis of the top 10 topics (i.e., those with the highest SBS) and their connections. Subsequently, we highlight a series of under-studied connections between the topics where there is potential for future research. Through this analysis, we build a map of the main gender-research trends in the last twenty-two years–presenting the most popular themes. We conclude by highlighting key areas on which research should focused in the future.

Our aim is to map a broad topic, gender equality research, that has been approached through a host of different angles and through different disciplines. Scoping reviews are the most appropriate as they provide the freedom to map different themes and identify literature gaps, thereby guiding the recommendation of new research agendas [ 38 ].

Several practical approaches have been proposed to identify and assess the underlying topics of a specific field using big data [ 39 – 41 ], but many of them fail without proper paper retrieval and text preprocessing. This is specifically true for a research field such as the gender-related one, which comprises the work of scholars from different backgrounds. In this section, we illustrate a novel approach for the analysis of scientific (gender-related) papers that relies on methods and tools of social network analysis and text mining. Our procedure has four main steps: (1) data collection, (2) text preprocessing, (3) keywords extraction and classification, and (4) evaluation of semantic importance and image.

Data collection

In this study, we analyze 22 years of literature on gender-related research. Following established practice for scoping reviews [ 42 ], our data collection consisted of two main steps, which we summarize here below.

Firstly, we retrieved from the Scopus database all the articles written in English that contained the term “gender” in their title, abstract or keywords and were published in a journal listed in the Academic Journal Guide 2018 ranking of the Chartered Association of Business Schools (CABS) ( https://charteredabs.org/wp-content/uploads/2018/03/AJG2018-Methodology.pdf ), considering the time period from Jan 2000 to May 2021. We used this information considering that abstracts, titles and keywords represent the most informative part of a paper, while using the full-text would increase the signal-to-noise ratio for information extraction. Indeed, these textual elements already demonstrated to be reliable sources of information for the task of domain lexicon extraction [ 43 , 44 ]. We chose Scopus as source of literature because of its popularity, its update rate, and because it offers an API to ease the querying process. Indeed, while it does not allow to retrieve the full text of scientific articles, the Scopus API offers access to titles, abstracts, citation information and metadata for all its indexed scholarly journals. Moreover, we decided to focus on the journals listed in the AJG 2018 ranking because we were interested in reviewing business and economics related gender studies only. The AJG is indeed widely used by universities and business schools as a reference point for journal and research rigor and quality. This first step, executed in June 2021, returned more than 55,000 papers.

In the second step–because a look at the papers showed very sparse results, many of which were not in line with the topic of this literature review (e.g., papers dealing with health care or medical issues, where the word gender indicates the gender of the patients)–we applied further inclusion criteria to make the sample more focused on the topic of this literature review (i.e., women’s gender equality issues). Specifically, we only retained those papers mentioning, in their title and/or abstract, both gender-related keywords (e.g., daughter, female, mother) and keywords referring to bias and equality issues (e.g., equality, bias, diversity, inclusion). After text pre-processing (see next section), keywords were first identified from a frequency-weighted list of words found in the titles, abstracts and keywords in the initial list of papers, extracted through text mining (following the same approach as [ 43 ]). They were selected by two of the co-authors independently, following respectively a bottom up and a top-down approach. The bottom-up approach consisted of examining the words found in the frequency-weighted list and classifying those related to gender and equality. The top-down approach consisted in searching in the word list for notable gender and equality-related words. Table 1 reports the sets of keywords we considered, together with some examples of words that were used to search for their presence in the dataset (a full list is provided in the S1 Text ). At end of this second step, we obtained a final sample of 15,465 relevant papers.

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https://doi.org/10.1371/journal.pone.0256474.t001

Text processing and keyword extraction

Text preprocessing aims at structuring text into a form that can be analyzed by statistical models. In the present section, we describe the preprocessing steps we applied to paper titles and abstracts, which, as explained below, partially follow a standard text preprocessing pipeline [ 45 ]. These activities have been performed using the R package udpipe [ 46 ].

The first step is n-gram extraction (i.e., a sequence of words from a given text sample) to identify which n-grams are important in the analysis, since domain-specific lexicons are often composed by bi-grams and tri-grams [ 47 ]. Multi-word extraction is usually implemented with statistics and linguistic rules, thus using the statistical properties of n-grams or machine learning approaches [ 48 ]. However, for the present paper, we used Scopus metadata in order to have a more effective and efficient n-grams collection approach [ 49 ]. We used the keywords of each paper in order to tag n-grams with their associated keywords automatically. Using this greedy approach, it was possible to collect all the keywords listed by the authors of the papers. From this list, we extracted only keywords composed by two, three and four words, we removed all the acronyms and rare keywords (i.e., appearing in less than 1% of papers), and we clustered keywords showing a high orthographic similarity–measured using a Levenshtein distance [ 50 ] lower than 2, considering these groups of keywords as representing same concepts, but expressed with different spelling. After tagging the n-grams in the abstracts, we followed a common data preparation pipeline that consists of the following steps: (i) tokenization, that splits the text into tokens (i.e., single words and previously tagged multi-words); (ii) removal of stop-words (i.e. those words that add little meaning to the text, usually being very common and short functional words–such as “and”, “or”, or “of”); (iii) parts-of-speech tagging, that is providing information concerning the morphological role of a word and its morphosyntactic context (e.g., if the token is a determiner, the next token is a noun or an adjective with very high confidence, [ 51 ]); and (iv) lemmatization, which consists in substituting each word with its dictionary form (or lemma). The output of the latter step allows grouping together the inflected forms of a word. For example, the verbs “am”, “are”, and “is” have the shared lemma “be”, or the nouns “cat” and “cats” both share the lemma “cat”. We preferred lemmatization over stemming [ 52 ] in order to obtain more interpretable results.

In addition, we identified a further set of keywords (with respect to those listed in the “keywords” field) by applying a series of automatic words unification and removal steps, as suggested in past research [ 53 , 54 ]. We removed: sparse terms (i.e., occurring in less than 0.1% of all documents), common terms (i.e., occurring in more than 10% of all documents) and retained only nouns and adjectives. It is relevant to notice that no document was lost due to these steps. We then used the TF-IDF function [ 55 ] to produce a new list of keywords. We additionally tested other approaches for the identification and clustering of keywords–such as TextRank [ 56 ] or Latent Dirichlet Allocation [ 57 ]–without obtaining more informative results.

Classification of research topics

To guide the literature analysis, two experts met regularly to examine the sample of collected papers and to identify the main topics and trends in gender research. Initially, they conducted brainstorming sessions on the topics they expected to find, due to their knowledge of the literature. This led to an initial list of topics. Subsequently, the experts worked independently, also supported by the keywords in paper titles and abstracts extracted with the procedure described above.

Considering all this information, each expert identified and clustered relevant keywords into topics. At the end of the process, the two assignments were compared and exhibited a 92% agreement. Another meeting was held to discuss discordant cases and reach a consensus. This resulted in a list of 27 topics, briefly introduced in Table 2 and subsequently detailed in the following sections.

https://doi.org/10.1371/journal.pone.0256474.t002

Evaluation of semantic importance

Working on the lemmatized corpus of the 15,465 papers included in our sample, we proceeded with the evaluation of semantic importance trends for each topic and with the analysis of their connections and prevalent textual associations. To this aim, we used the Semantic Brand Score indicator [ 36 ], calculated through the SBS BI webapp [ 37 ] that also produced a brand image report for each topic. For this study we relied on the computing resources of the ENEA/CRESCO infrastructure [ 58 ].

The Semantic Brand Score (SBS) is a measure of semantic importance that combines methods of social network analysis and text mining. It is usually applied for the analysis of (big) textual data to evaluate the importance of one or more brands, names, words, or sets of keywords [ 36 ]. Indeed, the concept of “brand” is intended in a flexible way and goes beyond products or commercial brands. In this study, we evaluate the SBS time-trends of the keywords defining the research topics discussed in the previous section. Semantic importance comprises the three dimensions of topic prevalence, diversity and connectivity. Prevalence measures how frequently a research topic is used in the discourse. The more a topic is mentioned by scientific articles, the more the research community will be aware of it, with possible increase of future studies; this construct is partly related to that of brand awareness [ 59 ]. This effect is even stronger, considering that we are analyzing the title, abstract and keywords of the papers, i.e. the parts that have the highest visibility. A very important characteristic of the SBS is that it considers the relationships among words in a text. Topic importance is not just a matter of how frequently a topic is mentioned, but also of the associations a topic has in the text. Specifically, texts are transformed into networks of co-occurring words, and relationships are studied through social network analysis [ 60 ]. This step is necessary to calculate the other two dimensions of our semantic importance indicator. Accordingly, a social network of words is generated for each time period considered in the analysis–i.e., a graph made of n nodes (words) and E edges weighted by co-occurrence frequency, with W being the set of edge weights. The keywords representing each topic were clustered into single nodes.

The construct of diversity relates to that of brand image [ 59 ], in the sense that it considers the richness and distinctiveness of textual (topic) associations. Considering the above-mentioned networks, we calculated diversity using the distinctiveness centrality metric–as in the formula presented by Fronzetti Colladon and Naldi [ 61 ].

Lastly, connectivity was measured as the weighted betweenness centrality [ 62 , 63 ] of each research topic node. We used the formula presented by Wasserman and Faust [ 60 ]. The dimension of connectivity represents the “brokerage power” of each research topic–i.e., how much it can serve as a bridge to connect other terms (and ultimately topics) in the discourse [ 36 ].

The SBS is the final composite indicator obtained by summing the standardized scores of prevalence, diversity and connectivity. Standardization was carried out considering all the words in the corpus, for each specific timeframe.

This methodology, applied to a large and heterogeneous body of text, enables to automatically identify two important sets of information that add value to the literature review. Firstly, the relevance of each topic in literature is measured through a composite indicator of semantic importance, rather than simply looking at word frequencies. This provides a much richer picture of the topics that are at the center of the discourse, as well as of the topics that are emerging in the literature. Secondly, it enables to examine the extent of the semantic relationship between topics, looking at how tightly their discourses are linked. In a field such as gender equality, where many topics are closely linked to each other and present overlaps in issues and solutions, this methodology offers a novel perspective with respect to traditional literature reviews. In addition, it ensures reproducibility over time and the possibility to semi-automatically update the analysis, as new papers become available.

Overview of main topics

In terms of descriptive textual statistics, our corpus is made of 15,465 text documents, consisting of a total of 2,685,893 lemmatized tokens (words) and 32,279 types. As a result, the type-token ratio is 1.2%. The number of hapaxes is 12,141, with a hapax-token ratio of 37.61%.

Fig 1 shows the list of 27 topics by decreasing SBS. The most researched topic is compensation , exceeding all others in prevalence, diversity, and connectivity. This means it is not only mentioned more often than other topics, but it is also connected to a greater number of other topics and is central to the discourse on gender equality. The next four topics are, in order of SBS, role , education , decision-making , and career progression . These topics, except for education , all concern women in the workforce. Between these first five topics and the following ones there is a clear drop in SBS scores. In particular, the topics that follow have a lower connectivity than the first five. They are hiring , performance , behavior , organization , and human capital . Again, except for behavior and human capital , the other three topics are purely related to women in the workforce. After another drop-off, the following topics deal prevalently with women in society. This trend highlights that research on gender in business journals has so far mainly paid attention to the conditions that women experience in business contexts, while also devoting some attention to women in society.

https://doi.org/10.1371/journal.pone.0256474.g001

Fig 2 shows the SBS time series of the top 10 topics. While there has been a general increase in the number of Scopus-indexed publications in the last decade, we notice that some SBS trends remain steady, or even decrease. In particular, we observe that the main topic of the last twenty-two years, compensation , is losing momentum. Since 2016, it has been surpassed by decision-making , education and role , which may indicate that literature is increasingly attempting to identify root causes of compensation inequalities. Moreover, in the last two years, the topics of hiring , performance , and organization are experiencing the largest importance increase.

https://doi.org/10.1371/journal.pone.0256474.g002

Fig 3 shows the SBS time trends of the remaining 17 topics (i.e., those not in the top 10). As we can see from the graph, there are some that maintain a steady trend–such as reputation , management , networks and governance , which also seem to have little importance. More relevant topics with average stationary trends (except for the last two years) are culture , family , and parenting . The feminine topic is among the most important here, and one of those that exhibit the larger variations over time (similarly to leadership ). On the other hand, the are some topics that, even if not among the most important, show increasing SBS trends; therefore, they could be considered as emerging topics and could become popular in the near future. These are entrepreneurship , leadership , board of directors , and sustainability . These emerging topics are also interesting to anticipate future trends in gender equality research that are conducive to overall equality in society.

https://doi.org/10.1371/journal.pone.0256474.g003

In addition to the SBS score of the different topics, the network of terms they are associated to enables to gauge the extent to which their images (textual associations) overlap or differ ( Fig 4 ).

https://doi.org/10.1371/journal.pone.0256474.g004

There is a central cluster of topics with high similarity, which are all connected with women in the workforce. The cluster includes topics such as organization , decision-making , performance , hiring , human capital , education and compensation . In addition, the topic of well-being is found within this cluster, suggesting that women’s equality in the workforce is associated to well-being considerations. The emerging topics of entrepreneurship and leadership are also closely connected with each other, possibly implying that leadership is a much-researched quality in female entrepreneurship. Topics that are relatively more distant include personality , politics , feminine , empowerment , management , board of directors , reputation , governance , parenting , masculine and network .

The following sections describe the top 10 topics and their main associations in literature (see Table 3 ), while providing a brief overview of the emerging topics.

https://doi.org/10.1371/journal.pone.0256474.t003

Compensation.

The topic of compensation is related to the topics of role , hiring , education and career progression , however, also sees a very high association with the words gap and inequality . Indeed, a well-known debate in degrowth economics centers around whether and how to adequately compensate women for their childbearing, childrearing, caregiver and household work [e.g., 30 ].

Even in paid work, women continue being offered lower compensations than their male counterparts who have the same job or cover the same role [ 64 – 67 ]. This severe inequality has been widely studied by scholars over the last twenty-two years. Dealing with this topic, some specific roles have been addressed. Specifically, research highlighted differences in compensation between female and male CEOs [e.g., 68 ], top executives [e.g., 69 ], and boards’ directors [e.g., 70 ]. Scholars investigated the determinants of these gaps, such as the gender composition of the board [e.g., 71 – 73 ] or women’s individual characteristics [e.g., 71 , 74 ].

Among these individual characteristics, education plays a relevant role [ 75 ]. Education is indeed presented as the solution for women, not only to achieve top executive roles, but also to reduce wage inequality [e.g., 76 , 77 ]. Past research has highlighted education influences on gender wage gaps, specifically referring to gender differences in skills [e.g., 78 ], college majors [e.g., 79 ], and college selectivity [e.g., 80 ].

Finally, the wage gap issue is strictly interrelated with hiring –e.g., looking at whether being a mother affects hiring and compensation [e.g., 65 , 81 ] or relating compensation to unemployment [e.g., 82 ]–and career progression –for instance looking at meritocracy [ 83 , 84 ] or the characteristics of the boss for whom women work [e.g., 85 ].

The roles covered by women have been deeply investigated. Scholars have focused on the role of women in their families and the society as a whole [e.g., 14 , 15 ], and, more widely, in business contexts [e.g., 18 , 81 ]. Indeed, despite still lagging behind their male counterparts [e.g., 86 , 87 ], in the last decade there has been an increase in top ranked positions achieved by women [e.g., 88 , 89 ]. Following this phenomenon, scholars have posed greater attention towards the presence of women in the board of directors [e.g., 16 , 18 , 90 , 91 ], given the increasing pressure to appoint female directors that firms, especially listed ones, have experienced. Other scholars have focused on the presence of women covering the role of CEO [e.g., 17 , 92 ] or being part of the top management team [e.g., 93 ]. Irrespectively of the level of analysis, all these studies tried to uncover the antecedents of women’s presence among top managers [e.g., 92 , 94 ] and the consequences of having a them involved in the firm’s decision-making –e.g., on performance [e.g., 19 , 95 , 96 ], risk [e.g., 97 , 98 ], and corporate social responsibility [e.g., 99 , 100 ].

Besides studying the difficulties and discriminations faced by women in getting a job [ 81 , 101 ], and, more specifically in the hiring , appointment, or career progression to these apical roles [e.g., 70 , 83 ], the majority of research of women’s roles dealt with compensation issues. Specifically, scholars highlight the pay-gap that still exists between women and men, both in general [e.g., 64 , 65 ], as well as referring to boards’ directors [e.g., 70 , 102 ], CEOs and executives [e.g., 69 , 103 , 104 ].

Finally, other scholars focused on the behavior of women when dealing with business. In this sense, particular attention has been paid to leadership and entrepreneurial behaviors. The former quite overlaps with dealing with the roles mentioned above, but also includes aspects such as leaders being stereotyped as masculine [e.g., 105 ], the need for greater exposure to female leaders to reduce biases [e.g., 106 ], or female leaders acting as queen bees [e.g., 107 ]. Regarding entrepreneurship , scholars mainly investigated women’s entrepreneurial entry [e.g., 108 , 109 ], differences between female and male entrepreneurs in the evaluations and funding received from investors [e.g., 110 , 111 ], and their performance gap [e.g., 112 , 113 ].

Education has long been recognized as key to social advancement and economic stability [ 114 ], for job progression and also a barrier to gender equality, especially in STEM-related fields. Research on education and gender equality is mostly linked with the topics of compensation , human capital , career progression , hiring , parenting and decision-making .

Education contributes to a higher human capital [ 115 ] and constitutes an investment on the part of women towards their future. In this context, literature points to the gender gap in educational attainment, and the consequences for women from a social, economic, personal and professional standpoint. Women are found to have less access to formal education and information, especially in emerging countries, which in turn may cause them to lose social and economic opportunities [e.g., 12 , 116 – 119 ]. Education in local and rural communities is also paramount to communicate the benefits of female empowerment , contributing to overall societal well-being [e.g., 120 ].

Once women access education, the image they have of the world and their place in society (i.e., habitus) affects their education performance [ 13 ] and is passed on to their children. These situations reinforce gender stereotypes, which become self-fulfilling prophecies that may negatively affect female students’ performance by lowering their confidence and heightening their anxiety [ 121 , 122 ]. Besides formal education, also the information that women are exposed to on a daily basis contributes to their human capital . Digital inequalities, for instance, stems from men spending more time online and acquiring higher digital skills than women [ 123 ].

Education is also a factor that should boost employability of candidates and thus hiring , career progression and compensation , however the relationship between these factors is not straightforward [ 115 ]. First, educational choices ( decision-making ) are influenced by variables such as self-efficacy and the presence of barriers, irrespectively of the career opportunities they offer, especially in STEM [ 124 ]. This brings additional difficulties to women’s enrollment and persistence in scientific and technical fields of study due to stereotypes and biases [ 125 , 126 ]. Moreover, access to education does not automatically translate into job opportunities for women and minority groups [ 127 , 128 ] or into female access to managerial positions [ 129 ].

Finally, parenting is reported as an antecedent of education [e.g., 130 ], with much of the literature focusing on the role of parents’ education on the opportunities afforded to children to enroll in education [ 131 – 134 ] and the role of parenting in their offspring’s perception of study fields and attitudes towards learning [ 135 – 138 ]. Parental education is also a predictor of the other related topics, namely human capital and compensation [ 139 ].

Decision-making.

This literature mainly points to the fact that women are thought to make decisions differently than men. Women have indeed different priorities, such as they care more about people’s well-being, working with people or helping others, rather than maximizing their personal (or their firm’s) gain [ 140 ]. In other words, women typically present more communal than agentic behaviors, which are instead more frequent among men [ 141 ]. These different attitude, behavior and preferences in turn affect the decisions they make [e.g., 142 ] and the decision-making of the firm in which they work [e.g., 143 ].

At the individual level, gender affects, for instance, career aspirations [e.g., 144 ] and choices [e.g., 142 , 145 ], or the decision of creating a venture [e.g., 108 , 109 , 146 ]. Moreover, in everyday life, women and men make different decisions regarding partners [e.g., 147 ], childcare [e.g., 148 ], education [e.g., 149 ], attention to the environment [e.g., 150 ] and politics [e.g., 151 ].

At the firm level, scholars highlighted, for example, how the presence of women in the board affects corporate decisions [e.g., 152 , 153 ], that female CEOs are more conservative in accounting decisions [e.g., 154 ], or that female CFOs tend to make more conservative decisions regarding the firm’s financial reporting [e.g., 155 ]. Nevertheless, firm level research also investigated decisions that, influenced by gender bias, affect women, such as those pertaining hiring [e.g., 156 , 157 ], compensation [e.g., 73 , 158 ], or the empowerment of women once appointed [ 159 ].

Career progression.

Once women have entered the workforce, the key aspect to achieve gender equality becomes career progression , including efforts toward overcoming the glass ceiling. Indeed, according to the SBS analysis, career progression is highly related to words such as work, social issues and equality. The topic with which it has the highest semantic overlap is role , followed by decision-making , hiring , education , compensation , leadership , human capital , and family .

Career progression implies an advancement in the hierarchical ladder of the firm, assigning managerial roles to women. Coherently, much of the literature has focused on identifying rationales for a greater female participation in the top management team and board of directors [e.g., 95 ] as well as the best criteria to ensure that the decision-makers promote the most valuable employees irrespectively of their individual characteristics, such as gender [e.g., 84 ]. The link between career progression , role and compensation is often provided in practice by performance appraisal exercises, frequently rooted in a culture of meritocracy that guides bonuses, salary increases and promotions. However, performance appraisals can actually mask gender-biased decisions where women are held to higher standards than their male colleagues [e.g., 83 , 84 , 95 , 160 , 161 ]. Women often have less opportunities to gain leadership experience and are less visible than their male colleagues, which constitute barriers to career advancement [e.g., 162 ]. Therefore, transparency and accountability, together with procedures that discourage discretionary choices, are paramount to achieve a fair career progression [e.g., 84 ], together with the relaxation of strict job boundaries in favor of cross-functional and self-directed tasks [e.g., 163 ].

In addition, a series of stereotypes about the type of leadership characteristics that are required for top management positions, which fit better with typical male and agentic attributes, are another key barrier to career advancement for women [e.g., 92 , 160 ].

Hiring is the entrance gateway for women into the workforce. Therefore, it is related to other workforce topics such as compensation , role , career progression , decision-making , human capital , performance , organization and education .

A first stream of literature focuses on the process leading up to candidates’ job applications, demonstrating that bias exists before positions are even opened, and it is perpetuated both by men and women through networking and gatekeeping practices [e.g., 164 , 165 ].

The hiring process itself is also subject to biases [ 166 ], for example gender-congruity bias that leads to men being preferred candidates in male-dominated sectors [e.g., 167 ], women being hired in positions with higher risk of failure [e.g., 168 ] and limited transparency and accountability afforded by written processes and procedures [e.g., 164 ] that all contribute to ascriptive inequality. In addition, providing incentives for evaluators to hire women may actually work to this end; however, this is not the case when supporting female candidates endangers higher-ranking male ones [ 169 ].

Another interesting perspective, instead, looks at top management teams’ composition and the effects on hiring practices, indicating that firms with more women in top management are less likely to lay off staff [e.g., 152 ].

Performance.

Several scholars posed their attention towards women’s performance, its consequences [e.g., 170 , 171 ] and the implications of having women in decision-making positions [e.g., 18 , 19 ].

At the individual level, research focused on differences in educational and academic performance between women and men, especially referring to the gender gap in STEM fields [e.g., 171 ]. The presence of stereotype threats–that is the expectation that the members of a social group (e.g., women) “must deal with the possibility of being judged or treated stereotypically, or of doing something that would confirm the stereotype” [ 172 ]–affects women’s interested in STEM [e.g., 173 ], as well as their cognitive ability tests, penalizing them [e.g., 174 ]. A stronger gender identification enhances this gap [e.g., 175 ], whereas mentoring and role models can be used as solutions to this problem [e.g., 121 ]. Despite the negative effect of stereotype threats on girls’ performance [ 176 ], female and male students perform equally in mathematics and related subjects [e.g., 177 ]. Moreover, while individuals’ performance at school and university generally affects their achievements and the field in which they end up working, evidence reveals that performance in math or other scientific subjects does not explain why fewer women enter STEM working fields; rather this gap depends on other aspects, such as culture, past working experiences, or self-efficacy [e.g., 170 ]. Finally, scholars have highlighted the penalization that women face for their positive performance, for instance when they succeed in traditionally male areas [e.g., 178 ]. This penalization is explained by the violation of gender-stereotypic prescriptions [e.g., 179 , 180 ], that is having women well performing in agentic areas, which are typical associated to men. Performance penalization can thus be overcome by clearly conveying communal characteristics and behaviors [ 178 ].

Evidence has been provided on how the involvement of women in boards of directors and decision-making positions affects firms’ performance. Nevertheless, results are mixed, with some studies showing positive effects on financial [ 19 , 181 , 182 ] and corporate social performance [ 99 , 182 , 183 ]. Other studies maintain a negative association [e.g., 18 ], and other again mixed [e.g., 184 ] or non-significant association [e.g., 185 ]. Also with respect to the presence of a female CEO, mixed results emerged so far, with some researches demonstrating a positive effect on firm’s performance [e.g., 96 , 186 ], while other obtaining only a limited evidence of this relationship [e.g., 103 ] or a negative one [e.g., 187 ].

Finally, some studies have investigated whether and how women’s performance affects their hiring [e.g., 101 ] and career progression [e.g., 83 , 160 ]. For instance, academic performance leads to different returns in hiring for women and men. Specifically, high-achieving men are called back significantly more often than high-achieving women, which are penalized when they have a major in mathematics; this result depends on employers’ gendered standards for applicants [e.g., 101 ]. Once appointed, performance ratings are more strongly related to promotions for women than men, and promoted women typically show higher past performance ratings than those of promoted men. This suggesting that women are subject to stricter standards for promotion [e.g., 160 ].

Behavioral aspects related to gender follow two main streams of literature. The first examines female personality and behavior in the workplace, and their alignment with cultural expectations or stereotypes [e.g., 188 ] as well as their impacts on equality. There is a common bias that depicts women as less agentic than males. Certain characteristics, such as those more congruent with male behaviors–e.g., self-promotion [e.g., 189 ], negotiation skills [e.g., 190 ] and general agentic behavior [e.g., 191 ]–, are less accepted in women. However, characteristics such as individualism in women have been found to promote greater gender equality in society [ 192 ]. In addition, behaviors such as display of emotions [e.g., 193 ], which are stereotypically female, work against women’s acceptance in the workplace, requiring women to carefully moderate their behavior to avoid exclusion. A counter-intuitive result is that women and minorities, which are more marginalized in the workplace, tend to be better problem-solvers in innovation competitions due to their different knowledge bases [ 194 ].

The other side of the coin is examined in a parallel literature stream on behavior towards women in the workplace. As a result of biases, prejudices and stereotypes, women may experience adverse behavior from their colleagues, such as incivility and harassment, which undermine their well-being [e.g., 195 , 196 ]. Biases that go beyond gender, such as for overweight people, are also more strongly applied to women [ 197 ].

Organization.

The role of women and gender bias in organizations has been studied from different perspectives, which mirror those presented in detail in the following sections. Specifically, most research highlighted the stereotypical view of leaders [e.g., 105 ] and the roles played by women within firms, for instance referring to presence in the board of directors [e.g., 18 , 90 , 91 ], appointment as CEOs [e.g., 16 ], or top executives [e.g., 93 ].

Scholars have investigated antecedents and consequences of the presence of women in these apical roles. On the one side they looked at hiring and career progression [e.g., 83 , 92 , 160 , 168 , 198 ], finding women typically disadvantaged with respect to their male counterparts. On the other side, they studied women’s leadership styles and influence on the firm’s decision-making [e.g., 152 , 154 , 155 , 199 ], with implications for performance [e.g., 18 , 19 , 96 ].

Human capital.

Human capital is a transverse topic that touches upon many different aspects of female gender equality. As such, it has the most associations with other topics, starting with education as mentioned above, with career-related topics such as role , decision-making , hiring , career progression , performance , compensation , leadership and organization . Another topic with which there is a close connection is behavior . In general, human capital is approached both from the education standpoint but also from the perspective of social capital.

The behavioral aspect in human capital comprises research related to gender differences for example in cultural and religious beliefs that influence women’s attitudes and perceptions towards STEM subjects [ 142 , 200 – 202 ], towards employment [ 203 ] or towards environmental issues [ 150 , 204 ]. These cultural differences also emerge in the context of globalization which may accelerate gender equality in the workforce [ 205 , 206 ]. Gender differences also appear in behaviors such as motivation [ 207 ], and in negotiation [ 190 ], and have repercussions on women’s decision-making related to their careers. The so-called gender equality paradox sees women in countries with lower gender equality more likely to pursue studies and careers in STEM fields, whereas the gap in STEM enrollment widens as countries achieve greater equality in society [ 171 ].

Career progression is modeled by literature as a choice-process where personal preferences, culture and decision-making affect the chosen path and the outcomes. Some literature highlights how women tend to self-select into different professions than men, often due to stereotypes rather than actual ability to perform in these professions [ 142 , 144 ]. These stereotypes also affect the perceptions of female performance or the amount of human capital required to equal male performance [ 110 , 193 , 208 ], particularly for mothers [ 81 ]. It is therefore often assumed that women are better suited to less visible and less leadership -oriented roles [ 209 ]. Women also express differing preferences towards work-family balance, which affect whether and how they pursue human capital gains [ 210 ], and ultimately their career progression and salary .

On the other hand, men are often unaware of gendered processes and behaviors that they carry forward in their interactions and decision-making [ 211 , 212 ]. Therefore, initiatives aimed at increasing managers’ human capital –by raising awareness of gender disparities in their organizations and engaging them in diversity promotion–are essential steps to counter gender bias and segregation [ 213 ].

Emerging topics: Leadership and entrepreneurship

Among the emerging topics, the most pervasive one is women reaching leadership positions in the workforce and in society. This is still a rare occurrence for two main types of factors, on the one hand, bias and discrimination make it harder for women to access leadership positions [e.g., 214 – 216 ], on the other hand, the competitive nature and high pressure associated with leadership positions, coupled with the lack of women currently represented, reduce women’s desire to achieve them [e.g., 209 , 217 ]. Women are more effective leaders when they have access to education, resources and a diverse environment with representation [e.g., 218 , 219 ].

One sector where there is potential for women to carve out a leadership role is entrepreneurship . Although at the start of the millennium the discourse on entrepreneurship was found to be “discriminatory, gender-biased, ethnocentrically determined and ideologically controlled” [ 220 ], an increasing body of literature is studying how to stimulate female entrepreneurship as an alternative pathway to wealth, leadership and empowerment [e.g., 221 ]. Many barriers exist for women to access entrepreneurship, including the institutional and legal environment, social and cultural factors, access to knowledge and resources, and individual behavior [e.g., 222 , 223 ]. Education has been found to raise women’s entrepreneurial intentions [e.g., 224 ], although this effect is smaller than for men [e.g., 109 ]. In addition, increasing self-efficacy and risk-taking behavior constitute important success factors [e.g., 225 ].

Finally, the topic of sustainability is worth mentioning, as it is the primary objective of the SDGs and is closely associated with societal well-being. As society grapples with the effects of climate change and increasing depletion of natural resources, a narrative has emerged on women and their greater link to the environment [ 226 ]. Studies in developed countries have found some support for women leaders’ attention to sustainability issues in firms [e.g., 227 – 229 ], and smaller resource consumption by women [ 230 ]. At the same time, women will likely be more affected by the consequences of climate change [e.g., 230 ] but often lack the decision-making power to influence local decision-making on resource management and environmental policies [e.g., 231 ].

Research gaps and conclusions

Research on gender equality has advanced rapidly in the past decades, with a steady increase in publications, both in mainstream topics related to women in education and the workforce, and in emerging topics. Through a novel approach combining methods of text mining and social network analysis, we examined a comprehensive body of literature comprising 15,465 papers published between 2000 and mid 2021 on topics related to gender equality. We identified a set of 27 topics addressed by the literature and examined their connections.

At the highest level of abstraction, it is worth noting that papers abound on the identification of issues related to gender inequalities and imbalances in the workforce and in society. Literature has thoroughly examined the (unconscious) biases, barriers, stereotypes, and discriminatory behaviors that women are facing as a result of their gender. Instead, there are much fewer papers that discuss or demonstrate effective solutions to overcome gender bias [e.g., 121 , 143 , 145 , 163 , 194 , 213 , 232 ]. This is partly due to the relative ease in studying the status quo, as opposed to studying changes in the status quo. However, we observed a shift in the more recent years towards solution seeking in this domain, which we strongly encourage future researchers to focus on. In the future, we may focus on collecting and mapping pro-active contributions to gender studies, using additional Natural Language Processing techniques, able to measure the sentiment of scientific papers [ 43 ].

All of the mainstream topics identified in our literature review are closely related, and there is a wealth of insights looking at the intersection between issues such as education and career progression or human capital and role . However, emerging topics are worthy of being furtherly explored. It would be interesting to see more work on the topic of female entrepreneurship , exploring aspects such as education , personality , governance , management and leadership . For instance, how can education support female entrepreneurship? How can self-efficacy and risk-taking behaviors be taught or enhanced? What are the differences in managerial and governance styles of female entrepreneurs? Which personality traits are associated with successful entrepreneurs? Which traits are preferred by venture capitalists and funding bodies?

The emerging topic of sustainability also deserves further attention, as our society struggles with climate change and its consequences. It would be interesting to see more research on the intersection between sustainability and entrepreneurship , looking at how female entrepreneurs are tackling sustainability issues, examining both their business models and their company governance . In addition, scholars are suggested to dig deeper into the relationship between family values and behaviors.

Moreover, it would be relevant to understand how women’s networks (social capital), or the composition and structure of social networks involving both women and men, enable them to increase their remuneration and reach top corporate positions, participate in key decision-making bodies, and have a voice in communities. Furthermore, the achievement of gender equality might significantly change firm networks and ecosystems, with important implications for their performance and survival.

Similarly, research at the nexus of (corporate) governance , career progression , compensation and female empowerment could yield useful insights–for example discussing how enterprises, institutions and countries are managed and the impact for women and other minorities. Are there specific governance structures that favor diversity and inclusion?

Lastly, we foresee an emerging stream of research pertaining how the spread of the COVID-19 pandemic challenged women, especially in the workforce, by making gender biases more evident.

For our analysis, we considered a set of 15,465 articles downloaded from the Scopus database (which is the largest abstract and citation database of peer-reviewed literature). As we were interested in reviewing business and economics related gender studies, we only considered those papers published in journals listed in the Academic Journal Guide (AJG) 2018 ranking of the Chartered Association of Business Schools (CABS). All the journals listed in this ranking are also indexed by Scopus. Therefore, looking at a single database (i.e., Scopus) should not be considered a limitation of our study. However, future research could consider different databases and inclusion criteria.

With our literature review, we offer researchers a comprehensive map of major gender-related research trends over the past twenty-two years. This can serve as a lens to look to the future, contributing to the achievement of SDG5. Researchers may use our study as a starting point to identify key themes addressed in the literature. In addition, our methodological approach–based on the use of the Semantic Brand Score and its webapp–could support scholars interested in reviewing other areas of research.

Supporting information

S1 text. keywords used for paper selection..

https://doi.org/10.1371/journal.pone.0256474.s001

Acknowledgments

The computing resources and the related technical support used for this work have been provided by CRESCO/ENEAGRID High Performance Computing infrastructure and its staff. CRESCO/ENEAGRID High Performance Computing infrastructure is funded by ENEA, the Italian National Agency for New Technologies, Energy and Sustainable Economic Development and by Italian and European research programmes (see http://www.cresco.enea.it/english for information).

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• Previous Article

This paper identifies five key issues that are important for the continued efforts to tackle gender inequality: (i) gender inequality needs to be distinguished from gender gaps. Not all gender gaps necessarily reflect gender inequality as some gender gaps are not driven by the lack of equal rights, responsibilities and opportunities bywomen and girls, and this has important implications on policy designs to address gender inequity. However, the literature has paid little attention to this issue, often using gender inequality and gender gaps interchangeably; (ii) the evolving focus of gender inequality suggests there is still a long way to go to fully address gender inequality. Particularly gender inequality is taking more subtle and implicit forms, though the social and economic benefits from addressing the remaininggender inequality is still likely to be substantial; (iii) addressing gender inequality benefits everyone, not just women. Thus, the entire society should work together, even for each individual’s own interest; (iv) both general policies and targeted gender policies can help address gender inequality.However, as gender inequality becomes more subtle and implicit, targeted gender policies will likely need to play an increasing role, which also makes separating gender inequality from gender gaps all that more important; and (v) addressing gender inequality does not need to start with policies targeted at its root causes, but needs to end with eliminating the root causes. Only then, any remaining gender gaps would only reflect preference and comparative advantage between men and women. The paper concludes by discussing gaps in the literature and policy challenges going forward.

• I. Introduction

Gender gaps have been observed in a broad range of social and economic dimensions and well-documented in the literature. Here gender gaps refer to the observed differences between men and women or between boys and girls in the relevant indicators. For example:

Gender gaps in nutritional intake have been often reported as a result of intra household allocation of resources in South Asia, with also evidence in sub-Sahara Africa ( Pal, 1999 ; World Bank, 2006 ; Hadley and others, 2007 ; Dasgupta, 2016 ; Hafeez and Quintana-Domeque, 2018 ).

In developing countries, while gender gaps in school enrollment have been narrowing rapidly over the recent decades, particularly for preprimary, primary and secondary education, considerable gaps still remain for tertiary education and there are large variations across countries ( Demery and Gaddis, 2009 ; Duflo, 2012 ; Austen and others, 2013 ; Evans and others, 2021 ). Furthermore, significant differences exist in the field of study between male and female students, likely in nearly all countries but with most evidence from advanced economies ( OECD, 2017 ; Cook and others, 2021 ).

Empirical studies, based on subjective self-reporting of unmet healthcare needs, find that women are more likely to report healthcare access related issues (Socías and others, 2016; Daher and others, 2021 ).

Access to formal financial services is generally lower for women than for men. Over time, access to financial services has increased worldwide, but significant gaps remain by gender, and both saving and borrowing services are more accessible to men than to women ( Demirgüç-Kunt and others, 2015 ; Sahay and others, 2020 ).

Differences between male and female labor force participation rates have narrowed, but the gaps remain high in most of the world, with large variations across regions and countries ( Field and others, 2010 ; Alesina and others, 2013 ; Bernhardt and others, 2018 ; Jayachandran, 2021 ). Even when women participate in the labor market, they tend to be overrepresented in certain sectors, often characterized by low status and low pay ( OECD, 2012 ; ILO, 2012 ). Particularly, women are strongly under-represented in corporate managerial positions and political leadership ( Profeta and others, 2014 ; OECD, 2017 ). Even for the same jobs and with similar qualifications, women tend to be paid less ( OECD, 2012 ; OECD, 2017 ; NSF, 2021 ).

Women are subject to more violence at home, in commuting, and at work ( Jayachandran, 2021 ). In addition, legal barriers to women’s rights and opportunities remain pervasive. Women on average have only three-quarters of the legal protections given to men during their working life, ranging from bans on entering some jobs to a lack of equal pay or freedom from sexual harassment ( World Bank, 2021 ).

Many research and policy work often equates gender gaps with gender inequality without clearly defining them. According to UN Women ,

“ Equality between women and men (gender equality) refers to the equal rights, responsibilities and opportunities of women and men and girls and boys. Equality does not mean that women and men will become the same but that women’s and men’s rights, responsibilities and opportunities will not depend on whether they are born male or female. Gender equality implies that the interests, needs and priorities of both women and men are taken into consideration, recognizing the diversity of different groups of women and men. ”

This suggests that not all gender gaps necessarily reflect gender inequality, as defined above. This has important policy implications, that is, policies should focus on eliminating gender inequality, not on achieving an equal gender share or fully closing all gender gaps.

The urgency to address gender inequality stems from its substantial social and economic consequences. First and foremost, gender inequality is a matter of fairness and concerning the wellbeing of women. 1 For example, some gender inequality reflects direct harmful actions against women —such as violence, harassment, and the resulting fear—or restrictions on women’s behaviors, legal or social. More generally, as gender inequality is the result of gender bias and social norms that restrict women’s rights and opportunities, it leads to lower welfare for women. Furthermore, as women account for half of the population, gender inequality means potentially a substantial misallocation of human capital, including both investment in women and utilization of women talent. A growing body of literature shows that reducing gender inequality can help foster better household decision-making, improve firm/institution performance, and generate substantial macroeconomic benefits, through boosting productivity and economic growth, strengthening macroeconomic and financial stability, and lowering income inequality ( Kochhar and others, 2017 ; Sahay and others, 2018 ; Cihák and Sahay, 2020 ; Gonzales and others, 2015 b).

There is clear evidence that gender inequality narrows as countries develop and new technologies, such as labor-saving household appliances, are being developed and widely adopted ( Jayachandran, 2015 ; Tewari and Wang, 2021 ). However, the interrelationships between women empowerment and economic development are probably too weak to be self-sustaining, and because of the social and economic significance of gender inequality, policy actions are needed to speed up the process ( Duflo, 2012 ). For example, around 82 percent of 40-year-old inventors are men, and while this gender gap in innovation is shrinking gradually, at the current rate of convergence, it will take another 118 years to reach gender parity ( Bell and others, 2019 ).

One of the United Nation’s Sustainable Development Goals (SDGs) is to achieve gender equality and empower all women and girls. 2 Many efforts have been taken over the past decades, particularly after the establishment of the SDGs in 2015, to tackle gender inequality. For example, public investment in education has nearly erased the gender gaps in primary and secondary school enrollment; legislative reforms have led to reductions in discrimination; countries have enacted reforms to boost women’s economic opportunities; countries have enacted laws or introduced policies to end child and early marriage, provide paternity and parental leave, reduce the gender wage gap, address violence against women including sexual harassment, and promote women in leadership ( World Bank, 2021 ; OECD, 2014 ; OECD, 2017 ). 3

While globally important progress has been made in some areas (e.g., enrollment in primary and secondary education), substantial gender inequality still remains in many other areas (e.g., enrollment in tertiary education, labor force participation, wages, and leadership positions). Furthermore, the COVID-19 pandemic has disproportionately affected women, further exacerbating pre-existing gender inequality, for example, as women shouldered more burden in taking care of young children when schools were closed ( Albanesi and Kim, 2021 ; Bluedorn and others 2021 ; Fabrizio and others, 2021 ; WEF, 2021 ).

Thus, much work still lies ahead to achieve gender equality, with some forms of gender inequality still existing in nearly all countries and often in relation to the SDGs. As countries seek to step up their efforts to address gender inequality, many questions remain for policymakers. This includes: (i) what are the main forms of gender inequality for countries at different stages of development? (ii) what are the economic benefits from continued efforts to reduce gender inequality, are the benefits diminishing as some gender inequality is being eliminated, and who would benefit from lower gender inequality? (iii) What policies are most effective in addressing gender inequality, what are the tradeoffs of adopting different types of policies, and are some of the policies more about ticking a box rather than making a real difference? And (iv) what are the roles of different types of policies at eliminating gender inequality, given the root causes of gender inequality is often social and cultural?

The literature on the economic impacts of gender inequality and the policies to address gender inequality has been growing rapidly over the recent decades. In addition, many countries have adopted policies to tackle gender inequality for many years, and there is a lot to learn from their experiences. This paper intends to draw on the vast literature—which tends to focus on specific aspects of gender inequality and policies —and the diverse country experiences to provide a holistic view of gender inequality and shed light on some of the key policy questions that can help countries approach gender issues in a more systematic manner. More specifically, the paper identifies five key lessons:

Gender inequality versus gender gaps . Gender inequality differs from gender gaps in important ways, and this has important policy implications. However, the literature often equates gender inequality with gender gaps and use them interchangeably. This paper defines gender gaps as the observed differences between men and women or between boys and girls in the various social and economic indicators, and gender inequality refers to the part of gender gaps that are driven by gender bias and unequal gender rights and opportunities. The rest of the gaps are driven by preference/comparative advantage between men and women. Therefore, policies should be targeted at reducing gender inequality, which does not necessarily mean to fully close all gender gaps.

The evolving focus of gender inequality . Gender inequality extends to nearly every dimension of social and economic activities. The policy focus often varies by country, depending on their circumstances and level of development. There appears to be a shift toward more subtle and implicit forms of gender inequality, as gender reforms deepen, for example, from school enrollment to quality of education and field of study and from labor force participation to distribution of employment across sector s and pay. However, this does not mean that the social and economic impacts of the remaining gender inequality are smaller. In fact, the literature has shown that they could have substantial economic consequences. Furthermore, for countries that are still at the early stage of addressing gender inequality, this suggests that they should learn from the experiences of other countries, and it may be more effective and efficient to tackle different forms of gender inequality simultaneously. For example, countries could consider policy measures to simultaneously address gender inequality in tertiary enrollment and field of study, rather than tackling gender imbalances in field of study only after gender inequality in enrollment is eliminated.

The benefits of reducing gender inequality go beyond women . Gender equality may be seen by some as a zero-sum game, from an economic point of view. Less unpaid work at home and higher labor force participation by women would mean more unpaid work at home and lower labor force participation for men. Better representation at leadership positions by women would mean less for men. It is, however, important to recognize that better gender equality benefits not just women, but it enlarges the economic pie and benefits everyone, through several potential channels: (i) women tend to make better decisions regarding children; (ii) gender-mixed teams are more productive; and (iii) lower gender inequality can bring important macroeconomic benefits to everyone, with stronger economic growth and financial stability, more jobs, and less income inequality.

Policies and their designs matter . Large variations in gender gaps among countries with a similar level of development and in the same region suggest that policy interventions and their designs can make a difference, and this is further illustrated with an econometric analysis of gender laws and regulations and selected gender gaps. In addition, the literature provides strong evidence that a broad range of policy reforms can help reduce gender inequality and ultimately improve social and economic outcomes. However, not all policy interventions work under all circumstances, and policy tradeoffs are often involved. The paper compares general policies and targeted gender policies and discusses some considerations in their designs and implementation.

Policy actions do not have to start with those targeted at the root causes . While gender inequality shows many symptoms, the root causes are typically traced to gender bias and social norms. Ideally, reforms should be directly targeted at the root causes. However, this appears difficult with limited policy options (e.g., educational programs, information campaign, and legal reforms to ensure women’s rights and opportunities), and it takes time to change people’s views and beliefs. Instead, policies have focused on reducing gender inequality in different areas such as education, labor market, and financial access. Not only do these policies have immediate impacts on gender inequality, but they could also help change social norms. While policies may not need to start with the root causes of gender inequality, fully eliminating gender inequality requires eventually addressing the root causes.

The rest of the paper is organized as follows. Section II to VI in turn take on the five key lessons discussed above. Section VII concludes with a discussion on the gaps in the literature and on some considerations in addressing gender inequality going forward.

• II. Gender gaps and gender inequality: definitions and drivers

Gender gaps are defined here as the observed differences between men and women or between boys and girls in the various social and economic indicators. Gender gaps can be considered to consist of two components, one that is caused by unequal rights, responsibilities, and opportunities for women and girls 4 and the other that is driven by women’s preference 5 or comparative advantage between men and women. 6 The former is what is defined in this paper as gender inequality, and the latter is the result of efficient allocation of human capital. For example, for school enrollment in primary and secondary education, it would be expected that most, if not all, of the gender gaps reflect gender inequality. For tertiary education in advanced economies, female enrollment rate is about 25 percent higher than that of male ( Figure 1 ). This gap, however, would not be expected to reflect gender inequality, that is, boys are facing less rights and opportunities. Instead, this likely reflects preference and choices (e.g., girls have comparative advantage in brain-based sectors and the returns to education are higher in such sectors ( Pitt and others, 2012 )). On the other hand, males represent a very small share of employment in registered nurses, some of which may indeed reflect social norms that hinder male’s entry into this profession. 7

Distinguishing between gender gaps and gender inequality has important policy implications. For the part of gender gaps that reflect preference/comparative advantage between men and women, there would be no need for policy intervention as there is no welfare loss from such gaps. For example, in many advanced economies where female tertiary enrollment rate is higher than that of male, there appears no need for policy interventions to further increase male tertiary enrollment rate to close the gap. On the other hand, there is a clear need to address gender inequality as it hurts women’s wellbeing, leads to distortions, and reduces overall social welfare. In many developing economies where female tertiary enrollment rate is lower than that of male due to gender bias, if it is left unaddressed, there would be an underutilization of women’s talent. Recognizing the difficulties often in separating gender inequality from gender gaps, Section V discusses some implications on policy designs.

Better understanding the drivers of gender inequality and gender gaps helps formulate effective policies. Both the theoretical and empirical literature offers evidence on the main drivers of gender gaps and gender inequality, particularly in the context of economic development:

Comparative advantage improves for women as countries develop. Women have a comparative advantage in mentally intensive tasks while men in physical intensive tasks; the process of development entails a growing capital stock and thus reduces the female-male wage gap, which in turn causes female labor force participation to increase ( Galor and Weil, 1996 ). 8 As brain-based sectors grow, if the returns to education are higher in brain-based than in brawn-based occupations, girls’ schooling could overtake that of boys ( Pitt and others, 2012 ). Gender differences in labor productivity as a driver of gender gaps are also supported by empirical evidence ( Qian, 2008 ; Alesina and others, 2013 ; Carranza; 2014 ). This strand of literature highlights the mechanism through which gender gaps narrow as countries develop, by largely reducing the part of gender gaps that reflect preference/comparative advantage between men and women.

Economic development is associated with better physical infrastructure and more advanced technology, making home production more efficient and less labor intensive. Because women perform the lion’s share of household chores, advances in home production technologies mainly free up women’s time and lead to an increase in female labor force participation ( Greenwood and others, 2005 ; Dinkelman, 2011 ; Tewari and Wang, 2021 ). As women performing much home production likely reflects both preference/comparative advantage between men and women and gender bias/social norms, better physical infrastructure and more advanced technology help reduce both components of gender gaps. 9

Gender bias and cultural barriers to women’s rights and opportunities are major drivers of gender gaps ( Jayachandran, 2015 ; Jayachandran, 2021 ; Alesina and others, 2013 ; Bernhardt and others, 2018 ). For example, Fernandez and Fogli (2009) show that whether a female second-generation immigrant in the United States works is strongly influenced by the female employment and fertility norms in her ancestral homeland. One form of barriers is the lack of basic legal rights, preventing women from joining the formal labor market or becoming entrepreneurs in many countries. Women are sometimes legally restricted from heading a household, pursuing a profession, or owning or inheriting assets. Such legal restrictions significantly hamper female labor force participation and pose a drag on female entrepreneurship (World Bank, 2015). Limiting gender bias and cultural barriers helps close gender gaps through reducing gender inequality.

Figure 1 illustrates conceptually the interrelationship between gender gaps, gender inequality, their drivers, and policy interventions to address them:

The root causes of gender inequality are gender bias and social norms that restrict women’s rights and opportunities, which, together with preference /comparative advantage between men and women, are the root drivers of gender gaps.

Gender bias/social norms and preference/comparative advantage between men and women interact with other factors (e.g., development, technological advances, and public policies) in determining gender gaps and gender inequality in different areas such as education, labor market and financial access. In other words, the root causes of gender inequality are gender bias and social norms; gender inequality in different areas are just symptoms of the root causes. This means that while some policies can help reduce gender inequality in some of these areas, fully addressing gender inequality would require the elimination of the root causes, gender bias/social norms.

As discussed above, development, technological advances, and public policies can affect gender bias/social norms and preference/comparative advantage between men and women.

Furthermore, interventions to lower gender inequality in different areas could also in turn alter gender bias/social norms. For example, as women become more educated and more women participate in the labor market, attitude toward women’s education and work may start to shift (see section VI for additional discussions).

Gender inequality, gender gaps and their causes

Citation: IMF Working Papers 2022, 232; 10.5089/9798400224843.001.A001

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III. The evolving focus of gender inequality: still a long way to go

Progress on gender equality has continually been made and differs substantially by country, particularly in relation to their stage of development. Consequently, the focus of gender inequality also varies by country and continue to evolve as some gender gaps are closed while others emerge and attract the attention of the public and policymakers. In general, the focus of gender inequality is shifting from gender gaps that are more explicit and visible to the public and policymakers to those that are more subtle and implicit. Given that gender inequality exists in broad areas, this section focuses on education, labor market, financial access, and legal barriers, as examples.

• A. Education

The focus of gender inequality in education appears to be shifting from access to education (e.g., school enrollment) to quality of education and field of study.

For emerging and developing economies as a group, the gender gaps in access to preprimary, primary and secondary education are being closed, though some countries are still lagging behind; however, there are still gaps for tertiary education ( Appendix Figure 1a-1d ). As a result, many emerging and developing economies are still trying to achieve gender equality in access to education, particularly for tertiary education ( Demery and Gaddis, 2009 ; Duflo, 2012 ; Austen and others, 2013 ; Evans and others, 2021 ).

Advanced economies, instead, have been focusing on gender equality in quality of education, including gender distribution by field of study, as they have largely achieved gender equality in access to preprimary, primary and secondary education decades ago and to tertiary education since mid-2000s. For example, across the OECD, boys outperformed girls in mathematics by an average of eight points in 2015 —equivalent to around one-fifth of a year of schooling—and by 5 points in 2018; on the other hand, girls significantly outperform boys in reading in all countries and economies that participated in PISA 2018 ( OECD, 2017 ; OECD, 2019 ).

One area that has received increasing attention is the large differences in field of study between boys and girls, with girls particularly underrepresented in the fields of science and engineering and overrepresented in social science related fields ( Appendix Figure 2 ). The distributions are remarkably similar between more developed economies and the rest of the world, indicating that this is an issue common for all economies ( Appendix Figure 2a shows the global distribution and Appendix Figure 2b shows the distribution for OECD countries during a similar period). For example, college-educated women in the United States have sorted into majors that systematically lower their potential wages relative to men; to what extent women choose a major in anticipation of future family demands, based on individual preferences, under the burden of restrictive social norms, or for any other reason remains an unanswered question ( Sloane and others, 2021 ).

Women appear to be particularly under-represented in science, technology, engineering, and math (STEM). In the United State, in 1970, only 9 percent of all doctorates in the science and engineering fields, including social sciences, were awarded to women; by 2018, that share was nearly 47 percent. A closer look indicates that a large part of this is driven by high shares of women in psychology and social sciences. Despite the progress, persistent barriers to women pursuing degrees in STEM fields abound (Cook and others, 20 21).

• B. Labor market

When it comes to the labor market, while efforts are continued to reduce gender inequality in labor force participation, narrow the gender wage gap, and boost the representation of women in political leadership, increasing attention is given to the large gaps in the sectoral distribution of female and male employment, women’s role in innovation, and women’s share in corporate management positions.

The differences between male and female labor force participation rates remain high, although the gaps have been narrowing over the past decades ( Appendix Figure 3a ). The gaps are smaller and also closing more rapidly in advanced economies. The gender gaps in emerging economies, in fact, have widened over the past two decade, and this is almost entirely driven by the declining female labor force participation in China and India. In China, the likely underlying factors include structural changes in the Chinese economy where households can afford to have only one wage earner, reduction in state childcare support, and rising gender-biased hiring practices; in India, the decline may reflect the declining employment in agriculture, safety concerns and the lack of transportation infrastructure for women to join the urban labor force , and the U-shaped relationship between education and labor force participation as education level improves for women ( Li, 2019 ; Zhang and Huang, 2020 ; Gupta and Bhamoriya, 2020 ; Hare, 2016 ). Excluding China and India, the gender gaps in labor force participation rates in emerging economies are still larger than those in developing economies, which partially reflects the large gaps in emerging MENAP countries. Low labor force participation, particularly for women, has been a major policy concern for many advanced economies and some emerging economies, as they face an aging population. As women in these economies tend to be well educated, it would be a considerable waste if they do not fully engage in economic activities.

There are also large gaps in the sectoral distribution of female and male employment, likely reflecting the differences in field of study. In advanced economies, women are less likely to work in the agriculture and industry sectors and more likely to work in the service sector; but there is a shift in the trend around 2018 from the service sector to the industry sector. Emerging and developing economies share broadly similarly trends over the past decade or so: relatively larger shares of women work in the agriculture sector; and women are moving rapidly from the agriculture and industry sectors to the service sector ( Appendix Figure 3b-3d ). In OECD countries, female employment in the service sector accounts for 80 percent of employed women, compared with 60 percent for men. Within this sector, women fill a disproportionately high share of occupations in health and community services, followed by education ( OECD, 2012 ). ILO (2012) finds that women are overrepresented in sectors characterized by low status and low pay.

Gender gaps in occupations within the science and engineering (S&E) field have been a particular concern. In the United States, by 2019 women made up 29 percent of the S&E workers, but female scientists and engineers are more likely to work in non-S&E occupations than in S&E occupations ( Cook and others, 2021 ). In 2019, 70 percent of psychologists were women, but just 14 percent of engineers and 29 percent of the workforce in computer and mathematical sciences were women. Women often start their careers working in the innovation economy, but then leave for various reasons, including the need to provide childcare, the lack of family-leave policies, and poor workplace climate ( Cook and others, 2021 ).

Increasing attention is also paid to women’s role in innovation, widely viewed as a central driver of productivity and economic growth. Gender inequality persists at every state of innovation, from education and training, to the practice of invention, and to the commercialization of those inventions ( Cook, 2019 ; Cook and others, 2021 ). Women hold only 5.5 percent of commercialized patents and represent just 10 percent of US patent inventors and only 15 percent of inventors in the life sciences. This in part reflects women’s underrepresentation in jobs involving development and design ( Hunt and others, 2013 ). In addition, discriminatory practice leads to inequality in patenting outcomes, even without discriminatory laws. Patent applications by women inventors were found to be more likely to be rejected than those of men, and those rejections were less likely to be appealed by the applicant teams. Conditional on being granted, patent applications by women inventors had a smaller fraction of their claims allowed, on average, than did applications by men. Further, those claims allowed had more words added during prosecution, thus reducing their scope and value. The granted patents of women inventors also received fewer citations than those of men and were less likely to be maintained by their assignees ( Cook and Kongcharoen, 2010 ; Jensen and others, 2018 ).

What has received particular attention is the underrepresentation of women in politics and corporate management positions. Representation of women in politics has improved substantially across all economies, with the proportion of seats held by women in national parliaments about doubled over the past two decades, likely due to the high public visibility; the gender gap, however, remains large ( Appendix Figure 4a ). For senior and middle management positions, there has been, however, little progress over the past two decades ( Appendix Figure 4b ). It appears that the success in political leadership has not been trickled down to the corporate world, highlighting the challenges to make changes in less visible areas. Across the 27 EU countries, only 25 percent of business owners with employees are women, and the low share of women had only marginally grown over 2000-2010 in EU27, Canada and United States ( OECD, 2014 ). A number of countries have enacted legislation requiring a set quota of female representation on corporate boards , the effectiveness and efficacy of such policy, however, has been intensely debated ( Kuzmina and Melentyeva, 2021 ; Greene and others, 2020 ; Lleras-Muney and others, 2019 ; Levi and others, 2014 ; Gregory-Smith and others, 2014 ; Strøm and others, 2014 ).

The gender wage gap has declined in most countries where data are available over the past two decades. Significant gap, however, still persist, averaging around 11 percent, and the gap varies substantially across countries ( Appendix Figure 5 ). While a large part of the gender gap in earnings can be explained by women working fewer hours in the labor market than men, women’s work force interruptions, gender differences in occupations and industries, a significant part of the gender pay gap remain unexplained, suggesting that factors such as discrimination and gender differences in psychological attributes and noncognitive skills are also important contributors to the gender pay gap ( OECD, 2017 ; Blau and Kahn, 2017 ). For example, using a personnel dataset from a large Chinese company, Chen and others (2021a) find that the gender wage gap is small in the early stages of careers and becomes increasingly evident when female employees get married and have children. Whereas the short-term peak around childbirth can be explained by women’ reduced working hours, the long-term trend is caused by women’s concentration in lower-level jobs.

• C. Financial access and legal barriers

More attention is gradually drawing to access to credit by female entrepreneurs, as to financial access by females as individuals.

On account ownership at a financial institution/with a mobile-money-service provider, advanced economies have largely closed the gender gap; emerging economies have been making steady progress, with the gap narrowing from 23 percent in 2011 to 7 percent in 2021; little progress, however, has been seen in low-income developing countries over the last decades, with the gap staying at around 27 percent ( Appendix Figure 6a ).

The evidence on whether fintech can help close gender gaps in financial access, particularly in developing and emerging economies, still appears limited. Sahay and others (2020) find that gender gaps are lower on average in digital financial inclusion than in traditional financial inclusion, but there are significant variations across and within geographical regions. Chen and others (2021b) find a large fintech gender gap: while 29 percent of men use fintech products and services, only 21 percent of women do. Various factors contribute to the gender gap in fintech, including financial and digital literacy and socio-culture factors, suggesting that fintech by itself may only have limited impacts in reducing gender inequality in financial access, and policies to address more fundamental drivers of gender inequality are essential ( Khera and others, 2022 ; Chen and others, 2021b ).

On entrepreneurship financing, a significant gender gap still exists, even in advanced economies. Women are less likely than men to report that they can access the financing needed to start a business in all countries except for Mexico and the United States, with an average gap of eight percentage points in OECD countries ( Appendix Figure 6c ).

On legal barriers to gender equality, substantial progress has been made in all country groups, but effective implementation of the enacted laws and regulations remain a challenge in some countries. According to the Women, Business and Law Index, advanced economies have removed almost all the legal barriers to gender equality; significant gaps, however, still exist in emerging and developing economies ( Appendix Figure 6b ). 10 The impact of adopting gender equality legislation, however, would be limited if they are not fully implemented and enforced. For example, there is evidence from Ghana that reforms to inheritance laws led to few positive changes in terms of women’s inheritance ( Gedzi, 2012 ); a positive legal change in Pakistan has not allowed women to claim their entitled inheritances because of factors such as lack of education, patriarchal behaviors, and forced marriages ( Ahmad and others, 2016 ). Furthermore, cultural and economic factors may pose challenges to women exerting their rights, as in the case of reforming gendered land ownership laws in Kenya, Rwanda, and Uganda ( Djurfeldt, 2020 ).

• D. Policy considerations

The literature suggests that there is still a long way to go to achieve gender equality for most economies:

Gender inequality remains large. While advanced economies have largely closed gender gaps in access to education and individual access to financial services, and removed legal barriers to gender equality, gender gaps in leadership positions, labor force participation, and pay remain sizable. Furthermore, more subtle gender gaps still persistent, such as in quality of education including field of study, sectoral distribution of employment, entrepreneurship financing, and innovation. 11 Emerging and developing economies faces additional challenges to achieve equality in access to tertiary education, individual access to financial services, and legal rights.

Closing the remaining gender inequality will likely be more challenging, as countries move to address gender inequality that is more implicit and subtle. This is because such inequality may be less visible to the public and thus may face less social pressures; with the difficulties in distinguishing preference/comparative advantage between men and women from gender bias/cultural barriers for such inequality, effective and efficient policies may be lacking; and addressing such inequality may require changing people’s mindset, which tends to be more difficult.

The social and economic impact of further reducing gender inequality is likely substantial. The more implicit and subtle nature of gender inequality does not necessarily mean less social and economic benefits from removing such forms of inequality. For example, in the case of United States, 94 percent of doctors and lawyers were white men in 1960; by 2010, the fraction was just 62 percent; similar changes in other high-skilled occupations have occurred throughout the U.S. economy during the last 50 years; given that the innate talent for these professions is unlikely to have changed differently across groups, the change in the occupational distribution since 1960 suggests that a substantial pool of innately talented women and black men in 1960 were not pursuing their comparative advantage ; it is estimated that between 20 and 40 percent of growth in aggregate market output per person during the period can be explained by the improved allocation of talent ( Hsieh and others, 2019 ). In a study of PhDs, GDP per capita could be 0.6 to 4.4 percent higher if women and African Americans were able to participate more fully in the innovation economy ( Cook and Yang, 2018 ).

One potential lesson, particular for emerging and developing economies, is that in addressing gender inequality, it may be more effective and efficient for policy designs to consider the whole spectrum of gender inequality, including both the highly visible ones and the more implicit and subtle ones. For example, in closing the gender gap in access to tertiary education, countries should also be mindful about the gender differences in field of study and actively help remove any barriers that may hinder the ability of female students in pursuing STEM fields. Another example would be to pay full attention to both the adoption and the implementation of gender equality laws.

• IV. The benefits of reducing gender inequality go beyond women

The literature has documented broad social and economic benefits from lowering gender inequality, including the increasing emphasis on its macroeconomic effects ( Kolovich and others, 2020 ). Reducing gender inequality affects not only women, but everyone.

First and foremost, women benefit from lower gender inequality. This includes, for example, better career development, higher pay, and less violence, less discrimination and more equal rights, through improvements in human capital development, job opportunities including in leadership positions and as entrepreneurs, access to finance, and legal and regulatory environment.

Second, children benefit from lower gender inequality and women’s empowerment. Women’s choices appear to emphasize child welfare more than those of men, and children seem to be better off when their mothers control relatively more of their family’s resources. For example, Miller (2008) presents evidence on how suffrage rights for American women helped children to benefit from the scientific breakthroughs of the bacteriological revolution, with child mortality declining by 8–15 percent (or 20,000 annual child deaths nationwide), through large increases in local public health spending. Leight and Liu (2020) document that more-educated mothers appear to compensate for differences between their children, investing more in children who exhibit greater noncognitive deficits, while no such effect is found for men. Pitt and others (2003) find that women’s access to credit has a large and statistically significant impact on two of three measures of the child health, but no such effect is found for men.

Third, reducing gender inequality could potentially help increase the productivity of teams and improve the performance of firms and other institutions. This is primarily through the diversity channel, in the sense that mixed-gender teams are more productive and creative and tend to make better decisions ( Rock and Grant, 2016 ; Ozgen, 2021 ). Cook and Kongcharoen (2010) find that all-male and all-female patent teams commercialize their patents less than mixed-gender patent teams, with a similar finding from Østergaard and others (2011 ). Herring (2009) finds that gender diversity is associated with increased sales revenue, more customers, and greater relative profits. A number of studies find that gender quotas at corporate board are associated with improvements in firm performances, though there is still no consensus in the literature ( Strøm and others, 2014 ; Levi and others, 2014 ; Kuzmina and Melentyeva, 2021 ; Owen and Temesvary, 2018 ; Green and others, 2020 ). 12

Fourth, lower gender inequality can bring important macroeconomic benefits to everyone, with stronger economic growth and financial stability, more jobs, and less income inequality ( Kochhar and others, 2017 ; Sahay and others, 2015; Sahay and others, 2018 ; Cihak and Sahay, 2020 ).

Better matching female talent to human capital development and employment, including as corporate and political leaders and entrepreneurs, can substantially boost economic growth and strengthen economic and financial stability. For example, higher female labor force participation can substantially boost economic growth ( Kochhar and others, 2017 ; Kolovich and others, 2020 ). As discussed earlier, between 20 to 40 percent of growth in aggregate market output per person between 1960 and 2010 in the United States can be explained by improved allocation of talent ( Hsieh and others, 2019 ). Innovation is widely viewed as a central driver of productivity growth and output, and gender inequality hinders innovation at every state of the process, particularly as a growing literature is showing better outcomes of more diverse and mixed-gender teams ( Rock and Grant 2016 ; Cook, 2019 ; Cook and others, 2021 ). The literature also finds positive association between financial inclusion and economic growth, and reducing gender inequality in financial access, including through fintech, can thus help increase economic growth, particularly in countries with low levels of financial inclusion (Sahay and others, 2015; Sahay and others, 2020 ). There is also evidence that female leadership, including as financial regulators, is associated with financial and political stability ( Sahay and others, 2018 ; Caprioli, 2005 ).

Reducing gender inequality could also help lower income inequality and, in turn, improve social stability and economic growth ( Gonzales and others, 2015b ). Gender wage gaps directly contribute to income inequality. Conversely, policies to address gender inequality benefit females in low-income households the most, also reducing income inequality. For example, reducing gender gaps in school enrollment means that girls from poor households are more likely to receive education , thereby increasing their lifetime earnings potential ( Demery and Gaddis, 2009 ). In addition, financial inclusion of women is found to have a strong link to lower income inequality; this is because, while financial inclusion benefits everybody, the gains for women are quantitatively larger (Aslan and others, 2017; Cihák and Sahay, 2020 ).

V. Policies and their designs matter: general versus targeted policies

There is strong evidence from the literature that government policies and their designs matter for gender gaps and gender inequality. The key question, however, is how government policies can be designed to achieve gender equality while minimizing their efficiency cost (or maximizing the efficiency benefit).

• A. The role of policies in closing gender gaps

A broad range of government policies and programs can affect gender gaps, such as public investment to improve access to education and healthcare, childcare subsides, paid parental leave, eliminating tax penalties for secondary earners, and laws and regulations to ensure women’s rights and opportunities ( Rim, 2021 ; Ruhm, 1998 ; Dustmann and Schönberg, 2012 ; Heath and Jayachandran, 2018 ; Evans and Yuan, 2022 ; Bick and Fuchs-Schündeln, 2017 ; Olivetti and Petrongolo, 2017 ; Gonzales and others, 2015 a; Hyland and others, 2020 ). For example, Rim (2021) finds that banning gender discrimination in admissions was successful in reducing gender disparity in graduate education. Sometimes, the policy interventions involve tradeoffs between different gender gaps. For example, Ruhm (1998) finds that parental leave is associated with increases in women’s employment, but with reductions in their relative wages at extended durations. Lalive and others (2014) find that, for parental leave, a system that combines cash benefits with job protection dominates other designs in generating time for care immediately after birth while maintaining mothers’ medium-term labor market attachment.

In addition to the large variations in gender gaps by level of development as shown in Section III, gender gaps also vary substantially among countries at a similar level of development and in the same region, for several selected gender gap measures ( Appendix Figure 7 ). Assuming countries in the same region have similar gender social norms, this suggests that government policies potentially play an important role in explaining cross-country variations in gender gaps.

As an illustration, here we estimate the effects of laws and regulations that ensure equal opportunities for women (measured by Women, Business and the Law Index) on five gender gaps (these gaps are selected as they are key measures of women’s economic opportunities, tend to present in many countries, and are widely reported). 13 The estimates are based on a fixed effects specification with a time trend and lagged key independent variable. The model uses per capita GDP in purchasing power parity (PPP) terms to control for level of development, country fixed effects to control for time-invariant factors (e.g., social norms), and a time trend to control for global trends. 14 The results suggest that gender laws and regulations are associated with lower gender gaps in some areas (e.g., account ownership at a financial institution /with a mobile-money-service provider and proportion of seats held by women in national parliaments). The estimates on gender gaps in labor force participation, female share of senor and middle management, and pay are not statistically significant ( Appendix Table 1 ). One likely explanation is that the introduction of gender equality laws and regulations helps raise awareness and can lead to changes that face relatively less barriers (e.g., financial access) or are highly visible by the public (parliament seats). More fundamental changes, however, may take time (e.g., labor force participation, senior and middle management, and pay).

• B. General versus targeted policies

The effects of government policies on gender inequality and economic efficiency would depend on their specific designs and country-specific social and economic structures and conditions, and thus should be assessed on a policy-by-policy basis. There are, however, also commonalities among government policies, and it would be useful to understand their advantages and disadvantages. For example, gender-sensitive government policies can be broadly classified into two groups: general policies that apply to all genders indiscriminately but affect one gender more than the other and targeted policies at a specific gender.

By definition, nearly all macro policies—including fiscal policies, monetary policies, and exchange rate policies as well as macro-financial and macro-structural policies—belong to general policies, as they are primarily intended to boost economic growth and employment and achieve macro and financial stability. This, however, does not necessarily mean that macro policies are gender neutral. In fact, many of these policies have implications on gender gaps and gender inequality, because they affect different segments of the economy differently, and the distributions of female and male population also differ across these segments of the economy. For example, on fiscal policies, family-based income taxation implicitly raises the marginal tax rate for the income of secondary earners—who tend to be women—and contributes to the gender inequality in labor force participation ( Bick and Fuchs-Schündeln, 2017 ); while public education and health spending on average may still favor boys, the benefits from additional spending tend to be captured more by poor girls, as they are more likely to be still lacking access to education and healthcare ( Demery and Gaddis, 2009 ). On financial sector policies, while financial inclusion benefits everyone, the gains for women are quantitatively larger ( Cihák and Sahay, 2020 ). Monetary, exchange rate policies and macro structural policies have also been found to have gender implications. 15

Micro policies refer to government programs that target specific entities such as firms and households, and thus gender-sensitive micro policies can be either general or targeted policies. This includes a variety of programs such as (un)conditional cash transfers, hygiene promotion and water treatment, educational programs on gender equality for students, legal reforms to enhance women’s rights, conditional cash transfers for dropped out girls, reservation of subway cars exclusively for women, and gender quotas on corporate boards or political seats. Many of these programs have been shown to improve outcomes for women or girls ( Hahn and others, 2018 ; Harari, 2019 ; Beaman and others, 2012 ).

In general, targeted gender policies conceptually are less efficient as they exclude males who may be better suited for the opportunities. However, with the presentence of gender inequality (e.g., gender bias and social norms that hinder women’s rights and opportunities), general programs can also be inefficient in the sense that preference may be given to less qualified males. Because gender gaps can be driven by gender inequality or preference/comparative advantage between men and women or most likely both, and empirically it is difficult to separate the two effects, the key challenge for targeted gender policies is how to set the policy target s, as fully closing gender gaps may not be appropriate. Below are a few considerations:

It is not even clear that targeted gender policies are more effective in closing gender gaps. For example, from 267 educational interventions in 54 low- and middle-income countries, general interventions deliver average gains for girls that are comparable to girl-targeted interventions in improving access and learning ( Evans and Yuan, 2022 ). However, the most effective programs may not be the most cost-effective. Due to the lack of cost data, the cost-effectiveness of the programs could not be assessed.

There is evidence that some gender targeted policies may have unintended consequences or lead to inefficiencies. For example, the findings from a program that reserves subway cars exclusively for women in Mexico City suggest that while the program seems to be successful at reducing sexual harassment toward women, it also increases aggression incidents among men ( Aguilar and others, 2021 ). While the policy of setting gender quotas on corporate boards is still intensely debated, some studies find that the policy is associated with negative returns, and the negative effect is less severe for firms with a greater supply of female candidates, and for those that can more easily replace male directors or attract female directors ( Green and others, 2020 ). This appears to indicate that this policy may indeed lead to less qualified women being selected in some circumstances. Furthermore, there is also evidence that the policy has very little discernible impact on women in business beyond its direct effect on the women who made it into boardrooms ( Bertrand and others, 2019 ). This suggests that the policy may be more of ticking a box exercise. Afridi and others (2017) find short-term costs of gender-affirmative action policies for political leadership positions, but that once initial disadvantages recede, women leaders are neither more nor less effective local politicians than men. 16 While this does not mean that these policies should not be pursued, it does raise the need for careful designing such programs, particularly as its long-run or economy-wide impact may be difficult to identify in the studies. 17

For some policies, there is less ambiguity on their efficiency implications. For example, legal reforms to provide equal rights to women, by definition, is addressing gender inequality directly. This may be one potential reason for the rapid progress in removing legal gender barriers. Another example is educational programs on gender inequality, it is in fact more effective to be targeted to both genders , as reducing gender inequality requires the active participation by men as well ( Dhar and others, 2022 ). In some instances, preference/comparative advantage between men and women are expected to play a limited role, such as access to basic education (e.g., preprimary, primary and secondary) and healthcare. In such cases, fully closing the gender gaps would unlikely introduce any major distortions.

General policies tend to introduce less gender-specific distortions, although they can only address gender inequality, often in a more gradual manner. For example, conditional cash transfer programs can help improve school attendance of both boys and girls and benefit girls more than boys because more girls lack access to education in the absence of the programs. However, on the margin, boys are likely still less qualified than girls, even if the programs have helped narrow the gap. With this in mind, general policies may be particularly useful in circumstances where it is difficult to assess to what extent that the gender gaps are due to gender inequality. One potential area is formal labor force participation for which it is unclear how much of the lower labor force participation for women is due to gender inequality and how much is due to preference. In such a case, targeted policies such as wage subsidies for women may not be advisable, while general policies such as childcare subsidies may be more appropriate. 18

In areas where only targeted gender policies may be effective (e.g., in situations where men and women compete with each other), it would make sense to be conservative, by setting the gender quotas low initially and gradually increase them as more evidence becomes available. For example, only targeted gender policies are likely effective in promoting female leadership (e.g., gender quotas on corporate boards), as the number of leadership positions is fixed, and more female leaders mean fewer male leaders. This appears to be the case in some countries that have adopted policies to set gender quotas on corporate boards, through it is unclear if the design is indeed driven by such a consideration. For example, Malaysia’s publicly traded firms must have at least one-woman director on their boards from September 1, 2022; and California requires public companies headquartered in California to have at least one female director by the end of 2019 and at least two (three) female directors on five (six or more) member boards by the end of 2021.

• VI. Policy actions do not have to start with those targeted at the root causes

As discussed in Section II, the root causes of gender inequality are gender bias/social norms that restrict women’s rights and opportunities. Only until the root causes are eliminated, gender equality can be fully achieved; some gender gaps may still remain but are driven by preference /comparative advantage between men and women. Before that, it is unlikely that gender inequality in different areas such as education, labor market, and financial access can be fully removed. With the difficulties in separating gender inequality from efficient allocation, general policies may have difficulties in fully eliminating gender inequality, while targeted gender policies run the risk of either not fully addressing gender inequality or introducing additional gender distortions. With these constraints, how should policies be designed? Should policies only focus on those that are directly targeted at gender inequality (e.g., removing legal barriers) and its root causes (e.g., educational programs and information campaigns)?

This paper argues that addressing gender inequality does not have to solely rely on policies that are targeted at gender inequality and its root causes, and other general and targeted policies can still play a key role in addressing gender inequality, for several reasons:

First, while social norms evolve as countries develop (e.g., higher income, better education, and technological advances), this is often slow, almost by definition. There is evidence that some interventions can help change social norms. This includes educational programs on gender inequality and exposure to (female) role models. For example, an intervention in India that engaged adolescent girls and boys in classroom discussions about gender equality for two years, aiming to reduce their support for societal norms that restrict women ’s and girls’ opportunities, is shown to have persistent effects and leads to shifts in behavior, more so for boys than girls ( Dhar and others, 2022 ). The findings from Bell and others (2019 ) suggest that if girls were as exposed to female inventors as boys are to male inventors in their childhood commuting zones, the current gender gap in innovation would shrink by half. 19 The scope for policies directly targeting gender inequality (e.g., removing legal barriers) also appears limited.

Second, policies to reduce gender inequality in different areas such as education and labor market can be effective, with substantial immediate benefits for women and for the entire society, as discussed throughout the paper and particularly in Section IV. Examples include general policies and targeted gen der policies to improve access to education (e.g., public investment in education and conditional cash transfers for girls) and boost labor force participation (e.g., childcare subsidies and eliminating tax penalties for secondary earners).

Third, policies to address gender inequality in different areas can also indirectly influence gender bias and social norms, the root causes of gender inequality. For example, policies that help narrow the gender inequality in education in turn also help shape gender attitude, as it increases women’s income and bargaining power at home ( Le and Nguyen, 2021 ). Gender quotas in political leadership can help influence adolescent girls’ career aspirations and educational attainment—reflecting primarily a role model effect of female leadership—and reduce gender discrimination in the long-term ( Beaman and others, 2012 ; Pande and Ford, 2012 ). A program to enhance financial inclusion of women—under which rural Indian women received bank accounts, training in account use, and direct deposit of public sector wages into their own (versus husbands ’) accounts— incentivizes women to work and helps liberalize women’s own work-related norms and shift perceptions of community norms ( Field and others, 2021 ).

While addressing gender inequality does not have to start with and solely focus on policies that are targeted at the root causes of gender inequality, it would need to end there, as fully eliminating gender inequality would require addressing the root causes of gender inequality, and policies aiming at reducing gender inequality in different areas can only go so far. Only then, while some gender gaps may still exist, the allocation of human capital would be fully efficient, reflecting preference /comparative advantages between men and women.

• VII. Discussions

This paper identifies five key issues that are particularly important for the continued efforts to tackle gender inequality:

It is critical to clearly define gender inequality and distinguish it from gender gaps. This has important implications on the policy designs to address gender inequity. However, the literature has paid little attention to this issue, often using gender inequality and gender gaps interchangeably. This paper defines gender gaps as the observed differences between men and women or between boys and girls in the various social and economic indicators, and gender inequality refers to the part that is driven by gender bias and unequal gender rights and opportunities. However, empirically estimating the corresponding gender inequality for each gender gap remains a challenge and requires more efforts on data collection and methodological developments.

The focus of gender inequality has been evolving over time. As some gender gaps are closed, other gender gaps are emerging (not necessarily new, but attracting the attentions of the public and policymakers). This suggests that there is still a long way to go to fully addressing gender inequality. Particularly, gender inequality is getting more subtle and implicit, though the social and economic benefits from addressing the remain gender inequality is still likely to be substantial. This highlights the need to apply a gender lens to a broad range of policies and practices to understand their potential implications on gender inequality. Such efforts help develop a comprehensive strategy, instead of a piece-meal approach with which only some gender inequality is addressed at a time.

Addressing gender inequality benefits everyone, not just women. Thus, the entire society should work together, even for each individual’s own interest. Lower gender inequality not only benefits women, but also benefits children—as women trend to emphasize child welfare more than men—and the entire economy through the positive productivity externality from more balanced gender roles, and improved economic growth, financial stability, and income inequality. In addition to further strengthening the empirical evidence in these areas, there is an urgent need for the findings to be incorporated into policy designs and decision-making.

Policies and their designs can help accelerate the decline of gender inequality from economic development and technological advances. Both general policies and targeted gender policies can play a role, and the pros and cons of such policies should be carefully assessed. As gender inequality becomes more subtle and implicit (e.g., in field of study, the distribution of employment across sectors, and mid-level management positions), general policies will typically not work, unlike for school enrollments and labor force participation. Thus, targeted gender policies will need to play a bigger role. More analytical work is needed on what programs work and under what conditions. Also, this means that analytical work geared at separating gender inequality from gender gaps is all that more important.

While fully addressing gender inequality requires the elimination of the root cause s of gender inequality (e.g., gender bias and social norms), this does not mean that policies are not targeted at the root causes of gender inequality do not have a role. In fact, they can still be effective, as they can generate immediate social and economic benefits and indirectly affect gender bias and social norms. Policies directly targeted at the root causes of gender inequality would be generally preferred but appear limited, and research to expand the policy toolkit would be particularly useful.

One general issue in the efforts to address gender inequality is the lack of gender disaggregated data. Great progress has been made. For example, The IMF’s Financial Access Survey (FAS) is a unique source of annual supply-side data on access to and use of basic financial services by gender. The World Development Indicators (WDI) from the World Bank now present many statistics by male and female separately. Missing data, however, are still widespread, particularly in low-income countries. Therefore, continued efforts are still needed to further expand data availability in terms of both coverage and quality.

Another important issue the paper only marginally touches upon is the challenge of turning policy designs into practices. The analysis of Women, Business, and the Law index on several gender gaps suggests that it is not automatic that laws and regulations to promote gender equality will lead to immediate improvements in gender outcomes. Implementation remains a challenge for many countries, particularly developing economies with limited administrative capacity. For example, as reported in Evans and Yuan (2022) , many similar policy interventions have substantially different impacts across countries. Conditional cash transfer in South Africa is the best intervention among the 267 educational interventions in 54 low- and middle-income countries, while conditional cash transfer in the Philippines is one of the ten worst interventions. Thus, the importance of effective implementation cannot be overstated.

Gender Gaps in Education

Gender Gaps in Field of Study

Gender Gaps in Labor Force Participation and Employment by Sector

Gender Gaps in Leadership Positions

Gender Wage Gap in Selected Economies

Gender Gaps in Financial Access and Legal Barriers to Gender Equality

Large Variations in Gender Gaps across Countries

Alternative Specifications on the Effects of Laws and Regulations on Selected Gender Gaps

Afridi Farzana , Vegard Iversen , and M. R. Sharan , 2017 , “ Women Political Leaders, Corruption, and Learning: Evidence from a Large Public Program in India ,” Economic Development and Cultural Change, Volume 66 , Number 1 : 1 – 30 .

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Strøm , Reidar , Bert D’Espallier , and Roy Mersland , 2014 , “ Female Leadership, Performance, and Governance in Microfinance Institutions .” Journal of Banking and Finance 42 : 60 –75.

Tewari , Ishani , and Yabin Wang , 2021 , “ Durable Ownership, Time Allocation, and Female Labor Force Participation: Evidence from China’s “Home Appliances to the Countryside” Rebate ,” Economic Development and Cultural Change , Volume 70 , Number 1 : 87 - 127 .

World Economic Forum (WEF) , 2021 , “ Global Gender Gap Report 2021 ,” World Economic Forum , Geneva .

World Bank , 2006 , “ Repositioning Nutrition as Central to Development: A Strategy for Large Scale Action ,” World Bank , Washington DC .

World Bank , 2021 , Women, Business and the Law 2021 , World Bank , Washington DC .

Xiao , Pengpeng , 2021 , “ Wage and Employment Discrimination by Gender in Labor Market Equilibrium ,” Working Paper 144 , VATT Institute for Economic Research .

Zhang , Eva , and Tianlei Huang , 2020 , “ The Gender Gap in Labor Force Participation Is Widening in China While Narrowing in Other Parts of the World ,” PIIE Charts , Peterson Institute for International Economics ( https://www.piie.com/research/piie-charts/gender-gap-labor-force-participation-widening-china-while-narrowing-other-parts ).

In the rest of the paper, the discussions typically center around gender inequality against women, but the same arguments can be made for gender inequality against men when applicable.

The global commitment to achieving gender equality an d accelerating efforts to end gender inequality is reflected in the 2030 Sustainable Development Goal 5 , which includes nine targets covering discrimination and violence against women, child marriage, unpaid care and domestic work, leadership role, access to reproductive health, rights to economic resources, and technology use to promote women empowerment. In addition, achieving other SDGs could also have important implications for gender equality, for example, under Sustainable Development Goal 4 on quality education .

For example, a number of countries have mandated gender diversity on corporate boards of directors, including Austria, Belgium, Finland, France, Germany, Iceland, India, Israel, Italy, Kenya, Netherlands, Norway, Pakistan, Portugal, Spain, Quebec of Canada, and California of United States. Malaysia is one recent case and mandates its publicly traded firms to have at least one-woman director on their boards from September 1, 2022.

This includes both taste-based and statistical discrimination; taste-based discrimination refers to less favorable attitudes and prejudice towards women, while statistical discrimination refers to the use of perception or statistics on women as a group in decision-making when information on a specific woman is lacking; for example, firms may make employment and pay decisions, based on average leave days taken and average job turnover rates for women and men; studies have found that statistical discrimination plays an important role in gender gaps , such as in wages and employment ( List, 2004 ; Xiao, 2020; Cordoba and others, 2021 ).

It should be noted that preference here refers to choices made in the absence of gender inequality. This is important as gender inequality and the associated social norms often operate through affecting the willingness of men and women in making certain choices.

For example, the comparative advantage of women often refers to the innate advantage of women in brain versus brawn jobs in the literature.

According to the Bureau of Labor Statistics, around 13 percent of registered nurses in the United States are male in 2021.

The empirical observation of U-shaped female labor force participation over the course of economic development reflects other factors that also influence the decision of women entering the labor market ( Jayachandran, 2021 ). This includes the less need for a second income earner in a household, women’s comparative advantage in rearing children, the need to balance employment with household responsibilities, and social/cultural norms on “suitable” jobs for women, for example, between manufacturing jobs and service sector jobs.

While there is little empirical evidence on to what extent unpaid work is driven by preference and social norms, it is generally recognized that both play a role ( Alonso and others, 2019 ).

The index measures laws and regulations that affect women’s economic opportunities, based on eight indicators structured around women’s interactions with the law as they move through their careers: mobility, workplace, pay, marriage, parenthood, entrepreneurship, assets, and pension. Although it is critical to ensuring women’s economic inclusion, implementation of laws is not currently measured. Instead, Women, Business and the Law identifies legal differences between men and women as one step toward a better understanding of where women’s economic rights may be restricted in practice ( World Bank, 2021 ).

While the paper focuses on education, labor market, financial access and legal barriers, similar patterns are also observed in other areas. For example, in advanced economies, while there are little gender differences in health insurance and the ability to seek healthcare, a growing body of evidence suggests that female patients—relative to male patients—receive less healthcare for similar medical conditions and are more likely to be told by providers that their symptoms are emotionally driven rather than arising from a physical impairment; recent evidence also shows that there are large gender gaps in receiving benefits from social insurance programs that rely on medical evaluations ( Cabral and Dillender, 2021a ). For example, Low and Pistaferri (2019) show that female applicants for Social Security Disability Insurance are 20 percentage points more likely to be rejected than similar male applicants. The gender imbalance in the physician workforce can explain a large part of the gap ( Cabral and Dillender, 2021b ).

The literature of broad diversity (e.g., gender, race, and age) on firm productivity and team performance also yields mixed effects (see OECD (2020) for a review).

The study sample covers all countries between 1990 and 2019, when data are available.

Please see Appendix Table 1 for alternative specifications. Without including a time trend, the estimates are larger, more statistically significant, and have the expected signs for all five gender gaps, including labor force participation. Gonzales and others (2015a) and Hyland and others (2020 ; 2021) do not include a time trend and show similar results. The results from a random effects specification often lie somewhere in between.

See, for example, Bergman and others (2022) on the gender employment implication of the Federal Reserve’s recent move from a strict to an average inflation targeting framework; Erten and Metzger (2019) on currency undervaluation and female labor force participation ; and Kim and Williams (2021) on the effects of the minimum wage on women’s intrahousehold bargaining power.

Beaman and other (2012) , however, find that quota policies for female leadership helps improve adolescent girls’ career aspirations and educational attainment.

For example, the studies typically do not consider the impact of gender quotas on reducing gender bias in the broad society.

One example of targeted policies at gender inequality in employment is a payroll tax cut for female hires, introduced in 2012 in Italy to spur female employment and to stimulate business activity by reducing labor costs. The preferential tax rate is only available in occupations with large gender employment gap and has requirement for length in unemployment, which varies by age, whether in economically disadvantaged areas, and occupation. In addition, the preferential payroll tax scheme is valid for up to 12 months for temporary jobs and 18 months for permanent jobs. Firms can use the payroll tax cut only if overall employment would not decrease with respect to past employment. The complex eligibility criteria highlight the challenges in designing targeted gender policies while limiting their efficiency cost. Rubolino (2022) finds that payroll tax cut generates long-lasting growth in female employment with little effect on net wages and without crowding out male employment. However, the efficiency implication of the reform is not fully analyzed, as it is unclear what would have happened had the tax cut not been gender targeted,

See also Cook and others (2021) and Becker and others (2016) , which show that targeted mentoring programs can have significant and long-lasting effects on inclusion in STEM careers, where income, race, and gender gaps in acquiring education have been due to a lack of mentoring and exposure to science and innovation careers rather than differences in ability.

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Twenty years of gender equality research: A scoping review based on a new semantic indicator

Paola belingheri.

1 Dipartimento di Ingegneria dell’Energia, dei Sistemi, del Territorio e delle Costruzioni, Università degli Studi di Pisa, Largo L. Lazzarino, Pisa, Italy

Filippo Chiarello

Andrea fronzetti colladon.

2 Department of Engineering, University of Perugia, Perugia, Italy

3 Department of Management, Kozminski University, Warsaw, Poland

Paola Rovelli

4 Faculty of Economics and Management, Centre for Family Business Management, Free University of Bozen-Bolzano, Bozen-Bolzano, Italy

Associated Data

All relevant data are within the manuscript and its supporting information files. The only exception is the text of the abstracts (over 15,000) that we have downloaded from Scopus. These abstracts can be retrieved from Scopus, but we do not have permission to redistribute them.

Gender equality is a major problem that places women at a disadvantage thereby stymieing economic growth and societal advancement. In the last two decades, extensive research has been conducted on gender related issues, studying both their antecedents and consequences. However, existing literature reviews fail to provide a comprehensive and clear picture of what has been studied so far, which could guide scholars in their future research. Our paper offers a scoping review of a large portion of the research that has been published over the last 22 years, on gender equality and related issues, with a specific focus on business and economics studies. Combining innovative methods drawn from both network analysis and text mining, we provide a synthesis of 15,465 scientific articles. We identify 27 main research topics, we measure their relevance from a semantic point of view and the relationships among them, highlighting the importance of each topic in the overall gender discourse. We find that prominent research topics mostly relate to women in the workforce–e.g., concerning compensation, role, education, decision-making and career progression. However, some of them are losing momentum, and some other research trends–for example related to female entrepreneurship, leadership and participation in the board of directors–are on the rise. Besides introducing a novel methodology to review broad literature streams, our paper offers a map of the main gender-research trends and presents the most popular and the emerging themes, as well as their intersections, outlining important avenues for future research.

Introduction

The persistent gender inequalities that currently exist across the developed and developing world are receiving increasing attention from economists, policymakers, and the general public [e.g., 1 – 3 ]. Economic studies have indicated that women’s education and entry into the workforce contributes to social and economic well-being [e.g., 4 , 5 ], while their exclusion from the labor market and from managerial positions has an impact on overall labor productivity and income per capita [ 6 , 7 ]. The United Nations selected gender equality, with an emphasis on female education, as part of the Millennium Development Goals [ 8 ], and gender equality at-large as one of the 17 Sustainable Development Goals (SDGs) to be achieved by 2030 [ 9 ]. These latter objectives involve not only developing nations, but rather all countries, to achieve economic, social and environmental well-being.

As is the case with many SDGs, gender equality is still far from being achieved and persists across education, access to opportunities, or presence in decision-making positions [ 7 , 10 , 11 ]. As we enter the last decade for the SDGs’ implementation, and while we are battling a global health pandemic, effective and efficient action becomes paramount to reach this ambitious goal.

Scholars have dedicated a massive effort towards understanding gender equality, its determinants, its consequences for women and society, and the appropriate actions and policies to advance women’s equality. Many topics have been covered, ranging from women’s education and human capital [ 12 , 13 ] and their role in society [e.g., 14 , 15 ], to their appointment in firms’ top ranked positions [e.g., 16 , 17 ] and performance implications [e.g., 18 , 19 ]. Despite some attempts, extant literature reviews provide a narrow view on these issues, restricted to specific topics–e.g., female students’ presence in STEM fields [ 20 ], educational gender inequality [ 5 ], the gender pay gap [ 21 ], the glass ceiling effect [ 22 ], leadership [ 23 ], entrepreneurship [ 24 ], women’s presence on the board of directors [ 25 , 26 ], diversity management [ 27 ], gender stereotypes in advertisement [ 28 ], or specific professions [ 29 ]. A comprehensive view on gender-related research, taking stock of key findings and under-studied topics is thus lacking.

Extant literature has also highlighted that gender issues, and their economic and social ramifications, are complex topics that involve a large number of possible antecedents and outcomes [ 7 ]. Indeed, gender equality actions are most effective when implemented in unison with other SDGs (e.g., with SDG 8, see [ 30 ]) in a synergetic perspective [ 10 ]. Many bodies of literature (e.g., business, economics, development studies, sociology and psychology) approach the problem of achieving gender equality from different perspectives–often addressing specific and narrow aspects. This sometimes leads to a lack of clarity about how different issues, circumstances, and solutions may be related in precipitating or mitigating gender inequality or its effects. As the number of papers grows at an increasing pace, this issue is exacerbated and there is a need to step back and survey the body of gender equality literature as a whole. There is also a need to examine synergies between different topics and approaches, as well as gaps in our understanding of how different problems and solutions work together. Considering the important topic of women’s economic and social empowerment, this paper aims to fill this gap by answering the following research question: what are the most relevant findings in the literature on gender equality and how do they relate to each other ?

To do so, we conduct a scoping review [ 31 ], providing a synthesis of 15,465 articles dealing with gender equity related issues published in the last twenty-two years, covering both the periods of the MDGs and the SDGs (i.e., 2000 to mid 2021) in all the journals indexed in the Academic Journal Guide’s 2018 ranking of business and economics journals. Given the huge amount of research conducted on the topic, we adopt an innovative methodology, which relies on social network analysis and text mining. These techniques are increasingly adopted when surveying large bodies of text. Recently, they were applied to perform analysis of online gender communication differences [ 32 ] and gender behaviors in online technology communities [ 33 ], to identify and classify sexual harassment instances in academia [ 34 ], and to evaluate the gender inclusivity of disaster management policies [ 35 ].

Applied to the title, abstracts and keywords of the articles in our sample, this methodology allows us to identify a set of 27 recurrent topics within which we automatically classify the papers. Introducing additional novelty, by means of the Semantic Brand Score (SBS) indicator [ 36 ] and the SBS BI app [ 37 ], we assess the importance of each topic in the overall gender equality discourse and its relationships with the other topics, as well as trends over time, with a more accurate description than that offered by traditional literature reviews relying solely on the number of papers presented in each topic.

This methodology, applied to gender equality research spanning the past twenty-two years, enables two key contributions. First, we extract the main message that each document is conveying and how this is connected to other themes in literature, providing a rich picture of the topics that are at the center of the discourse, as well as of the emerging topics. Second, by examining the semantic relationship between topics and how tightly their discourses are linked, we can identify the key relationships and connections between different topics. This semi-automatic methodology is also highly reproducible with minimum effort.

This literature review is organized as follows. In the next section, we present how we selected relevant papers and how we analyzed them through text mining and social network analysis. We then illustrate the importance of 27 selected research topics, measured by means of the SBS indicator. In the results section, we present an overview of the literature based on the SBS results–followed by an in-depth narrative analysis of the top 10 topics (i.e., those with the highest SBS) and their connections. Subsequently, we highlight a series of under-studied connections between the topics where there is potential for future research. Through this analysis, we build a map of the main gender-research trends in the last twenty-two years–presenting the most popular themes. We conclude by highlighting key areas on which research should focused in the future.

Our aim is to map a broad topic, gender equality research, that has been approached through a host of different angles and through different disciplines. Scoping reviews are the most appropriate as they provide the freedom to map different themes and identify literature gaps, thereby guiding the recommendation of new research agendas [ 38 ].

Several practical approaches have been proposed to identify and assess the underlying topics of a specific field using big data [ 39 – 41 ], but many of them fail without proper paper retrieval and text preprocessing. This is specifically true for a research field such as the gender-related one, which comprises the work of scholars from different backgrounds. In this section, we illustrate a novel approach for the analysis of scientific (gender-related) papers that relies on methods and tools of social network analysis and text mining. Our procedure has four main steps: (1) data collection, (2) text preprocessing, (3) keywords extraction and classification, and (4) evaluation of semantic importance and image.

Data collection

In this study, we analyze 22 years of literature on gender-related research. Following established practice for scoping reviews [ 42 ], our data collection consisted of two main steps, which we summarize here below.

Firstly, we retrieved from the Scopus database all the articles written in English that contained the term “gender” in their title, abstract or keywords and were published in a journal listed in the Academic Journal Guide 2018 ranking of the Chartered Association of Business Schools (CABS) ( https://charteredabs.org/wp-content/uploads/2018/03/AJG2018-Methodology.pdf ), considering the time period from Jan 2000 to May 2021. We used this information considering that abstracts, titles and keywords represent the most informative part of a paper, while using the full-text would increase the signal-to-noise ratio for information extraction. Indeed, these textual elements already demonstrated to be reliable sources of information for the task of domain lexicon extraction [ 43 , 44 ]. We chose Scopus as source of literature because of its popularity, its update rate, and because it offers an API to ease the querying process. Indeed, while it does not allow to retrieve the full text of scientific articles, the Scopus API offers access to titles, abstracts, citation information and metadata for all its indexed scholarly journals. Moreover, we decided to focus on the journals listed in the AJG 2018 ranking because we were interested in reviewing business and economics related gender studies only. The AJG is indeed widely used by universities and business schools as a reference point for journal and research rigor and quality. This first step, executed in June 2021, returned more than 55,000 papers.

In the second step–because a look at the papers showed very sparse results, many of which were not in line with the topic of this literature review (e.g., papers dealing with health care or medical issues, where the word gender indicates the gender of the patients)–we applied further inclusion criteria to make the sample more focused on the topic of this literature review (i.e., women’s gender equality issues). Specifically, we only retained those papers mentioning, in their title and/or abstract, both gender-related keywords (e.g., daughter, female, mother) and keywords referring to bias and equality issues (e.g., equality, bias, diversity, inclusion). After text pre-processing (see next section), keywords were first identified from a frequency-weighted list of words found in the titles, abstracts and keywords in the initial list of papers, extracted through text mining (following the same approach as [ 43 ]). They were selected by two of the co-authors independently, following respectively a bottom up and a top-down approach. The bottom-up approach consisted of examining the words found in the frequency-weighted list and classifying those related to gender and equality. The top-down approach consisted in searching in the word list for notable gender and equality-related words. Table 1 reports the sets of keywords we considered, together with some examples of words that were used to search for their presence in the dataset (a full list is provided in the S1 Text ). At end of this second step, we obtained a final sample of 15,465 relevant papers.

Text processing and keyword extraction

Text preprocessing aims at structuring text into a form that can be analyzed by statistical models. In the present section, we describe the preprocessing steps we applied to paper titles and abstracts, which, as explained below, partially follow a standard text preprocessing pipeline [ 45 ]. These activities have been performed using the R package udpipe [ 46 ].

The first step is n-gram extraction (i.e., a sequence of words from a given text sample) to identify which n-grams are important in the analysis, since domain-specific lexicons are often composed by bi-grams and tri-grams [ 47 ]. Multi-word extraction is usually implemented with statistics and linguistic rules, thus using the statistical properties of n-grams or machine learning approaches [ 48 ]. However, for the present paper, we used Scopus metadata in order to have a more effective and efficient n-grams collection approach [ 49 ]. We used the keywords of each paper in order to tag n-grams with their associated keywords automatically. Using this greedy approach, it was possible to collect all the keywords listed by the authors of the papers. From this list, we extracted only keywords composed by two, three and four words, we removed all the acronyms and rare keywords (i.e., appearing in less than 1% of papers), and we clustered keywords showing a high orthographic similarity–measured using a Levenshtein distance [ 50 ] lower than 2, considering these groups of keywords as representing same concepts, but expressed with different spelling. After tagging the n-grams in the abstracts, we followed a common data preparation pipeline that consists of the following steps: (i) tokenization, that splits the text into tokens (i.e., single words and previously tagged multi-words); (ii) removal of stop-words (i.e. those words that add little meaning to the text, usually being very common and short functional words–such as “and”, “or”, or “of”); (iii) parts-of-speech tagging, that is providing information concerning the morphological role of a word and its morphosyntactic context (e.g., if the token is a determiner, the next token is a noun or an adjective with very high confidence, [ 51 ]); and (iv) lemmatization, which consists in substituting each word with its dictionary form (or lemma). The output of the latter step allows grouping together the inflected forms of a word. For example, the verbs “am”, “are”, and “is” have the shared lemma “be”, or the nouns “cat” and “cats” both share the lemma “cat”. We preferred lemmatization over stemming [ 52 ] in order to obtain more interpretable results.

In addition, we identified a further set of keywords (with respect to those listed in the “keywords” field) by applying a series of automatic words unification and removal steps, as suggested in past research [ 53 , 54 ]. We removed: sparse terms (i.e., occurring in less than 0.1% of all documents), common terms (i.e., occurring in more than 10% of all documents) and retained only nouns and adjectives. It is relevant to notice that no document was lost due to these steps. We then used the TF-IDF function [ 55 ] to produce a new list of keywords. We additionally tested other approaches for the identification and clustering of keywords–such as TextRank [ 56 ] or Latent Dirichlet Allocation [ 57 ]–without obtaining more informative results.

Classification of research topics

To guide the literature analysis, two experts met regularly to examine the sample of collected papers and to identify the main topics and trends in gender research. Initially, they conducted brainstorming sessions on the topics they expected to find, due to their knowledge of the literature. This led to an initial list of topics. Subsequently, the experts worked independently, also supported by the keywords in paper titles and abstracts extracted with the procedure described above.

Considering all this information, each expert identified and clustered relevant keywords into topics. At the end of the process, the two assignments were compared and exhibited a 92% agreement. Another meeting was held to discuss discordant cases and reach a consensus. This resulted in a list of 27 topics, briefly introduced in Table 2 and subsequently detailed in the following sections.

Evaluation of semantic importance

Working on the lemmatized corpus of the 15,465 papers included in our sample, we proceeded with the evaluation of semantic importance trends for each topic and with the analysis of their connections and prevalent textual associations. To this aim, we used the Semantic Brand Score indicator [ 36 ], calculated through the SBS BI webapp [ 37 ] that also produced a brand image report for each topic. For this study we relied on the computing resources of the ENEA/CRESCO infrastructure [ 58 ].

The Semantic Brand Score (SBS) is a measure of semantic importance that combines methods of social network analysis and text mining. It is usually applied for the analysis of (big) textual data to evaluate the importance of one or more brands, names, words, or sets of keywords [ 36 ]. Indeed, the concept of “brand” is intended in a flexible way and goes beyond products or commercial brands. In this study, we evaluate the SBS time-trends of the keywords defining the research topics discussed in the previous section. Semantic importance comprises the three dimensions of topic prevalence, diversity and connectivity. Prevalence measures how frequently a research topic is used in the discourse. The more a topic is mentioned by scientific articles, the more the research community will be aware of it, with possible increase of future studies; this construct is partly related to that of brand awareness [ 59 ]. This effect is even stronger, considering that we are analyzing the title, abstract and keywords of the papers, i.e. the parts that have the highest visibility. A very important characteristic of the SBS is that it considers the relationships among words in a text. Topic importance is not just a matter of how frequently a topic is mentioned, but also of the associations a topic has in the text. Specifically, texts are transformed into networks of co-occurring words, and relationships are studied through social network analysis [ 60 ]. This step is necessary to calculate the other two dimensions of our semantic importance indicator. Accordingly, a social network of words is generated for each time period considered in the analysis–i.e., a graph made of n nodes (words) and E edges weighted by co-occurrence frequency, with W being the set of edge weights. The keywords representing each topic were clustered into single nodes.

The construct of diversity relates to that of brand image [ 59 ], in the sense that it considers the richness and distinctiveness of textual (topic) associations. Considering the above-mentioned networks, we calculated diversity using the distinctiveness centrality metric–as in the formula presented by Fronzetti Colladon and Naldi [ 61 ].

Lastly, connectivity was measured as the weighted betweenness centrality [ 62 , 63 ] of each research topic node. We used the formula presented by Wasserman and Faust [ 60 ]. The dimension of connectivity represents the “brokerage power” of each research topic–i.e., how much it can serve as a bridge to connect other terms (and ultimately topics) in the discourse [ 36 ].

The SBS is the final composite indicator obtained by summing the standardized scores of prevalence, diversity and connectivity. Standardization was carried out considering all the words in the corpus, for each specific timeframe.

This methodology, applied to a large and heterogeneous body of text, enables to automatically identify two important sets of information that add value to the literature review. Firstly, the relevance of each topic in literature is measured through a composite indicator of semantic importance, rather than simply looking at word frequencies. This provides a much richer picture of the topics that are at the center of the discourse, as well as of the topics that are emerging in the literature. Secondly, it enables to examine the extent of the semantic relationship between topics, looking at how tightly their discourses are linked. In a field such as gender equality, where many topics are closely linked to each other and present overlaps in issues and solutions, this methodology offers a novel perspective with respect to traditional literature reviews. In addition, it ensures reproducibility over time and the possibility to semi-automatically update the analysis, as new papers become available.

Overview of main topics

In terms of descriptive textual statistics, our corpus is made of 15,465 text documents, consisting of a total of 2,685,893 lemmatized tokens (words) and 32,279 types. As a result, the type-token ratio is 1.2%. The number of hapaxes is 12,141, with a hapax-token ratio of 37.61%.

Fig 1 shows the list of 27 topics by decreasing SBS. The most researched topic is compensation , exceeding all others in prevalence, diversity, and connectivity. This means it is not only mentioned more often than other topics, but it is also connected to a greater number of other topics and is central to the discourse on gender equality. The next four topics are, in order of SBS, role , education , decision-making , and career progression . These topics, except for education , all concern women in the workforce. Between these first five topics and the following ones there is a clear drop in SBS scores. In particular, the topics that follow have a lower connectivity than the first five. They are hiring , performance , behavior , organization , and human capital . Again, except for behavior and human capital , the other three topics are purely related to women in the workforce. After another drop-off, the following topics deal prevalently with women in society. This trend highlights that research on gender in business journals has so far mainly paid attention to the conditions that women experience in business contexts, while also devoting some attention to women in society.

Fig 2 shows the SBS time series of the top 10 topics. While there has been a general increase in the number of Scopus-indexed publications in the last decade, we notice that some SBS trends remain steady, or even decrease. In particular, we observe that the main topic of the last twenty-two years, compensation , is losing momentum. Since 2016, it has been surpassed by decision-making , education and role , which may indicate that literature is increasingly attempting to identify root causes of compensation inequalities. Moreover, in the last two years, the topics of hiring , performance , and organization are experiencing the largest importance increase.

Fig 3 shows the SBS time trends of the remaining 17 topics (i.e., those not in the top 10). As we can see from the graph, there are some that maintain a steady trend–such as reputation , management , networks and governance , which also seem to have little importance. More relevant topics with average stationary trends (except for the last two years) are culture , family , and parenting . The feminine topic is among the most important here, and one of those that exhibit the larger variations over time (similarly to leadership ). On the other hand, the are some topics that, even if not among the most important, show increasing SBS trends; therefore, they could be considered as emerging topics and could become popular in the near future. These are entrepreneurship , leadership , board of directors , and sustainability . These emerging topics are also interesting to anticipate future trends in gender equality research that are conducive to overall equality in society.

In addition to the SBS score of the different topics, the network of terms they are associated to enables to gauge the extent to which their images (textual associations) overlap or differ ( Fig 4 ).

There is a central cluster of topics with high similarity, which are all connected with women in the workforce. The cluster includes topics such as organization , decision-making , performance , hiring , human capital , education and compensation . In addition, the topic of well-being is found within this cluster, suggesting that women’s equality in the workforce is associated to well-being considerations. The emerging topics of entrepreneurship and leadership are also closely connected with each other, possibly implying that leadership is a much-researched quality in female entrepreneurship. Topics that are relatively more distant include personality , politics , feminine , empowerment , management , board of directors , reputation , governance , parenting , masculine and network .

The following sections describe the top 10 topics and their main associations in literature (see Table 3 ), while providing a brief overview of the emerging topics.

Compensation

The topic of compensation is related to the topics of role , hiring , education and career progression , however, also sees a very high association with the words gap and inequality . Indeed, a well-known debate in degrowth economics centers around whether and how to adequately compensate women for their childbearing, childrearing, caregiver and household work [e.g., 30 ].

Even in paid work, women continue being offered lower compensations than their male counterparts who have the same job or cover the same role [ 64 – 67 ]. This severe inequality has been widely studied by scholars over the last twenty-two years. Dealing with this topic, some specific roles have been addressed. Specifically, research highlighted differences in compensation between female and male CEOs [e.g., 68 ], top executives [e.g., 69 ], and boards’ directors [e.g., 70 ]. Scholars investigated the determinants of these gaps, such as the gender composition of the board [e.g., 71 – 73 ] or women’s individual characteristics [e.g., 71 , 74 ].

Among these individual characteristics, education plays a relevant role [ 75 ]. Education is indeed presented as the solution for women, not only to achieve top executive roles, but also to reduce wage inequality [e.g., 76 , 77 ]. Past research has highlighted education influences on gender wage gaps, specifically referring to gender differences in skills [e.g., 78 ], college majors [e.g., 79 ], and college selectivity [e.g., 80 ].

Finally, the wage gap issue is strictly interrelated with hiring –e.g., looking at whether being a mother affects hiring and compensation [e.g., 65 , 81 ] or relating compensation to unemployment [e.g., 82 ]–and career progression –for instance looking at meritocracy [ 83 , 84 ] or the characteristics of the boss for whom women work [e.g., 85 ].

The roles covered by women have been deeply investigated. Scholars have focused on the role of women in their families and the society as a whole [e.g., 14 , 15 ], and, more widely, in business contexts [e.g., 18 , 81 ]. Indeed, despite still lagging behind their male counterparts [e.g., 86 , 87 ], in the last decade there has been an increase in top ranked positions achieved by women [e.g., 88 , 89 ]. Following this phenomenon, scholars have posed greater attention towards the presence of women in the board of directors [e.g., 16 , 18 , 90 , 91 ], given the increasing pressure to appoint female directors that firms, especially listed ones, have experienced. Other scholars have focused on the presence of women covering the role of CEO [e.g., 17 , 92 ] or being part of the top management team [e.g., 93 ]. Irrespectively of the level of analysis, all these studies tried to uncover the antecedents of women’s presence among top managers [e.g., 92 , 94 ] and the consequences of having a them involved in the firm’s decision-making –e.g., on performance [e.g., 19 , 95 , 96 ], risk [e.g., 97 , 98 ], and corporate social responsibility [e.g., 99 , 100 ].

Besides studying the difficulties and discriminations faced by women in getting a job [ 81 , 101 ], and, more specifically in the hiring , appointment, or career progression to these apical roles [e.g., 70 , 83 ], the majority of research of women’s roles dealt with compensation issues. Specifically, scholars highlight the pay-gap that still exists between women and men, both in general [e.g., 64 , 65 ], as well as referring to boards’ directors [e.g., 70 , 102 ], CEOs and executives [e.g., 69 , 103 , 104 ].

Finally, other scholars focused on the behavior of women when dealing with business. In this sense, particular attention has been paid to leadership and entrepreneurial behaviors. The former quite overlaps with dealing with the roles mentioned above, but also includes aspects such as leaders being stereotyped as masculine [e.g., 105 ], the need for greater exposure to female leaders to reduce biases [e.g., 106 ], or female leaders acting as queen bees [e.g., 107 ]. Regarding entrepreneurship , scholars mainly investigated women’s entrepreneurial entry [e.g., 108 , 109 ], differences between female and male entrepreneurs in the evaluations and funding received from investors [e.g., 110 , 111 ], and their performance gap [e.g., 112 , 113 ].

Education has long been recognized as key to social advancement and economic stability [ 114 ], for job progression and also a barrier to gender equality, especially in STEM-related fields. Research on education and gender equality is mostly linked with the topics of compensation , human capital , career progression , hiring , parenting and decision-making .

Education contributes to a higher human capital [ 115 ] and constitutes an investment on the part of women towards their future. In this context, literature points to the gender gap in educational attainment, and the consequences for women from a social, economic, personal and professional standpoint. Women are found to have less access to formal education and information, especially in emerging countries, which in turn may cause them to lose social and economic opportunities [e.g., 12 , 116 – 119 ]. Education in local and rural communities is also paramount to communicate the benefits of female empowerment , contributing to overall societal well-being [e.g., 120 ].

Once women access education, the image they have of the world and their place in society (i.e., habitus) affects their education performance [ 13 ] and is passed on to their children. These situations reinforce gender stereotypes, which become self-fulfilling prophecies that may negatively affect female students’ performance by lowering their confidence and heightening their anxiety [ 121 , 122 ]. Besides formal education, also the information that women are exposed to on a daily basis contributes to their human capital . Digital inequalities, for instance, stems from men spending more time online and acquiring higher digital skills than women [ 123 ].

Education is also a factor that should boost employability of candidates and thus hiring , career progression and compensation , however the relationship between these factors is not straightforward [ 115 ]. First, educational choices ( decision-making ) are influenced by variables such as self-efficacy and the presence of barriers, irrespectively of the career opportunities they offer, especially in STEM [ 124 ]. This brings additional difficulties to women’s enrollment and persistence in scientific and technical fields of study due to stereotypes and biases [ 125 , 126 ]. Moreover, access to education does not automatically translate into job opportunities for women and minority groups [ 127 , 128 ] or into female access to managerial positions [ 129 ].

Finally, parenting is reported as an antecedent of education [e.g., 130 ], with much of the literature focusing on the role of parents’ education on the opportunities afforded to children to enroll in education [ 131 – 134 ] and the role of parenting in their offspring’s perception of study fields and attitudes towards learning [ 135 – 138 ]. Parental education is also a predictor of the other related topics, namely human capital and compensation [ 139 ].

Decision-making

This literature mainly points to the fact that women are thought to make decisions differently than men. Women have indeed different priorities, such as they care more about people’s well-being, working with people or helping others, rather than maximizing their personal (or their firm’s) gain [ 140 ]. In other words, women typically present more communal than agentic behaviors, which are instead more frequent among men [ 141 ]. These different attitude, behavior and preferences in turn affect the decisions they make [e.g., 142 ] and the decision-making of the firm in which they work [e.g., 143 ].

At the individual level, gender affects, for instance, career aspirations [e.g., 144 ] and choices [e.g., 142 , 145 ], or the decision of creating a venture [e.g., 108 , 109 , 146 ]. Moreover, in everyday life, women and men make different decisions regarding partners [e.g., 147 ], childcare [e.g., 148 ], education [e.g., 149 ], attention to the environment [e.g., 150 ] and politics [e.g., 151 ].

At the firm level, scholars highlighted, for example, how the presence of women in the board affects corporate decisions [e.g., 152 , 153 ], that female CEOs are more conservative in accounting decisions [e.g., 154 ], or that female CFOs tend to make more conservative decisions regarding the firm’s financial reporting [e.g., 155 ]. Nevertheless, firm level research also investigated decisions that, influenced by gender bias, affect women, such as those pertaining hiring [e.g., 156 , 157 ], compensation [e.g., 73 , 158 ], or the empowerment of women once appointed [ 159 ].

Career progression

Once women have entered the workforce, the key aspect to achieve gender equality becomes career progression , including efforts toward overcoming the glass ceiling. Indeed, according to the SBS analysis, career progression is highly related to words such as work, social issues and equality. The topic with which it has the highest semantic overlap is role , followed by decision-making , hiring , education , compensation , leadership , human capital , and family .

Career progression implies an advancement in the hierarchical ladder of the firm, assigning managerial roles to women. Coherently, much of the literature has focused on identifying rationales for a greater female participation in the top management team and board of directors [e.g., 95 ] as well as the best criteria to ensure that the decision-makers promote the most valuable employees irrespectively of their individual characteristics, such as gender [e.g., 84 ]. The link between career progression , role and compensation is often provided in practice by performance appraisal exercises, frequently rooted in a culture of meritocracy that guides bonuses, salary increases and promotions. However, performance appraisals can actually mask gender-biased decisions where women are held to higher standards than their male colleagues [e.g., 83 , 84 , 95 , 160 , 161 ]. Women often have less opportunities to gain leadership experience and are less visible than their male colleagues, which constitute barriers to career advancement [e.g., 162 ]. Therefore, transparency and accountability, together with procedures that discourage discretionary choices, are paramount to achieve a fair career progression [e.g., 84 ], together with the relaxation of strict job boundaries in favor of cross-functional and self-directed tasks [e.g., 163 ].

In addition, a series of stereotypes about the type of leadership characteristics that are required for top management positions, which fit better with typical male and agentic attributes, are another key barrier to career advancement for women [e.g., 92 , 160 ].

Hiring is the entrance gateway for women into the workforce. Therefore, it is related to other workforce topics such as compensation , role , career progression , decision-making , human capital , performance , organization and education .

A first stream of literature focuses on the process leading up to candidates’ job applications, demonstrating that bias exists before positions are even opened, and it is perpetuated both by men and women through networking and gatekeeping practices [e.g., 164 , 165 ].

The hiring process itself is also subject to biases [ 166 ], for example gender-congruity bias that leads to men being preferred candidates in male-dominated sectors [e.g., 167 ], women being hired in positions with higher risk of failure [e.g., 168 ] and limited transparency and accountability afforded by written processes and procedures [e.g., 164 ] that all contribute to ascriptive inequality. In addition, providing incentives for evaluators to hire women may actually work to this end; however, this is not the case when supporting female candidates endangers higher-ranking male ones [ 169 ].

Another interesting perspective, instead, looks at top management teams’ composition and the effects on hiring practices, indicating that firms with more women in top management are less likely to lay off staff [e.g., 152 ].

Performance

Several scholars posed their attention towards women’s performance, its consequences [e.g., 170 , 171 ] and the implications of having women in decision-making positions [e.g., 18 , 19 ].

At the individual level, research focused on differences in educational and academic performance between women and men, especially referring to the gender gap in STEM fields [e.g., 171 ]. The presence of stereotype threats–that is the expectation that the members of a social group (e.g., women) “must deal with the possibility of being judged or treated stereotypically, or of doing something that would confirm the stereotype” [ 172 ]–affects women’s interested in STEM [e.g., 173 ], as well as their cognitive ability tests, penalizing them [e.g., 174 ]. A stronger gender identification enhances this gap [e.g., 175 ], whereas mentoring and role models can be used as solutions to this problem [e.g., 121 ]. Despite the negative effect of stereotype threats on girls’ performance [ 176 ], female and male students perform equally in mathematics and related subjects [e.g., 177 ]. Moreover, while individuals’ performance at school and university generally affects their achievements and the field in which they end up working, evidence reveals that performance in math or other scientific subjects does not explain why fewer women enter STEM working fields; rather this gap depends on other aspects, such as culture, past working experiences, or self-efficacy [e.g., 170 ]. Finally, scholars have highlighted the penalization that women face for their positive performance, for instance when they succeed in traditionally male areas [e.g., 178 ]. This penalization is explained by the violation of gender-stereotypic prescriptions [e.g., 179 , 180 ], that is having women well performing in agentic areas, which are typical associated to men. Performance penalization can thus be overcome by clearly conveying communal characteristics and behaviors [ 178 ].

Evidence has been provided on how the involvement of women in boards of directors and decision-making positions affects firms’ performance. Nevertheless, results are mixed, with some studies showing positive effects on financial [ 19 , 181 , 182 ] and corporate social performance [ 99 , 182 , 183 ]. Other studies maintain a negative association [e.g., 18 ], and other again mixed [e.g., 184 ] or non-significant association [e.g., 185 ]. Also with respect to the presence of a female CEO, mixed results emerged so far, with some researches demonstrating a positive effect on firm’s performance [e.g., 96 , 186 ], while other obtaining only a limited evidence of this relationship [e.g., 103 ] or a negative one [e.g., 187 ].

Finally, some studies have investigated whether and how women’s performance affects their hiring [e.g., 101 ] and career progression [e.g., 83 , 160 ]. For instance, academic performance leads to different returns in hiring for women and men. Specifically, high-achieving men are called back significantly more often than high-achieving women, which are penalized when they have a major in mathematics; this result depends on employers’ gendered standards for applicants [e.g., 101 ]. Once appointed, performance ratings are more strongly related to promotions for women than men, and promoted women typically show higher past performance ratings than those of promoted men. This suggesting that women are subject to stricter standards for promotion [e.g., 160 ].

Behavioral aspects related to gender follow two main streams of literature. The first examines female personality and behavior in the workplace, and their alignment with cultural expectations or stereotypes [e.g., 188 ] as well as their impacts on equality. There is a common bias that depicts women as less agentic than males. Certain characteristics, such as those more congruent with male behaviors–e.g., self-promotion [e.g., 189 ], negotiation skills [e.g., 190 ] and general agentic behavior [e.g., 191 ]–, are less accepted in women. However, characteristics such as individualism in women have been found to promote greater gender equality in society [ 192 ]. In addition, behaviors such as display of emotions [e.g., 193 ], which are stereotypically female, work against women’s acceptance in the workplace, requiring women to carefully moderate their behavior to avoid exclusion. A counter-intuitive result is that women and minorities, which are more marginalized in the workplace, tend to be better problem-solvers in innovation competitions due to their different knowledge bases [ 194 ].

The other side of the coin is examined in a parallel literature stream on behavior towards women in the workplace. As a result of biases, prejudices and stereotypes, women may experience adverse behavior from their colleagues, such as incivility and harassment, which undermine their well-being [e.g., 195 , 196 ]. Biases that go beyond gender, such as for overweight people, are also more strongly applied to women [ 197 ].

Organization

The role of women and gender bias in organizations has been studied from different perspectives, which mirror those presented in detail in the following sections. Specifically, most research highlighted the stereotypical view of leaders [e.g., 105 ] and the roles played by women within firms, for instance referring to presence in the board of directors [e.g., 18 , 90 , 91 ], appointment as CEOs [e.g., 16 ], or top executives [e.g., 93 ].

Scholars have investigated antecedents and consequences of the presence of women in these apical roles. On the one side they looked at hiring and career progression [e.g., 83 , 92 , 160 , 168 , 198 ], finding women typically disadvantaged with respect to their male counterparts. On the other side, they studied women’s leadership styles and influence on the firm’s decision-making [e.g., 152 , 154 , 155 , 199 ], with implications for performance [e.g., 18 , 19 , 96 ].

Human capital

Human capital is a transverse topic that touches upon many different aspects of female gender equality. As such, it has the most associations with other topics, starting with education as mentioned above, with career-related topics such as role , decision-making , hiring , career progression , performance , compensation , leadership and organization . Another topic with which there is a close connection is behavior . In general, human capital is approached both from the education standpoint but also from the perspective of social capital.

The behavioral aspect in human capital comprises research related to gender differences for example in cultural and religious beliefs that influence women’s attitudes and perceptions towards STEM subjects [ 142 , 200 – 202 ], towards employment [ 203 ] or towards environmental issues [ 150 , 204 ]. These cultural differences also emerge in the context of globalization which may accelerate gender equality in the workforce [ 205 , 206 ]. Gender differences also appear in behaviors such as motivation [ 207 ], and in negotiation [ 190 ], and have repercussions on women’s decision-making related to their careers. The so-called gender equality paradox sees women in countries with lower gender equality more likely to pursue studies and careers in STEM fields, whereas the gap in STEM enrollment widens as countries achieve greater equality in society [ 171 ].

Career progression is modeled by literature as a choice-process where personal preferences, culture and decision-making affect the chosen path and the outcomes. Some literature highlights how women tend to self-select into different professions than men, often due to stereotypes rather than actual ability to perform in these professions [ 142 , 144 ]. These stereotypes also affect the perceptions of female performance or the amount of human capital required to equal male performance [ 110 , 193 , 208 ], particularly for mothers [ 81 ]. It is therefore often assumed that women are better suited to less visible and less leadership -oriented roles [ 209 ]. Women also express differing preferences towards work-family balance, which affect whether and how they pursue human capital gains [ 210 ], and ultimately their career progression and salary .

On the other hand, men are often unaware of gendered processes and behaviors that they carry forward in their interactions and decision-making [ 211 , 212 ]. Therefore, initiatives aimed at increasing managers’ human capital –by raising awareness of gender disparities in their organizations and engaging them in diversity promotion–are essential steps to counter gender bias and segregation [ 213 ].

Emerging topics: Leadership and entrepreneurship

Among the emerging topics, the most pervasive one is women reaching leadership positions in the workforce and in society. This is still a rare occurrence for two main types of factors, on the one hand, bias and discrimination make it harder for women to access leadership positions [e.g., 214 – 216 ], on the other hand, the competitive nature and high pressure associated with leadership positions, coupled with the lack of women currently represented, reduce women’s desire to achieve them [e.g., 209 , 217 ]. Women are more effective leaders when they have access to education, resources and a diverse environment with representation [e.g., 218 , 219 ].

One sector where there is potential for women to carve out a leadership role is entrepreneurship . Although at the start of the millennium the discourse on entrepreneurship was found to be “discriminatory, gender-biased, ethnocentrically determined and ideologically controlled” [ 220 ], an increasing body of literature is studying how to stimulate female entrepreneurship as an alternative pathway to wealth, leadership and empowerment [e.g., 221 ]. Many barriers exist for women to access entrepreneurship, including the institutional and legal environment, social and cultural factors, access to knowledge and resources, and individual behavior [e.g., 222 , 223 ]. Education has been found to raise women’s entrepreneurial intentions [e.g., 224 ], although this effect is smaller than for men [e.g., 109 ]. In addition, increasing self-efficacy and risk-taking behavior constitute important success factors [e.g., 225 ].

Finally, the topic of sustainability is worth mentioning, as it is the primary objective of the SDGs and is closely associated with societal well-being. As society grapples with the effects of climate change and increasing depletion of natural resources, a narrative has emerged on women and their greater link to the environment [ 226 ]. Studies in developed countries have found some support for women leaders’ attention to sustainability issues in firms [e.g., 227 – 229 ], and smaller resource consumption by women [ 230 ]. At the same time, women will likely be more affected by the consequences of climate change [e.g., 230 ] but often lack the decision-making power to influence local decision-making on resource management and environmental policies [e.g., 231 ].

Research gaps and conclusions

Research on gender equality has advanced rapidly in the past decades, with a steady increase in publications, both in mainstream topics related to women in education and the workforce, and in emerging topics. Through a novel approach combining methods of text mining and social network analysis, we examined a comprehensive body of literature comprising 15,465 papers published between 2000 and mid 2021 on topics related to gender equality. We identified a set of 27 topics addressed by the literature and examined their connections.

At the highest level of abstraction, it is worth noting that papers abound on the identification of issues related to gender inequalities and imbalances in the workforce and in society. Literature has thoroughly examined the (unconscious) biases, barriers, stereotypes, and discriminatory behaviors that women are facing as a result of their gender. Instead, there are much fewer papers that discuss or demonstrate effective solutions to overcome gender bias [e.g., 121 , 143 , 145 , 163 , 194 , 213 , 232 ]. This is partly due to the relative ease in studying the status quo, as opposed to studying changes in the status quo. However, we observed a shift in the more recent years towards solution seeking in this domain, which we strongly encourage future researchers to focus on. In the future, we may focus on collecting and mapping pro-active contributions to gender studies, using additional Natural Language Processing techniques, able to measure the sentiment of scientific papers [ 43 ].

All of the mainstream topics identified in our literature review are closely related, and there is a wealth of insights looking at the intersection between issues such as education and career progression or human capital and role . However, emerging topics are worthy of being furtherly explored. It would be interesting to see more work on the topic of female entrepreneurship , exploring aspects such as education , personality , governance , management and leadership . For instance, how can education support female entrepreneurship? How can self-efficacy and risk-taking behaviors be taught or enhanced? What are the differences in managerial and governance styles of female entrepreneurs? Which personality traits are associated with successful entrepreneurs? Which traits are preferred by venture capitalists and funding bodies?

The emerging topic of sustainability also deserves further attention, as our society struggles with climate change and its consequences. It would be interesting to see more research on the intersection between sustainability and entrepreneurship , looking at how female entrepreneurs are tackling sustainability issues, examining both their business models and their company governance . In addition, scholars are suggested to dig deeper into the relationship between family values and behaviors.

Moreover, it would be relevant to understand how women’s networks (social capital), or the composition and structure of social networks involving both women and men, enable them to increase their remuneration and reach top corporate positions, participate in key decision-making bodies, and have a voice in communities. Furthermore, the achievement of gender equality might significantly change firm networks and ecosystems, with important implications for their performance and survival.

Similarly, research at the nexus of (corporate) governance , career progression , compensation and female empowerment could yield useful insights–for example discussing how enterprises, institutions and countries are managed and the impact for women and other minorities. Are there specific governance structures that favor diversity and inclusion?

Lastly, we foresee an emerging stream of research pertaining how the spread of the COVID-19 pandemic challenged women, especially in the workforce, by making gender biases more evident.

For our analysis, we considered a set of 15,465 articles downloaded from the Scopus database (which is the largest abstract and citation database of peer-reviewed literature). As we were interested in reviewing business and economics related gender studies, we only considered those papers published in journals listed in the Academic Journal Guide (AJG) 2018 ranking of the Chartered Association of Business Schools (CABS). All the journals listed in this ranking are also indexed by Scopus. Therefore, looking at a single database (i.e., Scopus) should not be considered a limitation of our study. However, future research could consider different databases and inclusion criteria.

With our literature review, we offer researchers a comprehensive map of major gender-related research trends over the past twenty-two years. This can serve as a lens to look to the future, contributing to the achievement of SDG5. Researchers may use our study as a starting point to identify key themes addressed in the literature. In addition, our methodological approach–based on the use of the Semantic Brand Score and its webapp–could support scholars interested in reviewing other areas of research.

Supporting information

Acknowledgments.

The computing resources and the related technical support used for this work have been provided by CRESCO/ENEAGRID High Performance Computing infrastructure and its staff. CRESCO/ENEAGRID High Performance Computing infrastructure is funded by ENEA, the Italian National Agency for New Technologies, Energy and Sustainable Economic Development and by Italian and European research programmes (see http://www.cresco.enea.it/english for information).

Funding Statement

P.B and F.C.: Grant of the Department of Energy, Systems, Territory and Construction of the University of Pisa (DESTEC) for the project “Measuring Gender Bias with Semantic Analysis: The Development of an Assessment Tool and its Application in the European Space Industry. P.B., F.C., A.F.C., P.R.: Grant of the Italian Association of Management Engineering (AiIG), “Misure di sostegno ai soci giovani AiIG” 2020, for the project “Gender Equality Through Data Intelligence (GEDI)”. F.C.: EU project ASSETs+ Project (Alliance for Strategic Skills addressing Emerging Technologies in Defence) EAC/A03/2018 - Erasmus+ programme, Sector Skills Alliances, Lot 3: Sector Skills Alliance for implementing a new strategic approach (Blueprint) to sectoral cooperation on skills G.A. NUMBER: 612678-EPP-1-2019-1-IT-EPPKA2-SSA-B.

Data Availability

• DOI: 10.1007/s11150-020-09535-6
• Corpus ID: 158349832

Gender inequality as a barrier to economic growth: a review of the theoretical literature

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Gender Inequality at Work: A Literature Review

• September 2019

• University of Sri Jayewardenepura

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SYSTEMATIC REVIEW article

Linking gender differences with gender equality: a systematic-narrative literature review of basic skills and personality.

• Department of Social Research, University of Turku, Turku, Finland

There is controversy regarding whether gender differences are smaller or larger in societies that promote gender equality highlighting the need for an integrated analysis. This review examines literature correlating, on a national level, gender differences in basic skills—mathematics, science (including attitudes and anxiety), and reading—as well as personality, to gender equality indicators. The aim is to assess the cross-national pattern of these differences when linked to measures of gender equality and explore new explanatory variables that can shed light on this linkage. The review was based on quantitative research relating country-level measures of gender differences to gender equality composite indices and specific indicators. The findings show that the mathematics gender gap from the PISA and TIMMS assessments, is not linked to composite indices and specific indicators, but gender differences are larger in gender-equal countries for reading, mathematics attitudes, and personality (Big Five, HEXACO, Basic Human Values, and Vocational Interests). Research on science and overall scores (mathematics, science, and reading considered together) is inconclusive. It is proposed that the paradox in reading results from the interrelation between basic skills and the attempt to increase girls’ mathematics abilities both acting simultaneously while the paradox in mathematics attitudes might be explained by girls being less exposed to mathematics than boys. On the other hand, a more nuanced understanding of the gender equality paradox in personality is advanced, in which a gene–environment-cultural interplay accounts for the phenomenon. Challenges for future cross-national research are discussed.

1. Introduction

Despite Western countries having considerably advanced in gender equality, gender horizontal segregation remains among the main drivers of economic gender inequality ( Cech, 2013 ). Women have entered the labor market at increasingly high rates since the 70s, nevertheless, they often still work in specific sectors with substantial effects on their income ( Cortes and Pan, 2018 ). Gender segregation is already visible at the educational level where girls are overrepresented in disciplines such as Social Sciences and Humanities; these subjects are characterized by lower labor market prospects and income ( van de Werfhorst, 2017 ). On the other hand, boys prefer STEM fields which offer high-salaried and more status-related careers ( Barone and Assirelli, 2020 ). To explain the phenomenon, scholars in sociology and psychology have been particularly interested in basic skills and personality gender variances due to their influence on gendered career choices and outcomes ( Rosenbloom et al., 2008 ; Dekhtyar et al., 2018 ; Stoet and Geary, 2018 ).

Regardless of doubts about their magnitude ( Hyde, 2005 ; Archer, 2019 ; Hirnstein et al., 2022 ), gender differences in basic skills and personality are well-established in the literature ( Halpern, 2000 ; Halpern et al., 2007 ; Geary, 2010 ; Weisberg et al., 2011 ). The gender gaps favoring boys in mathematics and science are close to zero on average but observable at the upper and lower tails of the distribution ( Halpern et al., 2007 ; Wai et al., 2018 ). Conversely, differences in reading skills (women > men) are more pronounced and already noticeable when comparing men’s and women’s statistical means ( Halpern, 2000 ; Moè et al., 2021 ). Regarding personality (Big Five, HEXACO, Basic Human Values, and Vocational Interests), gender variances, although small to medium, occur across models and share a similar pattern. On the one hand, women score higher in negative emotions and reciprocity as well as prefer to “work with people.” On the other hand, men have more realistic preferences and regard status-related values more ( Schwartz and Rubel, 2005 ; Schmitt et al., 2008 ; Su et al., 2009 ; Lee and Ashton, 2018 ). On a national level, however, the link between these gender differences and gender equality, measured using conventional indicators such as the World Economic Forum’s Global Gender Gap Index (GGI), remains unclear with scholars making contrasting predictions.

Numerous social-role theories of gender differences expect that the gaps between men and women will decrease as equality between them is achieved ( Eagly and Mitchell, 2004 ; Else-Quest et al., 2010 ). These theories argue that cognitive and personality gender differences are derived from socially constructed gender identities based on erroneous essential beliefs (stereotypes) that men and women are intrinsically different ( Wood and Eagly, 2013 ). Gender stereotypes originate from the division of labor in ancient hunter-gatherer societies, in which greater strength allowed men to engage in more power-related activities, while women were tasked with nurturing duties because of their ability to breastfeed ( Eagly and Wood, 1999 ). Stereotypes would emerge early in life, with elementary school children already consistently engaging in gender essentialism, gender stereotyping, and implicit gender associations ( Meyer and Gelman, 2016 ). Parents, teachers, and friends are responsible for reinforcing them, rewarding children for behaving according to gendered expectations ( Gunderson et al., 2012 ), thereby making gender a “primary framing device for social relations” ( Ridgeway, 2006 ). As a result, boys and girls grow up into adults who have gender-specific roles in society and experience gender-conforming environments that shape their distinct skills and personalities ( Diekman and Schneider, 2010 ). The common assumption underlying these theories predicts that essentialist beliefs decrease in countries with higher gender equality. If this is true, empirical research will find smaller gender differences in more gender-equal nations.

Other studies have theorized an opposite trend, with men and women becoming increasingly dissimilar in gender-equal countries ( Charles and Bradley, 2009 ; Kaiser, 2019 ). Recently, Stoet and Geary (2018) labeled this phenomenon “the gender equality paradox.” Some have proposed that this paradox results from an emphasis on individualism and a societal system designed to accommodate women in what is perceived to be their gendered role ( Charles and Grusky, 2018 ). Others have applied an evolutionary approach and argued that in less unequal environments, men and women freely express their intrinsic differences as the privileged access to resources in “more prosperous and more egalitarian” societies favors the emergence of specific gender-evolved behaviors ( Schmitt et al., 2008 ).

Although the topic of gender difference has been widely discussed, whether men and women become progressively similar or different when greater equality between them has been achieved remains uncertain. This paper reviews several theories hypothesizing contrasting patterns, and then turns to the recent scientific debate on gender differences in basic skills from the PISA and TIMMS assessments, as well as personality (Big Five, HEXACO, Basic Human Values, and Vocational Interests) to consider how they relate to measures of gender equality on a national level. Several challenges for future cross-national research are also highlighted. Specifically, the present review indicates that the correlation between gender differences in mathematics and gender equality may derive from the lack of country-level effects in the models, while ecological stress (food consumption and historical levels of pathogen prevalence) may confound the results for personality. In addition, the paper examines explanations of the paradox in different domains and proposes a novel theory to explain the gender equality paradox in personality, where a “feedback-loop” effect (gene–environment-culture interplay) might account for the phenomenon.

The narrative approach was assessed to be the most suitable method for this study. Compared to more analytical methods, it allows for deeper insights into the ongoing debate ( Graham, 1995 ). However, issues may arise with this method due to bias in paper selection and interpretation ( Dijkers, 2009 ). To avoid these issues, the author implemented a systematic approach based on PRISMA guidelines together with the narrative method.

2.1. Eligibility criteria

To be eligible for inclusion, papers had to have been published between 2009 and 2022, and they had to describe quantitative cross-national research analyzing gender differences associated with measures of gender equality (composite indices or specific indicators) utilizing international data. The selected studies were divided into two groups—basic skills and personality—then further divided into multiple subgroups: mathematics, science (including attitudes and anxiety), reading, and overall scores for basic skills, as well as the Big Five, the HEXACO model, basic human values and vocational interests for personality factors. Since they had fewer available papers, the Big Five and HEXACO, as well as basic human values and vocational interests categories were combined.

2.2. Information sources

Published studies were selected from Scopus, Web of Science, Social Science Database, and Google Scholar. The final search was conducted on all databases in November 2022.

2.3. Search strategy

The research focused on gender differences in basic skills and personality due to their strong relationships with horizontal gender segregation. Thus, the main search words were “gender/sex differences in mathematics/reading/science,” “gender/sex differences in personality,” “gender/sex differences in basic human values” and “gender/sex differences in vocational interests.” The search was then refined using “gender equality/egalitarianism/inequality” as parameters.

2.4. Selection process

Only papers published in English were considered, and they were selected based on their titles, abstracts, and keywords. This study’s author was primarily responsible for the selection, although two other scholars supervised the process and ensured systematic application of the selection criteria.

Ninety-one papers were preselected; 35 were excluded after deeper screening because they did not match the selection criteria. An additional 25 studies were excluded because they studied gender differences outside the domains of interest. Consequently, 31 papers were included in the study.

3. Overview of gender differences in basic skills and personality and their possible relation to gender equality

3.1. gender differences.

On a national level, gender differences in basic skills and personality have been repeatedly described. Research has shown that boys slightly outperform girls in complex mathematical riddles ( Reilly et al., 2019 ); this difference has been associated with men’s overrepresentation in STEM fields ( Dekhtyar et al., 2018 ). Although the difference approaches zero, gaps are especially visible among the top and lower performers because of the higher variability in boys ( Lindberg et al., 2010 ; Wai et al., 2018 ). Stated otherwise, while there are barely any differences on average, the men’s distribution has a flatter curve, yielding higher values at both the lowest and highest ends. Similarly, men appear to have a small advantage over women in science, with differences particularly visible at the top end of the distribution; however, men are also overrepresented among the lowest performers ( Halpern, 2000 ).

Mathematics and science achievement is influenced not only by skills, but also by mathematics and science attitudes, test anxiety, and self-efficacy ( Ashcraft and Moore, 2009 ; Geary et al., 2019 ). These dimensions are believed to be strong determinants of STEM careers and contribute to the underrepresentation of women in these fields ( Moakler and Kim, 2014 ; Sax et al., 2015 ). Research has shown that men generally report more enjoyment and positive attitudes than women when engaging in mathematical activities ( Ganley and Vasilyeva, 2011 ; Devine et al., 2012 ).

By contrast, women perform substantially better than men on verbal tasks ( Moè et al., 2021 ), with girls using a broader vocabulary than boys on average by age two ( Halpern, 2000 ; van der Slik et al., 2015 ). Verbal abilities comprise various skills, and gender differences are most prominent in the reading dimension, where the girls’ advantage is three times wider than the boys’ advantage in mathematics ( Stoet and Geary, 2013 ). Nevertheless, Hirnstein et al. (2022) have cast some doubts on the magnitude of gender differences in verbal abilities claiming that publication bias might have influenced the results.

Cognitive abilities are largely interrelated. For example, high math skills predict higher reading scores and vice versa ( Bos et al., 2012 ; Reilly, 2012 ). Women’s mean overall scores considerably outperform men’s, even though the latter appears to be better positioned at the top and lower tails of the distribution, a finding that supports the higher men variability hypothesis ( Halpern et al., 2007 ; Bergold et al., 2017 ).

Turning to personality, gender differences are reported across the Big Five traits (openness, conscientiousness, extraversion, agreeableness, and neuroticism) and the HEXACO model (honesty–humility, emotionality, extraversion, agreeableness, conscientiousness, and openness), suggesting small to moderate gaps depending on the test and dimension analyzed. Specifically, women score higher in both neuroticism and agreeableness ( Costa et al., 2001 ; Schmitt et al., 2008 ; Murphy et al., 2021 ), although findings have been inconclusive for openness, extraversion, and conscientiousness, with some studies showing women and others showing a men’s advantage ( Goodwin and Gotlib, 2004 ; Shokri et al., 2007 ). The HEXACO model displays a similar pattern, with emotionality and honesty–humility both substantially higher in women than men ( Lee and Ashton, 2004 , 2018 ).

Men and women also differ in value priority and vocational interests. According to Schwartz’s theory ( Schwartz, 1999 ), values define the motivations behind behaviors that regulate attraction in diverse fields. Although the variations are small to medium, research has consistently shown gender gaps, with men scoring higher in power, stimulation, hedonism, achievement, and self-direction and women scoring higher in universalism and benevolence ( Schwartz and Rubel, 2005 ). On the other hand, vocational interests ( Holland, 1997 ) describe how personality interacts with career environments and are important determinants of gender-typed career trajectories ( Kuhn and Wolter, 2022 ). Previous studies have shown that men prefer to be employed in realistic fields, while women favor working with people ( Lippa, 2010a ), suggesting that men have more realistic and investigative interests, preferring careers in engineering, science, and mathematics. By contrast, women prefer “working with people” as they have more artistic, social, and conventional tendencies, which facilitate social science careers ( Su et al., 2009 ).

3.2. Theories predicting that gender equality is linked with smaller gender differences

The social role theory ( Eagly and Wood, 1999 ) posits that variations between men and women derive from the interaction, reinforced by socio-psychological processes, between evolved gender differences in physicality and the socio-cultural context in which these differences are expressed. Eagly and Wood (2012) have argued that, historically, men’s greater strength, endurance, and speed allowed them to conduct physically challenging duties. Conversely, women developed the ability to breastfeed, making them better suited for nurturing tasks. These evolved physical predispositions for specific activities shaped the domestic division of labor between men and women in ancient hunter-gatherer societies ( Eagly and Wood, 2012 ).

As societies developed, the division of labor began to be influenced by physical gender differences in interaction with the social environment ( Eagly and Wood, 1999 ). In modern countries, the socioeconomic setting dictates the relevance of those activities for which men and women have evolved peculiar physical predispositions. In this context, division of labor no longer relates solely to the domestic sphere but also encompasses paid labor, with men and women being segregated into different occupations. This gender segregation “derives in part from male and female biology—that is, mainly their evolved physical attributes, especially women’s reproductive activities and men’s size and strength, which can allow some activities to be more efficiently performed by one sex or the other depending on the socioeconomic and ecological context” ( Wood and Eagly, 2013 ). Thus, the interaction of evolved physical gender differences with the social environment in which they are expressed is likely to be the main process shaping gender segregation.

Within societies, social-psychological processes reinforce gender segregation and make it appear “natural and sensible” ( Wood and Eagly, 2013 ). Most people, when observing differential behaviors, assume that men and women are intrinsically dissimilar and construct specific “multifaceted” gender roles that include either essentially masculine or essentially feminine features ( Beckwith, 2005 ; Wood and Eagly, 2012 ). Individuals then internalize these roles through societal mechanisms that reward people who comply and penalize those who deviate, leading both men and women to develop specific skills and personality ( Friedman and Downey, 2002 ; Eagly and Wood, 2012 ). Consequently, gender differences in basic skills and personality are derived from the great effort that societies have undertaken to perpetuate gender segregation and comply with constructed gender roles ( Wood and Eagly, 2013 ). It follows that in countries where gender roles are relaxed, gender segregation and, as a result, gender differences in basic skills and personality will be smaller ( Eagly and Mitchell, 2004 ).

The gender stratification hypothesis ( Baker and Jones, 1993 ) is consistent with the theory presented above. Although originally formulated to explain gender gaps in mathematics, it has also been applied in other spheres. The theory suggests that essentialist gender beliefs interact with individual goals, thereby generating gender differences. These differences emerge because men in patriarchal societies can connect their skills with career outcomes, whereas women cannot do so due to unequal opportunities ( Else-Quest et al., 2010 ). In sum, societies that exhibit more gender stratification offer fewer opportunities for women to experience and develop the same skills and personalities as men.

Drawing from expectancy-value theory ( Wigfield, 1994 ) and cognitive social learning theory ( Bussey and Bandura, 1999 ), the gender stratification hypothesis argues that people undertake a task only if they value it and expect success. Perceptions of a task’s value are shaped by socio-cultural stereotypes about characteristics assumed to be gender-essential. Thus, women, due to gender stereotypes, would not find it valuable to invest in domains perceived as “masculine” because they would not expect to succeed in them. Instead, they would prefer to develop more “feminine” skills, and this predilection generates gender variances ( Frome and Eccles, 1998 ).

The above process is ostensibly reinforced by environmental processes that highlight those behaviors that are generally linked to gender in a given cultural setting. In this context, environment relates to the social influences that could be imposed, selected, or contracted according to “levels of personal agency,” that is, the extent to which people feel they are in charge of their decisions ( Bandura and Walters, 1977 ). According to this perspective, the immediate environment provides gender-essentialist information through parents, friends, and the media. Individuals regulate their behaviors according to the social expectations conveyed by this information and, through “direct tuition,” inform others about how different behaviors are linked to gender ( Bussey and Bandura, 1999 ).

According to the above theories, gender differences derive from false essentialist beliefs that diminish opportunities for subjective growth, making differences the result of unequal social treatment ( Figure 1 ). Gender essentialism is conceived as a “powerful ideological” force that legitimates gendered choices and limits personal development ( West and Zimmerman, 1987 ). Stated otherwise, gender not only represents the lens through which people see the world, but it also constitutes the basis for categorizing individuals ( Bussey and Bandura, 1999 ). However, as the above theories emphasize, any visible variation between men and women results not from innate biological differences but from social impositions. If men and women were treated alike, gender stereotypes would fade, exposing them to similar stimuli and, consequently, eliminating gender differences in both basic skills and personality ( Baker and Jones, 1993 ; Eagly and Wood, 1999 ). Thus, gender equality is likely to be associated with reduced gender variation. As Else-Quest et al. (2010) claimed, “where there is greater gender equity, gender similarities … will be evident.” Eagly et al. (2004) argued in the same vein, maintaining that “the demise of many sex differences with increasing gender equality is a prediction of social role theory.”

Figure 1 . Overview of social-role theories of gender differences. Gender differences are generated by essentialist beliefs that men and women are intrinsically different which are in turn influenced by social norms in tandem with the division of labor derived from gender physical specialization.

3.3. Theories predicting that gender equality is linked with wider gender differences

Drawing on gender essentialism, Charles and Bradley (2009) theorized an opposite effect—that gaps might increase with greater gender equality. They posited that, even if societies are gender equal, gender stereotypes endure because of the emphasis on individualism and self-expression in these societies. Specifically, gender equality stresses the expression of subjective preferences; however, it does not question how that preference emerges—an emergence that, Charles and Bradley (2009) ascribe to societal mechanisms influencing individuals based on their gender. These mechanisms strengthen essentialist beliefs about differences between men and women, in turn reinforcing gender-related roles ( Levanon and Grusky, 2018 ).

According to the foregoing analysis, societal systems are characterized by internal structural diversification that is conceptualized to accommodate individual “expressive choices” but functions, instead, to increase stereotypes as people act out their internalized gender identities rather than their subjective preferences ( Rawlings, 2007 ; Charles et al., 2014 ). In addition, long periods of care leave and advanced family policies, which are generally found in gender-equal countries, tend to influence horizontal gender segregation and compel women to enter into roles typically considered more gender-appropriate ( Freiberg, 2019 ), widening even further the prevailing gender gaps. Thus, even when a society becomes more gender equal, “a preponderance of gender-typical choices” and an increase in gender variances can be expected ( Charles and Bradley, 2009 ). Supporting this statement, some scholars have argued that gender stereotypes increase in more gender-equal nations ( Breda et al., 2020 ; Napp and Breda, 2022 ). Others have stated that “cultural individualism” is often the strongest predictor of gender gaps in equal societies ( Bleidorn et al., 2016 ; Kaiser, 2019 ).

Evolutionary theorists claim that differences between men and women are magnified in more gender-equal environments because privileged access to resources allows them to freely express specific gender “ambitions and desires” ( Schmitt et al., 2008 ; Stoet and Geary, 2018 ). These theorists argue that from an evolutionary perspective, the possibility that men and women evolved with identical characteristics is a “theoretical impossibility” and maintain that gender differences are derived, in part, from innate predispositions ( Vandermassen, 2011 ). Specifically, variations are expected to be visible in those domains in which the evolutionary pressure, mainly sexual selection, has influenced men and women differently ( Schmitt, 2015 ). According to this view, the interplay between “sex-linked” genes and environmental stressors is responsible for the more pronounced gender dimorphism in modern nations ( Schmitt et al., 2008 ).

In ancient hunter-gatherer societies, men and women evolved specific, intrinsic differences as a result of evolutionary adaptation ( Mealey, 2000 ). Nevertheless, environmental conditions suppressed these innate differences that have subsequently re-emerged in developed societies characterized by reduced ecological pressure stemming from favorable economic circumstances. Gender differences in sensitivity to environmental change have played a key role in explaining this re-emergence. Generally, in the animal kingdom, the larger animal between the two sexes shows sharper fluctuations in behavior when ecological settings vary. The same appears to be true among humans, where men are more influenced by environmental changes ( Teder and Tammaru, 2005 ). It follows that both men and women, but especially men, are less affected by environmental components in resource-rich countries, where they are free to follow their intrinsic characteristics ( Schmitt et al., 2017 ). Conversely, in countries that offer fewer economic opportunities, choices are constrained, and reduced gender differences might be evident ( Stoet and Geary, 2018 ).

Thus, according to the evolutionary hypothesis, increased gender variations in more gender-equal societies are mainly a product of the sexual selection that men and women have undergone during evolution together with gender differences in sensitivity to environmental changes ( Schmitt et al., 2008 ). This interplay of gender-linked genes and environmental influences is relevant for some gender variances, such as height, since men in more developed societies are reported to be more sensitive to environmental changes ( Sohn, 2015 ).

4. Basic skills and gender equality

Most studies on gender differences in basic skills have focused on the Trends in International Mathematics and Science Study (TIMMS) and the Program for International Student Assessment (PISA). TIMMS targets fourth- and eighth-grade students worldwide and reports their academic achievements every 4 years. Similarly, PISA is a triennial test of mathematics and science administered to 15-year-old adolescents in several countries. The PISA and TIMMS tests have been related to only a few gender equality indices; the most commonly used are the World Economic Forum’s Gender Gap Index (GGI) and the United Nations’ Gender Empowerment Measure (GEM). Both indicators are based on sub-indices that assess gender equality in numerous domains, such as educational attainment, political empowerment, and health.

4.1. Mathematics

As Table 1 shows, the math gender gap does not usually relate to gender equality when analyzing TIMMS data; in the PISA data, however, the findings appear to be more divergent.

Table 1 . Correlations between mathematics gender differences (men > women) and both composite indices and specific indicators of gender equality.

Else-Quest et al. (2010) found that higher gender equality leads to slightly smaller differences between men and women in mathematics, although with variation across indices ( r  = 0.09–0.14). Similarly, Hyde and Mertz (2009) showed that more equitable index scores result in more women being among the top performers; however, their analysis used a small country sample and excluded Scandinavian nations (more on this below). Moreover, Gevrek et al. (2018) argued that moving toward gender equality predicts a reduced gender gap in mathematics in the part that cannot be explained by “observable characteristics,” that is, explained by elements that can be controlled for in statistical analyses.

However, the results appear to depend on the years that were considered in the analysis. For example, Stoet and Geary (2013 , 2015) found that only the 2003 PISA assessment was consistent with theories hypothesizing that gender equality is linked with smaller gender differences. For other years, gender-equal practices were unrelated to a mathematics gap. Additionally, the results are sensitive to the inclusion of Scandinavian and gender-segregated, Muslim countries as well as gender-equal nations in which boys considerably underperform girls ( Fryer and Levitt, 2010 ; Kane and Mertz, 2012 ; Stoet and Geary, 2015 ). However, some have raised doubts about including Muslim countries in the sample ( Kane and Mertz, 2012 ). Other scholars have proposed that the positive findings derive from a spurious correlation between the GGI and country-specific unobserved variances ( Anghel et al., 2019 ). Finally, as reported in Table 1 , Gevrek et al. (2020) recently reversed their findings, strengthening the evidence that gender equality, measured by composite indicators, is not linked to gender differences in mathematics achievement.

However, composite indices may fail to account for explicit factors influencing the mathematics gender gap while specific indicators may be more suitable for measuring how gender differences vary in relation to gender equality. As Table 1 shows, having more women in research, higher levels of female participation in economic activities, a higher ratio of women to men holding parliamentary seats, and greater educational equality seem to predict reduced gender variation ( Else-Quest et al., 2010 ; Penner and Cadwallader Olsker, 2012 ). More recently, Gevrek et al. (2020) extended their research by decomposing the mathematics gender gap into that which could be explained by “observable characteristics” and that which could not. Their finding suggests that the men-to-women ratio in tertiary education and the lower gender wage gap are not related to the explainable part of the gender gap, although they predicted a reduction in the unexplained part.

As mentioned earlier, also the findings for specific indicators depend on the year and countries considered. For instance, the results for the “women in research” indicator are unreliable because they sharply fluctuate across PISA assessments ( r  = −0.16, r  = −0.68; Reilly, 2012 ; Stoet and Geary, 2015 ). The relation is mainly driven by countries that are, on average, less gender-equal but display lower gender discrepancies, such as Latvia, Serbia, Tunisia, and Thailand, as well as non-OECD nations ( Reilly, 2012 ; Stoet and Geary, 2015 ).

Regarding “women’s economic activity,” Stoet and Geary (2015) analyzed four PISA assessments (2000, 2003, 2006, and 2009) and concluded that only the 2000 and 2003 results were consistent with theories predicting that gender equality is linked to smaller gender differences. In addition, “females in parliamentary seats” never reached statistical significance; only in the 2003 assessment did a link appear by excluding either non-OECD or Nordic countries from the sample ( Stoet and Geary, 2015 ). Further, while Penner and Cadwallader Olsker (2012) showed that countries with more women participation in the labor force tended to have higher mathematics gender differences, the gender gap was not linked to gender equality in their analysis, contrary to the predictions. In sum, only “women in research” demonstrated a significant negative relationship with the gender gap in mathematics, although the magnitude of this relationship is in doubt. Additionally, the gender equality paradox had no empirical support when analyzing mathematics abilities. Girls outperformed boys in diverse socio-cultural environments, such as Finland and Qatar, demonstrating that egalitarian attitudes do not explain gender discrepancies in this dimension ( Stoet and Geary, 2015 ). However, more gender equality had a positive effect on individuals, with both men and women increasing their mathematics scores in this context, without any specific advantages for either group ( Kane and Mertz, 2012 ).

4.2. Mathematics attitudes and anxiety

In line with the gender equality paradox, mathematics attitudes and anxiety gender gaps are higher in gender-equal countries ( Else-Quest et al., 2010 ; Stoet et al., 2016 ). Else-Quest et al. (2010) explained this phenomenon by arguing that mathematics anxiety is “a luxury, most often experienced by individuals who are not preoccupied with meeting more basic needs.” However, at the national level, both men and women tend to be less anxious about mathematics in equal societies, even though men benefit more from this lack of anxiety, enhancing gender differences as a consequence ( Stoet et al., 2016 ). Only Goldman and Penner (2016) showed contrary results to that of the above research, arguing that gender differences in mathematics attitudes remain stable, even in gender-equal countries. Recently, Marsh et al. (2021) proposed that the gender equality paradox in these dimensions is “illusory” as it vanishes when accounting for country-level academic achievements and socioeconomic status; however, further studies are needed to support their argument. According to the women’s political representation index, gender-equal nations also have wider self-efficacy and motivation gaps. By contrast, other specific indicators, such as “equality in wages” and “parity in secondary and tertiary education,” predict smaller gaps ( Else-Quest et al., 2010 ; Gevrek et al., 2020 ). Similarly, anxiety differences decline when there is equal political representation between men and women because women gain more than men in politically equal environments ( Else-Quest et al., 2010 ; Gevrek et al., 2020 ).

In conclusion, gender equality is negatively related to gender differences in mathematics attitudes when analyzing composite indices; however, specific indicators are either inversely or directly related. It appears that pursuing equal political representation counteracts the results achieved by parity in wages and education, putting the overall advantage into question. Moreover, although self-efficacy and motivational gender gaps increase as equality is achieved in political representation, parity in tertiary education and wages shows an opposite trend.

4.3. Science, reading, and overall scores

Table 2 shows the science gender gap’s mixed results for composite indicators. Analyzing the GGI, Reilly (2012) concluded that the gender gap in science achievement decreases as gender equality increases ( r  = 0.29); nevertheless, men are better represented among the top scorers. By contrast, Ireson (2017) failed to replicate any meaningful relationships. However, a recent meta-analysis reported that gender-equal societies are characterized by “a pattern of higher male achievement, while for nations with lower gender equality, we see a pattern of higher female achievement” ( Reilly et al., 2019 ).

Table 2 . Correlations between gender differences in science, reading, and overall scores (men–women) and both composite indices and specific indicators of gender equality.

As reported in Table 2 , also the specific indicators provide mixed results. No connection with the science gender gap is established for the “relative status of women,” whereas “women in research” is linked with increased gender differences ( r  = −0.39; Reilly, 2012 ).

These studies were based on inter-group comparisons, which may not have been appropriate for analyzing the relationship in question given the small mean gender gap in science. However, analyzing intra-individual strengths could move the debate forward because these are strongly related to career choices ( Wang and Degol, 2017 ). Studies have shown that men are more likely to have higher abilities in mathematics or science than in reading, generating a “math tilt,” whereas women generate a “verbal tilt,” with differences more visible at the distribution’s right tail ( Wai et al., 2018 ). In other words, although the mean gender variation in science approaches zero, an increasing number of men as compared to women have their top skill in science as opposed to reading, whereas the opposite trend holds true for women (see below). Analyzing 67 nations, Stoet and Geary (2018) pointed out that gender variances in science (and mathematics) intra-individual strength are higher in favor of boys in gender-equal nations. This trend among men could facilitate their preference for scientific careers because they would have the highest likelihood of success and especially so in gender-equal environments ( Dekhtyar et al., 2018 ).

Regarding attitudes, “almost everywhere” girls display a lower science self-concept than boys, even when their academic skills are equal to those of their male peers ( Sikora and Pokropek, 2012 ). Supporting the gender equality paradox, research has noted that gender differences in science self-efficacy, science enjoyment, and interest tend to be larger in gender-equal nations ( Stoet and Geary, 2018 ; Liou et al., 2022 ).

Table 2 shows that studies on reading differences, although few, have substantially converged, demonstrating an increased gender gap in favor of women when there is more equality between genders. Although no correlation is found for the GGI, gender equality results in higher women representation among top-performing students ( Reilly, 2012 ). Notably, the GGI has recently been linked to an increased reading gender gap in advanced societies ( Gevrek et al., 2020 ). Analyzing specific indicators, Reilly (2012) showed that “women in research” directly relates to gender differences in reading achievement, thus predicting progressively higher variations. Gevrek et al. (2020) reached similar conclusions, arguing that the reading gender gap is wider in favor of girls in countries where there is more gender equality in the labor market. Furthermore, studies on intra-individual strengths have also been consistent, showing that girls’ tilt in reading skills is larger than that of boys in gender-equal societies ( Stoet and Geary, 2018 ).

Few studies have focused on gender differences at the aggregate skills level, and those that exist have shown mixed results (see Table 2 ). Similar to the results for mathematics ability, Stoet and Geary (2015) found a significant increase in aggregate skill differences between boys and girls in nations with higher gender equality (GGI), although only in the 2003 PISA assessment. However, excluding either Iceland or Finland from the sample significantly weakened the link, and it disappeared when considering other years ( Stoet and Geary, 2015 ; Ireson, 2017 ). Recently, inspired by research on gender differences in gray and white matter, Stoet and Geary (2020) argued that the basic skills pattern should be considered as a whole to understand the full magnitude of gender variation. Assessing the overall pattern in mathematics, science, and reading performance, it appears that the gap is greater than previously measured, corresponding to a large statistical difference, and it widens in more gender-equal environments.

Some researchers have proposed that egalitarian values, have a “more pervasive influence” and might offer a better understanding of the topic ( Eriksson et al., 2020 ). An examination of these values suggests that “one standard deviation higher in gender equal values is on average 5.2 points more beneficial for boys” ( Eriksson et al., 2020 ). This observation holds true for the GGI.

Contrary to theories predicting that gender equality is linked with smaller gender differences, “male/female enrollment in tertiary education” is inversely related to gender differences in overall achievement in countries with gender-neutral enrollment rates that also have more men among the top performers ( r  = 0.19; Bergold et al., 2017 ). Conversely, “women’s labor market participation,” “women’s share of research positions,” and “the ratio of women to men with at least a secondary education” have medium-size negative correlations (from r  = 0.33–0.42), which may account for 28.7% of the gender variation ( Bergold et al., 2017 ).

In sum, few studies have examined the link between gender equality and gender differences in science, reading, and overall scores, making it difficult to draw any firm conclusions. The findings for science and overall scores are contradictory, while for reading, there is substantial agreement about there being a gender equality paradox favoring women. Furthermore, due to their interrelatedness, a communal pattern between these skills emerges when examining intra-individual strengths. This pattern is characterized by increasingly wider science/mathematics and reading tilts for boys and girls, respectively. The tilt for girls shows that when girls have a science or mathematics score similar to boys, they tend to have better grades in reading, a trend that is especially observed in gender-equal nations ( Stoet and Geary, 2018 ). However, scholars have only recently begun to consider intra-individual strengths, which represent a great opportunity for future studies on gender segregation.

5. Personality and gender equality

5.1. the big five and the hexaco model.

Evidence supporting a paradox emerged as early as 2001 when Costa et al. (2001) concluded that men’s and women’s personalities differ more in gender-equal countries. Schmitt et al. (2008) replicated these findings across 55 nations, again suggesting a positive correlation between gender differences and gender equality. More recently, larger gender differences in agreeableness favoring women have been found in gender-equal nations (see Table 3 ), mainly because of lower agreeableness in men in these nations with gender being the strongest predictor of individual levels ( Lippa, 2010b ). Conversely, the gender gap in neuroticism (women > men) has not been found to be affected by gender equality, even though the UN’s gender development and empowerment index predicts a decrease in negative emotions in both men and women ( Lippa, 2010b ).

Table 3 . Correlations between gender differences in personality (men–women) and composite indices of gender equality.

While these findings are illuminating, looking only at single dimensions may lead to counterintuitive results because personality is multifaceted ( Vianello et al., 2013 ). Although the average gender gap for a given personality trait is small, the overall variance is conventionally regarded as large, implying a significant difference between men and women ( Del Giudice, 2009 ). Based on the latter premise, Mac Giolla and Kajonius (2019) noted a strong relationship between gender personality differences and gender equality, with overall differences being broader in “gender-friendly” countries ( r  = 0.69). Other studies have supported these results, observing the same widening pattern ( Kaiser, 2019 ). Similarly, the emotionality gap from the HEXACO model displays a direct relationship with the GGI ( r  = 0.56), with women having an increasingly higher level than men in more gender-equal countries. However, honesty–humility fails to display any association with gender equality ( Lee and Ashton, 2020 ).

Further evidence for a gender equality paradox in personality emerges from the study by Falk and Hermle (2018) that, building upon the above personality models, related gender differences in economic preferences – positive reciprocity, patience, altruism, trust, risk-taking (higher in women), and negative reciprocity (higher in men) – to gender equality measures. They concluded that the differences are characterized by sharp increases in more gender-equal countries ( r  = 0.67).

5.2. Basic human values and vocational interests

Basic human values (see Table 3 ) of power, achievement and stimulation are generally considered more important for men, whereas benevolence and universalism are valued among women. Past research has found that these gender differences are broader when men and women are treated equally, even though both genders regard masculine values to be less significant ( Schwartz and Rubel-Lifschitz, 2009 ). More recently, Fors Connolly et al. (2020) extended the research on human values by adding a temporal dimension. Their analysis replicated the results cross-nationally, although temporal examination displayed a convergence between men and women in benevolence (over time, Cohen’s d −15%), with universalism and stimulation gaps remaining constant ( Fors Connolly et al., 2020 ). However, as the authors noted, this convergence resulted from factors not linked to gender equality, indicating that the correlation might be spurious and caused by confounding factors related to both gender equality and personality. This additional finding suggests that gender equality could not cause gender differences in values and that the gender equality paradox needs further exploration.

For vocational interests, few studies have examined how gender differences change with gender equality. Using the Brinkman Model of Interests, one study found that ‘gender differences in musical and persuasive interests decreased in countries with high gender egalitarianism; nevertheless, clerical and scientific interests were higher when gender egalitarianism was high’ ( Ott-Holland et al., 2013 ). However, most differences did not show any variance. More recently Tao et al. (2022) offered a more comprehensive overview highlighting that across all dimensions of vocational interest analyzed, increased gender equality was associated with wider gender differences. As Table 3 shows, gender personality differences generally increase in gender-equal countries. This finding is consistent across models and it appears to be valid also for dimensions not analyzed in this review (see Discussion for a more in-depth analysis).

6. Discussion

The systematic narrative literature review investigated recent studies on gender differences in basic skills and personality to determine whether cross-national relationships can be found with gender equality. The goal was to assess whether theories predicting that gender equality is linked with smaller gender differences have empirical support or whether a gender equality paradox has emerged in recent years. The general trend considers gender equality as either being connected to an increase in gender variations or having no relation with them, with a gender equality paradox occurring for gender gaps in some cognitive domains (attitudes toward mathematics, mathematics self-efficacy, mathematics anxiety, and reading) and personality.

6.1. Summary of the review

Based on the foregoing literature review, it can be seen that research supporting reduced gender differences in more gender-equal countries is scarce and inconsistent. A negative correlation is generally detected when analyzing gender differences in mathematics skills utilizing PISA data, although the correlation is influenced by either the year considered in the study or the sample country (see below). Moreover, “women in research” is the only specific indicator consistently negatively linked to the mathematics gender gap, albeit with disagreement about the strength of the association. Lastly, no connection between gender differences in mathematics and gender equality indicators is found when analyzing the TIMMS assessment. However, many studies have focused solely on mean differences in mathematics abilities, which are small or non-existent. Only Bergold et al. (2017) and Hyde and Mertz (2009) assessed the right tail of the distribution, where gender differences are more pronounced. This lack of studies on top performers highlights a gap in the research that needs to be filled. Also important is analyzing intra-individual strengths when studying the mathematics gender gap, as Stoet and Geary (2018) have emphasized.

Research supporting a positive link between gender variances and gender equality measures appears to be more robust and consistent. The literature on mathematics attitudes and anxiety shows that composite indicators predict a widening gender gap as equality between men and women advances. In addition, scholars agree that gender equality is connected with a larger advantage for women in reading and evidence further shows that gender personality differences are larger in more gender-equal nations. Men and women are less alike, especially in personality traits and basic human values, in countries that have invested the most in gender equality. Further support for a gender equality paradox in personality also emerges when examining other personality domains not included in this review. For example, wider gender gaps in self-esteem and narcissism (higher in men) exist in more gender-equal nations where women have more reproductive control, more executive positions, and their education is either similar to or higher than that of men ( Bleidorn et al., 2016 ; Jonason et al., 2020 ).

Specific indicators are either directly or inversely related to the mathematics gender gap, raising doubt about them being related to a general advantage ( Table 4 ). In addition, findings on science and overall scores are uncertain, even though both science anxiety and science intra-individual strengths follow a trend opposite to that anticipated by theories predicting a link between gender equality and smaller gender differences. Interestingly, other skills, such as episodic memory and visuospatial ability, show the same widening tendency, strengthening the case for a possible paradox in this area ( Lippa et al., 2010 ; Asperholm et al., 2019 ).

Table 4 . Summary of the papers included in the review.

6.2. Implications of the gender equality paradox

Understanding the possible reasons for the increase in gender differences in countries that promote gender equality is important and relevant since these countries may be leading men and women toward gendered trajectories, a path that is already observable in higher education. Charles and Bradley (2009) noted that the most advanced societies demonstrate more pronounced gender segregation in education. Stoet and Geary (2018) also observed that more gender-equal nations (measured by the GGI) have the widest gender gap among STEM graduates. Supporting these results, research has shown that gender differences using “interest in math careers” as a predictor of future major subjects are greater in countries with higher gender equality, with both men and women being, on average, less interested in mathematics than those in other countries ( Goldman and Penner, 2016 ; Charles, 2017 ; Breda et al., 2020 ). The same pattern is observed in the job market, where horizontal segregation is more pronounced in more gender-equal environments ( Blackburn and Jarman, 2006 ; Wong and Charles, 2020 ). Several investigations have documented this phenomenon and concluded that “Scandinavian countries are notable for their exceptionally high degrees of segregation” despite their advancement in gender equality ( Jarman et al., 2012 ). However, more recent findings have also detected desegregation patterns in more gender-equal nations ( Hustad et al., 2020 ).

6.3. The gender equality paradox: Possible explanations

The question of why gender differences are sometimes higher in more gender-equal countries remains. Some have proposed that the paradox in mathematics anxiety and attitudes might originate from the better economic conditions needed for these emotions to emerge. In countries where women are highly oppressed, these are more concerned about meeting more basic needs. Conversely, where economic, political, and educational circumstances are more favorable for women, anxiety toward mathematics activities is more likely to emerge ( Else-Quest et al., 2010 ). However, at the national level, both men and women are less anxious about mathematics in developed, gender-equal countries, indicating that alternative explanations are needed ( Stoet et al., 2016 ). In fact, others have suggested that, in gender-equal nations, men and women set aside financial drives and follow more intrinsic career interests because of easier access to economic resources. Hence, women are less exposed than men to STEM activities, “giving them less opportunity to reduce their negative feelings about mathematics” ( Stoet et al., 2016 ).

With respect to reading abilities, the paradox might result from the interaction of two factors: the interrelation between basic skills and Western societies’ strong efforts to equalize boys’ and girls’ mathematics performance that has instead, paradoxically, increased reading skills in girls. Notably, where mathematics gender differences are reduced, the reduction is mainly due to an improvement in women’s reading ( Guiso et al., 2008 ). It follows that countries with smaller mathematics gender differences have the largest reading gaps ( Stoet and Geary, 2013 ). As mathematics is promoted in girls, their reading skills appear to benefit. However, because boys’ disadvantage in reading is, on average, less of a concern among policymakers, gender variations in this dimension have widened.

Some researchers have explained the gender equality paradox in personality by arguing that only differences in self-reported domains are increased ( Eagly and Wood, 2012 ). Here, the reference-group effect ( Heine et al., 2002 ) might conceal variances in less gender-equal countries, where men and women compare themselves with others of their own gender ( Guimond et al., 2007 ). If this explanation holds true, the gap in gender-equal nations would be a better estimate of personality differences between the genders because in these nations both women and men have a more accurate comparative term that includes the whole population rather than just a subset ( Schmitt et al., 2017 ).

Another explanation may be that personality is strongly culturally influenced. According to this view, individualism and self-expressive values act in tandem with gender stereotypes, promoting gender variance as individuals act out their “gendered self” ( Charles and Bradley, 2009 ; Breda et al., 2020 ). This explanation of the gender equality paradox corresponds to the findings in gender-equal nations that cultural mechanisms are at play accommodating women-typical roles, such as job flexibility and high parental care—roles that encourage women to embark on gendered paths and experience more communal traits ( Levanon and Grusky, 2018 ). Thus, it should not be surprising that, in gender-equal countries, men and women appear to differ more than in non-gender-equal countries and that this difference is expanding as women-typical roles are becoming more prevalent. Rather than expressing intrinsic gender differences, in these nations, there is a reinforcement of gender essentialist beliefs, which constitute an artifact of social expectations about how men and women should comply with gender stereotypes ( England, 2010 ).

While this argument is somewhat persuasive, research aiming at linking gender stereotypes with gender equality suffers from several theoretical and methodological limitations. Often scholars apply broad assumptions and rely on a limited, as well as unreliable, set of items to capture latent dimensions of implicit stereotypes hidden in survey data. For instance, in their recent article Napp and Breda (2022) used solely one item to grasp an alleged stereotype that girls lack talent by arguing that systematic gender difference in answering the question would highlight “the magnitude of the (internalized) stereotype associating talent with boys rather than girls.” In addition, several studies have argued that stereotypes about group features, when measured reliably, appear to be accurate ( Jussim et al., 2015 ; Moè et al., 2021 ). Löckenhoff et al. (2014) observed that perceived gender differences in personality substantially match those found in self- and observer-rated personality tests. The authors concluded that gender stereotypes constitute “valid social judgments about the size and direction of sex differences” that are more relevant than socialization processes and ascribed cultural gender roles ( Löckenhoff et al., 2014 ). This is not to say that culture plays no role in the emergence of gender differences, but that the social mechanisms amplifying gender variances—mechanisms that social-role theorists have identified—also capture intrinsic gender differences.

Evolutionary theorists propose a different explanation for the gender equality paradox. As they argue, some gender variations are sensitive to context-related fluctuations, demonstrating a gene–environment interplay. In societies in which conditions are favorable, gender-specific genes flourish due to a lower prevalence of diseases, lower ecological stressors, and lower starvation rates. Per this view, wider gender gaps in gender-equal nations most likely “reflect a more general biological trend toward greater dimorphism in resource-rich environments” ( Schmitt et al., 2008 ). If this explanation holds true, then heritability estimates will be higher in developed societies than in less-advanced cultures. Some evidence in this direction has recently emerged ( Selita and Kovas, 2019 ); however, the “WEIRD” gene problem—that nearly all twin studies have been conducted among Western, educated, industrialized, rich, and democratic societies—represents an obstacle for generalizing results and making inferences about cross-cultural heritability differences ( Henrich et al., 2010 ).

6.4. A novel socio-cultural evolutionary account of the gender equality paradox in personality

The present review proposes that the evolutionary explanation for the gender equality paradox might be more complex than it appears due to the presence of socio-cultural elements in the evolutionary process. As previously noted, genetic effects depend on the environmental conditions (diseases and ecological stress) under which they occur, yet the environment is embedded into society. Thus, the gene–environment interplay is enclosed within a cultural context with specific social norms and, by itself, cannot encompass all involved elements ( Figure 2 ). Stated otherwise, the gene–environment interplay is a function of culture ( Uchiyama et al., 2022 ). Therefore, gender-specific genes can be expected to be emphasized in societies embracing cultural values that would favor the expression of these genes. Consider, for example, individualism and self-expression. It is unsurprising that these values are related to the gender equality paradox, as Charles and Bradley (2009) have highlighted. In resource-rich environments that also value individualism and self-expression, intrinsic gender differences are more likely to emerge. This thesis points toward interpretation of Kaiser (2019) , which states that both cultural individualism and pathogen levels confound the gender equality paradox in personality (see below). Also, Murphy et al. (2021) reached similar conclusions. A coherent, yet opposite, prediction might see gender differences as remaining stable or even decreasing in those resource-rich environments that culturally constrain self-expression. Accordingly, favorable cultural values would trump social mechanisms that amplify gender-based genes to emerge via a feedback-loop effect or “reciprocal causation” ( Dickens and Flynn, 2001 ) according to which social structures adjust to distinct gender traits and vice versa, thus increasing gender differences.

Figure 2 . Socio-cultural evolutionary explanation of the gender equality paradox. The gears show the interrelations between gender-specific genes, social structures, and environmental components mediated by cultural values.

6.5. Challenges for future cross-national research

While searching and analyzing the literature, this review also highlighted some challenges that researchers might face when conducting cross-national studies relating gender differences to gender equality measures. For mathematics ability, results could depend on outlier countries such as Scandinavian and gender-segregated, Muslim countries. In addition, the restricted country samples in international student assessments might be problematic. Despite the strong effort of PISA and TIMMS to be more inclusive, wealthy countries have traditionally been overrepresented, although the latest rounds have had very high coverage, including over 75 participating nations worldwide. Nevertheless, researchers, when assessing gender differences in mathematics abilities, should pay close attention to the countries included in their study because either the inclusion of outliers or a lack of heterogeneity might lead to biased estimations.

Another possible source of bias in research linking gender differences to gender equality on a cultural level is participant sample sizes, with some nations being overrepresented in comparison to others. How countries are clustered may also be problematic since countries are not independent data points and, “as such, they are like members of the same family or pupils of the same classroom” ( Kuppens and Pollet, 2015 ). Therefore, appropriate statistical methods, multilevel modeling, for example, should be utilized to account for both unbalanced sample sizes and data structure.

Correlations between mathematics gender differences and gender equality might originate from a lack of country-level effects in the models. Anghel et al. (2019) argued that when time-invariant country unobserved heterogeneity is controlled for, no association between the two variables is found. Moreover, the link between gender equality and the gender gap in mathematics attitudes might be confounded by country-level academic achievements and socioeconomic status ( Marsh et al., 2021 ).

Further, the gender equality paradox could be due to measurement error. Given that many international assessments and personality models have been developed in WEIRD countries, it is plausible that measurement error could be higher in non-WEIRD nations generating an illusory gender equality paradox. However, international assessments have been constructed to prevent such bias. For instance, PISA computes each student’s score based on a set of 5/10 plausible values designed to prevent measurement error and simplify secondary data analysis ( Marsh et al., 2021 ). Also, the gender equality paradox in personality appears to hold even after correcting for measurement error ( Kaiser, 2019 ; Fors Connolly et al., 2020 ; Tao et al., 2022 ). Nevertheless, when analyzing the link between gender differences in personality and gender equality, statistical procedures that control for measurement error should be applied (see for example Schmidt and Hunter, 2015 ).

Fors Connolly et al. (2020) highlighted the need for more temporal analyses of personality because an observed cross-national pattern may result from “a spurious relationship between gender equality and differences in personality” due to different country-level elements. Kaiser (2019) identified these elements as cultural individualism, food consumption, and historical pathogen prevalence levels. Other research has also agreed that cultural individualism could be a possible confounding factor as gender differences in personality are more pronounced in nations that highly regard individual self-expression ( Costa et al., 2001 ; Schmitt et al., 2008 ; Tao et al., 2022 ).

Some scholars have called attention to the misuse of composite indicators of gender equality, raising several concerns thereof and arguing that they might not be suitable for empirical research ( Else-Quest et al., 2010 ; Hyde, 2012 ). One concern is that these indicators, which encompass various domains from politics to economics, do not measure opportunities ( Richardson et al., 2020 ). Another concern is that they are not interchangeable since they are differentially constructed. Thus, comparisons between research relying on different measures of gender equality might not be suitable. Some of the disparate findings concerning math ability might be driven by computational differences in the indices included in the analysis. Nevertheless, the gender equality composite indicators most commonly utilized (GGI, GEI, and GEM) show very high correlation coefficients ( r  ≥ 0.84), while other indicators substantially relate to one another, suggesting that, although some differences occur, these indices are similar in their ability to capture the general dimension of gender equality ( Else-Quest et al., 2010 ; van Staveren, 2013 ; Stoet and Geary, 2015 ). Lastly, composite indicators may present a biased view of society due to the way gender equality is understood in the models. Often, disadvantages pertaining mostly to men are not taken into account when computing the indicators ( Benatar, 2012 ). As an example of this bias, the GGI from the World Economic Forum assumes perfect gender equality in areas where women have an advantage over men. Specifically, values higher than 1, which would assume a men’s disadvantage, in each sub-index are capped. Thus, a more simplified approach to measuring national gender inequality is preferred ( Stoet and Geary, 2019 ).

In addition, methodological issues also arise when using these indices. Some scholars have pointed out that correlations between gender gaps and the indices of gender equality could be driven by the strong economic component in these indices ( Fors Connolly et al., 2020 ). Therefore, it is important to control for appropriate economic indicators, such as GDP per capita and the Human Development Index, when linking gender differences with gender equality ( Kuppens and Pollet, 2015 ). Another difficulty may arise when contrasting results between composite indices and specific indicators occur. For mathematics attitudes, for instance, although composite indices suggest a gender equality paradox, specific indicators are either positively or negatively related to the gender gap. This may suggest that composite indices either capture an overall influence of gender equality or are unsuitable for evaluating gender differences. However, evaluation may lie outside the scope of models using these indices. Research linking gender differences with gender equality indicators has not tried to explain the paradox emerging from the analysis on the basis of gender equality per se ; instead, it has just highlighted a paradoxical pattern that would otherwise have remained concealed. Since no theory has been put forward that fully unravels the paradox, further studies are needed.

Theories considered in this review that predict that gender equality is linked with smaller gender differences do not offer a valid explanation of gender differences in basic skills and personality. In addition, for some dimensions, the gender equality paradox raises further questions about how gender variation emerges, which calls for a new approach. Based on these premises, this review explored both social-role and evolutionary hypotheses and suggested new insights that combine these views, while also highlighting explanatory variables that might cause bias in the results. Thus, specific research that more closely examines the explanations proposed is needed, especially studies with an interdisciplinary focus. Notably, Fors Connolly et al. (2020) highlighted the importance of cross-temporal analyses of the gender equality paradox because these may reveal a different path. Since country comparisons may be insufficient for fully grasping the evolution of the paradox, future research should include a thorough cross-temporal examination for a more comprehensive understanding.

Lastly, the gender equality paradox is an emerging phenomenon that has gained substantial scientific support across subjects ( Falk and Hermle, 2018 ; Campbell et al., 2021 ; Block et al., 2022 ; Vishkin, 2022 ). It requires attention from both the scientific community and the public because attempting to close gender gaps following traditional social-role theories and applying conventional methods, might end up exacerbating gender variations. In addition, the general pattern of increased gender differences in more gender-equal countries might inform that achieving equal opportunities does not go hand in hand with a reduction of gender gaps. Thus, policymakers should consider this trend when justifying interventions attempting to achieve equality of outcome between men and women.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Author contributions

The author confirms being the sole contributor of this work and has approved it for publication.

The work was supported by the Finnish National Board for Education through a working grant.

Conflict of interest

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Keywords: gender equality paradox, gender equality, gender differences, basic skills, personality

Citation: Balducci M (2023) Linking gender differences with gender equality: A systematic-narrative literature review of basic skills and personality. Front. Psychol . 14:1105234. doi: 10.3389/fpsyg.2023.1105234

Received: 22 November 2022; Accepted: 27 January 2023; Published: 16 February 2023.

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Copyright © 2023 Balducci. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Marco Balducci, ✉ [email protected]

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Implications of public policies performance on social inequality worldwide

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• Published: 28 August 2024
• Volume 4 , article number  103 , ( 2024 )

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• Mahmoud Salameh Qandeel   ORCID: orcid.org/0000-0003-4509-9553 1  

This study probes the linkage between public policy (represented by GDP growth, inflation, CO 2 emissions, and unemployment factors) and social inequality indicators, paying attention to economic, environmental, and social elements. The study questions the impact of these policies on overall social inequality as one measure and its separate dimensions, which are gender, income, education, and life expectancy, whereas data was gathered between 2010 and 2021 from the World Bank and the United Nations Development Programme (UNDP) for 139 countries. The linear regression revealed a significant relationship that explained 51% of the variance in overall social inequality, except for unemployment. Regarding separate dimensions of social inequality, the findings point out that GDP growth and inflation both affect life and gender inequality, whereas unemployment only affects income inequality; on the other hand, the CO 2 emissions factor has an inverse effect on all dimensions of inequality (income, life expectancy, education, and gender inequalities). Considering the implications, increased CO 2 emissions would reduce income inequality by boosting job creation, but they also pose environmental and health hazards, necessitating sustainable development strategies. Rising unemployment exacerbates income distribution, demonstrating the need for policies that enhance job stability and reduce inequality. Additionally, it necessitates investing in healthcare and education, eradicating gender inequality, and implementing sustainable strategies to foster economic growth while considering the consequences of inflation on life and gender justice. Thus, realizing these principles would build a sustainable and equitable society that balances economic enhancement with environmental protection and achieves equal opportunity.

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Introduction

Social inequality in society is established and sustained through the unequal distribution of opportunities and resources among distinct social groups; therefore, the idea holds that social, economic, and political power differences foster systemic inequalities such as income discrepancy, racial and ethnic discrimination, and gender disparities (Monk 2022 ). Although there has been advancement in decreasing inequalities, it is still a global trend that impacts income, education, health, and gender. As a result, it remains and is exacerbated by rapid technological breakthroughs, climate change, and global economic uncertainties (UNDESA 2022 ). These changes and uncertainties influence citizens’ well-being. For example, experiencing unemployment in an economy markedly reduces life satisfaction, besides the indirect effect of income loss (Filomena 2023 ; Pittau et al. 2010 ).

In countries, the economic and environmental situation shows the performance of a government’s policies. Therefore, protocols linked to tax, investments, commerce, and infrastructure can affect GDP growth , as those protocols that impinge upon the labor markets, education, training, and employment generation could sway the unemployment rate. However, the deterioration due to monetary policies, such as interest rates and money supply, and fiscal policies would cause inflation , and those protocols connected to energy production, transportation, and industrial processes can form the levels of CO 2 emissions (Chishti et al. 2021 ).

Public policy stands for government, public organizations, and other politically selected choices that solve societal concerns, and it entangles a broad span of vital societal issues, such as economics, social welfare, healthcare, and the environment (Decker et al. 2019 ). Yet, policies that stimulate equitable and resilient economic growth, such as adequate accessibility to excellent education and health care and enriching social security systems, could overwhelm concerns regarding inequalities (Kanbur 2021 ). In some countries, for example, there is a gap between low- and high-income families and difficulty accessing health resources, education, or discrimination based on gender. This gap has implications for inequality. Consequently, the widening gap includes changes in family structure, residential segregation, and disparities in access to educational resources. In general, public policy strategies might alleviate this issue, for example, by lowering residential segregation, expanding access to high-quality early childhood education, and enhancing teacher quality (Dave and Vasavada 2022 ). Another example is gender disparities in the job market, enclosing pay gaps and uneven admission to parental leave, worsening health, and work-life imbalances. Effective public policy interventions may be critical in addressing these inequalities in the labor market (Birkelund et al. 2022 ). Distributive and regulatory policies construct the two primary categories of public policy. Distributive policies, such as social welfare programs or economic infrastructure projects, entail resource distribution or advantages to groups or individuals. Regulatory policies, on the other hand, impose rules or standards on behavior, such as environmental laws or requirements for workplace health and safety. Additional types of public policy, such as redistributive policies, aim to reduce economic inequality, and constitutive policies shape fundamental values and institutions of society (Howlett et al. 2009 ; Sánchez-Rodríguez et al. 2024 ). Public policy affects social inequality by using its power to either increase or decrease existing imbalances. Implementing a progressive taxing structure, for instance, would assist in reducing disparities, yet identifying the ideal tax rate is conditional on various elements involving income distribution, social disposition for redistribution, and the effects of taxes on economic advancement (Galbraith 2009 ). Again, multiple public policies can influence inequality, including an individual’s earnings, access to education, and transferring income from higher earnings to poorer earners (Chetty et al. 2016 ).

The indicators of public policy performance usually concentrate on policy areas, such as economic and environmental indicators. Legislators consider these factors to assess policies and programs, establish areas for improvement, and make informed decisions. On the other hand, social inequalities indicators address more extensive social concerns like income, education, life expectancy, and gender inequalities. The indicators of policy performance measure the extent and consequences of social inequalities in societies and guide policy discussions regarding tackling these issues (Camminatiello et al. 2023 ). GDP growth, unemployment, and inflation are primary indicators of a country’s economic shift. It illustrates the expansive health and stability of the economy, the status of employment prospects available, and the solidity of prices. Observing economic and environmental indicators helps assess the effectiveness of public policies on citizens’ welfare regarding the environment and sustainable economic growth in a country (Mazzanti et al. 2020 ). In addition, public policy implementations and regulations have environmental impacts on people’s well-being. Measuring the benefits of CO 2 emissions tracks progress and identifies areas for improvement. As a result, the CO 2 emissions indicator is critical because it relates to climate change and environmental sustainability in the area where people live. Handling CO 2 emissions would mitigate climate change, minimize environmental degradation, and nurture a sustainable future for citizens (Robinson and Herbert 2001 ; Yang et al. 2023 ).

With a concentration on economic, environmental, and social aspects, this study investigates the relationship between public policy (shown by GDP growth, inflation, CO 2 emissions, and unemployment variables) and social inequality indicators introduced by income, life expectancy, education, and gender inequalities within communities. Whereas past studies have attempted to look at each of these areas separately, there remains not much knowledge on whether public policy, economic development, environmental effects, and social inequality interact and impact one another within a broad framework. The main question propelling this study is: How do public policy decisions on economic development and the environment affect social inequalities in a global setting? This study uses economic and environmental statistics from 139 nations to determine how these policies affect social inequality. It benefits the academic community and politicians by providing a global perspective and establishing the effects of policies on social inequalities, as the results assist authorities in recognizing the existing position and its implications for socioeconomic life.

Literature review

Although much study on the topic has been conducted, recognition of the linkage between environmental and economic policies and their impact on social inequality remains lacking. Prior studies have shown that inflation , unemployment , and GDP growth are economic factors with complex effects on social inequality. Income inequality tends to rise in parallel with GDP growth, for example, because wealth gains are not distributed evenly (Piketty 2014 ; Stiglitz 2012 ). Similarly, inflation exacerbates economic disparities and falls disproportionately on low-income households (Blanchard and Sheen 2013 ; Easterly and Fischer 2001 ). Particularly those targeting CO 2 emissions , environmental policies are also important in determining socioeconomic inequality. Studies show that while initiatives to lower emissions might boost public health and generate employment, they could also result in higher costs for businesses and consumers, hence perhaps expanding the divide between various socioeconomic levels (Stern 2007 ; Boyce 2018 ).

Despite all of this, there are few comprehensive studies examining which public policies across economic, environmental, and social domains jointly influence several facets of social inequality, involving income, education, life expectancy, and gender differences. Previous research concentrates on individual issues, omitting to convey the multidimensional character of public policy consequences (Tuters 2012 ; Alesina and Rodrik 1994 ). This research attempts to fill this gap by utilizing a wide methodology integrating environmental and economic variables to measure their influence on social disparities and by delivering a deeper comprehension of which public policy choices may be optimized to promote fair social outcomes. The literature review’s subsequent subparts give an ordered overview of each social disparity that may originate from environmental and economic reasons.

Economic and environmental indicators with a relationship to inequality in income distribution

Policies intended to reduce income disparity could encourage investment, develop human capital, and foster social stability, leading to more sustained economic growth. In contrast, inequality has a detrimental impact on economic blossoming (Acheampong et al. 2023 ). According to the Nikolopoulos et al. ( 2015 ) study, income inequality and declining GDP growth rates are associated with increased transmission of a sort of epidemic among people in Europe. Another study, in contrast, uncovered that income disparity and GDP growth are negatively correlated in developed and underdeveloped nations. Moreover, the strength of this linkage may differ depending on the extent of economic progress. The study confirms that the relationship between GDP growth and income disparity could have a threshold effect, requiring a particular degree of economic expansion before it becomes vital (Luan and Zhou 2017 ). Milanovic ( 2016 ), on the other hand, emphasized the importance of economic growth and reducing inequality without affecting growth. It was suggested that education and training could help achieve this goal without impeding evolution. Socialism has been successful in reducing inequality, but it has not had a significant impact on growth and innovation. The relation between income inequality and GDP growth, as shown by Raza et al. ( 2017 ), necessitates careful policymaking to prevent weakened economic growth. For example, the study discovered that military spending in Pakistan increases income disparity.

Helpman et al. ( 2010 ) demonstrated a link between unemployment and income inequality in a global economy. Their theoretical framework contends that shifts in international trade and technology may include varying impacts on the labor market’s diverse skill sets, resulting in adjustments to income inequality and unemployment rates. Importantly, people with low skill levels are likely to have higher unemployment rates and lower salaries compared with workers with high skill levels, which might raise income inequality. Moreover, Pal et al. ( 2021 ) discovered that remittances, or the money migrants send home from foreign countries, contribute to reducing unemployment rates and income inequality, albeit to varying degrees across different countries. In addition, Michieka et al. ( 2022 ) uncovered a varying linkage between contaminants in the air and income disparity across various geographic areas. Countries with higher levels of income imbalance tend to maintain higher CO 2 emissions (Padilla and Serrano 2006 ).

However, a study used data from a sample of industrialized and emerging nations to explore the connection between income disparity and inflation. According to the findings, inflation has a powerful impact on income disparity, which means that when inflation increases, income inequality tends to follow suit. The study established that developing nations may experience more remarkable economic disparity because of inflation than wealthy countries (Bulíř 2001 ). Identically, across 44 developing nations from various parts of the world, including Africa, Asia, and Latin America, the study used empirical analysis, which discovered a positive association between income disparity and inflation. Higher inflation rates were linked to higher levels of income disparity. Higher inflation rates were linked to higher levels of income disparity in Nantob’s ( 2015 ) study, as it was stressed to address income inequality to reduce inflation in developing nations. Another study found that income inequality and inflation were positively correlated in both types of economies after conducting an empirical investigation. It emphasized the role of tackling income disparity to manage inflation in rich and emerging nations (Siami-Namini and Hudson 2019 ).

From 2007 to 2013, Fitzgerald et al. ( 2018 ) investigated the connection between working hours and CO 2 emissions in the United States. They discovered that more hours worked by low-income employees are positively correlated with increased CO 2 emissions. An increase in energy use brought on by more extended workdays and a lack of time for eco-friendly pursuits can be used to explain this association. While Muhammad et al. ( 2022 ) explained income inequality and CO 2 emissions as separate factors, income inequality affects renewable energy consumption, and carbon emissions and trade openness influence renewable energy consumption. However, the shared area of this study is that they suggest policies aimed at reducing income inequality and promoting the adoption of renewable energy to promote more sustainable development in the countries.

Economic and environmental indicators with a relationship to inequality in life expectancy

Alam et al. ( 2021 ) explored the relationship between life expectancy and financial development, which serves for GDP growth in Bangladesh and India. The authors discover that a rise in GDP can result in better health outcomes since monetary evolution has a favorable and significant impact on life expectancy. Measures focused on fostering financial development may benefit health outcomes, particularly in developing nations like Bangladesh and India. As indicated by GDP growth, another study discovered that life expectancy is impacted by economic expansion. The study revealed that measures that support economic globalization, such as boosting trade and investment, may affect health outcomes in low-income nations. It also concluded that new policies could support sustainable development and economic growth (Guzel et al. 2021 ). From 2005 to 2015, Russia’s GDP development exhibited a favorable link with life expectancy, due to a rise in medical treatment, improved accommodation circumstances, and healthy methods. This connection is multidimensional and might rely on personal satisfaction and access to medical treatment. Yet the link between GDP growth and life expectancy may not always be positive (Shkolnikov et al. 2019 ).

Zarulli et al. ( 2021 ) found that unemployment mediates life expectancy and healthcare system efficacy because individuals without work may have limited access to resources for supporting healthy lifestyles. Unemployment rates, a social problem, may impact life by generating tension, poor mental health, and a higher frequency of chronic diseases. However, unemployment may restrict access to healthcare and other services that promote health and well-being, which leads to a decline in life expectancy (Montez et al. 2020 ).

Notwithstanding, life expectancy and inflation maintained a negative association. Due to a variety of rising demands for goods and services and tight supply, inflation tends to increase as the population ages. This may result in pensioners’ actual incomes declining and their quality of life declining, which would shorten their life expectancy (Broniatowska 2019 ). Moreover, there are many ways that inflation may impact the need for life insurance. For instance, rising inflation may lower people’s purchasing power and make it more challenging to pay life insurance payments. In addition, inflation can increase healthcare expenses, which can also affect life expectancy (Alhassan and Biekpe 2016 ).

Markedly, higher levels of CO 2 emissions were attributed to a decreased life expectancy, which concluded that CO 2 emissions had a negative and significant impact on life yearning. According to the study, this could be due to air pollution, which is caused by CO 2 emissions that harm health (Ali and Ahmad 2014 ; Roy 2024 ).

Economic and environmental indicators with a relationship to inequality in education

Since education boosts productivity and human capital, it is a primary factor in economic growth. Educated people are more likely to innovate and contribute to the economy’s GDP growth rate. Ensuring that the advantages of education are dispersed more equally across society is crucial for economic stability and growth. Overspending on education compared to other areas, such as infrastructure or healthcare, may not result in faster GDP growth rates (Krueger 2018 ). Yet education is frequently cited as a significant factor in economic progress, as it can contribute to social inequality by consolidating existing power structures and giving some groups preferential treatment. People from working-class origins frequently face barriers to obtaining a high-quality education, which may reduce their chances of achieving upward social mobility and maintaining economic disparity (Reay 2018 ). In the Middle East and North Africa, those with less education have fewer opportunities to work in the authority area and are more likely to participate in official sectors linked to the informal economy, which is more vulnerable to inflationary tensions (Adeleye et al. 2022 ).

Significantly, measures taken to lessen educational differences and increase access to educational and training opportunities can aid in lowering unemployment and fostering better social and economic equality (Berghammer and Adserà 2022 ). When education was an influential factor, it was found that it resulted in unemployment. Furthermore, education is a primary part of explaining the disparities in unemployment rates among various socioeconomic categories. Additionally, measures taken to lessen educational inequalities and increase access to educational and training opportunities can aid in lowering unemployment and fostering better social and economic equality (Hakura et al. 2016 ). The initiative, which aimed to enhance health and educational outcomes for low-income families, was linked to significant gains in children’s health and educational outcomes and declines in poverty and inequality, according to Behrman and Hoddinott ( 2001 ). Spending money on social welfare and educational initiatives can lower unemployment and assist underprivileged people in achieving more equal outcomes. The impact of education on unemployment varies depending on the institutional framework. Education is a critical marker in explaining the disparities in unemployment rates among various socioeconomic categories.

Spending on education and enacting laws to reduce educational inequality may help to mitigate inflationary pressures and encourage long-term economic growth (Ncube et al. 2014 ). Another study examined the connection between inflation and educational inequality in the USA’s higher education spending. It found that financing higher education resulted in a considerable increase in tuition and fees, which raised the inflation ratio in the education sector. Low-income students who accessed education were impacted by these fees. Downsizing inflationary pressures and promoting more equitable student outcomes were suggested, and elevating higher education funding with targeted measures would reduce educational disparity (Mitchell et al. 2019 ). An examination of the consequences for energy demonstrated that elevated levels of schooling inequality contribute to higher CO 2 emanations , and the more educated individuals are bound to pay extra for vehicles that radiate less carbon dioxide, although provincial and metropolitan regions contrast in such a manner (Wang et al. 2020 ). According to Achtnicht ( 2012 ), highly educated people may be more aware of and concerned about the effects of carbon emissions on the environment.

Economic and environmental indicators with a relationship to gender inequality

Remarkably, a study noted that gender differences in schooling can hinder economic growth by lowering the pool of human capital available for projects. Discrimination and social conventions can prohibit girls and women from getting an education, which reduces literacy and rates of female labor sector participation (Seguino 2000 ). According to a Pakistani study, gender disparity, as demonstrated by differences in salaries, employment opportunities, and education, harms GDP growth , but tackling it and advancing gender equality can result in better economic growth because women’s participation in the workforce can lead to higher productivity and innovation (Ali 2015 ).

Considering differences in education levels and age groups, a study in the United States revealed that women still experience more extensive rates of unemployment than men. Observable traits like education and experience do not entirely account for the gender gap in unemployment. As demonstrated by the study, the significant reasons for this imbalance are abnormalities in people’s associations with the work market. Due to caring commitments, women are more likely than men to reach through vocational interferences, and they likewise experience more difficulties while looking to return to the labor force after leave (Albanesi and Şahin 2018 ). The “gender regime” in a particular society affects the relationship between gender and unemployment. Gender disparities are strengthened in countries where women are underrepresented in the workforce, which results in higher unemployment rates for women than for men, whereas Strandh et al. ( 2013 ) found that this gendered unemployment experience can impair mental health, particularly for women, as they explained that promoting the health and well-being of men and women requires significant resolution of gender disparities in the job market.

Due to women’s lesser negotiating power in the labor market, gender discrimination exacerbates how inflation affects the business cycle and creates an atmosphere where inflation is more persistent. The significance of taking gender into account when examining the macroeconomic effects of inflation is brought home by this (Neyer and Stempel 2021 ). Braunstein and Heintz ( 2008 ) clarified that gender bias can negatively affect central bank policy in achieving its goals of reducing unemployment and inflation, whereas establishing more egalitarian and prosperous economic policies can target both job creation and inflation reduction. Surprisingly, Corduas ( 2022 ) argues, regarding perceived inequalities between genders, that women are more susceptible to price increases and tend to perceive inflation as being higher than men. Gender roles and socialization may also be important.

Expressively, gender differences and CO 2 discharges could be connected, as per the concentration of CO 2 emissions in European Union nations. The gender imbalance estimates gender disparity, and raised levels of gender disparity are bonded to higher CO 2 emissions. Even after controlling variables such as population density and economic growth, which may affect CO 2 emissions, gender inequality persisted. In addition, addressing gender disparity may be a crucial first step in halting environmental deterioration and cutting CO 2 emissions (Koengkan and Fuinhas 2021 ). Moreover, the study by Pearse ( 2017 ) posits that climate change has gendered dimensions, with women frequently disproportionately experiencing its impacts. The author contends that addressing gender disparities is crucial for effectively mitigating and adapting to climate change. Nonetheless, the lack of equivalent access to data about environmental change for women may compel their support in arranging and executing strategies to address it. Subsequently, recognizing and handling the gendered parts of climate change is vital for achieving more equitable and effective solutions.

Methodology

The methodology is organized to clarify the utilized approach and ensure that the research questions are addressed systematically; thus, this section lays out the conceptual framework, data collection, analysis methods, research hypotheses and questions, and sample description to examine the effects of public policy performance on social inequality indicators.

The conceptual framework

Public policy performance, the independent variable, contains economic indicators that are employed to measure economic implications and stability due to government policy performance, as well as environmental indicators that are included to understand the environmental impact of public policies. Both indicators are utilized in this study to understand their effect on social well-being and inequalities, notably in Fig.  1 showing the study model:

Source: author

The study model.

Economic and environmental indicators are based on Chishti et al. ( 2021 ) (the independent variable): (A) Economic indicators : (1) GDP growth (annual%); (2) unemployment, the total percentage of the total labor force, modeled by the International Labor Organization (ILO) estimate; (3) inflation, consumer prices. (B) Environmental indicator : CO 2 emissions per capita.

Social inequality (the dependent variable) founded upon Monk ( 2022 ) and Camminatiello et al. ( 2023 ) is exemplified by (1) inequality in income percentage, (2) inequality in life expectancy percentage, (3) inequality in education percentage, and (4) gender inequality index (GII) percentage.

Data collection and description

This investigation utilizes data employed from credible secondary sources, which are the World Bank (extracting economic, environmental, and social index for income distribution indicators), which contains data for 189 countries, and the United Nations Development Programme (UNDP) for other social inequalities variables, which has information for 193 countries and territories.

To handle certain missing data, the study used national averages to fill up the blanks between known years or the average of consecutive two years for the last blank; however, countries with many missing records were eliminated to ensure reliability. Consequently, 139 nations were covered from 2010 to 2021 based on data that was available throughout the research period in the second half of 2023. Then the study measured the longitudinal average for each country’s indicator over the years between 2010 and 2021, as the calculated numbers used for analysis are presented in Appendix 1 . The data extracted from the World Bank is the Gini index (inequality in income distribution), CO 2 emissions (metric tons per capita), GDP growth (annual%), unemployment (total (%) of the total labor force, modeled ILO estimate), and inflation (consumer prices, annual%), yet, from the UNDP website, the data are the GII, inequality in education, inequality in life expectancy, and the human development index (HDI) rank.

Depending on the data description from the World Bank and the UNDP, the Gini index measures the inequality in income distribution in a country (0 = complete equality, 100% complete inequality). Life expectancy is the expected life for an infant by comparing the percent of mortality to the survival between different subgroups in a population, which has no fixed numerical scale. Similarly, the disparity in education indicator has no fixed scale, as it represents the inequality in education in children’s school attendance percentages among various groups (differences in access to and completion of education). GII (0 = complete gender equality, 1 = complete gender inequality), as it is estimated in areas of labor market participation, empowerment in areas like parliamentary seats held by women, reproductive health like maternal mortality ratio, and adolescent pregnancies between 15 and 19. CO 2 emissions (metric tons per capita) estimate the country’s role in global greenhouse gas emissions by measuring the quantity of carbon dioxide emissions generated per person. Yet, GDP growth describes the annual ratio change in a country’s gross domestic product, whereas unemployment represents the total percentage of the total labor force unemployed and actively seeking jobs and has a scale from 0 to 100%. Inflation reflects the annual percentage change in consumer prices in the average of goods and services, which might indicate inflation or deflation.

Methods for data analysis

Descriptive and correlational analytics were utilized to describe the variables included and show the link between each variable of the economic and environmental indicators and each variable of social inequalities. To investigate the direct link between public policy performance via economic and environmental indicators and social inequalities, linear regression was employed. Yet, to examine each indicator of public policy performance (GDP growth, unemployment, inflation, and CO 2 emissions) and know if there is an effect of each variable of public policy performance on social inequalities or not, stepwise regression analytics were used. The HDI was considered to describe the sample and show the inequality distribution among the continents.

Research questions and hypotheses

This study tackles two specific research questions, depending on the conceptual and theoretical frameworks:

(1) Do public policy implementations, measured by economic and environmental indicators carried out by governments, affect social equality?

(2) Does the significance of these economic and environmental indicators vary across distinct types of social inequalities?

According to these questions, the research proposes five hypotheses:

H1 : Public policy performance (measured by economic and environmental indicators including GDP growth, unemployment, inflation, and CO 2 ) affects social inequalities.

H2: Public policy performance has a greater impact on income inequality.

H3 : Public policy performance exerts a stronger influence on the disparity in life expectancy.

H4: Public policy performance has a considerable influence on inequality in education.

H5: Public policy performance has a greater effect on gender inequality.

Econometric modeling

The dependent variable = β0 + β1⋅GDP growth + β2⋅unemployment + β3⋅inflation + β4⋅CO 2 emissions + ϵ.

For H1, the dependent variable is social inequalities, an aggregated measure of income, life expectancy, education, and gender inequalities. For H2, the dependent variable is income inequality; H3 is life expectancy; H4 is inequality in education; and H5 is gender inequality.

β0 (intercept): The dependent variable’s baseline level while all independent variables are zero.

β1–β4 (coefficients): alterations in the dependent variable when the independent variable rises by one unit, while other variables are treated as constants.

ϵ (error term): the difference between the observed and predicted values by the models.

Description of the sample

As shown in Fig.  2 , the sample consists of 139 countries worldwide, spanning six continents, from 2010 to 2021. The UNDP website exhibits the HDI code. It is a broad benchmark that measures the extent of human development in a nation, assuming variables like life expectancy, education, income, and gender disparities. It is split into four scales, from “very high” to “low.”. Alternately, Europe had an ample share of development, while Africa was the lowest in the human development index between 2010 and 2021. Moreover, 63% of HDI in Asia was between medium and high, while in South America, 62.5% of the total index was classified as high. Similarly, in Oceania, 75% of the HDI classification was high, but in North America, it was differentiated between exceedingly high and medium.

Source: author, based on the UNDP database

The HDI distribution among continents.

Hypotheses testing and results

Description and correlation analytics.

Table 1 presents the descriptive and correlational analytics of the study indicators. As given below, the CO 2 emissions part is negatively and significantly linked to all social inequalities (inequality in income distribution, inequality in life expectancy, inequality in education, and GII) (r = −0.380, −0.650, −0.632, −0.744, p < 0.001, respectively); the correlations vary from moderate to strong. GDP growth is positively associated with imbalances in life expectancy, unevenness in education, and GII (r = 0.246, 0.252, 0.248, p < 0.01), from a weak to moderate linkage. On the contrary, unemployment has a humble connection with income distribution disparity (r = 0.211, p < 0.05). However, inflation has a weak positive link with inequality variables, except for income distribution inequality. Its relation to inequality in life expectancy and GII was (0.312 and 0.296, p < 0.001, sequentially), while inequality in education was (0.211, p < 0.05).

Linear regression to measure public policy performance via economic and environmental indicators on the social inequality variable

The H₁ model, in Table  2 , has a significant intercept estimated at (20.140) and (SE = 1.473), indicating a strong relationship with social inequality (t = 13.672, p < 0.001). Yet the CO 2 emissions variable illustrates a negative association with “social inequality” (β = −0.623, t = −9.659, p < 0.001), while GDP growth proposes a positive but weak association (β = 0.150, t = 2.131, p = 0.035). As inflation infers a significant positive but vulnerable relationship (β = 0.167, t = 2.598, p = 0.010), unemployment is unimportant. R (0.714) shows a strong correlation, the model could clarify 51% of the variation in social inequality (R 2  = 0.510), and the adjusted R 2 of (0.496) is liable for the number of forecasters and delivers a good estimation of the model’s fit. The ANOVA establishes the significance of the relationship (F = 34.889, 4, p < 0.001). Besides, Fig.  3 illustrates the marginal effects plots for economic and environmental indicators on social inequality.

Source: author, based on the World Bank and UNDP data

Marginal effects plots of economic and environmental indicators on social inequality.

Stepwise regression between economic and environmental indicators and inequality in income distribution

Two indicators of government public policy implementation (CO 2 emissions and unemployment) are investigated with inequality in income distribution in the stepwise regression based on the correlation test results. Table 3 and Fig.  4 are the outcomes of the regression analysis. In model 1, the intercept term is 40.48, which indicates the expected inequalities in income distribution when the CO 2 emissions indicator is zero. The unstandardized coefficient for CO 2 emissions showed an inverse relationship between the increase in CO 2 emissions and the decline in inequality in income distribution by 0.728 units. Yet, based on model 2, the projection of disparity in the income distribution is 38.104-0.734 (CO 2 emissions) + 0.301 (unemployment). In model 2, a substantial linkage between income inequality and CO 2 emissions was indicated by the coefficients of −0.734 and 0.301. However, the standardized coefficient in model 2 showed a weak negative relationship between CO 2 emissions and income inequality, and unemployment was positively weak with this inequality. In model 1, the R-squared can explain 15% of the variance in inequality in income distribution. The RMSE is 6.977, offering a slightly better fit analogized with Model 1. The ANOVA tests for both models indicate significant general model fits (F = 16.101, df = 2, p < 0.001), which tells us that the regression models are a statistically significant improvement over the null model.

Marginal effect of CO 2 and unemployment on inequality in income distribution.

Stepwise regression between economic and environmental indicators and inequality in life expectancy

Based on the correlated variables, three metrics of public policy implementation effect regarding economic and environmental measures (CO 2 emissions, inflation, and GDP growth) are examined with inequality in life expectancy in the stepwise regression. Table 4 presents an examination comprising three models, each shedding light on different predictors’ effects. Model 1 showed a substantial negative link between CO 2 emissions and income inequality (t = −10.017, p < 0.001). Expanding to Model 2, which includes CO 2 emissions and inflation as predictors, the negative association between CO 2 emissions and income inequality persists. The connection in model 2 is still statistically influential (t = −9.489, p < 0.001) and (t = 2.916, p = 0.004) sequentially. Building on the previous models, model 3 presents GDP growth as an additional predictor, where the model stands substantial (t = 2.651, p < 0.01). Accordingly, each one-unit increase in CO 2 emissions, inflation, and GDP growth is associated with shifts in income inequality (B = −1.584 + 0.241 + 0.911 units). The R-squared in model 3 enhances to predict 48.4%. The adjusted R-squared of 0.472 reflects the model’s enhanced fit, considering the number of predictors and the increased explained variance. Significantly, the ANOVA tests for all three models demonstrate significant overall model fits (F = 42.148, df = 3, p < 0.001), and R is strong (0.695), inferring that the regression models significantly outperform the null model. The plots in Fig.  5 , which depict marginal effects, elucidate the correlation between CO 2 emissions, inflation, GDP growth, and inequality in life expectancy.

Marginal effect of CO 2 emissions, inflation, and GDP growth on inequality in life expectancy.

Stepwise regression between economic and environmental indicators and inequality in education

Table 5 presents a regression analysis investigating the impact of three measures of public policy performance (CO 2 emissions, inflation, and GDP growth) on educational inequality. The focus is on the relationship between these measures and disparities in education, as evidenced by the correlation results. The unstandardized coefficient for CO 2 emissions demonstrated a negative association for every one-unit increase in it; there is a decrease in educational inequality by 2.328 units. The stepwise regression (t = −9.558, p < 0.001) emphasizes the significance of the connection. The R-squared explains 40% of the variance in educational inequality. The RMSE of 11.523 represents the average deviation between the predicted and actual values of educational inequality. The analysis revealed that the CO 2 emissions indicator is the only predictor of educational inequality (F = 91.347, < 0.001). In Fig.  6 , the marginal effect shows the direction between CO 2 emissions and education inequality.

Marginal effect of CO 2 emissions on inequality in education.

Stepwise regression between economic and environmental indicators and GII

In the stepwise regression, three public policy effectiveness indicators (CO 2 emissions, inflation, and GDP growth) were considered for gender disparities, as measured by GII. The results were illustrated for three models in Table  6 and Fig.  7 . In model 1, upon inspection of the unstandardized coefficient for CO 2 emissions, it can be concluded that an inverse connection exists between CO 2 emissions and GII (t = −13.052, p < 0.001). Model 2 incorporates CO 2 emissions and inflation as predictors. The negative association between CO 2 emissions and GII persists (t = −12.528, p < 0.001), and the inflation indicator is positively significant (t = 2.663, p = 0.009). Model 3 expands on the analysis by incorporating GDP growth as an extra predictor. It is observed that one unit increase in the independent variables (CO 2 emissions, inflation, and GDP growth) leads to changes in GII as follows: −0.033 + 0.003 + 0.013 units, respectively. Nevertheless, these three variables are significant predictors of the GII, where the third added variable, GDP growth, was (t = 2.438, p = 0.016). Model 3 improved to explain 59.4% of the variance in GII. However, the ANOVA examination for the three models established significant model fit (F = 65.892, df = 3, p < 0.001), which, in turn, exhibits that the regression models significantly outperform the null model.

Marginal effect of CO 2 emissions, inflation, and GDP growth on GII.

The links between economic development and environmental factors regarding public policy implications and social inequities have all been independently investigated in past research. This study, on the other hand, contributes by integrating these elements into a broad structure that demonstrates the complexity of the linkage between economic growth, environmental factors, and social inequalities. As a result, it addresses an essential gap by demonstrating that public policy performance has implications for social disparities (as evidenced by GDP growth, unemployment, inflation, and CO 2 emissions). For this reason, this research investigated the effects of public policy performance on various social inequalities as expressed by economic and environmental indicators.

Regarding the research questions, it is found, as summarized in Table  7 , that there is a positive, statistically significant association between social disparities and the implementation of public policies (p < 0.001), as well as a variation in the effect of policy performance implications (represented by economic and environmental factors including GDP growth, unemployment, inflation, and CO2) on different social inequalities (income, life, education, and gender inequalities).

According to the results of the five hypotheses, this study established that:

H1 : Public policy performance (measured by economic and environmental indicators including GDP growth, inflation, and CO 2 except for unemployment ) affects social inequalities.

H2: Public policy performance, represented only by employment and CO 2 emissions policies, has a greater impact on income inequality.

H3 : Public policy performance, introduced only by GDP growth, handling inflation, and CO 2 emissions policies, exerts a stronger influence on the disparity in life expectancy.

H4: Public policy performance, brought about just by CO 2 emissions policies, has a considerable influence on inequality in education.

H5: Public policy performance, resulting from GDP growth, handling inflation, and CO 2 emissions policies, has a greater effect on gender inequality.

The subparts below discuss income, life expectancy, education, and gender inequalities, along with indicators of public policy performance implications:

Income inequality and indicators of public policy performance implications

The results proved that CO 2 emissions and unemployment influence income inequality . Unemployment contributes to the expansion of income inequality via job losses and downsized payments. Those employed experience less job security and insufficient compensation. Comparable to our study results, Helpman et al. ( 2010 ) showed a connection between unemployment and income inequality in a global economy, as Pal et al. ( 2021 ) found those variables were influenced by other factors, such as cash flows among countries. While we have established that CO 2 has an inverse link with income inequality, this is consistent with Michieka et al. ( 2022 ) and Padilla and Serrano ( 2006 ), who explained that the increased income disparity is linked to higher CO 2 emissions because the wealthier purchase more energy-intensive goods and services. Additionally, the long working hours of low-income people lead to more production and additional CO 2 emissions to compensate for challenging living circumstances. On the other hand, we could not discover the relationship between the two factors: GDP growth and inflation, and income inequality. However, Mo ( 2000 ) found that income inequality, as an influencing factor, hurts economic growth. Because income inequality hurts economic growth, policies aimed at reducing it may lead to more sustained growth by promoting investment, human capital development, and social stability. Nikolopoulos et al. ( 2015 ) demonstrated that income inequality and GDP growth, taken separately, resulted in a kind of disease in Europe, while Luan and Zhou ( 2017 ) proved that they are negative signs.

Life expectancy inequality and indicators of public policy performance implications

Notably, in the stepwise regression tests, this study found a significant impact of CO 2 emissions, inflation, and the GDP growth rate on inequality in life expectancy . Comparably, Alam et al. ( 2021 ) found that since financial development has a positive and considerable impact on life expectancy, an expansion in GDP can lead to better health outcomes. Arguably, Shkolnikov et al. ( 2019 ) illustrated that although there might be a significant correlation between GDP growth and life expectancy, there are other complicated factors, such as living circumstances in an economy and healthcare resource availability. However, like our findings, Alhassan and Biekpe ( 2016 ) and Broniatowska ( 2019 ) explained that inflation would increase the cost of healthcare services, which prevents people from having the resources they need. Ironically, we revealed that as CO 2 rises, inequality in life expectancy drops. Thus, we consider that when CO 2 emissions increase due to industrialization and economic development, this leads to better healthcare, living conditions, and resource access. As a result of equitable resource distribution and improved healthcare infrastructure, life expectancy rises and inequalities fall. Contrarily, Ali and Ahmad ( 2014 ) demonstrated that CO 2 emissions negatively impact life expectancy due to air pollution. Whereas we did not prove the significance of unemployment to the inequality in life expectancy, Montez et al. ( 2020 ) and Zarulli et al. ( 2021 ) clarified that unemployment could restrict people’s access to healthcare.

Inequality in education and indicators of public policy performance implications

This study demonstrated the CO 2 impacts on education. Where carbon emissions decrease, inequalities in education increase. We attribute this to the fact that CO 2 emissions are decreasing as individuals implement healthier, less polluting habits, which causes changes in economic and industrial structures. Consequently, educational prospects and resources may be distributed unequally, causing increased educational inequalities. However, an increase in CO 2 emissions indicates that less educated individuals are not worried about the earth, resulting in no indication of educational equity. Likewise, Achtnicht ( 2012 ) explained that highly educated people tend to be more anxious about the impact of carbon emissions on the environment. Additionally, Ncube et al. ( 2014 ) discussed that individuals with lower levels of education could have fewer employment possibilities and are more likely to engage in activities that exhibit rising inflation. In the same way, Mitchell et al. ( 2019 ) clarified that inequalities in funding education create inflation. While it is believed that education is a substantial factor in explaining the differences in unemployment rates among different socio-economic groups, Behrman and Hoddinott ( 2001 ) explained that government spending on education would reduce unemployment in the future.

Gender inequality and indicators of public policy performance implications

The influences of CO 2 emissions, inflation, and GDP growth on gender inequality were established. Accordingly, this study suggests that women may be unjustly affected by economic dissimilarities caused by GDP growth, particularly if they confront restrictions. Economic growth would maintain the possibility of sustaining traditional gender roles by inhibiting women from gaining schooling, jobs, or positions of power. In addition, the decreased CO 2 emissions may result in diminished economic prospects, impacting impotent groups such as women. Inflation spikes may undermine purchasing power, affecting lower-income households disproportionately. The junction of economic opportunities and financial strain could worsen gender disparities, leading to rising inequality. On the contrary, Koengkan and Fuinhas ( 2021 ) found that gender inequality was linked to higher CO 2 emissions, and addressing it can help reduce environmental deterioration and carbon emissions. However, the literature, in contrast, found that lowering gender bias as a factor would play a vital role in economic prosperity. For example, Ali ( 2015 ) showed that gender equality leads to better economic growth by increasing productivity and innovation. On the other hand, gender discrimination exacerbates inflation, making it more persistent (Neyer and Stempel 2021 ). Likewise, gender bias can negatively affect institutions, unemployment, and the inflation rate in the economy (Braunstein and Heintz 2008 ). In the same way, policymakers must be aware of gender disparities, since women are more likely than men to believe that inflation is higher (Corduas 2022 ). While we did not prove the effect of unemployment on GII, Albanesi and Şahin ( 2018 ) and Strandh et al. ( 2013 ) illustrated that gender differences result in higher rates of unemployment for women than for men. Gender disparities prevent women from entering the labor force, and seeking job habits is a major reason behind unemployment.

Conclusions

Regarding the first research question, the linear regression revealed a significant relationship between public policy and social inequality indicators, except for unemployment. The model could explain 51% of the relationship. Second, the relevance of economic and environmental indicators, which measure public policy performance, varied by type and their relationships with distinct inequality elements.

Significantly, based on the study aims, economic, ecological, and social imbalances are introduced because of their interlaced connections, their impact on societies, and their alarming implications for governments to organize applicable policies. Concerning the results, increasing emissions tends to decrease inequalities, and the expansion of CO 2 emissions would improve occupational creation. Hence, this reduces income imbalances and enhances investment in social services, including education and medical care, consequently restricting educational and life expectancy irregularities. On the other hand, environmental degradation and well-being dangers result from expanded CO 2 releases, which means there is a current need for sustainable development behaviors and plans for economic, ecological, and social equality to bypass this. Additionally, mounting unemployment initiates economic disparity due to ineffective public policy performance, which causes job losses, lower work hours, and financial difficulties for the jobless. People with jobs could benefit from less competitiveness and better pay, but scaling unemployment compounds job instability, lower salaries, and income inequality. Moreover, rising economic activity worsens inequalities.

As GDP growth expands, income disparities increase, impeding attempts to reduce inequality. However, inequalities in education and life expectancy continue to exist as economic growth collapses to benefit communities. Because of traditional roles and economic expansion biases, gender inequalities persist. Inflation compounds socioeconomic disparities by increasing the cost of vital products and services, unfairly harming poor people, and impeding access to medical care and food resources. Likewise, inflation triggers educational unfairness by expanding education prices and curbing the chance for excellent education, especially for individuals from less wealthy families. Besides, inflation complicates gender deficits due to eliminating women’s economic power and entry to resources, which suffer from salary discrepancies, limited opportunities, and unpaid childcare duties, causing gap rises in expenses that aggravate disparity in genders. To encourage beneficial growth and fair resource distribution, policies such as directed investments in education and healthcare, considering both genders’ contributions, and sustainable policies that promote a more ethical and thriving society by balancing economic growth, environmental protection, and social equity must be introduced.

This study explained 51% of the variation in the analyzed factors and, subsequently, still has restrictions because it relied on secondary data concerning the examined indicator and the usage of regression analytics. Therefore, it is demanding that conducting survey investigations in different countries offer more outstanding attention to these links and support the results.

The implications and policy directives

This study carries substantial theoretical and practical implications. It theoretically illustrates a more complete picture of the interactions between public policy performance and inequality in society by considering economic, environmental, and social factors, while practically suggesting that a multiple-dimension strategy that takes into account economic development, labor market equilibrium, pricing stability, and environmental preservation would be the most practical way for legislators to solve social inequities. Besides, it is recommended to examine public policy performance on separate continents using other techniques and measuring indicators, for example, public policy performance on poverty and social mobility by employing general indicators such as the multidimensional poverty index, intergenerational income, and occupational mobility, and conducting surveys within poor and wealthy countries to compare the results.

As policy directives, this study recommends creating policies that boost GDP growth with benefits shared among all social groups and investing in creating jobs and businesses for stable incomes that help eradicate inequality in society using such policies. Establishing focused job programs in highly demanding skill sectors to prepare new skills, educate them, and train the unemployed also opens new opportunities. Still, encouraging clean energy deployment policies aims to minimize CO 2 emissions by promoting renewable energy sources and motivating organizations to lower their carbon footprint. Yet, regarding education, there should be an investment in educational infrastructure to ensure that everyone receives an excellent education via scholarships and financial assistance and to support personal continuous education. To alleviate life expectancy inequality, public health initiatives should promote well-being by encouraging universal medical access and providing preventative treatment. Besides, gender equality in the workplace could be enhanced via policies such as flexible working hours, affordable childcare, and legislation for equal pay, childbirth leave, and professional succession for women. However, subsidies and price control policies would protect low-income families from inflation and improve their financial inclusion through access to bank services, microfinance, and digital banking initiatives, which could keep consumer prices stable for essential products.

Data availability

All data generated and analyzed during this study are included in this manuscript. Additionally, for secondary data, you can visit the World Bank ( https://www.worldbank.org ) and UNDP ( https://www.undp.org ).

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Qandeel, M.S. Implications of public policies performance on social inequality worldwide. SN Bus Econ 4 , 103 (2024). https://doi.org/10.1007/s43546-024-00704-8

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