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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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Promoting Gender Equality: A Systematic Review of Interventions

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• Volume 35 , pages 318–343, ( 2022 )

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• Michaela Guthridge   ORCID: orcid.org/0000-0002-5157-9839 1 , 3 ,
• Maggie Kirkman 2 ,
• Tania Penovic 4 , 5 &
• Melita J. Giummarra 1 , 5  

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More than four decades have passed since the United Nation’s Convention on the Elimination of All Forms of Discrimination against Women (CEDAW) was adopted. Now is an opportune time to consider whether the interventions seeking to realise CEDAW’s aspirations have brought us closer to achieving gender equality. This systematic review aimed to identify and synthesise evidence for the effectiveness of social justice, cognitive, or behaviour-change interventions that sought to reduce gender inequality, gender bias, or discrimination against women or girls. Interventions could be implemented in any context, with any mode of delivery and duration, if they measured gender equity or discrimination outcomes, and were published in English in peer-reviewed journals. Papers on violence against women and sexuality were not eligible. Seventy-eight papers reporting qualitative (n = 36), quantitative (n = 23), and multi-methods (n = 19) research projects met the eligibility criteria after screening 7,832 citations identified from psycINFO, ProQuest, Scopus searches, reference lists and expert recommendations. Findings were synthesised narratively. Improved gender inclusion was the most frequently reported change (n = 39), particularly for education and media interventions. Fifty percent of interventions measuring social change in gender equality did not achieve beneficial effects. Most gender mainstreaming interventions had only partial beneficial effects on outcomes, calling into question their efficacy in practice. Twenty-eight interventions used education and awareness-raising strategies, which also predominantly had only partial beneficial effects. Overall research quality was low to moderate, and the key findings created doubt that interventions to date have achieved meaningful change. Interventions may not have achieved macrolevel change because they did not explicitly address meso and micro change. We conclude with a summary of the evidence for key determinants of the promotion of gender equality, including a call to address men’s emotional responses (micro) in the process of achieving gender equality (micro/meso/macrolevels).

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Introduction

The adoption of CEDAW was a remarkable achievement in the history of the women’s movement. Its ultimate aim was to catalyse social transformation that transcends cursory legislative reform (Facio & Morgan, 2009 ). Article 3 of CEDAW promotes this social transformation, calling for state parties to ‘take all appropriate measures’ to achieve gender equality. In practice this has included, but has not been limited to, gender-blind strategies, awareness raising, litigation, international advocacy, art and social media activism, and gender mainstreaming (see Table 1 for definition).

The Global Gender Gap Index 2022 benchmarks 146 countries on the evolution of gender-based gaps in economic participation and opportunity, educational attainment, health and survival, and political empowerment (World Economic Forum, 2022 ). Although the Index measures gender parity (defined in Table 1 ) rather than substantive equality, it is a useful tool for analysing progression and regression. With scores depicting the distance to parity on a scale of zero to one hundred, the 2022 Report found the average distance completed to parity was 68 per cent. With the present trajectory, it will take 132 years to close the gender gap and 151 years to achieve equal economic participation and opportunity (World Economic Forum, 2022 ). Moreover, these estimates are predicted to worsen as the world faces crises in politics, economics, health, food, and the environment. Now more than ever we must assess our successes and failures in attempting to reduce gender inequality and discrimination.

The aim of this systematic review was to identify and synthesise evidence of the effectiveness of social justice interventions that sought to reduce gender inequality, gender bias, or discrimination against women and girls. Because recent systematic reviews have examined the effectiveness of interventions targeting violence against women and sexuality (e.g. Karakurt et al., 2019 ; Bourey et al., 2015 ; Yakubovich et al., 2018 ) we did not include these types of interventions. We were unable, however, to identify systematic reviews examining other interventions targeting gender equality. Therefore, this review focused on interventions that sought to achieve gender equality in any political, social, cultural or economic context, except violence against women and sexuality.

Theoretical Framework

The truism ‘context matters’ is pertinent to this systematic review. According to contextual social psychology, effects brought about at a microlevel are modified by the mesolevel and macrolevel, and vice versa (Pettigrew, 2021 ). In this review, microlevel variables include individual characteristics, including biology, beliefs, behaviours, values, and emotions, such as empathy and resentment. Mesolevel contextual factors include interpersonal interactions in family, work, and school etc. (e.g. gender segregation), and macrolevel context includes broader social and cultural norms, including religion and politics. Social norms in this context are “rules of action shared by people in a given society or group; they define what is considered normal and acceptable behaviour for the members of that group” (Cislaghi & Heise, 2020 , p. 409). In this sense, social norms exist within the mind, while gender norms exist outside it, and both are produced and reproduced through social interaction. In contextual social psychology, beliefs are embedded in institutions that affect our relational behaviours. While there are psychological causes of macrophenomena (Pettigrew, 2021 ), these phenomena (such as patriarchy) also influence individual affect. For example, affirmative action laws (macro) should increase contact between genders (meso), which in turn should reduce individual prejudice (micro). While this is a top down example, it also works from the bottom up, whereby micro behaviours can affect macrophenomena. In this context, prejudice against women and girls is a “multilevel syndrome” (Pettigrew, 2021 , p. 74).

“Systems thinking” also recognises the intersection between problems and processes from local to global levels (Arnold & Wade, 2015 ). Systems thinking is a complex interplay of a multitude of constantly evolving factors (Banerjee & Lowalekar, 2021 ). According to systems thinking, gender equality will be realised when interventions at the micro, meso and macrolevel are configured holistically, rather than individualistically. Interventions at any level need to consider and accommodate the role of processes and factors that may support or hinder the effectiveness of the intervention to yield population benefits. The different contextual levels that impact on gender inequality may be successfully tackled by feminist movements, but integrating the interventions pluralistically rather than monistically remains elusive as feminist movements appear to continue to work in silos. In undertaking strategies across different contexts, however, we are more likely to achieve substantive equality. But we need to address this complexity in the three contextual levels (micro, meso, macro) in order to predict, modify and eliminate discrimination against women and girls. These theoretical frameworks are used throughout this review to aid the synthesis of the evidence and identification of implications for practice.

Review Design

The Sample, Phenomena of Interest, Design, Evaluation, Research type (SPIDER) tool was used to design the review (Cooke et al., 2012 ). SPIDER is appropriate for systematic reviews of quantitative, qualitative, and multi-methods research. We use the term multi method rather than mixed method because mixed method studies could be considered to have used multiple methods of data collection/analysis, but not all multi-methods studies follow “mixed methods” procedures as they do not always provide an integrated synthesis of findings across the methods used (Creswell, 2009 ). The search terms are documented in Supplementary Tables 1 and 2. The review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (Page et al., 2021 ). Rapid review methods were used for citation screening and data extraction (Plüddemann et al., 2018 ). Papers were eligible according to the criteria defined below.

The sample could include people of any age, race, or gender in local, global, or transboundary intervention contexts. The phenomena of interest included any social justice, cognitive or behaviour-change interventions that sought to reduce gender inequality, gender bias, or discrimination against women, with any mode of delivery and duration. Interventions could be any type of program (e.g. behaviour change), policy (e.g. gender mainstreaming), process (e.g. awareness raising) or experimental condition that aimed to influence gender-focused outcomes. An intervention was categorised as achieving its aim (e.g., having a beneficial effect on gender equality or reducing discrimination), partially achieving its aim, not achieving its aim according to the assessment in the paper (i.e. if the analyses in the respective paper found that the intervention did not work), or having a harmful effect (i.e. resulting in increased discrimination or inequality).

The intervention being investigated could have been administered by any party, including expert advocates, government or non-government organisations (NGOs), social justice enterprises, or academic researchers. The research design did not need to include a comparator or control group, but must have incorporated a between-groups or pre-post comparison, or retrospective assessment of the impact, feasibility or acceptability of the intervention or program. The primary outcome for evaluation was any measure of actual or perceived level of, or change in, gender (in)equality, gender bias, or discrimination against women or girls. Secondary outcomes were the perceived level of inclusion, solidarity, awareness, empowerment, or equity. The research methods could include qualitative, quantitative, and mixed- or multi-methods. Eligible papers were published in peer-reviewed journals in English from 1990 to 2022. Whilst CEDAW was adopted in 1979, this timeframe was selected to ensure contemporaneity. A protocol for the review was developed a priori, but not registered.

Search Strategy and Eligibility Screening

As this was a review of research across multiple disciplines, three databases were used: Scopus, ProQuest, and psycINFO, in addition to reviewing reference lists and recommendations by experts. Search terms were adapted to each database. After screening the first search results it was evident that the terms were not broad enough, so a second search including additional terms was undertaken (see Supplementary Tables 1 and 2 for terms of both search strategies). All search results were uploaded to Covidence for eligibility screening and duplicate removal by reviewer one. Using Abstrackr, a second author screened a minimum of 10 percent of citations, consistent with rapid review methods (Plüddemann et al., 2018 ), or until < 50 percent of citations were predicted to be relevant. Abstrackr is a machine-learning program that generates predictions of the likely relevance of records based on judgements made by the reviewer (Wallace et al., 2012 ), which has been found to have excellent sensitivity and to generate significant workload savings (Giummarra et al., 2020 ). After titles and abstracts were screened, full text articles were assessed against the eligibility criteria, noting reasons for exclusion. Both reviewers met to discuss any conflicts; if consensus could not be reached a third author was consulted. The authors included experts in gender equality who provided significant input into the search strategy, identification of relevant literature, and synthesis.

Quality Assessment

The quality of research was assessed by the first author using a standard method (Kmet et al., 2004 ) with the added criterion of whether papers reported approval by a formally constituted human research ethics committee. Supplementary Tables 3–5 specify the quality criteria. Overall quality was classified as poor (studies meeting < 0.50 criteria), adequate (0.50–0.69), good (0.70–0.80), or strong (> 0.80) consistent with previous studies (Parsons et al., 2017 ).

Data Extraction and Synthesis

Data were extracted in three categories: The authors and publication year of the paper ; research aims, theoretical approach, methods, sample size, eligibility criteria, and sample characteristics; and, the intervention , aim, type, sector, geographic region, description, duration, targeted outcomes, effects, and short- and long-term impacts. Figures to summarise the proportion of studies from different geographic regions were generated using www.sankeymatic.com/build/ . Ten percent of the full-text articles were randomly selected, stratified by research method, for independent data extraction by a second author, consistent with rapid review methods (Plüddemann et al., 2018 ). The data extracted from both reviewers was cross-checked for accuracy and completeness. Sources of heterogeneity were noted, particularly variation in study samples, settings, contexts and intervention designs or aims. Given the heterogeneity of the interventions and the research, meta-analysis and meta-synthesis were not appropriate. Therefore, the findings were thematically synthesised according to intervention sector (e.g. education, employment etc.) and context (i.e., micro, meso and macro levels).

A total of 7,832 records were screened for eligibility with the last search conducted on 18 July 2022 (Fig.  1 ). Seventy-eight papers, each reporting a single intervention and using qualitative (n = 36), multi (19), or quantitative (23) methods, met the inclusion criteria. The characteristics of qualitative, quantitative, and multi-methods studies are summarised in Supplementary Tables 6, 7, and 8, respectively. The intervention effects for each study are summarised in Supplementary Tables 9 and 10.

Preferred Reporting Items for Systematic review and Meta-Analysis Protocol (PRISMA) Flow Diagram

Five interventions were at the microlevel, 37 were at the mesolevel, and 17 were at the macrolevel. The final 19 interventions straddled micro-meso, meso-macro, or micro–macro. No intervention covered all three levels or took a systems thinking approach.

The overall quality of each paper is detailed in Supplementary Tables 6–8, and ratings for each quality domain are in Supplementary Tables 3–5. Studies using quantitative methods (range 0.58–1.00; median = 0.92, Q1 = 0.82, Q3 = 1.00) had significantly higher quality than qualitative (range 0.41–0.91; median = 0.73, Q1 = 0.67, Q3 = 0.79; χ2(1) = 13.71, p  < 0.001) and multi-method studies (range 0.48–0.94; median = 0.76, Q1 = 0.63, Q3 = 0.82; χ2(1) = 21.96, p  < 0.001). There was no difference in the quality of qualitative and multi-methods studies ( p  = 0.97).

All quantitative studies articulated the research question and reported the results adequately. Randomisation and blinding were used in most studies. While estimates of variance and controlling for confounding were not consistently reported, 18 studies using quantitative methods were considered to be strong quality, and seven had a perfect score.

In reports of qualitative studies, the study design, context, and conclusion were generally addressed well. However, only six studies used verification processes (see Table 1 for definition). No qualitative study received a perfect score; 20 studies were considered to be good quality.

For multi-method studies, the objective, context, data collection, analysis, and conclusion were generally reported well. Blinding was not applicable, and estimates of variance and control of confounding were generally not reported. No multi-method study received a perfect score although the quality of six of multi-methods papers was assessed as good.

Corresponding authors were contacted to confirm ethics approval; authors of two papers confirmed that the study did not receive ethics approval, and authors from 16 studies did not respond or confirm whether they had ethics approval. The omission of evidence of ethical approval is concerning and should be addressed in all future research with humans. The 18 studies with respect to which we either could not confirm ethics approval or did not receive ethics approval were all published in highly ranked journals. Furthermore, it was not, in general, clear in the majority of papers which agency or organisation conducted the intervention or undertook the study (e.g. government agency, NGO, academic researchers) making it difficult to assess reflexivity, and the prospect of future implementation.

Included Interventions

Intervention sectors.

Interventions were implemented and evaluated in various sectors: education (26 interventions); politics (10); employment (8); information, communications, and technology (6); legal (5); economics (6); health (3); sustainable development and land rights (3); sport (3); and women’s and girls’ rights (2). Interventions in the areas of conflict and of water, sanitation, and hygiene were reported in one paper each.

Intervention Settings

Interventions were set evenly throughout the Global South (35 papers) and the Global North (39 papers). Interventions were evaluated in Africa (15), Europe (12), North America (19), Asia (10), Latin America (6), the Middle East and North Africa (4), the United Kingdom (6), and the Pacific (4). Just under half of the Global South interventions were conducted in rural settings (16/35), whereas Global North interventions tended to be urban (22/39) (Fig.  2 ).

Settings for interventions in Global North and South Countries

Research Participant Characteristics

Twenty-seven interventions included both women and men as participants, 30 included only women, and one intervention included only men. Thirteen studies did not report the gender of the sample, and in seven studies gender of the sample or population was not applicable (e.g. intervention sought to affect a broad population approach irrespective of gender, such as a new law that applied to the whole population in order to improve gender equality, or a collective political party that sought to influence gender issues in parliament). Thirty papers did not report other participant demographic characteristics. Where sample characteristics were reported, participants were 10–80 years of age, with education level ranging from none to post-graduate.

Study Characteristics

All papers but one (Devasia, 1998 ) were published after 2005. Most papers reported data gathered across years, with twelve interventions taking place over hours or weeks. The timeframe did not appear to be associated with whether or not the intervention had a significant beneficial effect on the aims of the intervention. For example, McGregor and Davies’ ( 2019 ) two year study of the effects of a pay equity campaign achieved its aim (legislation was enacted), but Hayhurst’s ( 2014 ) girls’ entrepreneurship study that ran for several years had harmful effects (girls income was taken by men). Similarly, Zawadzki et al., ( 2012 ) board game intervention that takes 60–90 min achieved its aims but Krishnan et al. ( 2014 ) conditional cash transfer study over a month had no effect on social change.

In the qualitative and multi-method studies, theoretical frameworks were rarely reported. The few papers that did report theoretical frameworks used feminist standpoint theory, post-structuralist feminist theory, or social constructivist theory. Qualitative data collection methods were diverse: interviews (41 studies), focus groups (19), document analysis (18), observations (15), case studies (2), and visual techniques (e.g. PhotoVoice) (2). Quantitative and multi-method studies predominantly used surveys and questionnaires (22), with one study each using of the following tools: Gender Equitable Men’s Scale (Gottert et al., 2016 ), the Knowledge of Gender Equity Scale, the Empathy Questionnaire (Spreng et al., 2009 ), the Feminist Identity Scale (Rickard, 1989 ), and the Gender Related System Justification scale (Jost & Kay, 2003 ).

Few interventions aimed to achieve gender equality per se. Rather, they aimed to achieve components of gender equality (see Table 1 for definition), which ranged from gender neutrality through to striving towards a feminist revolution. Overall aims included greater awareness, inclusion, empowerment, parity, equity, and substantive equality (Supplementary Tables 6–8, column 3). The evaluation of whether interventions achieved their aims was usually assessed through surveying participants. The most common aim was to enhance “empowerment” (n = 18), which was generally not clearly defined. The interventions had various levels of effectiveness, with 37 studies having a significant beneficial effect on the aim of the intervention (i.e., they achieved their aims); 31 having a partial beneficial impact on the aim of the intervention; four studies having no beneficial or harmful impact on the aim of the intervention; and six studies having a harmful effect on the aim of the intervention (e.g., the intervention led to increased discrimination, inequality, or abuse). Examples of harmful effects include the ‘Girl Effect’ program in Uganda which resulted in participants being abused or robbed of the money they had earned (Hayhurst, 2014 ), and a girls’ resiliency program in the USA that resulted in increased abuse from male peers (Brinkman et al., 2011 ).

Intervention Design and Effectiveness by Sector

Education and training interventions.

Evaluations of education and training interventions were reported in 18 papers (6 qualitative, 6 quantitative, 6 multi-methods). Education interventions covered a range contexts (3 micro-meso, 11 meso, 3 meso-macro, 1 macro). Most interventions (14) used awareness-raising workshops targeting individual change, and reported only partially achieving the aim of the interventions. Five workshops were assessed in randomised controlled trials. Two qualitative studies targeted increasing girls’ enrolment in formal education in Morocco (Eger et al., 2018 ) and India (Jain & Singh, 2017 ), both of which achieved the aims of the interventions. One qualitative study in the Democratic Republic of Congo targeted behaviour change in men only (Pierotti et al., 2018 ), which had a partial beneficial effect because men increased their willingness to contribute to household chores but maintained control over the broader gender system. This intervention was an eight-week long mesolevel men’s discussion group focused on “undoing gender” through social interaction (e.g. promoting a more equal division of labour in the household, improving intra-household relationship quality, and questioning existing gender norms).

Gender parity in schools did not signal an end to, or transformation of, gender inequities in the schools or communities studied (Ralfe, 2009 ). To bring about education policy reform, Palmén et al. ( 2020 ) found that top-down institutional commitment to gender equality was essential to create change. However, bottom-up strategies were also needed as teachers had to foster cooperative learning that encouraged working together and valuing different abilities across genders (Sánchez-Hernández et al., 2018 ). Sufficient resources, in addition to monitoring and evaluation of education initiatives, were found to be a key to intervention success (Palmén et al., 2020 ). Ultimately, social norms did not change beyond the school environment (Chisamya et al., 2012 ; Jain & Singh, 2017 ).

While interventions in traditional education contexts only partially achieved their aims, experiential learning was found to be a powerful process to deliver knowledge about gender equity in a nonthreatening way (Zawadzki et al., 2012a ). Zawadzki’s study was a mesolevel intervention that used a board game to teach participants the cumulative effect of subtle, nonconscious bias, to discuss how bias hinders women’s promotion in the workplace, and to find solutions for what can be done to reduce that bias. They found that the delivery of information was less effective when new knowledge did not promote self-efficacy or lead participants to resist perceived attempts to influence their beliefs or behaviours. Furthermore, they established that learning about gender inequity was not sufficient for knowledge retention. Rather, participants had to link the knowledge to their own experiences and be empowered to feel that they could act on that knowledge.

Awareness-raising interventions in education and training generally only partially achieved the aims of the interventions, and did not necessarily translate into behaviour change (Ralfe, 2009 ). In the strong quality (0.93) quantitative mesolevel study by Moss-Racusin et al. ( 2018 ), the Video Interventions for Diversity in STEM (VIDS) intervention was found to achieve significantly greater awareness of bias in participants compared to the non-intervention control condition; however, effects on behaviour were not assessed. This intervention presented participants with short videos about findings from gender bias research in one of three conditions. One condition illustrated findings using narratives (compelling stories), the second presented the same results using expert interviews (straightforward facts), and a hybrid condition included both narrative and expert interview videos.

A lack of awareness, knowledge, or understanding of women’s human rights was found to be a key barrier to the achievement of gender equality in education-based interventions (Murphy-Graham, 2009 ). Gervais ( 2010 ) reported that awareness-raising can have direct effects on participants by giving them confidence to speak up against violations of their rights, although they noted that this might anger violators. Similarly, education was found in some cases to enable women to negotiate power-sharing with their husbands, while other women were verbally abused and threatened because their husbands disapproved of the education program (Murphy-Graham, 2009 ). Similar to the study by Pierotti et al. ( 2018 ), Murphy-Graham ( 2009 ) sought to “undo gender” by encouraging students to rethink gender relations in their everyday lives (mesolevel). Including men together with women in education programs enabled women to gauge men’s reactions to social change in a safe environment (Cislaghi et al., 2019 ). Potential harmful effects of interventions are further summarised under the ‘The problem of hostile affect’ header below.

STEM Education

Among education interventions were a subset of Science, Technology, Engineering and Maths (STEM) education interventions. These specifically targeted secondary school girls as a pathway to tertiary STEM education, and were reported in eight papers (1 qualitative, 3 quantitative, 4 multi methods). The design of interventions varied from science clubs, outreach programs, after school sessions, residential camps and immersion days. Archer et al. ( 2014 ), however, took a multipronged approach. Their intervention included school excursions, visits from STEM Ambassadors and a researcher-in-residence, a STEM ‘speed networking’ event, and participation in a series of teacher-led sessions for girls aged 13–14 years. Despite this significant investment, the intervention did not significantly change students’ aspirations of studying science, although it did appear to have a beneficial effect on broadening students’ understanding of the range of science jobs.

All STEM education interventions were aimed at the mesolevel and were located in the urban Global North. While the long-term impact (e.g. increased enrolment of women into tertiary STEM education) were inconsistent among studies. Gorbacheva et al. ( 2014 ) found that secondary same-sex education had no influence on this objective. Alternatively, Hughes et al. ( 2013 ) found having role models was more critical than sex segregation. Finally, Lackey et al. ( 2007 ), Lang et al. ( 2015 ) and Watermeyer ( 2012 ) all established that a network of support (e.g. family, school, industry) made a positive difference to girls equality in STEM education.

Employment Interventions

Eight interventions focused on women’s employment: 4 qualitative, 2 quantitative, 2 multi-methods studies. They covered a range of contexts (1 micro/meso, 5 meso, 2 meso/macro). Three interventions addressed women’s promotion (Eriksson‐Zetterquist & Styhre, 2008 ; Grada et al., 2015 ; Smith et al., 2015 ). Two interventions evaluated microenterprise; one produced harmful effects (Hayhurst, 2014 ), and the other only partially achieved its aim (Strier, 2010 ). Hayhurst ( 2014 ) evaluated an intervention auspiced by the Nike Foundation and concluded that it had an unfair and deleterious effect by placing the burden of social change on girls. In this intervention, focusing on the mesolevel, girls were taught to be entrepreneurs to enable them to escape abuse, buy land, grow food, and work. In practice, this economic empowerment strategy led to increased abuse by men who wanted to take the girls’ money to pay their own taxes and fines. This study was good quality (0.73). Participants in the study by Strier ( 2010 ) thought that microenterprise promised self-realisation and escape from the slavery of the labour market, but they found it to be a false promise, characterising the informal sector as both a disappointment and a fraud. Overall, employment interventions led to unreliable and inconsistent outcomes.

Economic Interventions

Six interventions (1 qualitative, 2 quantitative, 3 multi-methods studies) addressed various contexts (1 micro, 1 micro/macro, 2 meso/macro, 2 macro interventions) that targeted economic empowerment. Overall, the interventions partially achieved their aims. For microfinance interventions, women benefited less than men because they were given smaller loans for less lucrative businesses (Haase, 2012 ). Krishnan et al. ( 2014 ) conducted a good quality (0.79) multi-method study of a micro–macro level intervention that provided conditional cash transfers in India, and found minimal positive effects from the implementation of this scheme to address social behaviours related to valuing girls. In this study, parents had to register the birth of their daughter in order to receive financial benefit, but this did not transform the social mindset that daughters are a burden. In another study, the size and frequency of cash transfers directly influenced outcomes: large but infrequent payments enabled investment that could facilitate economic transformation (Morton, 2019 ). Lump-sum payments also challenged stereotypes about what women could invest in, and could transform the gender asset gap. Institution of a social protection floor (e.g. welfare benefits) enhanced women’s power and control over household decision-making in financial matters and household spending in South Africa (Patel et al., 2013 ). While a social protection floor had benefits for women’s empowerment at the microlevel, it did not transform unequal and unjust gendered social relations of power at the macrolevel.

Legal Interventions

Five interventions (3 qualitative, 2 quantitative studies) in two contexts (1 meso/macro, 4 macro) reported on legal interventions. In Zartaloudis’s ( 2015 ) qualitative macrolevel study of an employment strategy in Greece and Portugal, legislation was found to have an important but not transformative effect on gender equality in employment. Three other studies found that changes in law must be accompanied by incentives and penalties in order to be effective (Kim & Kang, 2016 ; Palmén et al., 2020 ; Singh & Peng, 2010 ). While the decline in levels of discrimination was at first sharp after enacting anti-discrimination legislation, its implementation plateaued over time, calling into question the long-term sustainable effects of law reform without adequate enforcement mechanisms. In this macrolevel study by Singh and Peng ( 2010 ), the Ontario Pay Equity Act was effective because it was proactive in persuing pay equity, rather than being complaint based.

Legal opportunity and litigation were strategic choices in campaign strategies in one study, playing an important role in effecting change to prevent discriminatory pay for work typically performed by women (McGregor & Davies, 2019 ). The strong quality (0.92) macrolevel study by Mueller et al. ( 2019 ) increased access to legal services in order to improve legal knowledge in rural Tanzania. It found that, despite increased access to legal services, women still had moderate to low knowledge of marital laws, and only 2.7 percent of women would refer someone to a paralegal for problems with a widow’s assets, divorce, or marital disputes. Mueller et al. ( 2019 ) concluded that an increased investment in access to justice needed to be made through informal channels (mesolevel change) in addition to the macrolevel law reform.

Political Interventions

Ten papers (4 qualitative, 3 quantitative, 3 multi-methods studies) that covered a variety of contexts (1 micro/meso, 2 meso, 2 meso/macro, 5 macro) reported assessments of political interventions. Electing women to council increased other women’s access to councillors because women had greater heterosocial networks (i.e., comprising women and men), but did not affect men’s access to councillors (Benstead, 2019 ; Levy & Sakaiya, 2020 ). However, increasing the number of women in public office did not necessarily improve equality (McLean & Maalsen, 2017 ). For example, an evaluation of gendered outcomes of Hon. Julia Gillard’s tenure as Prime Minister of Australia saw increased gender-based denigration and vilification of her leadership (McLean & Maalsen, 2017 ).

A qualitative macro study using interviews and ethnography to explore the impact of political gender quotas in Mali (Johnson, 2019 ) found that savings groups, together with political gender quotas, were important for catalysing the first steps towards social and political transformation. In Mali, gender quota laws required political parties to field a minimum of 30 percent women candidates, and to include a woman within the first three places on a party’s candidate list. In this context, savings and credit associations developed women’s self-efficacy and increased their confidence to become political candidates (Johnson, 2019 ).

An example of discursive change based on political activism was found by Cowell-Meyers’ ( 2017 ) multi-method study examining the impact of a new feminist political party in Sweden. Near consensus by political parties that gender equality needed to be tackled through government intervention was achieved through the efforts of the small women’s rights party. However, another multi-method mesolevel study examining the effects of Transnational Advocacy Networks (TANs) in Europe found that they either ignored or subverted gender mainstreaming language (S. Lang, 2009 ). Gender mainstreaming policy interventions were found to have only partially achieved their aims, but were successful when law and policy detailed specific roles and responsibilities for action (Kim & Kang, 2016 ). Policymakers in two other studies were found to avoid the responsibility of implementation not because they opposed gender mainstreaming itself, but because they objected to being forced into it (Hwang & Wu, 2019 ; Kim & Kang, 2016 ). Therefore, the attitude of bureaucrats (microlevel) was considered to be an important factor in implementing gender equality initiatives at the macrolevel.

The strong (perfect quality score) quantitative study by Saguy and Szekeres ( 2018 ) reported on the effect on gender-based attitudes (microlevel) following exposure to the 2017 Women’s March across the US and worldwide in response to Donald Trump’s inauguration. The research found that large-scale collective action had a polarising effect on those exposed to it. Over time, men who identified more closely with their own gender increased the degree to which they justified gender inequality after exposure to the protests, suggesting a backlash reaction (mesolevel). People who were found to be positively affected by collective action were already in favour of the protesters’ cause. The backlash found for high-identifying men was explained by reactance theory (Brehm, 1966 ) whereby people become motivationally aroused by a threat to or elimination of a behavioral freedom (Brehm, 1989 ).

Barriers to Achieving Gender Equality: The Problem of Hostile Affect

No study accounted for men’s and boys’ emotions (microlevel change) as part of the aim and design of the intervention, but their significance became apparent in the results of several studies. Men and boys reported feeling hostility, resentment, fear and jealousy when social norms were challenged. Attempts at addressing gender inequality were found to threaten men’s sense of entitlement, and it was theorised that boys expected to be the centre of attention (Brinkman et al., 2011 ). In the meso study by MacPhail et al. ( 2019 ) that evaluated a men’s participation program in South Africa, participants reported equality as a zero-sum game that meant respecting women equated to disrespecting men. In that intervention, activities included intensive small group workshops, informal community dialogue through home visits, mural painting to stimulate discussions of key messages, informal theatre, soccer tournaments, and film screenings. In another study, women’s oppression was maintained by men because they feared losing control of ‘their’ women (Devasia, 1998 ). In several studies, men shared their fear of being perceived as weak or feminine in front of their peers or community (Bigler et al., 2019 ; McCarthy & Moon, 2018 ; Murphy-Graham, 2009 ; Pierotti et al., 2018 ; Singhal & Rattine-Flaherty, 2006 ). Male participants in the study by Pierotti et al. ( 2018 ) believed that allowing women to be leaders in households would disintegrate society. They believed that upholding men’s lack of accountability and position as ‘boss’ was important to maintaining the fabric of society.

In contrast, Cislaghi ( 2018 ) found that men in Senegal did not resist increased political participation of women. And a radio program in Afghanistan that addressed gender equality was found not to offend men’s cultural or religious beliefs, and ultimately succeeded in changing attitudes and behaviours towards women and girls (Sengupta et al., 2007 ). The outcome included changes in the community, such as giving permission to women to leave their home alone, to vote, to go to school, and to reject child marriage. While participants expressed increased empowerment (micro), they also acknowledged that they may have their rights, but can never make decisions pertaining to their rights (Sengupta et al., 2007 ). For example, women may have the right to vote (macro), but they cannot go to vote or decide who to vote for without male guardianship (meso). In that study, 15 h of civic education material was promoted by radio, focusing on peace, democracy, and women’s rights. At the community level, interviews and focus groups with participants revealed that there was no resistance to listening to the radio program from men or families. However, the Sengupta et al. study was not longitudinal and had a relatively small sample of 115 people (72.2% women), and the women in the study may not have been in a position that allowed them to admonish the men in their community.

It was found in one study that resistance and backlash can be ameliorated by including men and boys in the development and delivery of interventions (Sengupta et al., 2007 ). Behaviour change in men required an increase in empathy to achieve the aim of gender equality (Becker & Swim, 2011 ). Hadjipavlou ( 2006 ) and Vachhani and Pullen (2019) found that empathy was a viable alternative feminist strategy. In their qualitative study, Hwang and Wu ( 2019 ) in Taiwan found that trust-building between civil servants and advocates reduced resistance and hostility. Activists in this intervention used four strategies: (1) Giving praise and encouragement instead of criticism and blame; (2) Engaging civil servants on a personal level to create bonding; (3) Appeasing fears about being blamed by offering assistance; (4) Attempting to invoke their identification with the values of gender mainstreaming through informal educational efforts, all of which are mesolevel strategies.

Promoting Social Change to Reduce Gender Inequality

There was a wide array of types of change in different aspects of gender equality, with interventions varying in their success across settings and contexts. Table 2 summarises the types of change (e.g. legal, financial, behaviour, social) and the context (i.e., micro, meso, macro) that were identified and whether interventions aims were fully or partially achieved, or were not achieved, or had a harmful effect. Physical change, such as increased physical presence of women through inclusion or solidarity (meso) was the most consistently achieved beneficial outcome. Interventions targeting macrolevel social change, however, predominantly failed to achieve their aims or had harmful effects, reflecting how hard it is to realise social change, especially from a single, usually localised, intervention. Quotas could perhaps achieve their aim, although this finding was derived mostly from one good quality study (Johnson, 2019 ). The largest group of interventions were those implemented in education-based contexts, but these generally only partially achieved their aims, and focused mostly on physical changes (e.g., inclusion, solidarity). Most gender mainstreaming interventions did not achieved their aims.

Altogether, the findings confirm that social transformation is not automatic, easy, nor necessarily sustainable (Murphy-Graham, 2009 ). Furthermore, economic transformation is constrained if it is not supported by concurrent social transformation (Haase, 2012 ). One researcher, reporting a good quality meso-macro multi-method educational study in rural Bangladesh, claimed to have achieved social transformation (Sperandio, 2011 ). The appointment of women into roles that are traditionally occupied by men (in this case, teaching) led to widespread acceptance and normalisation of women in other non-traditional roles in a conservative village. Because the researcher did not interview or survey members of the community in which the intervention was evaluated, it is not clear whether broader social change was achieved.

It was found in several studies that dialogue was key to creating change in gender norms (Hwang & Wu, 2019 ; MacPhail et al., 2019 ; McGregor & Davies, 2019 ; Murphy-Graham, 2009 ; Sánchez-Hernández et al., 2018 ). However, Matich et al.’s ( 2019 ) qualitative study of the #freethenipple campaign and Boling’s ( 2020 ) study of the #ShePersisted campaign found that small steps bring about only small changes. For instance, in the #freethenipple campaign, women took control of how they were represented (microlevel) in order to challenge patriarchal gender norms (macrolevel). The authors noted that, despite good intentions, a hashtag cannot erase stereotyping. Pierotti et al. ( 2018 ) also found that small changes (micro) in quotidian tasks (e.g., participation in household chores) did not lead to substantive social change (macrolevel change). That is, while changes in tasks occurred with relative ease, social transformation through the cumulative effect of small steps towards egalitarianism did not occur.

In comparison, the qualitative study by McCarthy and Moon ( 2018 ) examined a women’s program in Ghana and found that changing everyday practices did matter, but becoming cognisant of the need for revolution led people to become overwhelmed and immune to change efforts. The researchers found that a key challenge in achieving social transformation was the need to bring about changes in daily interactions. For instance, one participant stated that if a person is not empowered at home, no matter how much money you give them, they are going to need more (McCarthy & Moon, 2018 ).

All genders need to participate to achieve a re-socialisation (Brinkman et al., 2011 ). Sengupta et al. ( 2007 ) concluded that their radio program would have alienated men if it had targeted only women. By including all genders, potential resistance to change can be neutralised (Devasia, 1998 ). In summary, social transformation is possible, but transformation is not likely to be universal or successful across all contexts (Sánchez-Hernández et al., 2018 ), particularly from any single monistic intervention. Holistic responses that take account of system thinking may create the change needed.

Overall, despite concerted effort, it seems that in the past thirty years we have not uncovered the keys to social change in order to enhance gender equality and non-discrimination against girls and women. Perhaps the reviewed interventions did not achieve macrolevel change because they did not simultaneously and explicitly address meso and micro change. Whilst CEDAW seeks the ‘elimination of all forms of discrimination’, achievement of that aim is far from complete, although it is not surprising that no single intervention could catalyse social change that achieves CEDAW’s objective. This review demonstrates that it will take time and a variety of endeavours to achieve gender equality.

To summarise the substantive lessons from this systematic review, we offer the following distillation as a summary of the findings to date. This distillation includes definitive statements that should be viewed only in the context of this review and may not generalise across all efforts towards gender equality in all societies.

What is Ineffective in Promoting Gender Equality

Small changes do not lead to big changes. Small concessions are granted to maintain peace, while big changes are often denied to maintain power.

Men and boys can feel the micro effects of fear, hostility, resentment, and jealousy when meso-macro gendered social norms are challenged.

Increased confidence, agency, empowerment, or individual leadership (micro) is not sufficient to promote the structural changes required to increase gender equality (macro).

A lack of change in mindsets (micro) and poor enforcement can mean that laws (macro) are not realised or have little effect at the community level (meso).

The overall focus on women ignores the real problem, and the need to engage with all members of society.

Education and awareness-raising may establish the right to education but do not necessarily create gender equality.

Raising awareness alone does not translate into behaviour change (meso to micro).

Transnational advocacy networks are not effective.

Protests in western democracies can have a polarising and backlash effect.

Gender mainstreaming efforts generally fail to achieve positive outcomes.

Economic transformation does not automatically lead to social transformation.

What is Effective in Promoting Gender Equality

Eliciting positive affect in interventions garners positive outcomes.

Empathy is a viable feminist strategy, although evidence is limited.

All genders need to participate in re-socialisation of gender norms.

Dialogue is a key to success.

A large number of women must behave differently for new behaviours to be accepted (micro to meso).

Experiential learning is a powerful way to embed knowledge about gender equity in a nonthreatening, lasting way.

Investment in access to justice must include informal channels of the justice system.

Social transformation can be achieved in households through daily interactions (meso to macro).

Enabling environments (macro) are more effective than individual empowerment (micro), but should include top-down and bottom-up approaches.

Quotas are effective.

Laws must be proactive as well as reactive or complaint based.

The contextual levels of analysis developed by Pettigrew ( 2021 ) has also been adapted from these lists into Fig. 3 . These distillations challenge our thinking about how to achieve gender equality and therefore require greater discussion amongst feminist activists, advocates, and the general population for ecological validation. The key findings of this review have implications for policy and practice because they call into question the type of change sought by feminist movements, the type of intervention used to achieve that change, and whether that intervention is likely to be effective in practice. Overall, this review gives pause for thought. We hope it will inform future decisions about how to achieve gender equality.

Contextual levels of analysis for this review, adapted from Pettigrew ( 2021 )

Strengths and Limitations

Our broad inclusion criteria identified relevant interventions across a range of political, economic, social and cultural contexts, published over a thirty year period. Consistent with the recommendations by Garritty et al. ( 2021 ) we used rapid review methods; this may have led to the omission of some eligible studies. However, the use of a machine learning approach by reviewer two to rapidly screen a sample of the records predicted to be most relevant helped to limit the omission of relevant studies. Moreover, our restriction of literature to 1990 onwards may have omitted some studies conducted since the adoption of CEDAW in 1979. Given that only one study was published from 1990–2000, however, it is unlikely that this restricted timeframe had a significant impact on the review. Excluding papers not published in English is a limitation, and may have led to the omission of studies in some settings. We urge those who have non-peer-reviewed evaluations to submit them to peer-reviewed journals for future inclusion in reviews like the present one. The results of the large number of studies included in the review are difficult to generalise given the heterogenous study methods, intervention designs, populations, and settings. Because of a lack of reflexivity in most qualitative and multi-method studies, it is impossible to discern (for example) whether research undertaken in the Global South was conducted by Global North researchers. Moreover, there was no evidence of the ethical conduct of 16 studies and two studies did not have ethics approval. Together, these limitations may indicate potential problems with informed consent and implicit racial or other biases, although none were explicitly identifiable. There was insufficient evidence to assess whether and how culture played a part in attempts to achieve gender equality. Furthermore, while 86 percent of interventions predominantly or partially achieved their aims, this may inflate the effectiveness of such interventions because of reporting biases that favour publication of positive results (Sengupta et al., 2007 ; Sperandio, 2011 ).

This review has taken stock of successes and failures in seeking to promote gender equality. The findings reveal that undue reliance has been placed on the presumed efficacy of awareness raising, and that the race to achieve gender parity has not yet catalysed the desired social transformation. Entrepreneur programs can be exploitative, and legal actions have had limited effects, potentially failing because of men’s feelings about change. This review has shown that men can be fearful, resentful, jealous, and angry towards acts that disrupt the status quo . Until we adequately address these emotions and biases, the change that women (and potentially all genders) want, and the equality we all need will not be realised. Social context and systems thinking have shown us the importance of holism when tackling systemic discrimination. In this context, to be fully human is to be emotionally fulfilled. Ergo , human rights will be realised when there is dignity, humanity and positive emotionality among genders. Only then is the promise of CEDAW likely to be fulfilled.

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What does gender equality look like today?

Date: Wednesday, 6 October 2021

Progress towards gender equality is looking bleak. But it doesn’t need to.

A new global analysis of progress on gender equality and women’s rights shows women and girls remain disproportionately affected by the socioeconomic fallout from the COVID-19 pandemic, struggling with disproportionately high job and livelihood losses, education disruptions and increased burdens of unpaid care work. Women’s health services, poorly funded even before the pandemic, faced major disruptions, undermining women’s sexual and reproductive health. And despite women’s central role in responding to COVID-19, including as front-line health workers, they are still largely bypassed for leadership positions they deserve.

UN Women’s latest report, together with UN DESA, Progress on the Sustainable Development Goals: The Gender Snapshot 2021 presents the latest data on gender equality across all 17 Sustainable Development Goals. The report highlights the progress made since 2015 but also the continued alarm over the COVID-19 pandemic, its immediate effect on women’s well-being and the threat it poses to future generations.

We’re breaking down some of the findings from the report, and calling for the action needed to accelerate progress.

The pandemic is making matters worse

One and a half years since the World Health Organization declared COVID-19 a global pandemic, the toll on the poorest and most vulnerable people remains devastating and disproportionate. The combined impact of conflict, extreme weather events and COVID-19 has deprived women and girls of even basic needs such as food security. Without urgent action to stem rising poverty, hunger and inequality, especially in countries affected by conflict and other acute forms of crisis, millions will continue to suffer.

A global goal by global goal reality check:

Goal 1. Poverty

In 2021, extreme poverty is on the rise and progress towards its elimination has reversed. An estimated 435 million women and girls globally are living in extreme poverty.

And yet we can change this .

Over 150 million women and girls could emerge from poverty by 2030 if governments implement a comprehensive strategy to improve access to education and family planning, achieve equal wages and extend social transfers.

Goal 2. Zero hunger

The global gender gap in food security has risen dramatically during the pandemic, with more women and girls going hungry. Women’s food insecurity levels were 10 per cent higher than men’s in 2020, compared with 6 per cent higher in 2019.

This trend can be reversed , including by supporting women small-scale producers, who typically earn far less than men, through increased funding, training and land rights reforms.

Goal 3. Good health and well-being

Disruptions in essential health services due to COVID-19 are taking a tragic toll on women and girls. In the first year of the pandemic, there were an estimated 1.4 million additional unintended pregnancies in lower and middle-income countries.

We need to do better .

Response to the pandemic must include prioritizing sexual and reproductive health services, ensuring they continue to operate safely now and after the pandemic is long over. In addition, more support is needed to ensure life-saving personal protection equipment, tests, oxygen and especially vaccines are available in rich and poor countries alike as well as to vulnerable population within countries.

Goal 4. Quality education

A year and a half into the pandemic, schools remain partially or fully closed in 42 per cent of the world’s countries and territories. School closures spell lost opportunities for girls and an increased risk of violence, exploitation and early marriage .

Governments can do more to protect girls education .

Measures focused specifically on supporting girls returning to school are urgently needed, including measures focused on girls from marginalized communities who are most at risk.

Goal 5. Gender equality

The pandemic has tested and even reversed progress in expanding women’s rights and opportunities. Reports of violence against women and girls, a “shadow” pandemic to COVID-19, are increasing in many parts of the world. COVID-19 is also intensifying women’s workload at home, forcing many to leave the labour force altogether.

Building forward differently and better will hinge on placing women and girls at the centre of all aspects of response and recovery, including through gender-responsive laws, policies and budgeting.

Goal 6. Clean water and sanitation

In 2018, nearly 2.3 billion people lived in water-stressed countries. Without safe drinking water, adequate sanitation and menstrual hygiene facilities, women and girls find it harder to lead safe, productive and healthy lives.

Change is possible .

Involve those most impacted in water management processes, including women. Women’s voices are often missing in water management processes. 

Goal 7. Affordable and clean energy

Increased demand for clean energy and low-carbon solutions is driving an unprecedented transformation of the energy sector. But women are being left out. Women hold only 32 per cent of renewable energy jobs.

We can do better .

Expose girls early on to STEM education, provide training and support to women entering the energy field, close the pay gap and increase women’s leadership in the energy sector.

Goal 8. Decent work and economic growth

The number of employed women declined by 54 million in 2020 and 45 million women left the labour market altogether. Women have suffered steeper job losses than men, along with increased unpaid care burdens at home.

We must do more to support women in the workforce .

Guarantee decent work for all, introduce labour laws/reforms, removing legal barriers for married women entering the workforce, support access to affordable/quality childcare.

Goal 9. Industry, innovation and infrastructure

The COVID-19 crisis has spurred striking achievements in medical research and innovation. Women’s contribution has been profound. But still only a little over a third of graduates in the science, technology, engineering and mathematics field are female.

We can take action today.

 Quotas mandating that a proportion of research grants are awarded to women-led teams or teams that include women is one concrete way to support women researchers. 

Goal 10. Reduced inequalities

Limited progress for women is being eroded by the pandemic. Women facing multiple forms of discrimination, including women and girls with disabilities, migrant women, women discriminated against because of their race/ethnicity are especially affected.

Commit to end racism and discrimination in all its forms, invest in inclusive, universal, gender responsive social protection systems that support all women. 

Goal 11. Sustainable cities and communities

Globally, more than 1 billion people live in informal settlements and slums. Women and girls, often overrepresented in these densely populated areas, suffer from lack of access to basic water and sanitation, health care and transportation.

The needs of urban poor women must be prioritized .

Increase the provision of durable and adequate housing and equitable access to land; included women in urban planning and development processes.

Goal 12. Sustainable consumption and production; Goal 13. Climate action; Goal 14. Life below water; and Goal 15. Life on land

Women activists, scientists and researchers are working hard to solve the climate crisis but often without the same platforms as men to share their knowledge and skills. Only 29 per cent of featured speakers at international ocean science conferences are women.

 And yet we can change this .

Ensure women activists, scientists and researchers have equal voice, representation and access to forums where these issues are being discussed and debated. 

Goal 16. Peace, justice and strong institutions

The lack of women in decision-making limits the reach and impact of the COVID-19 pandemic and other emergency recovery efforts. In conflict-affected countries, 18.9 per cent of parliamentary seats are held by women, much lower than the global average of 25.6 per cent.

This is unacceptable .

It's time for women to have an equal share of power and decision-making at all levels.

Goal 17. Global partnerships for the goals

There are just 9 years left to achieve the Global Goals by 2030, and gender equality cuts across all 17 of them. With COVID-19 slowing progress on women's rights, the time to act is now.

Looking ahead

As it stands today, only one indicator under the global goal for gender equality (SDG5) is ‘close to target’: proportion of seats held by women in local government. In other areas critical to women’s empowerment, equality in time spent on unpaid care and domestic work and decision making regarding sexual and reproductive health the world is far from target. Without a bold commitment to accelerate progress, the global community will fail to achieve gender equality. Building forward differently and better will require placing women and girls at the centre of all aspects of response and recovery, including through gender-responsive laws, policies and budgeting.

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• Data Descriptor
• Open access
• Published: 17 April 2024

Towards Gender Harmony Dataset: Gender Beliefs and Gender Stereotypes in 62 Countries

• Natasza Kosakowska-Berezecka   ORCID: orcid.org/0000-0003-3503-3921 1 ,
• Tomasz Besta   ORCID: orcid.org/0000-0001-6209-3677 1 ,
• Paweł Jurek   ORCID: orcid.org/0000-0002-9958-3941 1 ,
• Michał Olech 2 ,
• Jurand Sobiecki 1 ,
• Jennifer Bosson   ORCID: orcid.org/0000-0003-2566-1078 3 ,
• Joseph A. Vandello 3 ,
• Deborah Best   ORCID: orcid.org/0000-0002-6715-0957 4 ,
• Magdalena Zawisza 5 ,
• Saba Safdar 6 ,
• Anna Włodarczyk   ORCID: orcid.org/0000-0003-2106-5324 7 &
• Magdalena Żadkowska 1  

Scientific Data volume  11 , Article number:  392 ( 2024 ) Cite this article

236 Accesses

Metrics details

• Human behaviour

The Towards Gender Harmony (TGH) project began in September 2018 with over 160 scholars who formed an international consortium to collect data from 62 countries across six continents. Our overarching goal was to analyze contemporary perceptions of masculinity and femininity using quantitative and qualitative methods, marking a groundbreaking effort in social science research. The data collection took place between January 2018 and February 2020, and involved undergraduate students who completed a series of randomized scales and the data was collected through the SurveyMonkey or Qualtrics platforms, with paper surveys being used in rare cases. All the measures used in the project were translated into 22 languages. The dataset contains 33,313 observations and 286 variables, including contemporary measures of gendered self-views, attitudes, and stereotypes, as well as relevant demographic data. The TGH dataset, linked with accessible country-level data, provides valuable insights into the dynamics of gender relations worldwide, allowing for multilevel analyses and examination of how gendered self-views and attitudes are linked to behavioral intentions and demographic variables.

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Background & Summary

The Towards Gender Harmony project ( https://towardsgenderharmony.ug.edu.pl/ ) started in September 2018 with more than 160 scholars who have built an international consortium that collected data in 62 countries and six continents. Our overarching goal was to analyze contemporary perceptions of masculinity and femininity using quantitative and qualitative methods, marking a groundbreaking effort in social science research. Such multinational research is important, as it helps us move beyond the WEIRD perspective of Western, Educated, Industrialized, Rich and Democratic countries which heavily predominates in psychology 1 , 2 , 3 .

It has been more than 30 years since a similar large cross-cultural study examined understandings of masculinity and femininity. John Williams and Deborah Best established that universally, across 26 countries, (1) communality is associated with femininity and agency is associated with masculinity, and (2) women view themselves as more communal than men and men view themselves as more agentic than women 4 . While communality and agency are universal dimensions of human evaluation 5 , 6 underlying gender stereotypes and gendered self-views, the measures used in Williams and Best to capture communality and agency were not subjected to rigorous psychometric procedures for ensuring scales’ cultural invariance and equivalence. Further, because some of the data reported in Williams and Best were originally collected around 1977, they do not reflect the influence of dramatic changes in gender roles that have altered contemporary gender stereotypes 7 . It is thus important to reexamine these gender constructs today but with culturally invariant and equivalent measures. Our dataset includes contemporary data reflecting individuals’ gendered self-views, their descriptive, prescriptive, and proscriptive stereotypes about women and men, and a selection of gender beliefs and attitudes reflecting the contemporary literature of social psychology and society as a whole.

What is more, our project is unique as it examines the under-researched topic of the universality of stereotypes about men who, according to results of research (carried out so far mainly within Western cultural contexts), face strong pressures for conformity to norms such as agency, dominance, pursuit of high social status, and avoidance of femininity 8 , 9 . Apart from including contemporary measures of gendered self-views, attitudes, and gender stereotypes, we have also collected relevant demographic data. As a result, our Towards Gender Harmony dataset, linked with accessible country/nation-level data, offers powerful insight into the dynamics of gender relations worldwide, allowing for multilevel analyses and examination of how gender beliefs are linked to behavioral intentions and demographic variables.

This dataset has been so far used to test men’s support for gender equality across countries 10 ; to establish cross-culturally valid, psychometric properties and correlates of precarious manhood beliefs 11 ; to examine binary gender gaps in agentic and communal self-views 12 ; to investigate whether the degree of endorsement of precarious manhood beliefs at the country level was associated with various risk-related health behaviors and outcomes 13 , to test the double standard in gender rules across countries 14 ; and to test whether country-level precarious manhood beliefs were associated with more negative attitudes, fewer rights, more restrictive laws, and reduced safety for LGBTQ+ groups 15 .

To gather data, we conducted a cross-sectional survey study employing a rigorous approach encompassing questionnaire development, data acquisition, data processing, and statistical analysis techniques. Our study aimed to investigate contemporary perceptions of masculinity and femininity across different regions of the world. We prioritize transparency and reproducibility, ensuring that our methods are accessible to fellow researchers.

Questionnaire development

To collect pertinent information, we meticulously designed comprehensive questionnaires (refer to the Measures section for detailed content). Participants completed a battery of scales measuring a broad range of variables concerning gender beliefs and gender stereotypes (the full list is available at https://osf.io/7tza3 ).

Data acquisition

We adopted the convenience sampling method, aiming to recruit a minimum of 200 participants from each country. We sent out invitations to researchers to participate in our project using mailing lists aimed at psychology researchers across the globe. These mailing lists included the International Association of Cross-Cultural Psychology, the International Academy for Intercultural Relations, and the European Association of Social Psychology. To reduce cross-national differences due to potential confounding variables (e.g., education, age) that might occur if relied on more heterogeneous samples, we asked each collaborator to obtain a university student sample of at least 100 women and men. We have also made special efforts to recruit colleagues from underrepresented countries and continents and contacted individual colleagues. Data collection occurred between January 2018 and February 2020, as part of a broader cross-cultural research project (accessible on OSF: https://osf.io/mq48y ). Our participants consisted of undergraduate students who volunteered their time and, in most countries, received no compensation. We obtained ethical approval from the Ethics Board for Research projects at the Institute of Psychology, University of Gdańsk (no. 11/2018) and local Institutional Review Boards, and all participants provided informed consent. The order of measures was randomized, and data collection was facilitated through the SurveyMonkey or Qualtrics platforms. In rare instances, participants completed paper surveys.

Data processing

We took steps to ensure data quality and integrity throughout the data processing phase. Subsequently, we conducted data cleaning procedures to identify and address missing values, outliers, and inconsistencies (detailed in the Data Records section).

By adhering to these rigorous data collection and processing procedures, we aimed to generate reliable and robust findings concerning contemporary perceptions of masculinity and femininity across diverse global contexts. This commitment to transparency and thorough methodology ensures that our research can be comprehended and replicated by other scholars in the field.

All the measures used in the project were translated into 22 languages (Armenian, Chinese, Croatian, Danish, Dutch, English, Filipino, French, Georgian, German, Italian, Lithuanian, Norwegian, Polish, Portuguese, Romanian, Russian, Serbian, Slovak, Spanish, Turkish, Ukrainian). Bilingual scholars in psychology used the back-translation procedure to create national versions of each scale. The English version of the scales was used as the basis for all translations.

Gendered self-views and gender stereotypes

Gendered self-views.

Participants indicated the extent to which 12 agency-related traits, 12 communality-related traits, 12 dominance-related traits, and 12 weakness-related traits described them on a scale from 1 (does not describe me at all) to 7 (describes me well). Traits were selected from a pool of 472 prescriptive gender stereotypes (see supplementary material for the adjectives selected, Table  S1 and https://osf.io/7tza3 ) 4 , 8 , 16 . In addition, using the same scale, they also rated the following traits: gifted in science, gifted in math, linguistically gifted, and gifted in humanities.

Descriptive stereotypes

Participants rated the same set of traits (12 agency-related, 12 communality-related, 12 dominance-related, 12 weakness-related) on a scale from 1 (more frequently associated with women than men) to 7 (more frequently associated with men than women). In addition, using the same scale, they also rated the following traits: gifted in science, gifted in math, linguistically gifted, and gifted in humanities.

Prescriptive and proscriptive stereotypes

Participants rated the prescriptive (desirable) and proscriptive (undesirable) nature of the traits (12 agency-related, 12 communality-related, 12 dominance-related, 12 weakness-related) by answering “How desirable is it in your society for a woman [man] to possess this trait?” on a scale from 1 (not at all desirable) to 7 (very desirable). In addition, using the same scale, they also rated the following traits: gifted in science, gifted in math, linguistically gifted, and gifted in humanities.

Gender Beliefs & Attitudes

Precarious manhood beliefs.

We administered a short version of the Precarious Manhood Beliefs (PMB) scale 17 . Based on an exploratory factor analysis of 7 items from Vandello et al . 17 , we selected four items with loadings >0.45 that conveyed beliefs that manhood is difficult to earn (“Some boys do not become men no matter how old they get,” “Other people often question whether a man is a ‘real man’”) and easy to lose (“It is fairly easy for a man to lose his status as a man,” “Manhood is not assured – it can be lost”). Participants indicated their agreement on a scale from 1 (strongly disagree) to 7 (strongly agree).

Gender essentialism

Participants’ essentialist beliefs were measured with five items (e.g., “Their underlying nature makes it difficult for men to learn to behave more like women 18 ) on a scale ranging from strongly disagree (1) to strongly agree (7).

Ambivalent sexism

We used six items from the short version of the Ambivalent Sexism Inventory (ASI) 19 , which measures Hostile Sexism (HS) and Benevolent Sexism (BS). We selected items from Rollero et al . 19 with factor loadings >0.50. HS items were: “Women seek to gain power by getting control over men,” “Women exaggerate problems they have at work,” and “When women lose to men in a fair competition, they typically complain about being discriminated against.” BS items were: “Women should be cherished and protected by men,” “Men are incomplete without women,” and “Women, compared to men, tend to have superior moral sensibility.” Items were answered on scales from 0 (strongly disagree) to 5 (strongly agree).

Ambivalence toward Men

We used six items from the short version of the Ambivalence toward Men Inventory (AMI) 20 , which measures Hostility toward Men (HM) and Benevolence toward Men (BM). We selected items from Rollero et al . 20 with factor loadings >0.50. HM items were: “Men will always fight to have greater control in society than women,” “Men act like babies when they are sick,” and “Most men sexually harass women, even if only in subtle ways, once they are in a position of power over them.” BM items were: “Men are more willing to put themselves in danger to protect others,” “Every woman needs a male partner who will cherish her,” and “A woman will never be truly fulfilled in life if she doesn’t have a committed, long-term relationship with a man.” Items were rated on a 0 (strongly disagree) to 5 (strongly agree) scale.

Collective action intentions to support gender equality

To measure intention to engage in collective behaviors for gender equality, we used items taken and modified from two scales. All items were rated on a scale from 1 (not likely at all) to 7 (very likely). Instructions started with a sentence stem (“To support gender equality, how likely it is that you would …”) followed by a list of actions. Four actions, modified from Tausch et al . 21 , included: “participate in demonstrations”; “sign a petition”; “block buildings or streets, and “disturb events, where advocates of inequality appear.” Six actions, modified from Alisat and Reimer 22 , included: “become involved with a group (or political party) focused on gender issues/gender equality (e.g., volunteer, summer job, etc.)”; “consciously make time to be able to work on gender issues/(support) gender equality (e.g., working part time for an organization, contribute to raise awareness about gender issues, choosing activities focused on gender issues over other leisure activities)”; “participate in a community event which focused on gender issues”; “Used online tools (e.g., Instagram, YouTube, Facebook, Wikipedia, Blogs) to raise awareness about gender issues/gender equality”; “Participated in an educational event (e.g., workshop) related to gender issues/gender equality”; “Spent time working with a group/organization that deals with the connection of the gender issues/gender equality to other societal issues such as justice or inequality”.

Identification with gender

Participants’ identification with their gender was measured with two items (“Being a member of my gender group is an important part of how I see myself”, “To what extent you consider yourself feminine/masculine”; based on van Breen et al . 23 . Responses ranged from 1 (not at all) to 7 (very much).

Awareness of gender inequalities

Participants’ awareness of gender inequalities was measured with one item: “Overall, our society currently treats women less fairly than it treats men”. Responses ranged from 1 (strongly disagree) to 7 (strongly agree).

Gender roles and expectations

The items “What do you think women should prioritize?” and “What do you think men should prioritize?” were asked to assess societal attitudes and beliefs regarding gender roles. Respondents answered using a scale from 1 (Having a family) to 7 (Having a career). These items provided insights into broader societal norms related to gender roles and expectations. Individual preferences were also measured by similarly asking respondents what they would prioritize themselves – having a family or having a career.

Zero-sum beliefs about gender status

Participants’ zero-sum beliefs about gender status were assessed in two ways. The first was by the six-item Zero-Sum Perspective on Gender Status Scale (ZSPGS) 24 . The scale consists of items reflecting zero-sum beliefs in specific domains: occupational (‘More good jobs for women mean fewer good jobs for men’), power (‘The more power women gain, the less power men have’), economic (‘Women’s economic gains translate into men’s economic losses’), political (‘The more influence women have in politics, the less influence men have in politics’), social status (‘As women gain more social status, men lose social status’), and familial (‘More family-related decision making for women means less family-related decision making for men’). The second method was a more general single-item zero-sum perspective of gender status measure: ‘Declines in discrimination against women are directly related to increased discrimination against men’. Response options for each item ranged from 0 (strongly disagree) to 5 (strongly agree).

Culture-related Relevant Measures

Autonomy and embeddedness values.

In this study, the 10-item scale for measuring Autonomy vs. Embeddedness values was employed, following Vignoles et al . 25 . This scale, derived from the Portrait Values Questionnaire 26 , assessed participants’ orientations towards Autonomy (e.g., “It is important to this person to think up new ideas and be creative; to do things one’s own way.”) vs. Embeddedness (e.g., “Living in secure surroundings is important to this person; to avoid anything that might be dangerous.”) values. Participants assessed how well the description matched their own characteristics or traits from 1 (very much like me) to 6 (not at all like me).

Power distance beliefs

Participants’ power distance beliefs were measured using four items 27 . These items (e.g., “There should be established ranks in society with everyone occupying their rightful place regardless of whether that place is high or low in the ranking”) measured attitudes about societal ranks, requesting salary increases, questioning authority decisions, and formal communication with superiors. Responses ranged from 1 (strongly disagree) to 7 (strongly agree).

Subjective socio-economic status

The Subjective Social Status Ladder 28 often referred to as the “Social Status Ladder”, was used to gauge an individual’s perception of their relative social position within their country. Respondents were asked to choose a number on the ladder from 0 (representing the lowest social status) to 10 (representing the highest social status) to indicate where they perceive themselves to be in comparison to others.

Attention checks

The survey also included three attention checks in which participants were asked to mark on a scale from 1 to 7 indicated numbers (“If you are reading this please choose 3”).

Demographic variables

At the end of the questionnaire demographic information was collected. We asked participants to declare their age, study major, gender identity, education level, marital status, number of children, citizenship, and sexual orientation/identity. We also measured migration background and ethnicity (with a list of major ethnic backgrounds, if necessary adjusted/extended to meet local cultural contexts). Additionally, we ask who fulfilled the role of financial provider in the family, who fulfilled the role of homemaker in the family, and how would they describe the place they grew up (a city, a town, the countryside/remote place/rural area. Finally, our demographic part included questions about religiosity and religious denomination as well as political orientation.

Data Records

The data comprising the TGH project results are stored in a single table. The data table is available in the repository 29 in three formats: csv, xlsx, and Rda. The dataset contains 33,313 observations, each in a separate row, and 286 variables, each in a separate column. A detailed description of the variables can be found in the Supplementary Excel File titled ‘CodebookTGH.xlsx’, available in the Towards Gender Harmony full dataset repository 29 , which also includes a link to an interactive map with descriptive statistics and a summary of selected published statistics – the map will be developed with more analyses. The variable description consists of the following components: ‘ID’ – a unique sequential number for the item/variable (ranging from 1 to 286); ‘Variable Name’; ‘Measure’ – reference to the measurement tool used to assess this variable (containing the respective item); ‘Scale’ – the dimension, the name of the theoretical variable composed of items assigned to the scale; ‘Label’ – the content of the survey item; ‘Level of measurement’ – information about the level at which the variable/response to the item is expressed (nominal, ordinal, interval, or ratio); ‘Values’ – the range of values the variable can take; ‘Value Labels’ – possible response categories.

The dataset contains only responses provided by the study participants. Aggregated variables requiring, for example, the averaging of selected items (according to the key) must be calculated separately. To facilitate this process, we provide R code enabling the calculation of selected variables ‘TGH total scores code.R’ is available in the repository 29 .

Sample composition

We summarize the sample composition, including sample size, gender distribution, and descriptive statistics regarding age, for 13 distinct world regions, as illustrated in Table  1 . Additionally, we have provided detailed data for the 62 countries under study in the Supplementary Table  1 . As previously mentioned, our participants consisted of undergraduate students who volunteered their time. After data cleaning, the final dataset comprises 33,313 observations from 62 countries across 13 world regions. As can be seen in Table  1 and Supplementary Table  1 , both country-level and regional-level samples exhibit variations not only in terms of sample size but also in gender distribution and age distribution parameters.

Technical Validation

Data cleaning procedure.

Data cleaning is a crucial preparatory step to ensure the quality and reliability of data for subsequent analysis and modeling tasks 30 . In the TGH project, the data-cleaning procedure involved the following steps:

Data Integration: Data from various countries were provided by collaborators in separate files. We combined data from multiple sources into a unified dataset, resolving any inconsistencies in variables or units.

Data Inspection: We examined the dataset to identify inconsistencies, missing values, or outliers. We paid particular attention to data integrity, making sure that values either fell within acceptable ranges or adhered to predefined rules including verification of completeness of the data in all the scales, congruity between nominal categories in different countries. During this stage, we removed records with incorrect responses to attention check questions.

Handling Missing Data: In the TGH database, no data imputation methods were applied. In most cases, records with missing values were retained in the database. Only observations with data gaps preventing the calculation of most measured variables were removed.

Outlier Treatment: Outliers were observed in the age variable. Some responses appeared to contain randomly entered numbers (e.g., 247). Observations with such responses were removed. In a few cases where birthdates were mistakenly entered as ages, we recalculated the age by subtracting the birthdate from the examination date and rounding to full years. Outliers in other variables that could potentially skew the analysis were neither removed nor adjusted.

Data Transformation and Scaling: Due to the use of different response scales (mainly single-item scales) in some countries compared to the standardized scale adopted for the entire study (e.g., using a scale from 0 to 6 instead of 0 to 5), linear transformations were applied to harmonize the data.

Data Formatting: To ensure data format consistency, some responses recorded as labels were encoded into numerical values. The mapping of labels to numbers can be found in the Supplementary Excel File titled ‘CodebookTGH.xlsx,’ available in the repository 29 .

Data Verification: The cleaned dataset underwent validation, including the estimation of reliability ratios for aggregated scores (see Technical Validation).

As a result of the aforementioned operations, 710 observations were removed from the initial dataset ( N  = 34,023). However, further processing is necessary, depending on the objectives of subsequent analyses and due to the presence of missing data in the dataset, to select a subset suitable for testing specific models that involve particular variables.

In addition to socio-demographic variables, the majority of variables under study are psychometric measures. As previously mentioned, the target variables are derived either by averaging/summing responses to items that make up the scale or by calculating them from the results of fitting CFA models. To assess the reliability of these measured variables, it is necessary to employ psychometric techniques. In this field, the most common method for estimating the reliability of such measurements is through the calculation of internal consistency coefficients, such as Cronbach’s (as recommended when raw scores are obtained by averaging/summing responses to items comprising a scale) 31 or McDonald’s omega (recommended when standardized scores are to be derived using CFA results) 32 . Table  2 presents both of these reliability measures for all target variables calculated on the total sample. Detailed data on reliability coefficients calculated for each country separately are provided in Supplementary Excel File titled ‘ReliabilityTGH.xlsx’, available in the repository 29 .

As can be seen in Table  2 , in the vast majority of cases, the reliability of variable measurements, as measured by the coefficient of internal consistency, exceeds the widely accepted cutoff point of >0.70 33 . Only in the case of five measures (i.e., Benevolent Sexism, Benevolence toward Men, Power Distance Beliefs, Autonomy Value, Embeddedness Value) did the results indicate reliabilities below the desired threshold. This partially can be attributed to the use of very short scales (<10 items) to measure these variables. Nevertheless it is advisable to exercise caution in interpreting the results, and it is recommended to thoroughly examine the reliability of measurements for these variables in individual countries (see Supplementary Excel File ‘ReliabilityTGH.xlsx’).

Given the cross-cultural nature of the data, it is essential to establish measurement invariance (MI) before conducting any analyses that compare results between countries. Measurement invariance refers to the consistency of a scale’s measurement properties across different groups or cultural contexts 34 . In simpler terms, it assesses whether the construct being measured is understood and interpreted in the same way across various groups or settings. Typically, researchers report three levels of measurement invariance, which are determined by parameters that are constrained to be equal across groups. The first level, configural invariance, requires the scale to demonstrate the same overall factor structure for all groups; the second level, metric invariance, necessitates that the scale items’ factor loadings be equal across the groups; and the third level, scalar invariance, demands that item intercepts be equal across groups.

For some variables in this study, such analyses have already been conducted and published 10 , 11 , 14 . These analyses involve assessing whether the measurement properties of a scale, such as factor loadings or item intercepts, remain consistent across different groups or countries. Establishing MI is crucial to ensure that any observed differences in the data result from genuine variations in the construct being measured and not from measurement bias or cultural differences.

Moreover, in the context of using the data to calculate country-level scores, it is advisable to test for psychometric isomorphism. Psychometric isomorphism extends the concept of MI by examining whether the underlying psychological structure of the measurement remains consistent across different levels, such as countries or cultures 35 . This analysis goes beyond examining the equivalence of mere measurement properties; it also investigates the constancy of the conceptual meaning and relationships among variables when considering the data at the country level.

These assessments of MI and psychometric isomorphism help ensure the validity and comparability of the data when conducting cross-cultural analyses and making country-level comparisons, providing a robust foundation for meaningful and reliable research findings.

Code availability

We provide R code enabling the calculation of selected variables. This code is available in the repository 29 under the name TGH total scores code.R. Its proper operation requires the use of the R environment at least version 4.3.1 and the tidyverse package 36 .

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Acknowledgements

The results presented in this paper are part of the larger project titled “Towards Gender Harmony” ( www.towardsgenderharmony.ug.edu.pl ), which involves many wonderful people. Here, we acknowledge our University of Gdańsk Research Assistants Team: Agata Bizewska, Mariya Amiroslanova, Aleksandra Globińska, Andy Milewski, Piotr Piotrowski, Stanislav Romanov, Aleksandra Szulc, and Olga Żychlińska for their assistance with programming the surveys and coordinating the collection of data at all sites. We are also thankful to all Towards Gender Harmony collaborators for their assistance in data collection.

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N.K.B. supervised the entire project and data collection. In addition, P.J. and M.O. and J.S. and T.B. were involved in dataset preparation, and P.J. and M.O. were responsible for data validation and data visualization. All Authors (N.K.B., T.B., P.J., M.O., J.S., J.B., J.V., D.B., S.S., A.W., M.Ż.) contributed to the acquisition, analysis, or interpretation of data for the work, and the drafting of the work or revising it critically for important intellectual contributions. All authors have approved this version of the manuscript and agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. All persons designated as authors qualify for authorship, and all those who qualify for authorship are listed.

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Kosakowska-Berezecka, N., Besta, T., Jurek, P. et al. Towards Gender Harmony Dataset: Gender Beliefs and Gender Stereotypes in 62 Countries. Sci Data 11 , 392 (2024). https://doi.org/10.1038/s41597-024-03235-x

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Research: How to Close the Gender Gap in Startup Financing

• Malin Malmström,
• Barbara Burkhard,
• Charlotta Sirén,
• Dean Shepherd,
• Joakim Wincent

Three ways policymakers, financiers, and other stakeholders can mitigate gender bias in entrepreneurial funding.

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• MM Malin Malmström is a professor of entrepreneurship and innovation at Luleå University of Technology, and a director of the research center Sustainable Finance Lab in Sweden.
• BB Barbara Burkhard is a postdoctoral researcher of entrepreneurship at the Institute of Responsible Innovation at the University of St.Gallen.
• CS Charlotta Sirén is an associate professor of management at the Institute of Responsible Innovation at the University of St.Gallen.
• DS Dean Shepherd is a professor of entrepreneurship, management, and organization at The Mendoza College of Business, University of Notre Dame.
• JW Joakim Wincent is a professor of entrepreneurship and management at the Hanken School of Economics and the Global Center for Entrepreneurship and Innovation at the University of St.Gallen.

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The persistence of pay inequality: The gender pay gap in an anonymous online labor market

Leib litman.

1 Department of Psychology, Lander College, Flushing, New York, United States of America

Jonathan Robinson

2 Department of Computer Science, Lander College, Flushing, New York, United States of America

3 Department of Health Policy & Management, Mailman School of Public Health, Columbia University, New York, New York, United States of America

Cheskie Rosenzweig

4 Department of Clinical Psychology, Columbia University, New York, New York, United States of America

Joshua Waxman

5 Department of Computer Science, Stern College for Women, New York, New York, United States of America

Lisa M. Bates

6 Department of Epidemiology, Mailman School of Public Health, Columbia University New York, New York, United States of America

Associated Data

Due to the sensitive nature of some of the data, and the terms of service of the websites used during data collection (including CloudResearch and MTurk), CloudResearch cannot release the full data set to make it publically available. The data are on CloudResearch's Sequel servers located at Queens College in the city of New York. CloudResearch makes data available to be accessed by researchers for replication purposes, on the CloudResearch premises, in the same way the data were accessed and analysed by the authors of this manuscript. The contact person at CloudResearch who can help researchers access the data set is Tzvi Abberbock, who can be reached at [email protected] .

Studies of the gender pay gap are seldom able to simultaneously account for the range of alternative putative mechanisms underlying it. Using CloudResearch, an online microtask platform connecting employers to workers who perform research-related tasks, we examine whether gender pay discrepancies are still evident in a labor market characterized by anonymity, relatively homogeneous work, and flexibility. For 22,271 Mechanical Turk workers who participated in nearly 5 million tasks, we analyze hourly earnings by gender, controlling for key covariates which have been shown previously to lead to differential pay for men and women. On average, women’s hourly earnings were 10.5% lower than men’s. Several factors contributed to the gender pay gap, including the tendency for women to select tasks that have a lower advertised hourly pay. This study provides evidence that gender pay gaps can arise despite the absence of overt discrimination, labor segregation, and inflexible work arrangements, even after experience, education, and other human capital factors are controlled for. Findings highlight the need to examine other possible causes of the gender pay gap. Potential strategies for reducing the pay gap on online labor markets are also discussed.

Introduction

The gender pay gap, the disparity in earnings between male and female workers, has been the focus of empirical research in the US for decades, as well as legislative and executive action under the Obama administration [ 1 , 2 ]. Trends dating back to the 1960s show a long period in which women’s earnings were approximately 60% of their male counterparts, followed by increases in women’s earnings starting in the 1980s, which began to narrow, but not close, the gap which persists today [ 3 ]. More recent data from 2014 show that overall, the median weekly earnings of women working full time were 79–83% of what men earned [ 4 – 9 ].

The extensive literature seeking to explain the gender pay gap and its trajectory over time in traditional labor markets suggests it is a function of multiple structural and individual-level processes that reflect both the near-term and cumulative effects of gender relations and roles over the life course. Broadly speaking, the drivers of the gender pay gap can be categorized as: 1) human capital or productivity factors such as education, skills, and workforce experience; 2) industry or occupational segregation, which some estimates suggest accounts for approximately half of the pay gap; 3) gender-specific temporal flexibility constraints which can affect promotions and remuneration; and finally, 4) gender discrimination operating in hiring, promotion, task assignment, and/or compensation. The latter mechanism is often estimated by inference as a function of unexplained residual effects of gender on payment after accounting for other factors, an approach which is most persuasive in studies of narrowly restricted populations of workers such as lawyers [ 10 ] and academics of specific disciplines [ 11 ]. A recent estimate suggests this unexplained gender difference in earnings can account for approximately 40% of the pay gap [ 3 ]. However, more direct estimations of discriminatory processes are also available from experimental evidence, including field audit and lab-based studies [ 12 – 14 ]. Finally, gender pay gaps have also been attributed to differential discrimination encountered by men and women on the basis of parental status, often known as the ‘motherhood penalty’ [ 15 ].

Non-traditional ‘gig economy’ labor markets and the gender pay gap

In recent years there has been a dramatic rise in nontraditional ‘gig economy’ labor markets, which entail independent workers hired for single projects or tasks often on a short-term basis with minimal contractual engagement. “Microtask” platforms such as Amazon Mechanical Turk (MTurk) and Crowdflower have become a major sector of the gig economy, offering a source of easily accessible supplementary income through performance of small tasks online at a time and place convenient to the worker. Available tasks can range from categorizing receipts to transcription and proofreading services, and are posted online by the prospective employer. Workers registered with the platform then elect to perform the advertised tasks and receive compensation upon completion of satisfactory work [ 16 ]. An estimated 0.4% of US adults are currently receiving income from such platforms each month [ 17 ], and microtask work is a growing sector of the service economy in the United States [ 18 ]. Although still relatively small, these emerging labor market environments provide a unique opportunity to investigate the gender pay gap in ways not possible within traditional labor markets, due to features (described below) that allow researchers to simultaneously account for multiple putative mechanisms thought to underlie the pay gap.

The present study utilizes the Amazon Mechanical Turk (MTurk) platform as a case study to examine whether a gender pay gap remains evident when the main causes of the pay gap identified in the literature do not apply or can be accounted for in a single investigation. MTurk is an online microtask platform that connects employers (‘requesters’) to employees (‘workers’) who perform jobs called “Human Intelligence Tasks” (HITs). The platform allows requesters to post tasks on a dashboard with a short description of the HIT, the compensation being offered, and the time the HIT is expected to take. When complete, the requester either approves or rejects the work based on quality. If approved, payment is quickly accessible to workers. The gender of workers who complete these HITs is not known to the requesters, but was accessible to researchers for the present study (along with other sociodemographic information and pay rates) based on metadata collected through CloudResearch (formerly TurkPrime), a platform commonly used to conduct social and behavioral research on MTurk [ 19 ].

Evaluating pay rates of workers on MTurk requires estimating the pay per hour of each task that a worker accepts which can then be averaged together. All HITs posted on MTurk through CloudResearch display how much a HIT pays and an estimated time that it takes for that HIT to be completed. Workers use this information to determine what the corresponding hourly pay rate of a task is likely to be, and much of our analysis of the gender pay gap is based on this advertised pay rate of all completed surveys. We also calculate an estimate of the gender pay gap based on actual completion times to examine potential differences in task completion speed, which we refer to as estimated actual wages (see Methods section for details).

Previous studies have found that both task completion time and the selection of tasks influences the gender pay gap in at least some gig economy markets. For example, a gender pay gap was observed among Uber drivers, with men consistently earning higher pay than women [ 20 ]. Some of the contributing factors to this pay gap include that male Uber drivers selected different tasks than female drivers, including being more willing to work at night and to work in neighborhoods that were perceived to be more dangerous. Male drivers were also likely to drive faster than their female counterparts. These findings show that person-level factors like task selection, and speed can influence the gender pay gap within gig economy markets.

MTurk is uniquely suited to examine the gender pay gap because it is possible to account simultaneously for multiple structural and individual-level factors that have been shown to produce pay gaps. These include discrimination, work heterogeneity (leading to occupational segregation), and job flexibility, as well as human capital factors such as experience and education.

Discrimination

When employers post their HITs on MTurk they have no way of knowing the demographic characteristics of the workers who accept those tasks, including their gender. While MTurk allows for selective recruitment of specific demographic groups, the MTurk tasks examined in this study are exclusively open to all workers, independent of their gender or other demographic characteristics. Therefore, features of the worker’s identity that might be the basis for discrimination cannot factor into an employer’s decision-making regarding hiring or pay.

Task heterogeneity

Another factor making MTurk uniquely suited for the examination of the gender pay gap is the relative homogeneity of tasks performed by the workers, minimizing the potential influence of gender differences in the type of work pursued on earnings and the pay gap. Work on the MTurk platform consists mostly of short tasks such as 10–15 minute surveys and categorization tasks. In addition, the only information that workers have available to them to choose tasks, other than pay, is the tasks’ titles and descriptions. We additionally classified tasks based on similarity and accounted for possible task heterogeneity effects in our analyses.

Job flexibility

MTurk is not characterized by the same inflexibilities as are often encountered in traditional labor markets. Workers can work at any time of the day or day of the week. This increased flexibility may be expected to provide more opportunities for participation in this labor market for those who are otherwise constrained by family or other obligations.

Human capital factors

It is possible that the more experienced workers could learn over time how to identify higher paying tasks by virtue of, for example, identifying qualities of tasks that can be completed more quickly than the advertised required time estimate. Further, if experience is correlated with gender, it could contribute to a gender pay gap and thus needs to be controlled for. Using CloudResearch metadata, we are able to account for experience on the platform. Additionally, we account for multiple sociodemographic variables, including age, marital status, parental status, education, income (from all sources), and race using the sociodemographic data available through CloudResearch.

Expected gender pay gap findings on MTurk

Due to the aforementioned factors that are unique to the MTurk marketplace–e.g., anonymity, self-selection into tasks, relative homogeneity of the tasks performed, and flexible work scheduling–we did not expect a gender pay gap to be evident on the platform to the same extent as in traditional labor markets. However, potential gender differences in task selection and completion speed, which have implications for earnings, merit further consideration. For example, though we expect the relative homogeneity of the MTurk tasks to minimize gender differences in task selection that could mimic occupational segregation, we do account for potential subtle residual differences in tasks that could differentially attract male and female workers and indirectly lead to pay differentials if those tasks that are preferentially selected by men pay a higher rate. To do this we categorize all tasks based on their descriptions using K-clustering and add the clusters as covariates to our models. In addition, we separately examine the gender pay gap within each topic-cluster.

In addition, if workers who are experienced on the platform are better able to find higher paying HITs, and if experience is correlated with gender, it may lead to gender differences in earnings. Theoretically, other factors that may vary with gender could also influence task selection. Previous studies of the pay gap in traditional markets indicate that reservation wages, defined as the pay threshold at which a person is willing to accept work, may be lower among women with children compared to women without, and to that of men as well [ 21 ]. Thus, if women on MTurk are more likely to have young children than men, they may be more willing to accept available work even if it pays relatively poorly. Other factors such as income, education level, and age may similarly influence reservation wages if they are associated with opportunities to find work outside of microtask platforms. To the extent that these demographics correlate with gender they may give rise to a gender pay gap. Therefore we consider age, experience on MTurk, education, income, marital status, and parental status as covariates in our models.

Task completion speed may vary by gender for several reasons, including potential gender differences in past experience on the platform. We examine the estimated actual pay gap per hour based on HIT payment and estimated actual completion time to examine the effects of completion speed on the wage gap. We also examine the gender pay gap based on advertised pay rates, which are not dependent on completion speed and more directly measure how gender differences in task selection can lead to a pay gap. Below, we explain how these were calculated based on meta-data from CloudResearch.

To summarize, the overall goal of the present study was to explore whether gender pay differentials arise within a unique, non-traditional and anonymous online labor market, where known drivers of the gender pay gap either do not apply or can be accounted for statistically.

Materials and methods

Amazon mechanical turk and cloudresearch.

Started in 2005, the original purpose of the Amazon Mechanical Turk (MTurk) platform was to allow requesters to crowdsource tasks that could not easily be handled by existing technological solutions such as receipt copying, image categorization, and website testing. As of 2010, researchers increasingly began using MTurk for a wide variety of research tasks in the social, behavioral, and medical sciences, and it is currently used by thousands of academic researchers across hundreds of academic departments [ 22 ]. These research-related HITs are typically listed on the platform in generic terms such as, “Ten-minute social science study,” or “A study about public opinion attitudes.”

Because MTurk was not originally designed solely for research purposes, its interface is not optimized for some scientific applications. For this reason, third party add-on toolkits have been created that offer critical research tools for scientific use. One such platform, CloudResearch (formerly TurkPrime), allows requesters to manage multiple research functions, such as applying sampling criteria and facilitating longitudinal studies, through a link to their MTurk account. CloudResearch’s functionality has been described extensively elsewhere [ 19 ]. While the demographic characteristics of workers are not available to MTurk requesters, we were able to retroactively identify the gender and other demographic characteristics of workers through the CloudResearch platform. CloudResearch also facilitates access to data for each HIT, including pay, estimated length, and title.

The study was an analysis of previously collected metadata, which were analyzed anonymously. We complied with the terms of service for all data collected from CloudResearch, and MTurk. The approving institutional review board for this study was IntegReview.

Analytic sample

We analyzed the nearly 5 million tasks completed during an 18-month period between January 2016 and June 2017 by 12,312 female and 9,959 male workers who had complete data on key demographic characteristics. To be included in the analysis a HIT had to be fully completed, not just accepted, by the worker, and had to be accepted (paid for) by the requester. Although the vast majority of HITs were open to both males and females, a small percentage of HITs are intended for a specific gender. Because our goal was to exclusively analyze HITs for which the requesters did not know the gender of workers, we excluded any HITs using gender-specific inclusion or exclusion criteria from the analyses. In addition, we removed from the analysis any HITs that were part of follow-up studies in which it would be possible for the requester to know the gender of the worker from the prior data collection. Finally, where possible, CloudResearch tracks demographic information on workers across multiple HITs over time. To minimize misclassification of gender, we excluded the 0.3% of assignments for which gender was unknown with at least 95% consistency across HITs.

The main exposure variable is worker gender and the outcome variables are estimated actual hourly pay accrued through completing HITs, and advertised hourly pay for completed HITs. Estimated actual hourly wages are based on the estimated length in minutes and compensation in dollars per HIT as posted on the dashboard by the requester. We refer to actual pay as estimated because sometimes people work multiple assignments at the same time (which is allowed on the platform), or may simultaneously perform other unrelated activities and therefore not work on the HIT the entire time the task is open. We also considered several covariates to approximate human capital factors that could potentially influence earnings on this platform, including marital status, education, household income, number of children, race/ethnicity, age, and experience (number of HITs previously completed). Additional covariates included task length, task cluster (see below), and the serial order with which workers accepted the HIT in order to account for potential differences in HIT acceptance speed that may relate to the pay gap.

Database and analytic approach

Data were exported from CloudResearch’s database into Stata in long-form format to represent each task on a single row. For the purposes of this paper, we use “HIT” and “study” interchangeably to refer to a study put up on the MTurk dashboard which aims to collect data from multiple participants. A HIT or study consist of multiple “assignments” which is a single task completed by a single participant. Columns represented variables such as demographic information, payment, and estimated HIT length. Column variables also included unique IDs for workers, HITs (a single study posted by a requester), and requesters, allowing for a multi-level modeling analytic approach with assignments nested within workers. Individual assignments (a single task completed by a single worker) were the unit of analysis for all models.

Linear regression models were used to calculate the gender pay gap using two dependent variables 1) women’s estimated actual earnings relative to men’s and 2) women’s selection of tasks based on advertised earnings relative to men’s. We first examined the actual pay model, to see the gender pay gap when including an estimate of task completion speed, and then adjusted this model for advertised hourly pay to determine if and to what extent a propensity for men to select more remunerative tasks was evident and driving any observed gender pay gap. We additionally ran separate models using women’s advertised earnings relative to men’s as the dependent variable to examine task selection effects more directly. The fully adjusted models controlled for the human capital-related covariates, excluding household income and education which were balanced across genders. These models also tested for interactions between gender and each of the covariates by adding individual interaction terms to the adjusted model. To control for within-worker clustering, Huber-White standard error corrections were used in all models.

Cluster analysis

To explore the potential influence of any residual task heterogeneity and gender preference for specific task type as the cause of the gender pay gap, we use K-means clustering analysis (seed = 0) to categorize the types of tasks into clusters based on the descriptions that workers use to choose the tasks they perform. We excluded from this clustering any tasks which contained certain gendered words (such as “male”, “female”, etc.) and any tasks which had fewer than 30 respondents. We stripped out all punctuation, symbols and digits from the titles, so as to remove any reference to estimated compensation or duration. The features we clustered on were the presence or absence of 5,140 distinct words that appeared across all titles. We then present the distribution of tasks across these clusters as well as average pay by gender and the gender pay gap within each cluster.

The demographics of the analytic sample are presented in Table 1 . Men and women completed comparable numbers of tasks during the study period; 2,396,978 (48.6%) for men and 2,539,229 (51.4%) for women.

In Table 2 we measure the differences in remuneration between genders, and then decompose any observed pay gap into task completion speed, task selection, and then demographic and structural factors. Model 1 shows the unadjusted regression model of gender differences in estimated actual pay, and indicates that, on average, tasks completed by women paid 60 (10.5%) cents less per hour compared to tasks completed by men (t = 17.4, p < .0001), with the mean estimated actual pay across genders being $5.70 per hour.

*Model adjusted for race, marital status, number of children and task clusters as categorical covariates, and age, HIT acceptance speed, and number of HITs as continuous covariates.

In Model 2, adjusting for advertised hourly pay, the gender pay gap dropped to 46 cents indicating that 14 cents of the pay gap is attributable to gender differences in the selection of tasks (t = 8.6, p < .0001). Finally, after the inclusion of covariates and their interactions in Model 3, the gender pay differential was further attenuated to 32 cents (t = 6.7, p < .0001). The remaining 32 cent difference (56.6%) in earnings is inferred to be attributable to gender differences in HIT completion speed.

Task selection analyses

Although completion speed appears to account for a significant portion of the pay gap, of particular interest are gender differences in task selection. Beyond structural factors such as education, household composition and completion speed, task selection accounts for a meaningful portion of the gender pay gap. As a reminder, the pay rate and expected completion time are posted for every HIT, so why women would select less remunerative tasks on average than men do is an important question to explore. In the next section of the paper we perform a set of analyses to examine factors that could account for this observed gender difference in task selection.

Advertised hourly pay

To examine gender differences in task selection, we used linear regression to directly examine whether the advertised hourly pay differed for tasks accepted by male and female workers. We first ran a simple model ( Table 3 ; Model 3A) on the full dataset of 4.93 million HITs, with gender as the predictor and advertised hourly pay as the outcome including no other covariates. The unadjusted regression results (Model 4) shown in Table 3 , indicates that, summed across all clusters and demographic groups, tasks completed by women were advertised as paying 28 cents (95% CI: $0.25-$0.31) less per hour (5.8%) compared to tasks completed by men (t = 21.8, p < .0001).

*Models adjusted for race, marital status, number of children, and task clusters as categorical covariates, and age, HIT acceptance speed, and number of HITs as continuous covariates.

Model 5 examines whether the remuneration differences for tasks selected by men and women remains significant in the presence of multiple covariates included in the previous model and their interactions. The advertised pay differential for tasks selected by women compared to men was attenuated to 21 cents (4.3%), and remained statistically significant (t = 9.9, p < .0001). This estimate closely corresponded to the inferred influence of task selection reported in Table 2 . Tests of gender by covariate interactions were significant only in the cases of age and marital status; the pay differential in tasks selected by men and women decreased with age and was more pronounced among single versus currently or previously married women.

To further examine what factors may account for the observed gender differences in task selection we plotted the observed pay gap within demographic and other covariate groups. Table 4 shows the distribution of tasks completed by men and women, as well as mean earnings and the pay gap across all demographic groups, based on the advertised (not actual) hourly pay for HITs selected (hereafter referred to as “advertised hourly pay” and the “advertised pay gap”). The average task was advertised to pay $4.88 per hour (95% CI $4.69, $5.10).

The pattern across demographic characteristics shows that the advertised hourly pay gap between genders is pervasive. Notably, a significant advertised gender pay gap is evident in every level of each covariate considered in Table 4 , but more pronounced among some subgroups of workers. For example, the advertised pay gap was highest among the youngest workers ($0.31 per hour for workers age 18–29), and decreased linearly with age, declining to $0.13 per hour among workers age 60+. Advertised houry gender pay gaps were evident across all levels of education and income considered.

To further examine the potential influence of human capital factors on the advertised hourly pay gap, Table 5 presents the average advertised pay for selected tasks by level of experience on the CloudResearch platform. Workers were grouped into 4 experience levels, based on the number of prior HITs completed: Those who completed fewer than 100 HITs, between 100 and 500 HITs, between 500 and 1,000 HITs, and more than 1,000 HITs. A significant gender difference in advertised hourly pay was observed within each of these four experience groups. The advertised hourly pay for tasks selected by both male and female workers increased with experience, while the gender pay gap decreases. There was some evidence that male workers have more cumulative experience with the platform: 43% of male workers had the highest level of experience (previously completing 1,001–10,000 HITs) compared to only 33% of women.

Table 5 also explores the influence of task heterogeneity upon HIT selection and the gender gap in advertised hourly pay. K-means clustering was used to group HITs into 20 clusters initially based on the presence or absence of 5,140 distinct words appearing in HIT titles. Clusters with fewer than 50,000 completed tasks were then excluded from analysis. This resulted in 13 clusters which accounted for 94.3% of submitted work assignments (HITs).

The themes of all clusters as well as the average hourly advertised pay for men and women within each cluster are presented in the second panel of Table 5 . The clusters included categories such as Games, Decision making, Product evaluation, Psychology studies, and Short Surveys. We did not observe a gender preference for any of the clusters. Specifically, for every cluster, the proportion of males was no smaller than 46.6% (consistent with the slightly lower proportion of males on the platform, see Table 1 ) and no larger than 50.2%. As shown in Table 5 , the gender pay gap was observed within each of the clusters. These results suggest that residual task heterogeneity, a proxy for occupational segregation, is not likely to contribute to a gender pay gap in this market.

Task length was defined as the advertised estimated duration of a HIT. Table 6 presents the advertised hourly gender pay gaps for five categories of HIT length, which ranged from a few minutes to over 1 hour. Again, a significant advertised hourly gender pay gap was observed in each category.

Finally, we conducted additional supplementary analyses to determine if other plausible factors such as HIT timing could account for the gender pay gap. We explored temporal factors including hour of the day and day of the week. Each completed task was grouped based on the hour and day in which it was completed. A significant advertised gender pay gap was observed within each of the 24 hours of the day and for every day of the week demonstrating that HIT timing could not account for the observed gender gap (results available in Supplementary Materials).

In this study we examined the gender pay gap on an anonymous online platform across an 18-month period, during which close to five million tasks were completed by over 20,000 unique workers. Due to factors that are unique to the Mechanical Turk online marketplace–such as anonymity, self-selection into tasks, relative homogeneity of the tasks performed, and flexible work scheduling–we did not expect earnings to differ by gender on this platform. However, contrary to our expectations, a robust and persistent gender pay gap was observed.

The average estimated actual pay on MTurk over the course of the examined time period was $5.70 per hour, with the gender pay differential being 10.5%. Importantly, gig economy platforms differ from more traditional labor markets in that hourly pay largely depends on the speed with which tasks are completed. For this reason, an analysis of gender differences in actual earned pay will be affected by gender differences in task completion speed. Unfortunately, we were not able to directly measure the speed with which workers complete tasks and account for this factor in our analysis. This is because workers have the ability to accept multiple HITs at the same time and multiple HITs can sit dormant in a queue, waiting for workers to begin to work on them. Therefore, the actual time that many workers spend working on tasks is likely less than what is indicated in the metadata available. For this reason, the estimated average actual hourly rate of $5.70 is likely an underestimate and the gender gap in actual pay cannot be precisely measured. We infer however, by the residual gender pay gap after accounting for other factors, that as much as 57% (or $.32) of the pay differential may be attributable to task completion speed. There are multiple plausible explanations for gender differences in task completion speed. For example, women may be more meticulous at performing tasks and, thus, may take longer at completing them. There may also be a skill factor related to men’s greater experience on the platform (see Table 5 ), such that men may be faster on average at completing tasks than women.

However, our findings also revealed another component of a gender pay gap on this platform–gender differences in the selection of tasks based on their advertised pay. Because the speed with which workers complete tasks does not impact these estimates, we conducted extensive analyses to try to explain this gender gap and the reasons why women appear on average to be selecting tasks that pay less compared to men. These results pertaining to the advertised gender pay gap constitute the main focus of this study and the discussion that follows.

The overall advertised hourly pay was $4.88. The gender pay gap in the advertised hourly pay was $0.28, or 5.8% of the advertised pay. Once a gender earnings differential was observed based on advertised pay, we expected to fully explain it by controlling for key structural and individual-level covariates. The covariates that we examined included experience, age, income, education, family composition, race, number of children, task length, the speed of accepting a task, and thirteen types of subtasks. We additionally examined the time of day and day of the week as potential explanatory factors. Again, contrary to our expectations, we observed that the pay gap persisted even after these potential confounders were controlled for. Indeed, separate analyses that examined the advertised pay gap within each subcategory of the covariates showed that the pay gap is ubiquitous, and persisted within each of the ninety sub-groups examined. These findings allows us to rule out multiple mechanisms that are known drivers of the pay gap in traditional labor markets and other gig economy marketplaces. To our knowledge this is the only study that has observed a pay gap across such diverse categories of workers and conditions, in an anonymous marketplace, while simultaneously controlling for virtually all variables that are traditionally implicated as causes of the gender pay gap.

Individual-level factors

Individual-level factors such as parental status and family composition are a common source of the gender pay gap in traditional labor markets [ 15 ] . Single mothers have previously been shown to have lower reservation wages compared to other men and women [ 21 ]. In traditional labor markets lower reservation wages lead single mothers to be willing to accept lower-paying work, contributing to a larger gender pay gap in this group. This pattern may extend to gig economy markets, in which single mothers may look to online labor markets as a source of supplementary income to help take care of their children, potentially leading them to become less discriminating in their choice of tasks and more willing to work for lower pay. Since female MTurk workers are 20% more likely than men to have children (see Table 1 ), it was critical to examine whether the gender pay gap may be driven by factors associated with family composition.

An examination of the advertised gender pay gap among individuals who differed in their marital and parental status showed that while married workers and those with children are indeed willing to work for lower pay (suggesting that family circumstances do affect reservation wages and may thus affect the willingness of online workers to accept lower-paying online tasks), women’s hourly pay is consistently lower than men’s within both single and married subgroups of workers, and among workers who do and do not have children. Indeed, contrary to expectations, the advertised gender pay gap was highest among those workers who are single, and among those who do not have any children. This observation shows that it is not possible for parental and family status to account for the observed pay gap in the present study, since it is precisely among unmarried individuals and those without children that the largest pay gap is observed.

Age was another factor that we considered to potentially explain the gender pay gap. In the present sample, the hourly pay of older individuals is substantially lower than that of younger workers; and women on the platform are five years older on average compared to men (see Table 1 ). However, having examined the gender pay gap separately within five different age cohorts we found that the largest pay gap occurs in the two youngest cohort groups: those between 18 and 29, and between 30 and 39 years of age. These are also the largest cohorts, responsible for 64% of completed work in total.

Younger workers are also most likely to have never been married or to not have any children. Thus, taken together, the results of the subgroup analyses are consistent in showing that the largest pay gap does not emerge from factors relating to parental, family, or age-related person-level factors. Similar patterns were found for race, education, and income. Specifically, a significant gender pay gap was observed within each subgroup of every one of these variables, showing that person-level factors relating to demographics are not driving the pay gap on this platform.

Experience is a factor that has an influence on the pay gap in both traditional and gig economy labor markets [ 20 ] . As noted above, experienced workers may be faster and more efficient at completing tasks in this platform, but also potentially more savvy at selecting more remunerative tasks compared to less experienced workers if, for example, they are better at selecting tasks that will take less time to complete than estimated on the dashboard [ 20 ]. On MTurk, men are overall more experienced than women. However, experience does not account for the gender gap in advertised pay in the present study. Inexperienced workers comprise the vast majority of the Mechanical Turk workforce, accounting for 67% of all completed tasks (see Table 5 ). Yet within this inexperienced group, there is a consistent male earning advantage based on the advertised pay for tasks performed. Further, controlling for the effect of experience in our models has a minimal effect on attenuating the gender pay gap.

Another important source of the gender pay gap in both traditional and gig economy labor markets is task heterogeneity. In traditional labor markets men are disproportionately represented in lucrative fields, such as those in the tech sector [ 23 ]. While the workspace within MTurk is relatively homogeneous compared to the traditional labor market, there is still some variety in the kinds of tasks that are available, and men and women may have been expected to have preferences that influence choices among these.

To examine whether there is a gender preference for specific tasks, we systematically analyzed the textual descriptions of all tasks included in this study. These textual descriptions were available for all workers to examine on their dashboards, along with information about pay. The clustering algorithm revealed thirteen categories of tasks such as games, decision making, several different kinds of survey tasks, and psychology studies.We did not observe any evidence of gender preference for any of the task types. Within each of the thirteen clusters the distribution of tasks was approximately equally split between men and women. Thus, there is no evidence that women as a group have an overall preference for specific tasks compared to men. Critically, the gender pay gap was also observed within each one of these thirteen clusters.

Another potential source of heterogeneity is task length. Based on traditional labor markets, one plausible hypothesis about what may drive women’s preferences for specific tasks is that women may select tasks that differ in their duration. For example, women may be more likely to use the platform for supplemental income, while men may be more likely to work on HITs as their primary income source. Women may thus select shorter tasks relative to their male counterparts. If the shorter tasks pay less money, this would result in what appears to be a gender pay gap.

However, we did not observe gender differences in task selection based on task duration. For example, having divided tasks into their advertised length, the tasks are preferred equally by men and women. Furthermore, the shorter tasks’ hourly pay is substantially higher on average compared to longer tasks.

Additional evidence that scheduling factors do not drive the gender pay gap is that it was observed within all hourly and daily intervals (See S1 and S2 Tables in Appendix). These data are consistent with the results presented above regarding personal level factors, showing that the majority of male and female Mechanical Turk workers are single, young, and have no children. Thus, while in traditional labor markets task heterogeneity and labor segmentation is often driven by family and other life circumstances, the cohort examined in this study does not appear to be affected by these factors.

Practical implications of a gender pay gap on online platforms for social and behavioral science research

The present findings have important implications for online participant recruitment in the social and behavioral sciences, and also have theoretical implications for understanding the mechanisms that give rise to the gender pay gap. The last ten years have seen a revolution in data collection practices in the social and behavioral sciences, as laboratory-based data collection has slowly and steadily been moving online [ 16 , 24 ]. Mechanical Turk is by far the most widely used source of human participants online, with thousands of published peer-reviewed papers utilizing Mechanical Turk to recruit at least some of their human participants [ 25 ]. The present findings suggest both a challenge and an opportunity for researchers utilizing online platforms for participant recruitment. Our findings clearly reveal for the first time that sampling research participants on anonymous online platforms tends to produce gender pay inequities, and that this happens independent of demographics or type of task. While it is not clear from our findings what the exact cause of this inequity is, what is clear is that the online sampling environment produces similar gender pay inequities as those observed in other more traditional labor markets, after controlling for relevant covariates.

This finding is inherently surprising since many mechanisms that are known to produce the gender pay gap in traditional labor markets are not at play in online microtasks environments. Regardless of what the generative mechanisms of the gender pay gap on online microtask platforms might be, researchers may wish to consider whether changes in their sampling practices may produce more equitable pay outcomes. Unlike traditional labor markets, online data collection platforms have built-in tools that can allow researchers to easily fix gender pay inequities. Researchers can simply utilize gender quotas, for example, to fix the ratio of male and female participants that they recruit. These simple fixes in sampling practices will not only produce more equitable pay outcomes but are also most likely advantageous for reducing sampling bias due to gender being correlated with pay. Thus, while our results point to a ubiquitous discrepancy in pay between men and women on online microtask platforms, such inequities have relatively easy fixes on online gig economy marketplaces such as MTurk, compared to traditional labor markets where gender-based pay inequities have often remained intractable.

Other gig economy markets

As discussed in the introduction, a gender wage gap has been demonstrated on Uber, a gig economy transportation marketplace [ 20 ], where men earn approximately 7% more than women. However, unlike in the present study, the gender wage gap on Uber was fully explained by three factors; a) driving speed predicted higher wages, with men driving faster than women, b) men were more likely than women to drive in congested locations which resulted in better pay, c) experience working for Uber predicted higher wages, with men being more experienced. Thus, contrary to our findings, the gender wage gap in gig economy markets studied thus far are fully explained by task heterogeneity, experience, and task completion speed. To our knowledge, the results presented in the present study are the first to show that the gender wage gap can emerge independent of these factors.

Generalizability

Every labor market is characterized by a unique population of workers that are almost by definition not a representation of the general population outside of that labor market. Likewise, Mechanical Turk is characterized by a unique population of workers that is known to differ from the general population in several ways. Mechanical Turk workers are younger, better educated, less likely to be married or have children, less likely to be religious, and more likely to have a lower income compared to the general United States population [ 24 ]. The goal of the present study was not to uncover universal mechanisms that generate the gender pay gap across all labor markets and demographic groups. Rather, the goal was to examine a highly unique labor environment, characterized by factors that should make this labor market immune to the emergence of a gender pay gap.

Previous theories accounting for the pay gap have identified specific generating mechanisms relating to structural and personal factors, in addition to discrimination, as playing a role in the emergence of the gender pay gap. This study examined the work of over 20,000 individuals completing over 5 million tasks, under conditions where standard mechanisms that generate the gender pay gap have been controlled for. Nevertheless, a gender pay gap emerged in this environment, which cannot be accounted for by structural factors, demographic background, task preferences, or discrimination. Thus, these results reveal that the gender pay gap can emerge—in at least some labor markets—in which discrimination is absent and other key factors are accounted for. These results show that factors which have been identified to date as giving rise to the gender pay gap are not sufficient to explain the pay gap in at least some labor markets.

Potential mechanisms

While we cannot know from the results of this study what the actual mechanism is that generates the gender pay gap on online platforms, we suggest that it may be coming from outside of the platform. The particular characteristics of this labor market—such as anonymity, relative task homogeneity, and flexibility—suggest that, everything else being equal, women working in this platform have a greater propensity to choose less remunerative opportunities relative to men. It may be that these choices are driven by women having a lower reservation wage compared to men [ 21 , 26 ]. Previous research among student populations and in traditional labor markets has shown that women report lower pay or reward expectations than men [ 27 – 29 ]. Lower pay expectations among women are attributed to justifiable anticipation of differential returns to labor due to factors such as gender discrimination and/or a systematic psychological bias toward pessimism relative to an overly optimistic propensity among men [ 30 ].

Our results show that even if the bias of employers is removed by hiding the gender of workers as happens on MTurk, it seems that women may select lower paying opportunities themselves because their lower reservation wage influences the types of tasks they are willing to work on. It may be that women do this because cumulative experiences of pervasive discrimination lead women to undervalue their labor. In turn, women’s experiences with earning lower pay compared to men on traditional labor markets may lower women’s pay expectations on gig economy markets. Thus, consistent with these lowered expectations, women lower their reservation wages and may thus be more likely than men to settle for lower paying tasks.

More broadly, gender norms, psychological attributes, and non-cognitive skills, have recently become the subject of investigation as a potential source for the gender pay gap [ 3 ], and the present findings indicate the importance of such mechanisms being further explored, particularly in the context of task selection. More research will be required to explore the potential psychological and antecedent structural mechanisms underlying differential task selection and expectations of compensation for time spent on microtask platforms, with potential relevance to the gender pay gap in traditional labor markets as well. What these results do show is that pay discrepancies can emerge despite the absence of discrimination in at least some circumstances. These results should be of particular interest for researchers who may wish to see a more equitable online labor market for academic research, and also suggest that novel and heretofore unexplored mechanisms may be at play in generating these pay discrepancies.

A final note about framing: we are aware that explanations of the gender pay gap that invoke elements of women’s agency and, more specifically, “choices” risk both; a) diminishing or distracting from important structural factors, and b) “naturalizing” the status quo of gender inequality [ 30 ] . As Connor and Fiske (2019) argue, causal attributions for the gender pay gap to “unconstrained choices” by women, common as part of human capital explanations, may have the effect, intended or otherwise, of reinforcing system-justifying ideologies that serve to perpetuate inequality. By explicitly locating women’s economic decision making on the MTurk platform in the broader context of inegalitarian gender norms and labor market experiences outside of it (as above), we seek to distance our interpretation of our findings from implicit endorsement of traditional gender roles and economic arrangements and to promote further investigation of how the observed gender pay gap in this niche of the gig economy may reflect both broader gender inequalities and opportunities for structural remedies.

Supporting information

Funding statement.

The authors received no specific funding for this work.

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Gender pay gap in U.S. hasn’t changed much in two decades

The gender gap in pay has remained relatively stable in the United States over the past 20 years or so. In 2022, women earned an average of 82% of what men earned, according to a new Pew Research Center analysis of median hourly earnings of both full- and part-time workers. These results are similar to where the pay gap stood in 2002, when women earned 80% as much as men.

As has long been the case, the wage gap is smaller for workers ages 25 to 34 than for all workers 16 and older. In 2022, women ages 25 to 34 earned an average of 92 cents for every dollar earned by a man in the same age group – an 8-cent gap. By comparison, the gender pay gap among workers of all ages that year was 18 cents.

While the gender pay gap has not changed much in the last two decades, it has narrowed considerably when looking at the longer term, both among all workers ages 16 and older and among those ages 25 to 34. The estimated 18-cent gender pay gap among all workers in 2022 was down from 35 cents in 1982. And the 8-cent gap among workers ages 25 to 34 in 2022 was down from a 26-cent gap four decades earlier.

The gender pay gap measures the difference in median hourly earnings between men and women who work full or part time in the United States. Pew Research Center’s estimate of the pay gap is based on an analysis of Current Population Survey (CPS) monthly outgoing rotation group files ( IPUMS ) from January 1982 to December 2022, combined to create annual files. To understand how we calculate the gender pay gap, read our 2013 post, “How Pew Research Center measured the gender pay gap.”

The COVID-19 outbreak affected data collection efforts by the U.S. government in its surveys, especially in 2020 and 2021, limiting in-person data collection and affecting response rates. It is possible that some measures of economic outcomes and how they vary across demographic groups are affected by these changes in data collection.

In addition to findings about the gender wage gap, this analysis includes information from a Pew Research Center survey about the perceived reasons for the pay gap, as well as the pressures and career goals of U.S. men and women. The survey was conducted among 5,098 adults and includes a subset of questions asked only for 2,048 adults who are employed part time or full time, from Oct. 10-16, 2022. Everyone who took part is a member of the Center’s American Trends Panel (ATP), an online survey panel that is recruited through national, random sampling of residential addresses. This way nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race, ethnicity, partisan affiliation, education and other categories. Read more about the ATP’s methodology .

Here are the questions used in this analysis, along with responses, and its methodology .

The  U.S. Census Bureau has also analyzed the gender pay gap, though its analysis looks only at full-time workers (as opposed to full- and part-time workers). In 2021, full-time, year-round working women earned 84% of what their male counterparts earned, on average, according to the Census Bureau’s most recent analysis.

Much of the gender pay gap has been explained by measurable factors such as educational attainment, occupational segregation and work experience. The narrowing of the gap over the long term is attributable in large part to gains women have made in each of these dimensions.

Related: The Enduring Grip of the Gender Pay Gap

Even though women have increased their presence in higher-paying jobs traditionally dominated by men, such as professional and managerial positions, women as a whole continue to be overrepresented in lower-paying occupations relative to their share of the workforce. This may contribute to gender differences in pay.

Other factors that are difficult to measure, including gender discrimination, may also contribute to the ongoing wage discrepancy.

Perceived reasons for the gender wage gap

When asked about the factors that may play a role in the gender wage gap, half of U.S. adults point to women being treated differently by employers as a major reason, according to a Pew Research Center survey conducted in October 2022. Smaller shares point to women making different choices about how to balance work and family (42%) and working in jobs that pay less (34%).

There are some notable differences between men and women in views of what’s behind the gender wage gap. Women are much more likely than men (61% vs. 37%) to say a major reason for the gap is that employers treat women differently. And while 45% of women say a major factor is that women make different choices about how to balance work and family, men are slightly less likely to hold that view (40% say this).

Parents with children younger than 18 in the household are more likely than those who don’t have young kids at home (48% vs. 40%) to say a major reason for the pay gap is the choices that women make about how to balance family and work. On this question, differences by parental status are evident among both men and women.

Views about reasons for the gender wage gap also differ by party. About two-thirds of Democrats and Democratic-leaning independents (68%) say a major factor behind wage differences is that employers treat women differently, but far fewer Republicans and Republican leaners (30%) say the same. Conversely, Republicans are more likely than Democrats to say women’s choices about how to balance family and work (50% vs. 36%) and their tendency to work in jobs that pay less (39% vs. 30%) are major reasons why women earn less than men.

Democratic and Republican women are more likely than their male counterparts in the same party to say a major reason for the gender wage gap is that employers treat women differently. About three-quarters of Democratic women (76%) say this, compared with 59% of Democratic men. And while 43% of Republican women say unequal treatment by employers is a major reason for the gender wage gap, just 18% of GOP men share that view.

Pressures facing working women and men

Family caregiving responsibilities bring different pressures for working women and men, and research has shown that being a mother can reduce women’s earnings , while fatherhood can increase men’s earnings .

Employed women and men are about equally likely to say they feel a great deal of pressure to support their family financially and to be successful in their jobs and careers, according to the Center’s October survey. But women, and particularly working mothers, are more likely than men to say they feel a great deal of pressure to focus on responsibilities at home.

About half of employed women (48%) report feeling a great deal of pressure to focus on their responsibilities at home, compared with 35% of employed men. Among working mothers with children younger than 18 in the household, two-thirds (67%) say the same, compared with 45% of working dads.

When it comes to supporting their family financially, similar shares of working moms and dads (57% vs. 62%) report they feel a great deal of pressure, but this is driven mainly by the large share of unmarried working mothers who say they feel a great deal of pressure in this regard (77%). Among those who are married, working dads are far more likely than working moms (60% vs. 43%) to say they feel a great deal of pressure to support their family financially. (There were not enough unmarried working fathers in the sample to analyze separately.)

About four-in-ten working parents say they feel a great deal of pressure to be successful at their job or career. These findings don’t differ by gender.

Gender differences in job roles, aspirations

Overall, a quarter of employed U.S. adults say they are currently the boss or one of the top managers where they work, according to the Center’s survey. Another 33% say they are not currently the boss but would like to be in the future, while 41% are not and do not aspire to be the boss or one of the top managers.

Men are more likely than women to be a boss or a top manager where they work (28% vs. 21%). This is especially the case among employed fathers, 35% of whom say they are the boss or one of the top managers where they work. (The varying attitudes between fathers and men without children at least partly reflect differences in marital status and educational attainment between the two groups.)

In addition to being less likely than men to say they are currently the boss or a top manager at work, women are also more likely to say they wouldn’t want to be in this type of position in the future. More than four-in-ten employed women (46%) say this, compared with 37% of men. Similar shares of men (35%) and women (31%) say they are not currently the boss but would like to be one day. These patterns are similar among parents.

Note: This is an update of a post originally published on March 22, 2019. Anna Brown and former Pew Research Center writer/editor Amanda Barroso contributed to an earlier version of this analysis. Here are the questions used in this analysis, along with responses, and its methodology .

What is the gender wage gap in your metropolitan area? Find out with our pay gap calculator

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