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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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An IERI – International Educational Research Institute Journal

• Open access
• Published: 30 July 2019

Trends in gender gaps: using 20 years of evidence from TIMSS

• Sabine Meinck 1 &
• Falk Brese   ORCID: orcid.org/0000-0001-8504-6507 1  

Large-scale Assessments in Education volume  7 , Article number:  8 ( 2019 ) Cite this article

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Potential differences in achievement between female and male students have always been an interesting topic in educational research, as well as having policy and economic implications. This study provides an overview of the so-called “gender gap” in mathematics and science knowledge, based on an in-depth analysis of both extremes of student ability distributions. Evidence underpinning debate on gender inequality in education can be explored by analyzing trends in these distributions over the last 20 years. This new approach to gender gap analysis shows that while the gender gaps that existed 20 years ago have persisted, gender equality in education has increased. The persistent trend of an overrepresentation of male students in the group of high-achievers in both mathematics and science is striking, but male and female students are often also unequally represented at the lower end of the ability distributions. Patterns differ between countries and cycles. In many countries, male students constitute the majority of the lower end of the ability distribution, while in others, more female students are failing to achieve, especially at grade eight. Some countries have shown a reversed inequality trend over the last two decades. With the proposed approach in analyzing gender gaps, differences at the tails of the achievement distributions can be investigated even if the gender distribution is skewed. Policymakers could make use of the approach to closely monitor the development of achievement gaps in their countries and initiate measures to tackle potential causes of inequity, leading to gender inequalities regarding educational achievement.

Introduction

Differences in achievement between female and male students, often termed the “gender gap”, have always been of interest, not only in educational research, but also from a political and economic context (UNESCO 2015a ; Hausmann et al. 2009 ). These differences are frequently seen as a matter of inequality (Klasen 2002 ). Achieving strict gender equality in all situations or domains may seem to be a utopian goal. However, laying the foundations of gender equity has become a political issue and is seen as a general measure of justice and fairness, especially in the education context (EGREES 2005 ).

At an international level, gender equality is of high importance, leading UNESCO to declare gender equality as one of the most important goals for education (UNESCO 2015b ), and ultimately to incorporate this aim within the framework of sustainable development goals (United Nations 2018 ). International comparative research is addressing the issue of gender differences continuously, and the topic was prominent in many recently conducted international large-scale assessments in education, including for example the 2015 TIMSS and PISA cycles (Mullis et al. 2016a ; Mullis et al. 2016b ; OECD 2016 ).

Literature review and theoretical framework

Gender equality and equity in education and society.

Gender equality and equity in education is an issue under discussion for more than a century. At the time the right and obligation of schooling was introduced, single-sex schools dominated the educational landscape in many countries. Subjects taught to male and female students differed, reflecting the expected course of life of these children. Consequently, various subjects aimed at a certain gender group; for example cooking would be aimed at girls (Trueman 2015 ). Nowadays, fairly equal opportunities to learn have been established in the vast majority of countries for female and male students. However, the traditional patterns keep influencing in very powerful ways the life course of male and female students. For example, girls—as opposed to boys—still opt more for professions within the social sector and less often for sectors related to the so-called STEM (science, technology, engineering and mathematics) subjects. These patterns can be observed with career and study choices already prior to entering the work force (UNESCO 2017 ).

Moreover, stereotypes related to these traditional understandings of role models persist into the present, and they do influence what happens in the classroom today. Stereotypes affect professional action of teachers, parental influence and expectations, and consequently students’ self-concept, decisions, actions, and achievement. For example, Nguyen and Ryan ( 2008 ) review existing literature regarding negative effects of stereotyping for girls in mathematics, and prove in an experiment the effect of stereotype threats on achievement. Retelsdorf et al. ( 2015 ) found negative associations between teachers’ gender stereotype and boys’ reading self-concept, disadvantaging male students in their reading achievement. They point out that stereotypes can explain the long-term development of self-concepts as a relatively stable personal characteristic. This, in relation with theories on self-fulfilling prophecy, may be one explanation of manifested differences over the course of schooling.

One aspect of gender differences receiving high attention is related to STEM education. UNESCO ( 2017 ) reports on girls’ and women’s education in STEM find that, to date, girls are still underrepresented in choosing STEM disciplines for studying and as their career paths. Also international comparative studies observe similar patterns. The IEA TIMSS-Advanced study on upper secondary students studying advanced mathematics and science conducted in 2015 found (far) more male students in these advanced courses in most of the participating countries (Mullis et al. 2016c ). Further, male students on average achieve significantly higher than girls in—again—most of the countries.

However, countries have tried (in some cases since decades) to counteract gender inequity. UNESCO ( 2017 ) has compiled examples of the various kinds of interventions and programs regarding the gender differences in STEM education and outcomes. The list of examples comprises action targeted at the individual (female) students, for example single-sex workshops for girls to act as scientists led by same-sex tutors in the UK to facilitate girls’ interest in STEM subjects and careers. Other examples are one-week STEM camps (Kenya), where female students carry out experiments and visit companies offering STEM jobs, or “Science, Technology and Mathematics Education (STME) Clinics” (Ghana) which bring together girls in secondary schools with female scientists who could act as role models. Also on education-system level there was action taken: Improvements regarding school safety, education of teachers, smaller mathematics and science classes and better curriculum coverage could be identified (Mullis et al. 2016 ). Finally, at the level of the country or society itself, policies like quotas or financial investments to promote the image of women in STEM jobs are only some examples that have been implemented so far.

Measuring gender equality and equity in education

While the aforementioned studies aim towards showing causes of gender inequity in education, large-scale assessment data cannot provide this type of insight. Rather, figures of equality (i.e., comparing for example the achievement of gender groups) can be used as indicators of gender equity within educational systems, assuming there are no other factors determining differences between the sexes, such as genetic disposition. In other words, gender equity is understood in this context as a synonym for fairness and equal opportunities for female and male students, while gender equality represents an empirically measurable outcome of equity.

But what constitutes equality? As Allison ( 1978 ) states, already the choice of the measure to represent equality can make a difference regarding the perception of equality. Especially in cross-country comparisons, the question of what or who to compare to becomes a deciding factor on the resulting perception of the magnitude of inequality or even the lack of equality. In general terms, there are two types of comparisons.

First, comparisons could be based on absolute figures or distributions, setting or according to some standard across all compared entities, for example education systems. These standards could be related to various aspects of equality, like conditions, or outcomes. UNESCO, for example, classifies concepts for measuring equity in education accordingly (cf. 2018 , 23ff.): ‘Equality of conditions’ would mean that conditions of education are the same for everyone. ‘Equality of outcomes’ entails minimum educational outcomes (e.g., a certain completed level of schooling) for everyone. When ‘education is independent from personal characteristics’, equality is related to the impartial implementation of education. A ‘positive relation between education and ability’ would be a kind of equality where students with higher ability are provided with higher quality education. This would be the case in countries with a tracked school system. Finally, a ‘positive relation between education and being disadvantaged’ (e.g., regarding some criteria like income) would provide higher quality, more or special focused education to disadvantaged students. In international large-scale studies in education, outcome measures often translate to certain benchmarks of achievement or proficiency levels (see, for example, Mullis et al. 2016 , 61ff; Schulz et al., 2017 , 44ff; OECD 2016 , 59ff.). Gender equality is reported in relation to some standard or threshold applied to each country. While giving highly valuable information, this type of comparison has one disadvantage, that is, the overall achievement distributions vary greatly between countries, and there may be very few overlap at the ends of the distributions, leaving few room to compare gender equalities at these ends. For example, if one wishes to compare gender ratios of eights grade students reaching the advanced TIMSS mathematics benchmark in 2015, Footnote 1 results are scanty, as more than two-thirds of the countries have less than 10% of their students reaching this benchmark; a significant number of countries even have no students at all in this category. Switching to the next lower benchmark level (the “high benchmark”) will not help, as the majority of students in high-ranking countries achieve this category, hence, instead of focusing on the distribution tail, one would again rather focus on major population parts.

A different approach would be to compare achievement distributions across countries, using measures of relative equality within countries as comparison criterion. There are several implications of the latter approach: First, there is no need for (agreeing upon) a criterion acceptable and applicable across different education systems and countries. Second, the notion of comparability implies taking into account the country contexts. Looking at relative distributions in specific contexts or situations can provide more detailed insights into variation, especially in cases where international standards do not fit well. Subsequently, such analysis has the potential to reveal tailored options for addressing issues. For example, if gender gaps exist especially at the lower end of the achievement distribution, measures addressing weaknesses of low achieving students (tailored to the affected gender) will be more effective to tackle the issues.

The vast majority of related previous research based on international large-scale assessment data has focused on comparisons of the mean achievement of female and male students, only relatively few studies have addressed gender differences at different levels of achievement. Moreover, according to observations by Halpern et al. ( 2007 ), research focusing on gender differences at the tails of the ability distributions concentrates most on the upper tail (for example, Benbow and Stanley 1983 ). Only little research is concerned with the lower tail of the ability distribution. Research on the upper tail of the distribution looked at the absolute distribution of female and male students (for example, Hedges and Nowell 1995 ; Strand et al. 2006 ).

Baye and Monseur ( 2016 ) looked at differences in the variability of students’ achievement using TIMSS, PIRLS and PISA data spanning more than 20 years. They point out that gender differences at the extreme tails of the achievement distribution are often more substantial than average differences. Males were more frequently among the highest performing students in mathematics and science, but male students also varied more than female students in their level of performance. Bergold et al. ( 2016 ) also identified a higher variability in the achievement of male students, with male students overrepresented as a group among both highest and lowest performing students. The study looked at 17 countries participating in TIMSS and PIRLS in 2011 with fourth-grade students, and at various achievement domains (reading, mathematics and science) simultaneously. Significant variations between country profiles have been acknowledged, suggesting that a single generic model of explaining gender differences may not be reasonable. Both publications (Baye and Monseur 2016 ; Bergold et al. 2016 ) include a comprehensive review of the literature related to gender differences in general and theories on the greater variability of males regarding achievement.

Recently, some research used quantile regression analysis (Davino et al. 2014 ) as another way of differentiating inequalities along ability distributions. For example, Costanzo and Desimoni ( 2017 ) found varying gender inequalities for the different quantiles of the mathematics and reading scores distributions, using data from an Italian study of second and fifths grade students.

Objectives and research questions

The research presented in this paper was stimulated by the following considerations: (1) we acknowledge the fact that gender equity remains an important issue on the political agenda of many countries; (2) many countries have introduced measures tackling gender inequity over the past decades; (3) previous findings suggest that average achievement is not the most comprehensive indicator of gender equality, rather, unequal gender ratios can be observed especially at the tails of the achievement distributions. Consequently, we would like to expand current knowledge by adding a perspective on trends over time regarding gender differences at the tails of the ability distributions. Deviating from approaches used in previous research, we will implement a statistical analysis method that accounts for potentially skewed overall gender distributions within education systems. Focusing on relative ability distributions of female and male students rather than on absolute distributions, for example according to internationally defined proficiency levels, we will be able to identify gender inequalities better for countries where students’ results do not show (enough) variation across these standardized levels. Further, we can compare countries with regard to gender differences even if the average achievement of students varies a lot between these countries. By analyzing the tails of the relative distribution, we will get information on gender inequalities for the highest and lowest achieving students. We argue that these more fine-grained results—compared to overall achievement averages—could be used to develop measures and policies tailored more specifically to these groups of students. Regarding equal opportunities as an aspect of equity, the lower tail of the achievement distribution might be of special interest, as extremely low performance can seriously affect future options in school and later on in life. Finally, the proposed method is robust to skewed overall distributions of female and male students. This is an important aspect when including countries into the analyses were school enrolment is gender-dependent, or for trend analysis if overall gender ratios change over time.

Using TIMSS data, we sought to evaluate whether differences between girls and boys regarding their mathematics and science achievement at fourth and eighth grade changed over the last 20 years. We considered four central research questions.

Considering mathematics and science achievement of fourth and eighth graders, is there an equal gender distribution at the top and bottom end of the achievement distribution within participating countries?

If there is a gender gap, did it change over time? More specifically, did gender gaps change at the fourth grade from 1995 to 2015, and similarly at the eighth grade?

Looking at specific student cohorts in the fourth grade and again 4 years later at the eighth grade by following up the cohort, did the achievement gap widen or narrow?

Are these developments internationally generalizable or can different patterns be observed in different countries or groups of countries?

Data and methods

We analyzed data collected from education systems participating in TIMSS 1995, TIMSS 2015, and at least one additional intermediate cycle of TIMSS. Eighteen education systems at Grade 4 and 20 education systems at Grade 8 satisfied the requirements.

To enable a longitudinal analysis of specific cohorts at country level for addressing research question (3), we reduced the scope of our research further. From the countries included above, we chose only those who participated at grade four in 1995 and 2011, and at grade eight in 1999 and 2015. Consequently, we followed up two cohorts in 11 countries: students who attended grade four in 1995 and grade eight in 1999, and a cohort born 16 years later, with students attending grade four in 2011 and grade eight in 2015. It should be noted that only representative samples of the same cohorts were tested, and not the same students at different ages.

We first identified the 20% best and poorest performers in each country and cycle per grade and subject domain, using the overall mathematics and science achievement scores, by performing a percentile analysis. This analysis resulted in two benchmark scores per population, subject domain, country, and cycle, dividing the best and the poorest performing 20% from the remaining populations (see Fig.  1 ). Readers should be reminded that the achievement levels of these groups differ greatly among countries as shown in Fig. 2 , but these differences are not of interest for this paper. Instead, our focus is purely on the gender gap within and across countries, and time.

Identifying the tails of the achievement distributions

Ability distributions vary greatly among countries

In a second step, we estimated the differences in percentages of male and female students reaching or failing respective benchmarks resulting from the first step as illustrated in Fig.  3 . Again, separate analyses were run for different grades, subject domains, countries and cycles. The results of these analyses were the relative distributions of female and male students in the groups of “high” and “low” performers. The percentages were computed in a way that allows a direct comparison between the relative distributions even in populations with overall skewed gender distributions (i.e., populations with more female than male students comprising a grade, or vice versa).

Estimating relative distributions of female and male students in the groups of ‘high’ and ‘low’ performers

We accounted for the complex sample and assessment design by using sampling weights for the estimation of population parameters, and applying jackknife repeated replication and plausible values for the estimation of standard errors (Foy 2017 ).

General trends in gender gaps

Overall, there are more differences in trends for gender gaps between grades for the same subject than between subjects within grades. Gender gaps for mathematics achievement at grade four and their trends over time are more similar to those for science achievement at the same grade than trends at grade eight. In other words, it seems that, regarding gender differences, grade matters more than subject.

Trends in gender gaps in mathematics achievement at grade four

Twenty percent highest performing students (above 80th percentile).

Overall, there were relatively more boys than girls among the top 20% of students by achievement (Table  1 ). This applies to almost all countries and cycles. In about two-thirds of all observed cases, this difference was significant.

Kuwait was the only exception to this general finding; in 2007 and 2011, female students were significantly overrepresented in this group. Singapore was the only country to possess remarkable gender balance in all cycles from 1995 to 2015. Finally, only in Japan has inequality reduced, with an initially significant gender gap in favor of boys reducing from 2003 (and becoming insignificant in later cycles).

In 2015, in 12 out of 18 countries there was a significant gender gap favoring boys; the same tendency was observed in the other countries, but the differences were not significant. In seven countries (Australia, Czech Republic, Hong Kong (SAR), Hungary, New Zealand, Slovenia, and the United States), a gender gap in favor of boys widened over the last 20 years, starting from a small and mostly insignificant difference in 1995 and 2003 to a significant gap in 2015, posing questions surrounding potential causes of this apparent increase in inequality.

Twenty percent lowest performing students (below 20th percentile)

Overall, fewer significant gender gaps were observed in this group of low-performers (Table  1 ). Moreover, there were no generalizable trend patterns. In The Netherlands, female students were significantly overrepresented in this group compared to their male peers, a gap that has remained fairly constant over the last two decades. In Iran, Kuwait and Singapore, there were significantly more boys represented among these low-performing students in more recent cycles.

A gender gap existing in New Zealand in 1995 (again, with more male students being part of this group) became insignificant in all later cycles.

Trends in gender gaps in mathematics achievement at grade eight

As with grade four, there are relatively higher percentages of boys than girls among the top 20% highest achieving eighth grade students in mathematics (see Table  2 ). Gaps predominantly favor boys, in all significant gaps but one (Thailand). In 1995, there was a significant gender gap in nine countries, whereas, in 2015, this was true for only five countries, showing a reduction somewhat of the gap among the countries considered.

Over the last two decades, relatively constant gender gaps favoring boys can be observed for Italy, Japan, Korea and the United States. In three countries (England, Iran and Israel), gender inequality decreased over the same period. Only in Thailand, there was a tendency to have relatively more girls in this group (gap significant only in 2007).

Overall, the gender gaps in mathematics achievement of the 20% lowest performing students in grade eight differ by country (see Table  2 ). Gender gaps are often smaller than at the upper end of the achievement distribution of this grade. Further, gaps can be observed in both directions, with a higher relative percentage of boys as well as the opposite, a higher percentage of girls.

In 1995, six countries had significant gender differences, with three countries showing relatively more female students in this group, and another three with relatively more boys. Twenty years later, five (but now mostly other) countries still showed significant gender differences, two countries with relatively more girls, and three countries with relatively more boys. No generalizable trend can be observed for this group.

In Hong Kong (SAR), Kuwait, Lithuania, Singapore and Thailand, boys were significantly more likely to be among the low-performing students than girls in more than one cycle. In none of these countries could a tendency towards increased gender equality be observed. While girls were more likely to be in the group of low-performing students in England, Iran, Israel and Korea in the early cycles, this was no longer the case by 1999 or 2003. Russia showed a tendency toward a reversed gender inequality: while boys were overrepresented in this group in earlier cycles of TIMSS, significantly more girls belong to this group in 2015.

Trends in gender gaps in science achievement at grade four

Boys were notably overrepresented in the group of the 20% highest performing students in science achievement at grade four across countries and cycles (see Table  3 ). This finding is consistent with observations related to mathematics. Similarly, Kuwait was again a remarkable exception, with having constantly more female high-achievers (gap significant in 2007 and 2011).

In 1995, 14 countries had significant gender gaps, all in favor of boys. In 2015, this number reduced to nine countries, indicating some reduction of the gap.

There were no clear group patterns for the bottom 20% of students in science achievement at grade four (see Table  3 ). Gender differences were smaller than for the high-achiever group for many countries and while some countries had more boys than girls, others had more girls than boys.

In 1995, eight countries showed significant differences in the relative percentages of male and female students in this group, six with relatively more girls, and two with more boys. In 2015, only three countries out of these eight still showed significant differences, one with a higher percentage of low-performing girls, and two again with relatively more low-performing boys. Kuwait again proved an exception, with remarkably large gender gaps (up to 18% more boys in the group). Differences in all other countries were not significant.

Trends in gender gaps in science achievement at grade eight

Very similar to the findings relating to grade four, the patterns were striking: There were more boys than girls among the 20% highest performing students in science at grade eight in most countries and cycles (see Table  4 ). In 19 out of the 20 countries investigated, significant gender gaps for this group were evident in two or more cycles.

In 1995, 17 countries showed a significant gender gap, all in favor of boys. In 2015, only nine countries had a significant gap, with only one country (Kuwait) having more girls than boys in this group. A tendency towards a reduction of the gender gap can be observed in many countries. In England¸ Iran, Israel, Lithuania, Slovenia and Sweden, the gender differences were significant in earlier TIMSS cycles but, by 2015, they were no longer significant. This suggests these countries improved gender equality among their top-performing eighth grade students in science during the last decade.

Overall, gender inequalities are less pronounced among the 20% lowest performing students compared to the 20% best performing students (see Table  4 ). The differences, however, mostly favor boys, meaning here that there is, in many cases, a higher percentage of female students in this group compared to the percentage of male students.

In 1995, 14 countries had significant gender gaps, 13 countries with a relatively higher percentage of girls, and one country (Kuwait) with a relatively higher percentage of boys. In 2015, only four countries had significant gaps, one with a higher percentage of girls, and three with a higher percentage of boys. This trend shows that the gap favoring boys (a lower percentage of boys in the low-performing group) is closing and even beginning to reverse.

Canada, England, Hong Kong (SAR), Japan, Korea, New Zealand, Russia, Slovenia, Sweden and United States have managed to close a previously existing significant gender gap over the last 20 years. Israel has reversed its gender gap: while relatively more girls belonged to the low-performing group in 1995, boys were overrepresented in 2015. A similar (but insignificant) tendency can also be observed in other countries.

A remarkably large gender gap within the group of low-performing students can be observed in Kuwait: three out of four students are male; 30% of all male students in the country are among the 20% of eighth grade students performing lowest in science, while this is the case for only 9% of all female eighth graders. This difference seems to be stable over the last 20 years.

Trends in gender gaps in mathematics achievement across countries

When looking at the trends in gender differences in mathematics achievement across countries, we can observe differences (i) between the two tails of the mathematics achievement distribution and (ii) between grades four and eight. As Fig.  4 shows, in the majority of countries (12 out of 18) there are gender differences at grade four, either persisting or developed newly since 1995, within the group of 20% highest achieving students in mathematics. In six countries, there is no gap: either there has been none in 1995 already, or a gap existing in 1995 closed in 2015. On the contrary, there is no gender gap (any longer) in 2015 in the majority of countries (15 out of 18) for fourth graders at the lower end of the achievement distribution (20% lowest achieving students in mathematics). A similar pattern exists for students at eighth grade. For students at both tails of the mathematics achievement distribution, there are significant gender differences in the majority of countries (15 out of 20).

Changes in gender gaps in mathematics achievement in grades four and eight in the group of the 20% highest achieving and the group of 20% lowest achieving students between 1995 and 2015. Example description of upper left pie chart (grade 4, upper 20% of distribution of math achievement): In five countries, a gender gap remained; in seven countries, a gender gap opened, i.e., developed where there was none before; in six countries, the gender gap closed or there is none in 2015 and there has not been a gap in 1995 neither

Trends in gender gaps in science achievement across countries

Figure  5 shows the change in gender differences in science achievement for the 20% highest and lowest achieving students at fourth and eighth grade in a similar way. While there are some similarities in the overall picture to the findings regarding mathematics achievement, we see also different results. Again, for fourth grade students, differences are found in more countries at the upper tail of the achievement distribution than at the lower end. In half of the countries (9 out of 18), there are persisting or newly developed gender differences for the 20% highest achieving students. For the 20% lowest achieving students, however, gaps have closed or never existed in the majority of countries (15 out of 18). We find rather similar patterns for eighth grade students. Almost half of the countries show gender differences for the 20% highest achieving students (9 out of 20), with mostly persisting gaps, whereas for the 20% lowest achieving students there are no differences in the majority of countries (15 out of 20).

Changes in gender gaps in science achievement in grades four and eight in the group of the 20% highest achieving and the group of 20% lowest achieving students between 1995 and 2015. Example description of upper left pie chart (grade 4, upper 20% of distribution of math achievement): In four countries, a gender gap remained; In five countries, a gender gap opened, i.e., developed where there was none before; In nine countries, the gender gap closed or there is none in 2015 and there has not been a gap in 1995 neither

Comparing trends in gender gaps in mathematics and science achievement across countries

Overall, we observe fewer countries with significant gender gaps at the lower tail of the achievement distribution. This holds for both subjects, mathematics and science, as well as for both grade 4 and grade 8. In the upper tail of the achievement distribution, i.e. the 20% highest achieving students, there are more countries with existing gender differences. Here, we find more countries with gender differences for the lower grade students (grade 4) than for the upper grade students (grade 8). However, as the selection of countries included in this analysis is different for grade 4 and grade 8, a direct comparison between results from the two grades is not appropriate.

Trends in gender gaps in mathematics achievement within cohorts

We also examined the gender gaps in mathematics achievement and their trends following up two cohorts from grade four to eight (Table  5 ).

While most of the figures and bars displayed in Tables  5 and 6 resemble information from Tables  1 , 2 , 3 , 4 in a different format, the columns “Gap difference between grade 4 and 8” show the development of gender gaps over 4 years of schooling within the same cohort of students. The first cohort represents students attending grade four in 1995 and grade eight in 1999. The second cohort represents students attending grade four in 2011 and grade eight in 2015.

The first cohort (students attending grade four in 1995) exhibited a significant gender gap favoring boys in England, Iran, Japan and Korea (Table  5 , left upper part). For the first three of these countries, the gap persisted or even widened by grade eight. In Korea, however, the gap decreased over those 4 years and was no longer significant at the eighth grade. Conversely, a gender gap opened up between grades four and eight in the United States. The gap changes were not significant in any country.

In two countries (Hong Kong (SAR) and Korea), the second cohort (fourth graders in 2011) contained significantly more male students among the top-performers at both grades. Australia, Hungary, Slovenia and the United States managed to close an existing gender gap in favor of boys at the fourth grade over the ensuing 4 years: the gaps were no longer significant at grade eight. Finally, Slovenia and England showed a significant change in the gender gaps between grades four and eight. In both cases, more boys belonged to the high-performers at grade four. However, while Slovenia achieved gender equality at grade eight, in England, the inequality gap had reversed, and by 2015 favored girls, with significantly more girls achieving high mathematics scores than in 2011 at grade four.

In the countries considered, there was no general trend in terms of the gender composition of the bottom twenty percent of students in mathematics, nor were there identifiable trends over the years or among cohorts (lower part of Table  5 ).

The picture is very diverse across countries, particularly for the first cohort. Four countries showed significant gender differences in the group allocation; two of them, Iran and Korea, had significantly more girls in this group, while the other two, New Zealand and Singapore, had more boys. These gender gaps reduced over the ensuing 4 years in all countries but Iran, where the gap doubled instead. Moreover, Iran was the only country with a significant gender gap in this group at grade eight in 1999.

Regarding the second cohort, only very few significant gender differences existed in the eleven countries in both grades. In Singapore, significantly more boys belonged to the group of low-performers at fourth grade, and this percentage had doubled by grade eight. A small, yet significant gender difference in the United States with a higher proportion of female students vanished over the years and could no longer be observed at grade eight.

Trends in gender gaps in science achievement between grade four and eight

In contrast to the gender gap trends in mathematics achievement, science achievement showed a very clear and quite generalizable pattern (see upper part of Table  6 ). Male students were overrepresented in the group of the upper 20th percentile of science achievers at grade four in all considered countries in both cycles (1995 and 2011). This overrepresentation was even more pronounced 4 years later at grade eight, with New Zealand being the only country where this group allocation was insignificant at both grades (cohort 1). Further, there was a significant increase in male students in this group in three countries (England, Hungary and the United States). Fortunately, however, the second considered cohort (students that attended grade four 16 years later) painted a less severe picture. While, similarly, relatively more male students were among the top-performers at grade four, 4 years later this was only still true in five countries (Hong Kong, SAR, Hungary, Korea, Singapore and the United States). The gap did not widen significantly in any country.

The gender gap is less pronounced in the group of low performing students (Table  6 , lower part). In 1995, at grade four only four countries showed significant gender gaps. In Hong Kong (SAR), Hungary and Korea more female students were among the low-performing students, but more male students were in this group in New Zealand. While New Zealand reached gender equity 4 years later, the gap persisted in the other countries and widened in England and Iran, again with a higher proportion of girls.

However, the cohort attending grades four and eight 16 years later showed minimal gender gaps at both grades, with the exception of Hungary, where again more girls comprised the lowest percentile of science achievers at eighth grade. Patterns indicate a tendency towards an increase in male students in this group, a yet insignificant trend that should be closely monitored in the future.

Changes over time

Overall, our findings suggest that girls are catching up with boys. In the group of high achieving students in both subject areas, the overrepresentation of boys continued or even extended from grade four to eight from 1990 to 1995. 16 years later, the overrepresentation of boys found at grade four in 2011 rather reduced at grade eight in 2015. For the lower achieving students, at least regarding science achievement, we can observe a similar change. From grade four in 1995 to grade eight in 1999, there was an increase in the overrepresentation of girls in that group. However, there was no such increase from grade four in 2011 to grade eight in 2015. Even more, the overrepresentation of girls at grade four was much lower already in 2011 than that in 1995.

Discussion, conclusions and policy implications

We investigated the gender gaps in mathematics and science knowledge at both extremes of students’ ability distributions. We found that gender gaps that existed 20 years ago have persisted into the present, but also identified encouraging evidence that gender equality in education is increasing. Moreover, data suggests that no general favorable genetic disposition of male students towards mathematics and/or science exists. Otherwise, patterns would be consistent across countries and time. Overwhelmingly obvious is, however, the persisting trend of more male students in the group of high-achievers for both mathematics and science in many educational systems. These subjects have a long history of being more often favored by male students, a situation that fosters gender differences in academic competencies and an underrepresentation of woman in scientific careers. Male and female students may benefit from different teaching approaches and methods to motivate engagement. Several countries have adopted initiatives to address this problem, and the findings indicate that some may have shown success. Similarly, at the lower end of the ability distributions, male and female students are not always equally represented. Patterns differ between countries and cycles. In many countries, male students are overrepresented in this group, while in others, more female students are at risk, predominantly in the upper grades. Policymakers should closely monitor the development of these gaps and initiate measures to tackle gender inequalities. The trends identified in this paper included promising changes in several countries that were able to diminish gender differences in mathematics and science achievement, that have been existing in the past. Furthermore, findings suggest that girls in general are catching up. A closer look at the specific contexts and policy changes might reveal successful measures to counteract gender differences.

This paper adds to existing research into gender gaps in mathematics and science education over the last 20 years, and offers a new approach to gender gap analysis. Investigating the tails of the achievement distributions provide a more differentiated picture of potential gender differences. It thus extends findings of analysis comparing the mean achievement of female and male students. For example, Mullis et al. ( 2016 ) report a decrease of the number of countries without achievement differences between boys and girls in math of fourth grade students. However, our analysis revealed that there is quite some variation between high and low achieving students (cf. Tables  1 and 5 ). For the highest achieving 20% students, the number of countries showing gender differences is rather increasing. For the lowest achieving 20% students, most of the countries included in this analysis showing no gender differences (any longer). This example indicates that the approach can reveal more (detailed) information on gender differences and their changes over time.

Furthermore, rather than looking at groups of students reaching various benchmarks, we focused on the gender composition of the groups of students comprising the 20th highest and lowest achievement percentiles respectively, for each country. This approach overcomes the problem of only very small samples of students reaching the highest or the lowest benchmark in some countries.

Our research revealed trends in these gaps over 20 years of TIMSS, but it does not explain the mechanisms causing these gaps or any of the underlying factors. Further research is needed to understand these mechanisms better and refine implications and recommendations for policy. IEA contextual data is a valuable research resource to uncover such relations. Although this paper focused only on specific countries and cohorts, it may serve as a template for similar analyses of data from other countries and cohorts that have participated in TIMSS, PIRLS or similar large-scale assessments in education.

Finally, the approach of investigating the relative distribution of a characteristic at the tails of an ability distribution within a country or education system could be used for characteristics other than gender as well. As for gender as such a characteristic, we see that in education systems with several (hundreds of) thousands of students in a certain grade or within a certain age group, this characteristic is fairly equally distributed. That might be very different for other characteristics, for example, students’ family background, ethnicity or other student characteristics, for which inequality is perceived as an issue of concern. One of the advantages of the suggested approach is that it is robust in respect to non-equal distributions of such characteristics.

Further research needs

A secondary aim of this paper was to introduce and evaluate a specific approach to identifying (gender) differences in certain outcomes of education (mathematics and science achievement of fourth and eighth grade students). With this more detailed look at tails of ability distributions, the approach could provide information that is more specific and foster the interpretation and explanation of possible inequalities in education. With the analysis presented, trends over time within a set of countries could be identified and support for some common narratives on gender inequalities could be provided, whereas for other narratives we could not find support. In order to investigate possible correlates for changes in or persistence of inequalities, a more detailed look needs to include country, school and classroom contexts, as well as student characteristics. The TIMSS data provides this kind of information and can serve as a valuable source.

Availability of data and materials

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Acknowledgements

We thank Gillian Wilson and Mark Cockle for their thorough editorial review and Adeoye Oyekan for supporting with the analyses.

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SM developed the research questions and design, conducted parts of the statistical analysis and interpretation of results and drafted major parts of the manuscript. FB conducted major parts of the statistical analysis, drafted minor parts of the manuscript and critically revised all other parts of the manuscript. FB was responsible for data compilation, preparation of data analysis, and manuscript revision. All authors have given final approval of the manuscript version to be published 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. Both authors read and approved the final manuscript.

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Meinck, S., Brese, F. Trends in gender gaps: using 20 years of evidence from TIMSS. Large-scale Assess Educ 7 , 8 (2019). https://doi.org/10.1186/s40536-019-0076-3

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DOI : https://doi.org/10.1186/s40536-019-0076-3

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Accepted 2024 Feb 14; Collection date 2023.

This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Version Changes

Revised. amendments from version 2.

We have read very carefully the reviewer's comments, which we have responded to and additionally we have incorporated a limitations paragraph in the scientific article to comply with the reviewer's comments.

Despite significant advancements in closing the global gender gap, there is still much progress to be made, particularly in the field of science and scientific research. Numerous studies have addressed this issue and identified a variety of factors that contribute to gender asymmetries in research.

This study aimed to identify the determinants of gender gaps in scientific research present in the most cited studies of the past ten years as a first step towards closing these differences. Through a systematic literature review that incorporated the Proknow-C Knowledge Development Process and Constructivism methodology.

The results lead us to identify four dimensions to classify the determinants of the gaps in scientific research: academic supply, research policies, scientific production and researcher profile with their respective quantitative or qualitative indicators.

Conclusions

As a potential basis for further modeling that offers greater analytical and correlational depth, as well as the identification of targeted strategies aimed at reducing gender gaps in research.

Keywords: Gender gaps, discrimination, segregation, research

Introduction

As indicated by the Global Gender Gap Report 2022, it will take 132 years to close the global gender gap, statistics designed to measure gender equality and inequality such as income, political representation, wealth accumulation, tertiary education levels, stress levels ( World Economic Forum, 2022 ). In the case of Latin America and the Caribbean, based on the current rate of progress, the region will close the gap in 67 years ( Mujeres 360, 2022 ). Despite notable advancements in the region, some countries (Argentina, Brazil, and Mexico) appear to have stagnated, while others (Peru, Guyana, and Chile) are improving their gender parity outcomes. At the other end of the spectrum, countries such as Colombia, Honduras, Barbados, and Belize have widened the gender gap. These results contrast with the Sustainable Development Goals, which aim to reduce gender inequalities by 2030 ( United Nations, 2015 ) and hinder the increase in scientific productivity by 15% to 20% ( International Labor Organization, 2017 ; Science Metrix Inc., 2018 ), highlighting the impact that achievements in reducing inequality have on the global economy and closing social gaps.

In recent years, numerous studies have been conducted to investigate differences in scientific productivity ( Abramo et al ., 2013 ; López-Bassols et al ., 2018 ; Pinho-Gomes et al ., 2020 ) in various areas such as psychology ( Mayer & Rathmann, 2018 ), elite researchers ( Sá et al ., 2020 ), science and technology ( Pons et al ., 2013 ). Others have focused on identifying achievements and challenges ( Osorio, 2005 ), obstacles and barriers ( García-Jiménez & Herrero, 2022 ; Ramírez López & Bermúdez Urbina, 2015 ; Valenzuela et al ., 2022 ), to mention a few. The diversity of research interests exploring aspects of the problem related to the determinants and/or explanatory factors of gender gaps in scientific research leads us to consider it relevant to conduct a comprehensive literature review focused on gender differences in scientific research. The aim is to identify the determinants of gender gaps in scientific research as a first step towards closing these differences.

In order to conduct a relevant literature review, this research focused on a systematic review that incorporated the Proknow-C Knowledge Development Process and Constructivism methodology ( Ensslin et al ., 2013 ) to identify potential factors or determinants that make a difference in research for female researchers in the most relevant studies.

This research proposes classifying the determinants into four dimensions: i) Academic Offerings, ii) Research Policies, iii) Scientific Production, and iv) Researcher Profile. The proposed classification allows the recognition of each factor and the definition of indicators, whether quantitative or qualitative, that reflect the situation in the respective field. These indicators serve as a basis for subsequent modelling, offering greater analytical and correlational depth, as well as identifying strategies to address and reduce gender gaps in research.

This qualitative research, based on a documentary design, relies on a systematic review and bibliometric analysis, which enables the study of quantitative aspects of production, dissemination, and use of recorded information ( Araújo & Arencibia, 2002 ; Morales-Morejón & Cruz Paz, 1995 ). The Proknow-C methodology ( Ensslin et al ., 2010 ) is employed, consisting of three stages: Development of the Relevant Bibliographic Portfolio (RBP), bibliometric analysis and systemic analysis. The research proposes to classify the information into 4 factors: i) academic offer, ii) research policy, iii) scientific production and iv) research profile ( Rivera-Lozada et al. , 2022 ).

I) Development of the Relevant Bibliographic Portfolio (RBP). The Relevant Bibliographic Portfolio (RBP) refers to the result of the sampling conducted on the relevant scientific literature concerning the gender gap in research. For defining the databases, we established the axes and keywords for the search, as shown in Table 1 .

Table 1. Definition of Axes and Keywords.

The databases were selected to enable filtering with Boolean equations in English and/or Spanish, differentiating the type of publication (book or article) and the temporal horizon (2012-2023).

The verification of compliance with these requirements is presented in Table 2 .

Table 2. Selection of databases.

All documents that provided relevant information on the determinants of scientific research and the gender gaps in it were selected ( Table 2a ). The selected documentation was tabulated and classified according to objectives, methodology, variables, population, year of publication, results and conclusions. This classification served as a reference to identify the most pertinent, novel, curious or relevant documents that required special attention.

Table 2a. Bank articles according to the database.

Table 2b. total potential results of each database..

Once the mentioned filters were applied, the Gross Bank Articles (GBA) was defined, resulting in 636 articles. The GBA was subjected to an adherence test by calculating a sample with a 95% confidence level and a maximum error of 10% to verify that each article contains at least one of the established keywords ( Table 1 ).

The formula used is the following:

Where: n is the sample size of the GBA; Z is the parameter for the confidence level (1.96 for a confidence level of 95%); E is the allowable error (10%); p and q (50% each); and N = 636, being n = 83 elements of GBA (13%).

With these conditions, the sample size was determined to be 83 articles to be classified in descending order of citability, along with their main authors, as shown in Table 3 .

Table 3. Relevant Bibliographic Portfolio (RBP) and respective main authors.

After the review, it was confirmed that the 83 articles contain the defined keywords in the fields of keywords, title, and/or abstract, verifying adherence. The representation of the used databases in obtaining the RBP is shown in Figure 1 . It highlights that the Redalyc database contributes the highest number of articles to the RBP, accounting for 47% of the total. Base-search.net follows with 14%, while the remaining 39% of the RBP is derived from databases such as Web of Science , Scopus , Scielo , La referencia , DOAJ , Ebsco , Redalyc and Base-search.net .

Figure 1. Representativeness test of the RBP.

On the other hand, it is possible to evidence representativeness of RBP through citability of the articles. Table 4 shows the list of 21 articles of RBP which stand out due to a citability of 10 or higher. This number is determined to highlight the 25% of RBP.

Table 4. Table of highlighted articles.

Data processing.

Regarding the documentation selection process, it took the documents that met all the keywords proposed in the Boolean equation. The review, filtering of this documentation and analysis was done using the Mendeley literature manager Desktop version 1.19.8 and then exported the metadata of the publications obtained from the search were downloaded from the databases in Microsoft Excel format. For the underlying data, see Rivera-Lozada et al . (2023a) . The phases of definition of the protocol, search and extraction of the initial data from the databases were carried out by all the authors of this publication. The search results are current as of the second week of May. The subsequent filtering of the successive phases was carried out by peer review among the authors.

Figure 2 contains the diagram of the process flow carried out to obtain our RBP. In the first instance, duplicate articles and articles that despite carrying out the search determining the interval of years, did not comply with this, are eliminated. Subsequently, those that contain the keywords in the title or in the abstract but any of both shows they are not related to the subject of study are excluded, such as the article “Analysis of the world scientific production on forced sterilization of women with disabilities between 1997 and 2016” ( Concha & Ferrer, 2019 ), our Keyword: scientific production and women or the article “Women and aging in social research (1950-2018)” ( Torralbo & Guizardi, 2020 ), our keywords: women and research. Then we obtained reports sought for retrieval but we do not have access to 40 of these, like the “Chapter 4 Gender and Economics in Latin America: a Systematic Analysis of Scientific Production in Scopus” ( Maldonado & Quiñonez, 2021 ) and finally, the articles are organized from highest to lowest citation ability and the articles that are outside the sample of 83 articles that were determined with the aforementioned formula are excluded.

Figure 2. Temporal distribution of RBP articles.

Bibliometric analysis

Figure 3 shows the number of articles from the RBP published in the period 2012-2022, as well as the number of highlighted articles from the RBP based on their citability, published within this time interval. It can be observed that the year 2014 marked a turning point in the decline of publications, reaching its peak in 2016, which also had the highest number of highlighted articles. However, this pattern does not repeat when considering the lowest number of published articles. For the RBP, the year with the lowest number of publications is 2014, while in terms of highlighted articles, the year with the fewest was 2021, where not a single article surpassed 10 citations.

Figure 3. Specialized journals publishing topics related to the gender gap in research.

Figure 3 highlights the articles from the RBP according to the journals where they were published. The list includes 32 journals that published the respective articles from the RBP, ordered according to the Scimago Journal Rank (SJR) index, which measures the prestige of the journals based on the citation count received by each publication. Notable journals such as BMJ Global Health, Journal of Technology Transfer, and Journal of English for Academic Purposes stand out with SJR indexes of 2.37, 1.70, and 1.32, respectively. There is a significant difference of 0.67 points between the first and second-ranked journals. However, the subsequent journals show a smaller decrease in their scores, as seen in the difference of 0.38 points between the second and third-ranked journals. The journal with the lowest index is Biblios, with 0.10 points.

Based on these prominent journals, the coverage years of the SJR index for the top 10 journals were identified, as shown in Table 5 .

Table 5. Coverage years of the SJR index for the highlighted journals in the RBP.

Furthermore, the year of the first publication on the gender gap in research was determined for the highlighted journals in the RBP, as shown in Table 6 .

Table 6. Determination of the year when publications on the gender gap in research began.

Along with the highlighted journals, information about the prominent authors in the RBP was also collected, selecting authors with the highest citability within the RBP, as shown in Figure 4 . The figure compares the total number of publications of each author with the publications they have made related to the gender gap in research. Among these authors, Ana Pinho Gomes stands out with the highest number of total publications. Giovanni Abramo, Sanne Peters, and Ciriaco Andrea D’Angelo also have a percentage of their total publications related to the topic of study, with 1%, 3%, and 4% respectively. In contrast, authors such as Pessoa de Carvalho, Ana Guil Bozal, and Alejandra Montané have a smaller number of publications but a higher involvement in the subject at hand, with 14%, 31%, and 8% respectively.

Figure 4. Prominent authors.

It is possible to identify the geographical areas where the studies on the gender gap in research included in the RBP were conducted, as shown in Figure 5 .

Figure 5. Spatial Focus.

Geographical location of studies in the RBP.

Based on the information recorded in Figure 5 , the evidence indicates that the country with the highest number of studies related to the gender gap in research was Mexico (29%), significantly surpassing other territories. Spanish publications ranked second (12%), followed by Colombian (8%), Argentinean (7%), Brazilian (7%), European (6%), Global Studies (6%), publications without a specific territory (5%), United States (4%), Chile (2%), and to a lesser extent, Venezuela, Turkey, Peru, Norway, Italy, Hungary, Honduras, Ecuador, Costa Rica, Saudi Arabia, and Africa (1% each). This provides the study with global perspectives.

In addition to the spatial focus, the classification of the RBP was incorporated according to the methodological approach of the studies, indicating that 42% were qualitative and 58% were quantitative.

The review made it possible to classify the potential determinants of scientific research: geographical location, position, dedication time, type of call, teaching classification or hierarchy, age, dependency load, inclusive financing funds, sexist bias, gender stereotypes, discrimination, institutional determinants, biases of evaluation committees, income, glass ceiling, scientific recognition or status, salary, motivations, sticky floor, labor contract, marital status, professional segregation, visibility of scientific production, among others.

The systemic analysis of the RBP allowed us to categorize the barriers contributing to the gender gap in research into four dimensions with their respective indicators: i) Academic Offerings: number of higher education institutions total number of careers, female and male enrolment, science careers, female and male enrolment in sciences (natural and social) ii) Research Policies: agency, call for proposals, inclusive funding, evaluation committee, institutional policy, discrimination, recognition or incentive, types of hiring, iii) Scientific Production: research category, publications number, patents, collaboration, segregation, citability, and iv) Researcher Profile: motivations, sticky floor, income, position, geographic location, education level, age, gender, marital status, dependency load, scale, acknowledgment.

i) Academic offerings: Ten percent of the RBP focused on explaining or analyzing gender gaps in research based on considerations that incorporate variables such as the number of educational institutions, professional careers offered, total enrolment, STEM (Science, Technology, Engineering, and Mathematics) careers, and enrolment of women in natural sciences or STEM. In this regard, the results presented by López-Bassols et al . (2018) indicate that beyond acknowledging the existence of gender gaps in research, it is necessary to identify the areas or fields of knowledge where these gaps are most significant in order to correlate them with women’s participation and presence in higher education across various disciplines. Research conducted for Latin America and the Caribbean identifies family pressures, stereotypes, expectations, lack of mentors and role models, as well as vertical segregation and the “leaky pipeline” phenomenon as causal factors contributing to the existing differentials in women’s participation in research.

The authors propose considering three dimensions: actors within the national science and innovation system (higher education institutions, government, companies, NGOs), activities related to science and technology (teaching, research, publications, patents, funding, and innovative entrepreneurship), and obstacles or motivations (opportunities, attitudes, financial support, other incentives, role models, discrimination, and social biases). Based on the aforementioned, they establish a set of 16 indicators classified under higher education, careers in science and technology, scientific research, and innovation and innovative entrepreneurship.

On the other hand, the research conducted by García and Ortíz (2014) explores the individual and institutional determinants affecting gender gaps in scientific production in Ecuadorian universities. Using a linear regression model, they propose individual determinants that include academic degree, number of scholarship recipients, hours of dedication, age, and gender, as well as institutional determinants that incorporate the number of R&D projects, laboratories, executed budget for R&D, number of collaborators, among others. The research results indicate that institutional determinants have a significant and positive impact, while the number of PhDs still does not have a significant effect on scientific production. In particular, they find that women are less productive than men and that the age range with the highest productivity is between 30 and 39 years. Additionally, they found that the number of collaborators has a negative effect.

The UNESCO study on STEM education (2019) identifies four factors affecting gender gaps in research based on women’s participation in STEM education: individual factors (biological and psychological), family factors (parents’ beliefs, parents’ level of education, household socioeconomic status, and other characteristics), school factors (teaching staff, pedagogical strategies, teachers’ perceptions, interactions with students, textbooks, educational materials, curriculum, STEM equipment and resources, evaluation strategies and tools), and social factors (gender equality, social norms, policies and legislation, media and social communication).

The document debunks the belief in biological differences in the brains of men and women as an explanatory factor for their participation in STEM disciplines and research in this field. In this regard, neurological plasticity, understood as the brain’s ability to create new connections, is the essence of the learning process. The self-selection bias is the reason why girls and women decline STEM education, and this selection is influenced by stereotypes and androcentric biases acquired during upbringing and socialization moments. Similarly, social norms and stereotypes disseminated by the media have a significant impact on the internalization of gender roles, occupations, skills, and capabilities by girls and boys ( UNESCO, 2019 ).

Based on the literature review conducted, the indicators in the dimension of Academic Offerings are:. Based on our review of the literature, we identified the following indicators: number of higher education institutions, total number of offered careers, number of careers in science, total enrolment, total female enrolment, total male enrolment, enrolment of women and men in Natural Sciences, enrolment of male and female in Social Sciences, and enrolment of women and men in Health Sciences ( Carrillo & Florez, 2023 ; Del Valle et al ., 2012 ; Ortiz-Ortega & Sánchez, 2017 ).

ii) Research policies: Twenty-nine percent of the RBP disseminated results regarding the impact of research policies on gender gaps in research. Mexico is the country where the highest number of studies in this field has been conducted, and thus, the equity of gender in research was evaluated in 2012, 2013, and 2015 ( Cárdenas Tapia, 2015 ). With a database that provided gender-specific information, knowledge area, SNI (National System of Researchers) classification, and public universities, it was possible to demonstrate that the participation of female researchers in Mexico is lower than that of men, and they also have a lower level in the SNI in all categories. In more detail, women are not the majority in any of the seven knowledge areas proposed by the SNI. Despite this, from highest to lowest, women are found in 1) biology and chemistry, 2) humanities and behavioural sciences, 3) social and economic sciences, 4) physics, mathematics, and earth sciences, and 5) engineering.

The results confirm the scissor effect or pyramid effect regarding women’s participation in science, as the number of women decreases as their professional career progresses. This situation demands effective policies that encourage and ensure greater presence and participation of women in scientific research in Mexico.

On the other hand, the research by González and Álvarez (2016) aimed to determine the factors that influence the achievement of efficient research formulation in Mexico through a descriptive-correlational study. With a sample of 42 researchers, they identified the following factors influencing the achievement of research formulation: development of analytical thinking, efficient database searching, management of research projects, efficient use of software, data analysis and modelling, innovation, and proper time management.

Pons-Peregort et al. (2013) analyzed gender equality of opportunities in science and technology to understand the career paths of female scientists in Spain. Using a mixed methodology, they identified that equality of opportunities in internal promotion, salary disparities, and work-life balance are challenges that can be overcome with research policies. The low presence of women in scientific professional careers is based on the hegemony of masculine values, which calls for rethinking the achievement of the critical mass needed to bring about structural transformations in the research field, estimated to be 35% ( Langford et al ., 1995 ). The authors propose policies that support maternity, childcare services, tax deductions for women who stay at home to care for children because their absence perpetuates women’s inferior position in the labor market and keeps them away from the research field.

In this sense, the indicators that contribute to the definition of the dimension of research policies are entities proposing the policy (State, Ministries, Universities, NGOs), research calls, inclusive financing, composition of the evaluation committee, institutional university policy, incentives and/or recognition, organizational culture (discriminatory biases), types of contracts.

iii) Scientific production: Twenty-four percent of the RPB aims to study the gender gap in scientific production. Luna & Luna (2018) analyzed Mexico’s scientific production recorded in Web of Science from 1900 to 2000 in the fields of exact sciences and engineering to characterize the involvement of female researchers in these fields of study. As these areas have traditionally been associated with men, the research sought to highlight the breakthroughs and achievements of women, considering that they are not the majority in either field. The research used indicators in regard to gender, scientific production and impact, bibliometric and co-authorship network analysis.

The results showed that scientific production increased from the 1980s and 1990s, mainly due to increases in postgraduate studies, the consolidation of research groups, and an increase in national and international scientific collaborations ( Luna & Collazo, 2002 ). Scientific production is concentrated in five institutions and is led by UNAM, which has had female representation for 29 years. Additionally, the co-authorship network of research groups in the physical, chemical, mathematical, and engineering studies emerges in various specialties. The increase in scientific productivity is associated with the growth of female enrolment in higher education in natural sciences (47%) and engineering (25%) ( UNESCO, 2019 ), as well as the creation of new educational institutions since the 1960s and the emergence of women in traditionally male-dominated disciplines.

Continuing with Mexican research, Castro (2018) explores how a group of female researchers breaks paradigms and reshapes the line of women in scientific production. To achieve this, they incorporated qualitative-quantitative methods that included Participatory Action Research (PAR), Véster’s sensitive model, as well as validation indexes and indicators; monitoring and prospecting systems. This allowed them to conclude that women construct knowledge supported by a real and symbolic world.

García (2014) explores the situation of women in the research field in Mexico and discovers that difficulties arise in reconciling academic degrees with motherhood, publishing requires fulfilling the double workload, supervising degree projects requires time and mobility availability, lack of transparency in selection processes, and being evaluated by men negatively impact women’s research.

In Ecuador, Basurto and Ricaurte (2016) used a mixed study to examine the situation of women in the academic field of tourism. The results identified the low representation of women in teaching positions (53%) compared to the percentage of female students in tourism (75%), as well as male leadership in research from participating educational institutions, which are mostly organized by women. The explanatory factors found were difficulty reconciling research roles with motherhood, gender stereotypes, and the social perception of tourism as a feminine disciplinary field.

López and Farías (2022) propose a quantitative analysis of the temporal trajectories of gender parity in scientific publications in Colombia. The country ranks fifth in scientific productivity in Latin America and allocates 0.5% of its GDP to R+D ( MINCIENCIAS, 2020 ). English language proficiency is a disadvantage for Colombian researchers and is closely related to socioeconomic status. Similarly, gender stereotypes in the workplace negatively affect female researchers, leading the authors to assert that it is not simply about increasing the number of women in science and their scientific production, but rather reevaluating how science is done and valued in Colombia, which requires an inclusive and equitable ecosystem.

They highlight that the scientific areas with the highest number of female publications are medical sciences (37.76%), social sciences (35.51%), and natural sciences (29.09%).

Regarding gender gaps in research productivity and recognition among elite scientists in the United States, Canada, and South Africa, as studied by Sá et al . (2020) , it was found that women in science in these countries are under-cited, underpaid, underpromoted, and receive less professional recognition compared to their male counterparts, which puts women at a disadvantage when considering the principle of cumulative advantage, indicating that greater recognition leads to more productivity. They identified factors such as differences in family responsibilities, different patterns in the use of time (women spend more time teaching, advising students, and participating in committees), unequal allocation of resources, gender bias in peer review, gender stratification in disciplines, as well as different patterns in academic collaboration and network building as explanatory factors for the low productivity of female researchers.

In an Italian study conducted by Abramo et al . (2013) using a bibliometric approach, they sought to identify academics’ propensity for collaboration, leading them to conclude that women demonstrate a greater capacity to collaborate in all the analysed forms (intramural, extramural, national, and international), except in international collaboration, where the gap with male counterparts persists.

Among the explanatory factors for productivity gaps in research compared to men, they found that the low percentage of female academics, discrimination affecting job opportunities, biases, difficulty accessing funding, and limitations due to family responsibilities negatively affect female researchers.

Pinho-Gomes et al . (2020) investigate the authorship of women in COVID-19 research by asking “where are the women?” The study shows that women are underrepresented in research, particularly in first and last author positions. These gender biases point to broader inequalities that include authorship in other scientific areas and senior authorship.

Based on the aforementioned, the indicators in the scientific production factor are the following researcher category, number of publications, number of patents, collaborative authorship, type of publication (scientific article, popular article, book chapter, book, collections), segregation (female collaborations), and citability.

iv) Profile. Thirty-seven percent of the RBP presented research results addressing the characteristics, motivations, uniqueness, and distinctive traits of researchers. In this perspective, Prieto de Alizo (2008) provides a theoretical approach to the characteristics of researchers in the humanities field using a phenomenological, hermeneutic, and ethnographic approach with successive interviews until theoretical saturation of categories is reached. The study conducted in Venezuela defined researchers as individuals with an active interest in understanding, learning, deconstructing, and constructing their reality. Beyond defining the characteristics of the group of researchers, the research made it possible to address two other fundamental aspects: the researchers’ conception of conducting research and the context in which research is carried out. This includes aspects such as institutional identification, the academic space where they work, the research-teaching relationship, the appreciation of the profession, administrative strategies in terms of institutional and state policies, as well as quality criteria.

In Spain, Dapía et al . (2019) explored whether science has a gender association among primary school students. Using a gender perspective analysis, they administered the PANA instrument (Project on Attitudes Toward Science in Children and Adolescents) by Pérez and Pro (2005) and the ROSE questionnaire ( Schreiner and Sjøberg, 2004 ) to 378 students. The investigation showed a weak association between gender and science, a more positive attitude towards science among boys, and no gender bias in the desire to become a scientist. This led to the conclusion that there is a need to improve knowledge about the contributions of science and that career aspirations to become scientists in primary education are not associated with gender.

Research conducted in Mexico by Carrillo and Flores (2023) , aimed at describing the educational trajectory of women during their professional studies, surveyed 152 women scientists from the National System of Researchers (SNI) selected through non-probability sampling. The study aimed to identify obstacles, challenges, and experiences in the scientific field. Using descriptive quantitative methodology, they found that women face gender-related barriers such as invisibility, lack of recognition for scientific contributions, inequity, stereotypes associated with the care economy, dual burden of work, and difficulties in achieving work-life balance.

In the same vein, they identified variables associated with gender such as the choice of professional career, gender division of labour, sociocultural conditions, social valuation, stereotypes, and gender roles. Regarding the academic trajectory, the variables that influence it are discipline, postgraduate studies, periodic publications, difficulties in entering and advancing in the SNI (lower hierarchy, prolonged stagnation, institutional conditions, as well as symbolic and social gender aspects).

As a result, the main challenges faced by female researchers are related to bureaucracy, in terms of paperwork and procedures, the evaluation system based on quantity rather than quality, funding, lack of job security, competition in what they call the “wolf environment,” excessive workload due to lack of staff, and career advancement understood as continuous training and development processes.

Therefore, the selected indicators for the profile dimension are motivations, existence of a sticky floor, income level, position, geographic location, educational level, career, age, gender, marital status, dependent care burden, rank or category, and recognition.

It is important to mention that this study presented limitations in the number of articles included from databases such as Scopus and Wos, which can be explained by the selection of Boolean expressions used that privileged certain repositories for the search of information. However, this does not affect the value of the information found in this research.

Research focused on gender differences in scientific research has identified a diverse range of determinants or explanatory factors for these gaps. Through a systematic review using the Proknow-C Knowledge Development Process and Constructivism methodology, this research identified the most relevant studies and potential factors influencing scientific research for female researchers and academics.

The research facilitated the identification of relevant indicators grouped into four dimensions. The first dimension analysed was academic offerings, considering variables such as the number of educational institutions, offered professional careers, total enrolment, STEM careers, and enrolment of women in natural sciences or STEM. Additional indicators included total female enrolment, female enrolment in natural sciences, female enrolment in social sciences, and female enrolment in health sciences.

The second dimension examined was research policies. The research found that 29% of the Relevant Bibliographic Portfolio (RBP) presented results regarding the impact of research policies on gender gaps in research. Mexico stood out as the country with the highest number of studies in this field. Indicators considered in the analysed documents included policy-proposing organizations (government, ministries, universities, NGOs), research calls, inclusive funding, composition of evaluation committees, university institutional policies, incentives/recognition, organizational culture (discriminatory biases), and types of employment contracts.

The third dimension focused on scientific production. The research found that 24% of the RBP aimed to study the gender gap in research production. The selected indicators were researcher category, number of publications, number of patents, collaborative authorship, publication types (scientific articles, popular articles, book chapters, books, collections), gender segregation in collaborations, and citability.

The final dimension addressed the profile of researchers. Approximately 37% of the RBP included studies investigating characteristics, motivations, singularities, and distinctive traits of researchers. Qualitative and quantitative indicators were selected, including motivations, the existence of glass ceiling, income level, position, geographical location, educational level, field of study, age, gender, marital status, dependency burden, rank or category, and recognition.

Systematization processes in the literature review are usually processes whose complexity becomes more evident in the discussion of the results. In particular, because in this case the largest number of documents was concentrated in a single country, Mexico, a situation that leads to conceptual and, above all, contextual biases.

The proposed dimensions of analysis, along with the derived indicators from the conducted research, aim to contribute to the development of explanatory models for the determinants of gender differentials in research. Furthermore, this research aims to help the formulation of effective public policies that address and reduce these gender gaps.

Funding Statement

Universidad del Cauca Cross ref 501100005682 assigned to researchers Rivera-Lozada. Call Young Researchers and Innovators of the Department of Cauca, BPIN code No. 2020000100043 to Escobar Pérez.

The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

[version 3; peer review: 1 approved

Data availability

Underlying data.

Zenodo: Gender gaps in research: Literature review, https://doi.org/10.5281/zenodo.8206068 ( Rivera-Lozada et al ., 2023a ).

This project contains the following underling data:

METHODOLOGY.xlsx

Reporting guidelines

Zenodo: PRISMA checklist and flow diagram for ‘Gender gaps in research: a systematic review’, https://doi.org/10.5281/zenodo.8267629 ( Rivera-Lozada et al ., 2023b ).

Data are available under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0 International).

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Reviewer response for version 3

Salomon huancahuire-vega.

Competing interests: No competing interests were disclosed.

This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

This is a very interesting work that aims, through a systematic bibliographic analysis, to identify the determinants of the gender gap in scientific research. The work is well structured, written, and presented. I only have a few comments:

1. There is an apparent database selection bias (despite having included Scopus and WoS, there is an inclination towards regional databases that publish in Spanish and Portuguese, but databases with a different language have not been considered to English). Because of this, I suggest that this limitation of the study be mentioned.

2. Be careful with the range of years of the bibliographic analysis. In the methodology section, it says 2012 - 2023 but in the results they mention 2012 - 2022

3. Could you mention the exact Boolean equation? Table 1 has keywords but the equation is not shown.

4. I do not understand why to calculate the sample size if the complementary materials show that they have followed the Prisma methodology.

5. In the Methodology - data processing section, it says Figure 2 contains the diagram of the process flow carried out to obtain our RBP. However, Figure 2 doesn't look like a flowchart, why not use the Prism flowchart instead?

5. Correct in the results section the first paragraph says figure 3 but it seems to refer to figure 2.

6. In my opinion, tables 5 and 6 and figure 4 are not of major relevance to what this study proposes. The journal, its SJR, the researcher, do not have greater relevance in the identification of determinants of gender gap in research.

7. On page 12 it says: The systemic analysis of the RBP allowed us to categorize the barriers contributing to the gender gap in research into four dimensions with their respective indicators... I consider this to be the main result of the study, on which you should mention more details of how this determination was reached. All the previous results are analyzes of bibliometric aspects. The latter comes from a review of the content of the selected works, from which these 4 dimensions are determined.

8. There is no mention of a discussion section. I think the discussion has been going on since he begins to describe the 4 dimensions on page 12.

9. It would be good to mention the limitations of the study

Are the rationale for, and objectives of, the Systematic Review clearly stated?

Is the statistical analysis and its interpretation appropriate?

Not applicable

If this is a Living Systematic Review, is the ‘living’ method appropriate and is the search schedule clearly defined and justified? (‘Living Systematic Review’ or a variation of this term should be included in the title.)

Are sufficient details of the methods and analysis provided to allow replication by others?

Are the conclusions drawn adequately supported by the results presented in the review?

Reviewer Expertise:

public health

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

Yolvi Javier Ocaña Fernandez

I have read this latest version very carefully, and as I have commented since the first review, the article is very well structured from a methodological point of view, complying with all the characteristics that a systematic review article should have, the topic is very interesting and relevant. in the line of gender research, which is why I once again congratulate the authors for such an excellent work.

Education and invation

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

Lorenzo Escot

I have reviewed the three versions of the article with interest, care and dedicating the time necessary for its rigorous revision. But the authors' response to the problem of sample selection is clearly insufficient. They clearly recognized in their response that there is a Latin American bias to their systematic review, but they have not revised the article in line with this problem. The authors stated in their comments that this selection bias “was added in the limitations of the article, since incorporating more sample would involve changing results, discussion, conclusions, among others”.

They also recognize that “the results cannot be generalized to all countries although it makes a good reading of what happens in Latin America and definitely cannot be generalized to all times that are so changing and respond to situational contexts”.

The problem is that they recognize that all their results are biased in the referee response but not in the main text of the new version 3 of the paper.

A systematic review of Latin American literature is a very valuable exercise when it is made explicit that it is an analysis of Latin American literature, and it is not intended to be a systematic analysis of "all" literature and generalized to all countries. That is all.

The paper should make this selection bias problem explicit from the beginning, in the title, in the abstract, in the introduction, in the methodology, in the results - in fact I have not seen anywhere in the paper that they have made explicit reference to this problem. Only at the end of the conclusions is there a brief, insufficient, and not at all clear and explicit, reference to this problem (Is the inclusion of this sentence at the end of the conclusions the incorporation of the limitations referred to by the authors in their response?). This is not acceptable for an article that claims to be systematic, serious, rigorous and scientific.

In fact, the entire introduction and description of the methodology should be changed to make it explicit, and not hide the existence of this strong bias until the last sentence of the conclusions. This has been the response of the authors to my comments to version 2, to insert at the end of the conclusions a short, vague, and imprecise sentence about the problem of sample selection bias in their entire analysis. The problem of sample selection bias is not a conclusion to your work, it is a limitation of your entire analysis that should be acknowledged and made explicit from the outset.

In this sense, the title of the paper "Gender gaps in research: a systematic review" is misleading and should be changed to one that reflects the bias "Gender gaps in research: a Latin American literature systematic review", or other that explicitly recognizes the bias in the selection of the literature.

Also, it has to be explicit in the description of methodology (Methods, Data processing) and in Bibliometric analysis results, not only in the final sentence of the conclusion. Because otherwise, as it now appears at the end of the conclusions, it seems that it is something that authors do not want to recognize. As I have just said, a review of Latin American literature is a very valuable exercise when it is made explicit that it is an analysis of Latin American literature, and it is not intended to be a general and complete systematic analysis of "all" literature.

After these changes that clear acknowledge at the outset and throughout the title, the methodology and data description, the existence of this bias, then the article will be ready to be accepted for indexing.

So I have to maintain my decision of 'Approved with reservations', and I insist, because of the insufficient authors' response to solve the problem (or make explicit) the recognized problem of selection bias in their research.

This is just my opinion. And I am sorry for any inconvenience my opinion may cause to the authors. I understand the need for all of us in academia to publish in scientific journals. But I think it is my duty to try to be serious and rigorous when one accepts the review of scientific papers. Article review processes should be a dialogue and an exchange of ideas and opinions, in which the arbitration of an editorial team is always positive, or at least that is how I understand it. And in this sense, I am open to further discussion with the authors on the need or not to address the issue of sample selection in their article.

Applied Economics, Gender Economics

Reviewer response for version 2

I have been able to read the authors' response to my comments. I don't think they really answer my questions. 

For example, they have added table 2a with the queries, but they do not show the results from the main database they claim to use (Scopus). Maybe I am wrong, but I have not been able to identify this database in table 2a. Perhaps you could clarify this (I have run a Scopus query with "scientific publications" + "women" and got 502 potential papers). Authors states that "These repositories were filtered with the keywords shown in table 2a and it is precisely there where Redalyc presents the largest number of documents related to the keywords of interest for this research". I do not see Scopus in table 2a, I am sure I am missing something because I do not see any result both in Scopus or WoS in this Table2a.

Also, authors states:  "Perhaps another type of Boolean expressions in search offers repository participations different from those presented in this document. According to the above, we consider that there is no selection bias."

I think this statement is not accurate. In my opinion, the paper has a large sample selection bias precisely because of the type of queries selected and the type of databases selected. Then the systematic review is not representative of all the literature about gender gaps because its selection bias.

In any case, the authors do not answer my question "Do the authors think that their results could be generalized to all countries and to any time? Would the resulting papers selected change if other repositories had been used?"

This bias is important because the analysis of the results has mainly analysed Spanish-speaking authors, is this correct? are they the ones with the most citations, and what about English-speaking authors?

In any case, I think it is not the time to redo the analysis now. So, I maintain my congratulations to the authors and also my decision "approved with reservations".

ORIANA RIVERA LOZADA

Competing interests: We present no conflict of interest

Dear Reviewer

We apologize for not having answered the questions appropriately. Surely the selection of Boolean expressions and repositories privileged Spanish-speaking authors in consideration of the subjectivities that are implicit in the perception and situated knowledge that we as Latin American researchers may have. In this case we share the assertion that searches in Scopus and WOS repositories with Boolean expressions in English will yield a greater number of literature, which would lead to new results that demand other analyses.  Therefore, this was added in the limitations of the article, since incorporating more sample would involve changing results, discussion, conclusions, among others.

In spite of the above, we believe that the results cannot be generalized to all countries although it makes a good reading of what happens in Latin America and definitely cannot be generalized to all times that are so changing and respond to situational contexts.

Reviewer response for version 1

The research addresses a very interesting and relevant topic at this time as is the identification of the determinants of gender asymmetries in scientific research and provide ideas on how to close these gaps.

The dimensions they used to classify gender gaps: academic supply, research policies, scientific production and researcher profile, seem to me appropriate and based on the existing literature, so I congratulate the authors for their choice.  The tables and figures help to understand all the information in the article. The conclusions are clear, well-structured and in accordance with the research objectives. The bibliography is well selected and well used throughout the document.

Additionally, the document is well structured and well written. As a suggestion I propose:

1. Consider the inclusion of more articles from other databases, in order to avoid some biases, such as selection bias. It could also help to broaden the discussion of the research.

2.       Expand the introduction so that references from other countries can be included, so that this valuable information can be used in other countries and especially background information related to the dimension of the research.

Finally, I would like once again to congratulate the authors for their excellent work.

Competing interests: No conflict of interest

Consider the inclusion of more articles from other databases, in order to avoid some biases, such as selection bias. It could also help to broaden the discussion of the research.

The repositories used for the research were, in order: Scopus, Redalyc, La referencia, Base-Search.net, Web of Science, Scielo, DOAJ and Ebsco, considering that they met the criteria of fields Boolean expressions, temporal horizon and type of publication (Table 2). These repositories were filtered with the keywords shown in table 2a and it is precisely there where Redalyc presents the largest number of documents related to the keywords of interest for this research.

Perhaps another type of Boolean expressions in search offers repository participations different from those presented in this document. According to the above, we consider that there is no selection bias.

are included in the text

The paper provides a systematic review that aims to identify the determinants of gender asymmetries in scientific research and provide insights on how to close these differences.

The subject is of great interest and relevance. The study identified four dimensions to classify the determinants of gender gaps in scientific research: academic supply, research policies, scientific production, and researcher profile.

The document appears to be well structured and provides detailed information on how the systematic review has performed. 

1) My main drawback with respect to the systematic analysis of the literature is in the selected databases. There is a clear sample selection bias towards Latin American and Hispanic journals as a consequence of the inclusion of the Redalyc index, since almost 50% of the bibliographic references found come from this repository. 

Do the authors think that their results could be generalized to all countries and to any time?. Would the resulting papers selected change if other repositories had been used?

2) In some cases, the information on the characteristics of the sample of bibliographic references obtained does not allow relevant conclusions to be drawn. For example, Table 5 shows the Coverage years of the SJR index for the highlighted journals in the RBP. I understand that it is total coverage years in SJR, not the years used in the RBP. Is the information in this table really relevant? this information is only of the 5% of the RBP used?, are all the journals and all the papers included in SJR?

3) Section "iii) Scientific production" summarizes the main findings of a selection of articles. It is not clear what criteria were used to select the articles explained in this section. How were they selected?, are they the most cited articles? are those published in the highest impact journals? or is it a purely random selection?

It is not clear, therefore, how the factors explained in section iii) are select as the main factors behind the gender gap. Are these factor in scientific literature  supported by the quality of the journal?, or by the number of cites?, or whether this is simply a purely historical review?.

In any case, I would like to congratulate the authors for their excellent work.

My main drawback with respect to the systematic analysis of the literature is in the selected databases. There is a clear sample selection bias towards Latin American and Hispanic journals as a consequence of the inclusion of the Redalyc index, since almost 50% of the bibliographic references found come from this repository.

R/ The repositories used for the research were, in order: Scopus, Redalyc, La referencia, Base-Search.net, Web of Science, Scielo, DOAJ and Ebsco, considering that they met the criteria of fields Boolean expressions, temporal horizon and type of publication (Table 2). These repositories were filtered with the keywords shown in table 2a and it is precisely there where Redalyc presents the largest number of documents related to the keywords of interest for this research.

2) In some cases, the information on the characteristics of the sample of bibliographic references obtained does not allow relevant conclusions to be drawn. For example, Table 5 shows the Coverage years of the SJR index for the highlighted journals in the RBP. I understand that it is total coverage years in SJR, not the years used in the RBP. Is the information in this table really relevant? this information is only of the 5% of the RBP used?, are all the journals and all the papers included in SJR?

R/ Accordingly, the years shown in Table 5 refer to the period of JRS coverage for journals and we show this to highlight 10 of the journals of the PBR, the ones with the highest score, thus informing the reader of the time of measurement of these journals by the JRS index and, therefore, their prestige.

There are 10 journals that contain 1 PBR article each, so they represent 12.04% of the PBR used.

Regarding the last question, not all articles are included in JRS, 44.57% of the PBR is found in journals that are measured with the JRS index, 42.16% of the PBR are articles from journals that have not been measured with this international index, so they are not found in figure 3 or table 5 and 13.26% are articles from web pages.  Thesis, a book and a paper.

R/ The research proposes to classify the systematic review of literature into four classifications: i) academic offer, ii) research policy, iii) scientific production and iv) research profile. Scientific production grouped the documents that focus their attention on scientific production by profession or by female researchers. In this perspective, the research presented in this section was presented due to its thematic relevance and proposed contributions, as it was selected from the documents with the highest citation.  This was added to the paper

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Report | Wages, Incomes, and Wealth

“Women’s work” and the gender pay gap : How discrimination, societal norms, and other forces affect women’s occupational choices—and their pay

Report • By Jessica Schieder and Elise Gould • July 20, 2016

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What this report finds: Women are paid 79 cents for every dollar paid to men—despite the fact that over the last several decades millions more women have joined the workforce and made huge gains in their educational attainment. Too often it is assumed that this pay gap is not evidence of discrimination, but is instead a statistical artifact of failing to adjust for factors that could drive earnings differences between men and women. However, these factors—particularly occupational differences between women and men—are themselves often affected by gender bias. For example, by the time a woman earns her first dollar, her occupational choice is the culmination of years of education, guidance by mentors, expectations set by those who raised her, hiring practices of firms, and widespread norms and expectations about work–family balance held by employers, co-workers, and society. In other words, even though women disproportionately enter lower-paid, female-dominated occupations, this decision is shaped by discrimination, societal norms, and other forces beyond women’s control.

Why it matters, and how to fix it: The gender wage gap is real—and hurts women across the board by suppressing their earnings and making it harder to balance work and family. Serious attempts to understand the gender wage gap should not include shifting the blame to women for not earning more. Rather, these attempts should examine where our economy provides unequal opportunities for women at every point of their education, training, and career choices.

Introduction and key findings

Women are paid 79 cents for every dollar paid to men (Hegewisch and DuMonthier 2016). This is despite the fact that over the last several decades millions more women have joined the workforce and made huge gains in their educational attainment.

Critics of this widely cited statistic claim it is not solid evidence of economic discrimination against women because it is unadjusted for characteristics other than gender that can affect earnings, such as years of education, work experience, and location. Many of these skeptics contend that the gender wage gap is driven not by discrimination, but instead by voluntary choices made by men and women—particularly the choice of occupation in which they work. And occupational differences certainly do matter—occupation and industry account for about half of the overall gender wage gap (Blau and Kahn 2016).

To isolate the impact of overt gender discrimination—such as a woman being paid less than her male coworker for doing the exact same job—it is typical to adjust for such characteristics. But these adjusted statistics can radically understate the potential for gender discrimination to suppress women’s earnings. This is because gender discrimination does not occur only in employers’ pay-setting practices. It can happen at every stage leading to women’s labor market outcomes.

Take one key example: occupation of employment. While controlling for occupation does indeed reduce the measured gender wage gap, the sorting of genders into different occupations can itself be driven (at least in part) by discrimination. By the time a woman earns her first dollar, her occupational choice is the culmination of years of education, guidance by mentors, expectations set by those who raised her, hiring practices of firms, and widespread norms and expectations about work–family balance held by employers, co-workers, and society. In other words, even though women disproportionately enter lower-paid, female-dominated occupations, this decision is shaped by discrimination, societal norms, and other forces beyond women’s control.

This paper explains why gender occupational sorting is itself part of the discrimination women face, examines how this sorting is shaped by societal and economic forces, and explains that gender pay gaps are present even  within  occupations.

Key points include:

• Gender pay gaps within occupations persist, even after accounting for years of experience, hours worked, and education.
• Decisions women make about their occupation and career do not happen in a vacuum—they are also shaped by society.
• The long hours required by the highest-paid occupations can make it difficult for women to succeed, since women tend to shoulder the majority of family caretaking duties.
• Many professions dominated by women are low paid, and professions that have become female-dominated have become lower paid.

This report examines wages on an hourly basis. Technically, this is an adjusted gender wage gap measure. As opposed to weekly or annual earnings, hourly earnings ignore the fact that men work more hours on average throughout a week or year. Thus, the hourly gender wage gap is a bit smaller than the 79 percent figure cited earlier. This minor adjustment allows for a comparison of women’s and men’s wages without assuming that women, who still shoulder a disproportionate amount of responsibilities at home, would be able or willing to work as many hours as their male counterparts. Examining the hourly gender wage gap allows for a more thorough conversation about how many factors create the wage gap women experience when they cash their paychecks.

Within-occupation gender wage gaps are large—and persist after controlling for education and other factors

Those keen on downplaying the gender wage gap often claim women voluntarily choose lower pay by disproportionately going into stereotypically female professions or by seeking out lower-paid positions. But even when men and women work in the same occupation—whether as hairdressers, cosmetologists, nurses, teachers, computer engineers, mechanical engineers, or construction workers—men make more, on average, than women (CPS microdata 2011–2015).

As a thought experiment, imagine if women’s occupational distribution mirrored men’s. For example, if 2 percent of men are carpenters, suppose 2 percent of women become carpenters. What would this do to the wage gap? After controlling for differences in education and preferences for full-time work, Goldin (2014) finds that 32 percent of the gender pay gap would be closed.

However, leaving women in their current occupations and just closing the gaps between women and their male counterparts within occupations (e.g., if male and female civil engineers made the same per hour) would close 68 percent of the gap. This means examining why waiters and waitresses, for example, with the same education and work experience do not make the same amount per hour. To quote Goldin:

Another way to measure the effect of occupation is to ask what would happen to the aggregate gender gap if one equalized earnings by gender within each occupation or, instead, evened their proportions for each occupation. The answer is that equalizing earnings within each occupation matters far more than equalizing the proportions by each occupation. (Goldin 2014)

This phenomenon is not limited to low-skilled occupations, and women cannot educate themselves out of the gender wage gap (at least in terms of broad formal credentials). Indeed, women’s educational attainment outpaces men’s; 37.0 percent of women have a college or advanced degree, as compared with 32.5 percent of men (CPS ORG 2015). Furthermore, women earn less per hour at every education level, on average. As shown in Figure A , men with a college degree make more per hour than women with an advanced degree. Likewise, men with a high school degree make more per hour than women who attended college but did not graduate. Even straight out of college, women make $4 less per hour than men—a gap that has grown since 2000 (Kroeger, Cooke, and Gould 2016).

Women earn less than men at every education level : Average hourly wages, by gender and education, 2015

The data below can be saved or copied directly into Excel.

The data underlying the figure.

Source :  EPI analysis of Current Population Survey Outgoing Rotation Group microdata

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Steering women to certain educational and professional career paths—as well as outright discrimination—can lead to different occupational outcomes

The gender pay gap is driven at least in part by the cumulative impact of many instances over the course of women’s lives when they are treated differently than their male peers. Girls can be steered toward gender-normative careers from a very early age. At a time when parental influence is key, parents are often more likely to expect their sons, rather than their daughters, to work in science, technology, engineering, or mathematics (STEM) fields, even when their daughters perform at the same level in mathematics (OECD 2015).

Expectations can become a self-fulfilling prophecy. A 2005 study found third-grade girls rated their math competency scores much lower than boys’, even when these girls’ performance did not lag behind that of their male counterparts (Herbert and Stipek 2005). Similarly, in states where people were more likely to say that “women [are] better suited for home” and “math is for boys,” girls were more likely to have lower math scores and higher reading scores (Pope and Sydnor 2010). While this only establishes a correlation, there is no reason to believe gender aptitude in reading and math would otherwise be related to geography. Parental expectations can impact performance by influencing their children’s self-confidence because self-confidence is associated with higher test scores (OECD 2015).

By the time young women graduate from high school and enter college, they already evaluate their career opportunities differently than young men do. Figure B shows college freshmen’s intended majors by gender. While women have increasingly gone into medical school and continue to dominate the nursing field, women are significantly less likely to arrive at college interested in engineering, computer science, or physics, as compared with their male counterparts.

Women arrive at college less interested in STEM fields as compared with their male counterparts : Intent of first-year college students to major in select STEM fields, by gender, 2014

Source:  EPI adaptation of Corbett and Hill (2015) analysis of Eagan et al. (2014)

These decisions to allow doors to lucrative job opportunities to close do not take place in a vacuum. Many factors might make it difficult for a young woman to see herself working in computer science or a similarly remunerative field. A particularly depressing example is the well-publicized evidence of sexism in the tech industry (Hewlett et al. 2008). Unfortunately, tech isn’t the only STEM field with this problem.

Young women may be discouraged from certain career paths because of industry culture. Even for women who go against the grain and pursue STEM careers, if employers in the industry foster an environment hostile to women’s participation, the share of women in these occupations will be limited. One 2008 study found that “52 percent of highly qualified females working for SET [science, technology, and engineering] companies quit their jobs, driven out by hostile work environments and extreme job pressures” (Hewlett et al. 2008). Extreme job pressures are defined as working more than 100 hours per week, needing to be available 24/7, working with or managing colleagues in multiple time zones, and feeling pressure to put in extensive face time (Hewlett et al. 2008). As compared with men, more than twice as many women engage in housework on a daily basis, and women spend twice as much time caring for other household members (BLS 2015). Because of these cultural norms, women are less likely to be able to handle these extreme work pressures. In addition, 63 percent of women in SET workplaces experience sexual harassment (Hewlett et al. 2008). To make matters worse, 51 percent abandon their SET training when they quit their job. All of these factors play a role in steering women away from highly paid occupations, particularly in STEM fields.

The long hours required for some of the highest-paid occupations are incompatible with historically gendered family responsibilities

Those seeking to downplay the gender wage gap often suggest that women who work hard enough and reach the apex of their field will see the full fruits of their labor. In reality, however, the gender wage gap is wider for those with higher earnings. Women in the top 95th percentile of the wage distribution experience a much larger gender pay gap than lower-paid women.

Again, this large gender pay gap between the highest earners is partially driven by gender bias. Harvard economist Claudia Goldin (2014) posits that high-wage firms have adopted pay-setting practices that disproportionately reward individuals who work very long and very particular hours. This means that even if men and women are equally productive per hour, individuals—disproportionately men—who are more likely to work excessive hours and be available at particular off-hours are paid more highly (Hersch and Stratton 2002; Goldin 2014; Landers, Rebitzer, and Taylor 1996).

It is clear why this disadvantages women. Social norms and expectations exert pressure on women to bear a disproportionate share of domestic work—particularly caring for children and elderly parents. This can make it particularly difficult for them (relative to their male peers) to be available at the drop of a hat on a Sunday evening after working a 60-hour week. To the extent that availability to work long and particular hours makes the difference between getting a promotion or seeing one’s career stagnate, women are disadvantaged.

And this disadvantage is reinforced in a vicious circle. Imagine a household where both members of a male–female couple have similarly demanding jobs. One partner’s career is likely to be prioritized if a grandparent is hospitalized or a child’s babysitter is sick. If the past history of employer pay-setting practices that disadvantage women has led to an already-existing gender wage gap for this couple, it can be seen as “rational” for this couple to prioritize the male’s career. This perpetuates the expectation that it always makes sense for women to shoulder the majority of domestic work, and further exacerbates the gender wage gap.

Female-dominated professions pay less, but it’s a chicken-and-egg phenomenon

Many women do go into low-paying female-dominated industries. Home health aides, for example, are much more likely to be women. But research suggests that women are making a logical choice, given existing constraints . This is because they will likely not see a significant pay boost if they try to buck convention and enter male-dominated occupations. Exceptions certainly exist, particularly in the civil service or in unionized workplaces (Anderson, Hegewisch, and Hayes 2015). However, if women in female-dominated occupations were to go into male-dominated occupations, they would often have similar or lower expected wages as compared with their female counterparts in female-dominated occupations (Pitts 2002). Thus, many women going into female-dominated occupations are actually situating themselves to earn higher wages. These choices thereby maximize their wages (Pitts 2002). This holds true for all categories of women except for the most educated, who are more likely to earn more in a male profession than a female profession. There is also evidence that if it becomes more lucrative for women to move into male-dominated professions, women will do exactly this (Pitts 2002). In short, occupational choice is heavily influenced by existing constraints based on gender and pay-setting across occupations.

To make matters worse, when women increasingly enter a field, the average pay in that field tends to decline, relative to other fields. Levanon, England, and Allison (2009) found that when more women entered an industry, the relative pay of that industry 10 years later was lower. Specifically, they found evidence of devaluation—meaning the proportion of women in an occupation impacts the pay for that industry because work done by women is devalued.

Computer programming is an example of a field that has shifted from being a very mixed profession, often associated with secretarial work in the past, to being a lucrative, male-dominated profession (Miller 2016; Oldenziel 1999). While computer programming has evolved into a more technically demanding occupation in recent decades, there is no skills-based reason why the field needed to become such a male-dominated profession. When men flooded the field, pay went up. In contrast, when women became park rangers, pay in that field went down (Miller 2016).

Further compounding this problem is that many professions where pay is set too low by market forces, but which clearly provide enormous social benefits when done well, are female-dominated. Key examples range from home health workers who care for seniors, to teachers and child care workers who educate today’s children. If closing gender pay differences can help boost pay and professionalism in these key sectors, it would be a huge win for the economy and society.

The gender wage gap is real—and hurts women across the board. Too often it is assumed that this gap is not evidence of discrimination, but is instead a statistical artifact of failing to adjust for factors that could drive earnings differences between men and women. However, these factors—particularly occupational differences between women and men—are themselves affected by gender bias. Serious attempts to understand the gender wage gap should not include shifting the blame to women for not earning more. Rather, these attempts should examine where our economy provides unequal opportunities for women at every point of their education, training, and career choices.

— This paper was made possible by a grant from the Peter G. Peterson Foundation. The statements made and views expressed are solely the responsibility of the authors.

— The authors wish to thank Josh Bivens, Barbara Gault, and Heidi Hartman for their helpful comments.

About the authors

Jessica Schieder joined EPI in 2015. As a research assistant, she supports the research of EPI’s economists on topics such as the labor market, wage trends, executive compensation, and inequality. Prior to joining EPI, Jessica worked at the Center for Effective Government (formerly OMB Watch) as a revenue and spending policies analyst, where she examined how budget and tax policy decisions impact working families. She holds a bachelor’s degree in international political economy from Georgetown University.

Elise Gould , senior economist, joined EPI in 2003. Her research areas include wages, poverty, economic mobility, and health care. She is a co-author of The State of Working America, 12th Edition . In the past, she has authored a chapter on health in The State of Working America 2008/09; co-authored a book on health insurance coverage in retirement; published in venues such as The Chronicle of Higher Education ,  Challenge Magazine , and Tax Notes; and written for academic journals including Health Economics , Health Affairs, Journal of Aging and Social Policy, Risk Management & Insurance Review, Environmental Health Perspectives , and International Journal of Health Services . She holds a master’s in public affairs from the University of Texas at Austin and a Ph.D. in economics from the University of Wisconsin at Madison.

Anderson, Julie, Ariane Hegewisch, and Jeff Hayes 2015. The Union Advantage for Women . Institute for Women’s Policy Research.

Blau, Francine D., and Lawrence M. Kahn 2016. The Gender Wage Gap: Extent, Trends, and Explanations . National Bureau of Economic Research, Working Paper No. 21913.

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Corbett, Christianne, and Catherine Hill. 2015. Solving the Equation: The Variables for Women’s Success in Engineering and Computing . American Association of University Women (AAUW).

Current Population Survey Outgoing Rotation Group microdata (CPS ORG). 2011–2015. Survey conducted by the Bureau of the Census for the Bureau of Labor Statistics [ machine-readable microdata file ]. U.S. Census Bureau.

Goldin, Claudia. 2014. “ A Grand Gender Convergence: Its Last Chapter .” American Economic Review, vol. 104, no. 4, 1091–1119.

Hegewisch, Ariane, and Asha DuMonthier. 2016. The Gender Wage Gap: 2015; Earnings Differences by Race and Ethnicity . Institute for Women’s Policy Research.

Herbert, Jennifer, and Deborah Stipek. 2005. “The Emergence of Gender Difference in Children’s Perceptions of Their Academic Competence.” Journal of Applied Developmental Psychology , vol. 26, no. 3, 276–295.

Hersch, Joni, and Leslie S. Stratton. 2002. “ Housework and Wages .” The Journal of Human Resources , vol. 37, no. 1, 217–229.

Hewlett, Sylvia Ann, Carolyn Buck Luce, Lisa J. Servon, Laura Sherbin, Peggy Shiller, Eytan Sosnovich, and Karen Sumberg. 2008. The Athena Factor: Reversing the Brain Drain in Science, Engineering, and Technology . Harvard Business Review.

Kroeger, Teresa, Tanyell Cooke, and Elise Gould. 2016.  The Class of 2016: The Labor Market Is Still Far from Ideal for Young Graduates . Economic Policy Institute.

Landers, Renee M., James B. Rebitzer, and Lowell J. Taylor. 1996. “ Rat Race Redux: Adverse Selection in the Determination of Work Hours in Law Firms .” American Economic Review , vol. 86, no. 3, 329–348.

Levanon, Asaf, Paula England, and Paul Allison. 2009. “Occupational Feminization and Pay: Assessing Causal Dynamics Using 1950-2000 U.S. Census Data.” Social Forces, vol. 88, no. 2, 865–892.

Miller, Claire Cain. 2016. “As Women Take Over a Male-Dominated Field, the Pay Drops.” New York Times , March 18.

Oldenziel, Ruth. 1999. Making Technology Masculine: Men, Women, and Modern Machines in America, 1870-1945 . Amsterdam: Amsterdam University Press.

Organisation for Economic Co-operation and Development (OECD). 2015. The ABC of Gender Equality in Education: Aptitude, Behavior, Confidence .

Pitts, Melissa M. 2002. Why Choose Women’s Work If It Pays Less? A Structural Model of Occupational Choice. Federal Reserve Bank of Atlanta, Working Paper 2002-30.

Pope, Devin G., and Justin R. Sydnor. 2010. “ Geographic Variation in the Gender Differences in Test Scores .” Journal of Economic Perspectives , vol. 24, no. 2, 95–108.

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