gender economics research topics

Department of Economics

College of social & behavioral science, main navigation, research in gender.

Gender inequalities have been persistent and pervasive in all economies. Until recently, the importance of gender inequalities was not well-appreciated in economic theory or policies. Today, gender-equitable approaches to economic policy-making are becoming increasingly common. In 2015, with the adoption of Sustainable Development Goals, the United Nations declared achieving gender equality as a central goal in economic development outcomes to be achieved by 2030.

Commitment to Gender Equality

We are one of a handful of Ph.D.-granting economics departments in the world with a core strength in feminist economics. We use gender as a central category of analysis alongside, class and race.

TOPICS OF RESEARCH

Our feminist economics research focuses on theory, empirical work and economic policy, addressing the causes and consequences of gender inequalities in economic life and development of economic policies that aim to eradicate gender inequalities. Topics include

• Gender and macroeconomics
• Gender and international trade
• Gender differences in income and time poverty
• The interface of paid and unpaid work
• Gender differences in labor market behaviors and outcomes
• Causes and consequences of violence against women
• Care policies
• Role of institutions in promoting gender equality

COURSE WORK THROUGH A GENDER LENS

Since 1995 we have offered undergraduate and graduate students the opportunity to study economics through a gender lens.

Undergraduate and Master's Level

• ECON 5170/6170
• ECON 5560/6560/GNDR 5560 
• ECON 2040/GNDR 2040

Ph.D. Field Courses

• ECON 7150
• ECON 7180

Faculty Focusing on Gender Equality

We would love to hear from you! Our faculty would be happy to share more about their current projects.

Diksha Arora

Diksha Arora's current research focuses on the relationship between climate change, macro policy and gender disparities in access to decent work in Latin America. She has also conducted research examining gendered time poverty and its impact on household income and resilience to climate change, and gender-based constraints to adoption of climate-smart practices. 

Besides academic research, she has worked with local- and national-level policymakers in several Latin American countries to promote uptake of gender considerations in their local development programs and climate-change adaptation policies. She has also worked with several international organizations such as the World Bank, FAO and CGIAR. Most recently, her work with World Bank and Canada Caribbean Resilience Fund helped promote gender mainstreaming in disaster risk management policies in Caribbean countries.

Günseli Berik

Günseli Berik ’s research examines gender inequalities in livelihood and well-being outcomes—earnings, working conditions, training, population sex ratios, time use in the household. Her empirical research has focused on Turkey, Taiwan, Korea, Bangladesh, the US, and Utah. In recent research she examines aggregate well-being measures that incorporate unpaid work and the environment; street harassment in South Asia; and the feminist project in economics. She served as editor of the journal  Feminist Economics  between 2010 and 2017. 

Haimanti Bhattacharya

Haimanti Bhattacharya's research falls under the broad rubric of applied microeconomics and has three specific themes: environment, resource & food, psychology & economics, and gender. Her gender research examines different aspects of violence against women, including the relationship between engaging in paid work and spousal violence. She has also engaged with interdisciplinary research teams to examine perceptions and implications of sexual violence against women. The main geographic focus of her research is India.

Pavitra Govindan

Pavitra Govindan is a behavioral and experimental economist specializing in the topics of social norms and behavioral change, gender differences in self-promotion, and role of institutions in promoting gender diversity.  She has conducted lab experiments with university students, lab-in-the-field experiments in rural India, and online experiments on Qualtrics and Amazon Mechanical Turk. She has been a faculty member in the Economics department at the University of Utah since 2018.

Eunice Han is a labor economist, specializing in labor relations and educational policy. Her research focuses on workers’ well-being and inequality. Because the goals of labor unions are aligned with these topics, many of her studies examine the relationship between unions and labor market outcomes in both the private and public sectors. In particular, she is interested in understanding gender differences in employment, labor earnings, and other labor market conditions, as well as identifying tools to close the gender gap.

Codrina Rada

Codrina Rada is a macroeconomist with an interest in issues of growth and income distribution. She uses theoretical and empirical tools to study trends in income inequality in modern economies and the effect of rising inequality on economic activity within the context of global economic integration. Her gender research uses computable general equilibrium (CGE) framework to examine the impacts of gender inequality on food security in Mozambique and Ethiopia.

Catherine Ruetschlin

Catherine Ruetschlin studies labor market inequalities and public policy. Her current research is focused on markets for childcare and the labor market for childcare workers. In 2021 and 2022, she worked with Utah’s Department of Workforce Services Office of Child Care to evaluate access to and affordability of childcare services across the state. She also contributed to a forthcoming interdisciplinary study with the US Department of Veterans Affairs examining the labor market challenges facing female veterans. Catherine has taught at the University of Utah since 2018.  

Sarah Small

Sarah Small’s research falls under the broad umbrella of feminist economics. Her current research focuses on a variety of topics including intrahousehold bargaining, care work, the occupational crowding hypothesis, and history of feminist economic thought. Much of her work aims to understand how households allocate unpaid labor in the United States, especially within couples facing differences in income, union membership, and business ownership. She also studies how economics courses can be made more inviting to women. Before joining the University of Utah in 2022, she was a Feminist Economics Fellow and a postdoctoral researcher at the Center for Women and Work at Rutgers University. 

Current PhD Student Research

Latest department publications.

Utah 2021 Child Care Market Rate Study

Author: Catherine Ruetschlin and Yazgi Genc

Published: Utah Department of Workforce Services Office of Child Care

The Routledge Handbook of Feminist Economics

Author: Günseli Berik and Ebru Kongar (PhD, 2003, U of Utah)

Published: Routledge International Handbooks , 2021

Gender norms and intra-household allocation of labor in Mozambique: a CGE application to household  and agricultural economics

Authors:  Diksha Arora  and Codrina Rada

Published: Agricultural Economics, 2020

The Effects of Teachers' Unions on the Gender Pay Gap among US Public School Teachers

Authors: Eunice Han

Published: Industrial Relations A Journal of Economy and Society,2020

What is Eve Teasing? A Mixed Methods Study of Sexual Harassment of Young Women in the Rural Indian Context

Authors:  Haimanti Bhattacharya with Sharon Talboys, Manmeet Kaur, Jim VanDerslice, Lisa Gren, and Steve Alder

Published:  Sage Open , 2017

A Gendered Model of the Peasant Household: Time Poverty and Farm Production in Rural Mozambique

Authors:  Diksha Arora  (Ph.D. 2016, U of Utah) and Codrina Rada

Published:  Feminist Economics , 2017

Rape Myth Acceptance among College Students in the United States, Japan and India

Author: Haimanti Bhattacharya et. al.

Published: Sage Open, 2016

Spousal Violence and Women's Employment in India 

Author: Haimanti Bhattacharya

Published: Feminist Economics , 2015

Utah's Labor Market for Child Care Professionals

Author: Catherine Ruetschlin

Forthcoming: Utah Department of Workforce Services Office of Child Care

The Cost of Quality Childcare in Utah

The gender gap in labor market self-promotion: discrimination, beliefs, and norms: An experiment 

Author: Pavitra Govindan

Forthcoming: Working Paper

Do Meritocracies Increase Females Selecting Into Male-dominated Environments?

Selected Recent Publications from Alumni

Adem elveren.

Ph.D. 2008, Associate Professor at Fitchburg State University

Militarization and Gender Inequality: Exploring the Impact  

Co-author: Valentine M. Moghadam

Journal of Women, Politics & Policy, 2022

Ph.D. 2010, Assistant Professor, Fitchburg State University

Expanding Understanding of Poverty: Time Poverty Revealed Time-Use Data

Harnessing Time-Use Data for Evidence-based Policy, the 2030 Agenda for Sustainable Development and the Beijing Platform for Action: A Resource for Data Analysis , United Nations ESCAP, 2021

Nursel Aydiner-Avsar

Ph.D. 2011, Associate professor  at  Akdeniz University

The Gender Impact of Unemployment on Mental Health: A Micro Analysis for the United States

Forum for Social Economics, 2021

  Chiara Piovani  

Ph.D. 2011, Associate Professor, University of Denver

Gender and Development Programme

Work Time Matters for Mental Health: A Gender Analysis of Paid and Unpaid Labor

Review of Radical Polical Economics, 2021

Ph.D. 2016, PostDoctoral Fellow, Colorado State University

Gender norms and intrahousehold allocation of labor in Mozambique: A CGE application to household and agricultural economics

Agricultural Economics, 2020

Jacqueline Strenio

Ph.D. 2018, Assistant Professor of Economics at Norwich University

Time Heals All Wounds? A Capabilities Approach for Intimate Partner Violence

Feminist Economics, 2020

Emel Memis  

Ph.D. 2007, Associate Professor, Ankara University

“Changes in Global Trade Patterns and Women’s Employment in Manufacturing, 1995-2011”

Feminist Economics, 2018

Ph.D. 2011, Assistant Professor, Istanbul University

Engendering Welfare States: How Fa(i)r are Scandinavian Welfare States

Journal of Economic and Social Thought, 2017

Ph.D. 2011, Associate Professor at University of Wisconsin, Whitewater

“Gender Empowerment and Educational Attainment of US Immigrants and their Home-Country Counterparts”

Feminist Economics, 2017

Ebru Kongar

Ph.D. 2003, Associate Professor at Dickinson College

Gender and Time Use in a Global Context

Co-Author: Rachel Connelly

Palgrave, 2017

Gender distribution across topics in the top five economics journals: a machine learning approach

• Original Article
• Open access
• Published: 25 November 2021
• Volume 13 , pages 269–308, ( 2022 )

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• J. Ignacio Conde-Ruiz 1 , 3 ,
• Juan-José Ganuza 2 ,
• Manu García 4 &
• Luis A. Puch 3  

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We analyze text data in all the articles published in the top five (T5) economics journals between 2002 and 2019 in order to find gender differences in their research approach. We implement an unsupervised machine learning algorithm: the structural topic model (STM), so as to incorporate gender document-level meta-data into a probabilistic text model. This algorithm characterizes jointly the set of latent topics that best fits our data (the set of abstracts) and how the documents/abstracts are allocated to each topic. Latent topics are mixtures over words where each word has a probability of belonging to a topic after controlling by journal name and publication year (the meta-data). Thus, the topics may capture research fields but also other more subtle characteristics related to the way in which the articles are written. We find that females are unevenly distributed over the estimated latent topics. This and other findings rely on “automatically” generated built-in data given the contents in the abstracts of the articles in the T5 journals, without any arbitrary allocation of texts to particular categories (as JEL codes, or research areas).

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1 Introduction

Despite the efforts undertaken for the whole economic profession to fight against discrimination, women are underrepresented in academia. Lundberg and Stearns ( 2019 ) make an assessment of the presence of female economists in the profession, and they report a very slow improvement in the last two decades. The picture is as follows. In the beginning of this century, 35% percent of PhD students and 30% of assistant professors were female. Since then, these numbers have not increased. Footnote 1 Additionally, Siniscalchi and Veronesi ( 2020 ) summarizing Chevalier ( 2020 ) (Report of the Committee on the Status of Women in the Economics Profession) point out that the proportion of women assistant professors in the “top 10” schools has declined to less than 20% by 2019. They document also that female have been less successful in promoting to tenured associate or full professors.

In economics, the tenure path often requires to publish in the top five (Top 5, or just T5) journals, namely American Economic Review ( AER ), Econometrica ( ECA ), Journal of Political Economy ( JPE ), Quarterly Journal of Economics ( QJE ) and Review of Economic Studies ( REStud ). Heckman and Moktan ( 2020 ) analyze the tenure decisions of the top 35 Economics departments in the USA, and they conclude that T5 publications are a very powerful explanatory variable of the promotion to tenure. Publishing in a T5 is becoming the main goal of young professors in economics because their professional career may depend on succeeding on this target. In addition, the content published in these journals is also determining the path of research in economics. As a consequence of these facts, the competition to publish in any of these journals has increased in recent years. Card and DellaVigna ( 2013 ) analyze the publication records in the Top 5 from 1970 to 2012 showing that the acceptance rate has fallen from 15% (1970) to 6% (2012). They explain this fact as a combination of the increasing number of submissions and a declining number of published papers. Card et al. ( 2019 ) further analyze the publication records from two of the T5 journals (the QJE and REStud ), together with the Journal of European Economic Association and the Review of Economics and Statistics . They report that the current proportion of accepted papers is 3%. Is the T5 entry barrier harder for women? The answer provided by Card et al. ( 2019 ) to this question is ambiguous. On the one hand, these authors do not find any gender biases in the refereeing process, and editors decisions are gender-neutral conditional on the referee advises. On the other hand, they find that conditional on referee process, female authored papers end up accumulating more citations in later years. Footnote 2 A potential explanation for this second result is that journals hold female-authored papers to higher standards. Hengel ( 2020 ) uses readability scores and finds that female-authored papers are better written and improve during peer review and as they publish more papers. These results could be related to some “horizontal” features or characteristics of female-authored papers that lead to more citations or better writing standards, but not to higher acceptance rates in the editorial process. As Card et al. ( 2019 ) control by research fields (JEL codes), their results may be linked to more subtle horizontal differences. For instance, in the same research field, males may choose a more theoretical approach and females a more applied perspective (which tends to be more cited or subject to less complicated wording), leading to particular career outcomes. Footnote 3

Several papers have pointed out persistent gender differences in the choice of research fields in economics. Dolado et al. ( 2012 ) analyze the gender distribution of research fields in the top-50 economics departments in 2005, and show that women are unevenly distributed across fields. Similarly, Chari and Goldsmith-Pinkham ( 2017 ) use data from submissions to the National Bureau of Economic Research Summer Institute (2001–2016) and show that the distribution of female researchers is not uniform across fields. From these, we learnt that women are particularly underrepresented in macro, finance and economic theory, and more prevalent in labor or applied microeconomics fields. Beneito et al. ( 2021 ) find similar results using data from the annual AEA meetings from 2010–2016, while Lundberg and Stearns ( 2019 ) focus on PhD dissertations in Economics from 1991–2017, in almost all major PhD-granting departments in the USA. Using the JEL code for research areas, they find that women are more prone to study topics in Labor and Public Economics than in Macro and Finance. They also show that this pattern has not changed over time.

We want to contribute to this literature in two directions. First, we focus on exploring the gender horizontal distribution across research topics in the leading economics journals. We do so by using a new methodological approach based on machine learning techniques. This classifies our abstracts’ database into latent topics . We collect all the articles published in T5 journals for the period 2002–2019. We obtain 5311 articles, and we keep track for each article of the authors’ names, year of publication, journal and the abstract. With this information, we can provide a very accurate picture of the performance of men and women publishing record in these leading journals. Our primary objective is to describe what these latent topics are and the gender distribution across them. Notice this is a very particular sample of researchers though.

Second, from the universe of algorithms for topic modeling we implement and develop the structural topic model (STM) developed by Roberts et al. ( 2019 ). This choice is because the algorithm allows to incorporate document-level meta-data into a probabilistic text model. Precisely, we keep track of journal names and publication years as covariates to improve the estimation of the prevalence of topics in our data. Our abstracts come from different sources and different periods of time, so it is natural to allow this meta-data to affect the frequency with which a topic appears. The output of the algorithm is a stochastic model that generates latent topics and allocate the documents to them in a probabilistic way. The main advantage of this unsupervised machine learning approach is that latent topics are mixtures over words where each word has a probability to belong to the different topics. Therefore, these topics can capture, conditional on covariates and without human intervention, research fields, information regarding the style of writing, methodology, conversational patterns or even different ways of thinking.

We start by identifying the number of latent topics for which the stochastic model fits best our data. The result is that female authors are unevenly distributed across latent topics. It turns out that female prevalence dispersion is higher across these topics compared to other approaches. Moreover, we show that although the proportion of females is slightly increasing among the population of T5 authors over the years, the identified horizontal differences persist. We compute the empirical distribution of latent topics by gender and we show some striking differences between male and female expected proportions. We want to emphasize the importance of these results, not only because latent topics may capture subtle horizontal differences, but also because the gender differences we estimate are “automatically” generated given the documents, without any arbitrary allocations to particular categories (as JEL codes, or declared areas). Thus, they are possibly more robust.

Notwithstanding, the choice of the number of latent topics, even if optimal as we discuss, is subject to clustering issues. To address these issues, we also choose to reduce the number of topics the algorithm has to generate, and in order to capture the mixtures of words that more closely resemble to research areas. There is a trade-off when choosing ex ante the number of latent topics. On the one hand, a relatively high number of topics usually fits better the data. On the other hand, a lower number of latent topics facilitates the broad semantic interpretation of them. In our setting, a lower number of topics turns out to make them closer to traditional research fields. Consistently with our main findings, we corroborate the uneven distribution of topic/research fields by gender, but now, much more in line with the existing literature cited above. Thus, we can also discuss the link between the existing literature and our class of probabilistic results. Our approach provides complementary evidence from previous literature over horizontal research differences between males and females. The idea is that the larger set of research topics may allow to identify more precisely the gender gaps, and what is more important, may help to understand the driving forces behind these gaps.

There are several channels for which the gender differences in the choice of research topic that we identify can have an impact on the probability of publishing in top journals, earning tenure and in general on career success. Conde-Ruiz et al. ( 2017 , 2021 ) and Siniscalchi and Veronesi ( 2020 ) provide two dynamic mechanisms that may explain how “horizontal” gender differences, together with an initially uneven distribution of gender researchers, may generate an unintentional discrimination trap linked with the functioning of academic organizations (journals, departments, etc.). In particular, Conde-Ruiz et al. ( 2017 , 2021 ) analyze a promotion setting in which workers’ skills are assessed by committees whose members have different abilities to evaluate workers’ signals (they are better at evaluating workers from the same group). This “homo-accuracy” assumption naturally translates to the present academic setting, where promotions and editorial processes are done by “committees” and where evaluators making research in the same research field are able to assess better the underlying quality of the candidate. Under this “ homo-accuracy bias ,” the group that is most represented in the evaluation committee generates more accurate signals, and, consequently, has a greater incentive to invest in human capital. This gives rise to a discrimination trap. If, for some exogenous reason, one group is initially poorly evaluated (less represented into evaluation committees), this translates into lower investment in human capital of individuals of such group, which leads to lower representation in the evaluation committee in the future, generating a persistent discrimination process. Siniscalchi and Veronesi ( 2020 ) focus specifically on the academic labor market and point out a similar unintentional discrimination trap linked to the so-called self-image bias . Research evaluation is biased toward young researchers with similar characteristics to them. The authors build up an overlapping-generations model with two groups of researchers with equally desirable (but a little bit different) research characteristics and identical ex ante productivity distributions. If one group is slightly over-represented into the evaluation group, this group (and its specific research characteristics) may dominate forever. These theoretical results go in line with the empirical findings of Dolado et al. ( 2012 ) that show that the probability for a female researcher to work on a given field is positively related to the share of women already working on that field (path-dependence). The proportions these authors find based on JEL codes are very similar to what we find automatically at the same level of aggregation, but we can set forth a lot more field idiosyncrasy under an extended optimal topic choice. At the end of the paper, we discuss various issues for further research in related applications.

The paper is organized as follows: the next section presents the raw data and the descriptive analysis of the patterns of publication in T5 journals. Section  3 presents the structural topic model. Section  4 studies the gender differences in the latent estimated topics. Section  5 extends the model to analyze topics as research fields. Last section concludes, and in Appendix we explore several extensions and provide details about the functioning of the structural topic model (STM) algorithm.

2 Raw Data and Descriptive Analysis

We collect the publicly available information from all articles published between 2002 and 2019 in the T5 leading journals in economics, as already indicated: The American Economic Review , Econometrica , The Journal of Political Economy , The Quarterly Journal of Economics , and The Review of Economic Studies . For each article, we collect the information about the journal, year of publication, authors and the abstract of the paper.

Number of articles published per year in T5. Note Publications exclude notes (without abstract), comments, announcements, and Papers and Proceedings (P&P)

We have 5311 articles in total over the period 2002–2019, the average number of papers published in top-5 journals per year is 295, with a maximum of 351 (on year 2017), and a minimum of 234 (on year 2002). Figure  1 shows that the distribution of published papers by journal is uneven. AER accounts for 34.3%, while JPE only represent 13.4% of the sample. AER publishes regular articles as well as shorter papers. Footnote 4 We include in our sample the shorter papers (as long as they have abstract) since their editorial processes is similar to regular articles. We exclude the articles published in AER as Papers and Proceedings since their requirements and editorial processes are different. Footnote 5 We want to compare this descriptive information with Card and DellaVigna ( 2013 ) who analyze all the articles published in the T5 from 1970 to 2012. They obtain several interesting facts, among them, that the total number of articles published in these journals declined from 400 per year in the late 1970s to 300 per year in 2012. They also show that one journal, the American Economic Review , accounted in 2012 for 40% of T5 publications, up from 25% in the 1970s. In our updated sample, as it is shown in the figure, we find that this trend has stabilized after 2012.

Card and DellaVigna ( 2013 ) also find that the number of authors per paper has increased from 1.3 in 1970 to 2.3 in 2012. We observe the same trend in the recent years, in particular in 2019 the average number of authors was above 2.5. Figure  2 reports the share of articles by number of authors, one to five or more. Clearly, the steepest trend downward is for solo authorship, whereas the three-author case (or even the four-author case) exhibits the opposite pattern. The two-author case share has remained fairly stable over the entire sample at around 40% of articles (base, not augmented). Five or more authors in economics articles at leading journals are still a rare event.

Number of authors of published papers in T5

Next, we move to analyze gender issues. We do not observe directly gender in our data. For solving that problem, we classify authors by gender according to their first name. We rely on three different databases: the first-names’ database published by the USA. Social Security Administration, created using data from Social Security card applications; the database constructed by Tang et al. ( 2011 ), who use Facebook to collect data on first names and self-reported gender; and finally, the names’ database developed by Bagues and Campa ( 2017 ). We check manually any candidate who (a) falls within the [0.05 0.95] probability interval of being male/female or (b) cannot be found in any of the databases.

We convert the original sample of articles into an articles-authors sample. We transform the original 5311 articles to a total sample of 11,721 (with implied 9840 articles-men authors, and 1881 articles-women authors). Except otherwise indicated all measures below are computed over this augmented articles-authors sample.

Number of article-author observations by gender and the share of female articles

Figure  3 depicts the share of female authors (right axis), which has been increasing (with fluctuations) at a rate of 6.2% per year, (compared to men’s share average rate at 3.7%), reaching 20% share during a couple of years in the recent past. Despite female authors are increasing at a higher rate, and that there have been an important improvement in the last decades, women are clearly under-represented in T5 publications. These data are consistent with the data from the report of the Committee on the Status of Women in the Economics Profession, Chevalier ( 2020 ). Figure  4 compares the evolution of the share of women in the different professor categories of the top 20 Schools of Economics in the USA in 2020 with the proportion of female authors in top 5. Notice that the share of female authors is very similar to the 20,4% share of women in the faculty of the top 20 Schools in the USA on 2020. In line with Heckman and Moktan ( 2020 ), the rate of increase of female coauthors in T5 seems to parallel the rate of increase of female full Professors in these departments. The average proportion of females that are full professor in Spain and the EU average are very similar as well.

The pipeline for top 20 economics departments: percent and numbers of faculty and students who are women. Source CSWEP Report, 2020 and own elaboration

Co-authorships patterns in T5 journals

Distribution of number of T5 papers published by gender

We have split the description of the data into two figures: one for single gender groups and another for mixed teams. Figure  5 a shows the corresponding co-authorships pattern when the set of co-authors are single gender groups. The more salient feature of these data is that while the share of sole male authors has been declining from 30% of total, to slightly above 10%, the share of sole female authors has been stable over the entire sample, at a share close to 5%. We want also to point out that despite the slow decline, two males are the most common co-authors team.

The equal share of male-female authors has been fairly stable at about 12% (92.7% of these articles are, in particular, one male-one female). Alternatively, the share of articles with at least one woman and at least two men has been increasing from nearly 5% over total to around 14%. Thus, the strongest trend in data seems to be associated with the participation of female authors in articles with relatively more male authors.

Figure  6 shows the distribution of the number of published papers by gender. Conditioning on having published in T5 journals, females are more likely than males to publish only one or two papers, while the proportion of authors that have published more than three papers is greater for males than for females. Clearly though, more than 80% of either female (15% of the distribution) or male authors have published less than two T5 over the last 20 years. This is an important fact for understanding the role of superstars in the profession as well as the mechanisms underlying the formation of networks of coauthors.

3 The Empirical Model: Structural Topic Model (STM)

Our empirical strategy is to use unsupervised machine learning techniques to uncover the hidden structure of our text documents. Footnote 6 By unsupervised we denote the absence of human intervention in order to identify the latent topics behind the abstracts of articles published in the T5 journals during the period 2002–2019. For us, an abstract is a set of words and these words have different probabilities to belong to one or several latent topics. Informally, when we are writing on a particular topic there are words that are used more often than others. Our objective is to provide a low-dimensional representation (topics) of a high-dimensional object (abstracts) while retaining as much as possible its informational content.

The baseline for topic modeling is the LDA algorithm (latent Dirichlet allocation) developed by Blei et al. ( 2003 ) and also the most popular machine learning algorithm in reducing the dimensionality of text documents. Footnote 7 In this paper, we use an algorithm called STM (structural topic model) developed by Roberts et al. ( 2019 ), which can be understood as a refinement for this LDA algorithm. This topic model is said to be structural because it allows the use of “covariates” to inform about the structure (partial pooling of parameters). These covariates in our case are going to be the different journal names and the different years in the sample. The idea is to better capture along these dimensions the changing relationship between words in abstracts and the latent topics. Next, we want to explain the algorithm and the outcome variables, and in “Appendix A” we provide a more technical discussion over STM and LDA.

We start by describing the inputs. We have our 5311 abstracts (or documents) to extract all the words. First, we have to “clean” this set of words in order to reduce the vocabulary and select terms with more informational content. This helps us for a better estimation of more semantically meaningful topics. The corpora is the set of unique words that we obtain, after converting to lower case and remove from the original raw text common stopwords, Footnote 8 as “for” or “in.” Also, we prune the words until we get their original linguistic root (“educ” instead of “education”) and eliminate the words that appears one or two times only. Footnote 9 In our case, we start with a set of 13,835 different terms and end up in a corpora of 4241 of unique words.

The second step is to represent our text data in a document-term matrix of D rows (5311 abstracts) and V columns (4182 unique words in our corpus) where the element ( d ,  v ) of the matrix is the number of times the \(v_{th}\) unique word appears in the \(d_{th}\) abstract. This document-term matrix that reduces the dimensionality of our original text variables is the input of the algorithm. Our objective is to find a probabilistic topic model that is able to explain the document-term-matrix in two additional steps. First by identifying K topics in our corpora and then by representing documents as a combination of those topics. What is a topic? The topic k is a probability distribution \(\beta _k\) over all the unique words of our corpus, where \(\beta _k^v\) is the probability that topic k generates word v . Each document d has its own distribution over the set of topics \(\theta _d\) . This captures that each document/abstract can refer to several topics. Then, \(\theta _d^k\) would mean the weight of topic k in document d . The probabilistic topic model is described by these topic \(\beta _k\) and document \(\theta _d\) distributions. Given that, we can compute the probability that an arbitrary word in the document d coincides with the \(v_{th}\) term is \(p_{dv}=\sum _k\beta _k^v\theta _d^k\) . Using these probabilities, we can obtain the total likelihood of our data, \({\prod _{d}}{\prod _{v}}p_{d,v}^{n_{d,v}}\) , where the \(n_{d,v}\) corresponds to the elements in the document-term matrix (the number of times the \(v_{th}\) unique word appears in the \(d_{th}\) abstract). Footnote 10

This total likelihood is our “objective” function. In a nutshell, the LDA and the STM algorithms are designed for finding numerically the stochastic model of latent topics (the distributions \(\beta _k\) and \(\theta _d\) ) that better suit our document-term matrix, that is that maximizes this total likelihood. We are going to skip here further details on the algorithms we use, and we refer the interested reader to “Appendix A” (and also to Roberts et al. 2014 ). However, we want to make two important observations.

First, as indicated above, we are implementing STM instead of LDA. The main advantage of STM for our data is that we can use very relevant covariate information about our documents in order to improve parameter estimation. Footnote 11 In particular, for each document/abstract we interact the year of publication as well as the journal name. We take advantage of the variability of the abstract along the time and across journals for improving the estimation of our stochastic model in particular of the distribution \(\theta _d\) ).

The second important observation refers to the determination of the number of topics. We can follow two strategies. One, it is to find the number of topics that better fits the data, which usually leads to a large (optimal) K . The alternative is to force the algorithm to use a given number of topics for facilitating the interpretation of those. For our baseline analysis, we use the first approach and we work with 54 topics, but we also pursue the estimation of our stochastic model using a fixed number of topics to facilitate comparison with the results in existing literature.

Previous literature, using JEL codes (for example, in Card et al. 2019 ) or research areas in top departments (for example, in Dolado et al. 2012 ) have concentrated in a broad definition of topics as fields of research, say, Labor or Econometrics. However, the unsupervised learning methodology we use allows us to go beyond pre-labeled research areas so as to capture more subtle differences, such as writing style, particular methodologies, or the variation in research questions. For example, our methodology allow us, when identifying latent topics, to separate two papers of labor economics, but one more applied and other with a theoretical contribution. We consider our approach a promising tool to analyze if there are horizontal gender differences in economics research, that is, whether or not male and female write different articles even within the same research field. For this reason, in the next section we will analyze our stochastic model with \(K=54\) topics, while in Sect.  5 , we will be focusing on estimating our stochastic model with \(K=15\) topics. In addition to these two exercises, in Appendix we extend our original sample for including the abstracts of 1117 articles published as Papers and Proceeding in AER , between 2011 and 2018 (before 2011 these types of papers do not have abstracts and after 2018 are published in a different journal). We will show that for this extended sample the optimal number of topics increases to \(K=70\) . While we have preferred to exclude these papers of the main baseline analysis because these are very short papers with very different editorial processes than regular submissions, this extended sample generates interesting new insights.

4 Gender Differences in Latent Estimated Topics

As we said above, the number of topics that best fits the text data is 54. Footnote 12 We estimate probabilities for each document to belong to this set of built-in latent topics using the structural topic model. The STM output is summarized by the latent topics displayed in Fig.  7 that shows the key words associated with each of the 54 topics. The words within each row are ordered left to right by the probability they appear in each latent topic. Eventually, we could assign some labels to latent topics, based on well-known fields names in economics. For instance, we can associate the more prevalent topic in the sample in expectation, topic 28, to international trade. Likewise, the second more prevalent topic in the distribution, topic 9, may be associated with Econometric Theory. However, this is not the goal of the analysis as we have indicated above. The important thing is that latent topics may be related to something beyond research fields, as methodology or style of writing. These latent characteristics hide gender differences too.

4.1 Topic Prevalence

Once we have identified the estimated latent topics, we can analyze how our documents/abstracts are distributed among them. In allocating an abstract to a particular topic, we consider our underlying \(\theta _d\) distribution. Then, we assign document d to different topics with different probability weights. Following this approach, Fig.  8 shows latent estimated topics in a way that also illustrates the number of documents in each topic, notice that in Fig.  8 the size of the circle is proportional to the expected number of documents in the topic (we have also reproduced numerically this information in a column in Fig.  7 ). As we cannot make a mapping of our 54 topics to particular fields of research, it is difficult to interpret the information of Fig.  8 regarding the size of the topics. For example, topics 11, 9 and 21, in Fig.  8 are related to “Econometric Theory,” and are relatively large compared with other topics. However, if the algorithm would have introduced more topics within “Econometric Theory,” each topic would have had a smaller mass, the weight of the research field being the same. In other words, our perception of the successful topics is affected by how the research field is split into topics.

Optimal K topics ranked by prevalence in the corpus

Connectedness between topics and the fraction documents/abstracts in each topic ( \(\theta _d\) distribution)

Figure  8 also contains information over the connectedness between topics. For example, if the latent topic k is closer to \(k'\) than \(k''\) , it means that the distribution \(\beta _k\) is more alike to the distribution \(\beta _{k'}\) than to distribution \(\beta _{k''}\) . Looking at Fig.  7 and the description of the latent topics in Fig.  8 , some interesting patterns arise. For example, the previous discussed topics 11, 9 and 21 (“Econometric Theory”) are in someway isolated from the rest of topics. In Fig.  8 , we can also identify some other clusters of topics, for example (east in Fig.  8 ) 51, 34, 23, 2, etc., are topics related to Macro-Finance, closer to those in Econometric Theory, but not that much; (west in Fig.  8 ) 50 is a central node of a set of topics related to Political Economy and Institutions); (southwest in Fig.  8 ) 29, 32, 22, etc., are topics related to microeconomics (contract theory, decision theory, etc.). Finally, applied areas as labor, international-development, or public economics are located around topics 19, 49, 28, and 48 (north in Fig.  8 ). In “Appendix D”, we undertake a more formal analysis of the distance between topics using a simple correspondence analysis of the probability matrix for documents to belong to the different latent topics. We find the corpus organized along two dimensions: Dimension 1 can be interpreted as going from Applies to Theory, whereas Dimension 2 goes from, say, Economics to Econometrics.

Connectedness between topics and the female authors documents/abstracts in each topic

Topic Word Clouds: Topic 49 vs Topic 16

On the presence of women, by topic: mean and one standard deviation across time

Using our classification of authors’ names by gender and the allocation of documents to latent topics, we can build up a similar figure with information about the gender distribution. Figure  9 shows latent topics where the sizes of circles are proportional to the percentage of female authors working in such topics (we have also reproduced numerically this information in the last column in Fig.  7 ).

Figure  9 provides interesting evidence of the main message of this paper, male and female display different patterns when doing research. Independently of the grade of under-representation of women in the profession, if there were not significant gender horizontal differences we would expect that sizes of latent topics measure for the proportion of females were similar. On the contrary, we observe an uneven distribution of sizes.

There is a small subset of topics (north in Fig.  9 ), specially topic 49, with a relative high proportion of females, that moreover seem to be closely connected (according to the terminology for applied economics fields). On the contrary, there is other set of topics (for example, southwest in Fig.  9 ) that are also closely connected and where the presence of females is scarce (around terms common to economic theory research questions).

4.2 Topic Analysis and the Gender Distribution

As we said above, it is difficult to describe the precise semantic meaning of the latent topics when we are working with \(K=54\) . We are able, however, to look closer to the latent topics where females are more or less prevalent and its potential implications. In particular, Fig.  10 shows that the latent topic with the highest proportion of female authors is topic 49 (32.8% as indicated in Fig.  7 ). On the contrary topic 16 turns out to be the topic with the lowest proportion of females (10.1% as indicated in Fig.  7 ). As a simple illustration, Fig.  10 represents these topics as word clouds, where the size of terms in the cloud is equivalent to its probability in the latent topic distribution \(\beta _k\) .

Empirical distributions across topics between males and females (conditional of having published an article in Top 5)

Relative propensity of publishing papers by females over topics

Empirical distributions across topics between males, females and mixed authorship (conditional of having published an article in top 5)

Diversify across latent topics by gender (HHI)

The words that seem to be more prominent in the cloud 49 are women, men, parent, children, health, etc. These words could be easily linked to research fields, as gender or health economics, traditionally associated with women. Similarly, the word cloud of topic 16 seems to be related to Micro theory that has been often labeled (while not statistically) as an area where there are less female than average.

Latent topics may differ in other dimensions beside semantic content. For instance, Hengel ( 2020 ) uses readability scores to measure the quality of writing of article abstracts. Footnote 13 We have implemented E. Hengel’s Python module Textatistic to compute readability results over the article abstracts across our latent topics. The finding is that scores across more female topics are better rated than across more male topics. However, it is hard to disentangle the role of the prevalence of female authors face to face the wording within a topic. Moreover, scores that are outliers should be properly treated to ease comparisons. We leave the study of these readability issues implying fundamental gender differences for further research.

Rather, Fig.  11 shows the mean of the presence of women authors by topic, together with the standard deviation of this presence over the sample of years. For some latent topics, the proportion of females is larger than the average (which is 15.9% over the period 2002–2019), reaching a proportion of 33% for topic 49. On the contrary, females are specially underrepresented in other topics, as topic 16, with only a 10%. Dispersion over time differs also across topics, and it seems that is higher for topics with higher proportion of females (the correlation between dispersion and the proportion of females is 0.35). While it is true that the proportion of female authors has been increasing in the last two decades from around 13% on 2002 to 19% on 2019, we do not see a trend in the dispersion of the proportion of females by topic. Consequently we see the prevalence of females across topics as a signal of gender “horizontal” differences in research.

Nevertheless, for having a more accurate picture of this “horizontal” differences, we need to add the information regarding the relative prevalence of the topics. It could be possible that females are unrepresented in a particular topic, and this circumstance having little impact as far as this topic contains very few published papers.

Figure  12 shows the distribution between males and females across topics normalized for having the same size. This gives us the propensity that, say, a female authored paper belongs to any of the 54 topics. We rank the topics according to probability of being chosen by a male author. This figure provides evidence that male and female authors either have different preferences or follow different strategies when pursuing and publishing their research. We observe that topics with higher “demand” by males are also highly demanded by females. However, there is a set of topics, for which the proportion of published papers for men are high, which are less attractive (o more difficult to publish) for females. In general, male and female distributions are different, with the salient feature of topic 49 for females, that it is a clear spike in the female distribution of published papers.

We confirm this evidence with a complementary Fig.  13 representing the dispersion of published female authored papers across topics, but accounting also for the prevalence of latent topics. In particular, for each topic we have the proportion of published papers by female authors (taken from Fig.  12 ) minus the proportion of published papers in this topic overall. Conditioning on having published a paper, male and female would be equally likely to publish a paper in a specific topic, this difference would be zero. Then, we can interpret this difference as the excess propensity to publish a paper in a particular topic by females. These differences can be positive or negative, and the sum over all topics is zero. The figure shows that there are topics for which the propensity of publishing papers by females is higher than males, and the opposite. Again topic 49 but also topics 41 (health) and 30 (applied IO) are in one side, while theory topics as 16 or 37 are in the other side.

In order to analyze the pattern of coauthor-ships we have pooled the articles in three groups, papers written by male authors, by female authors, and gender mixed team of authors. The main results are summarized in Fig.  14 that shows that there is a important difference between the pattern of latent topics between sole male teams and sole female teams, while mixed teams generate an intermediate distribution over the latent topics.

Finally, we want to address a related but different question, how male and female diversify across topics. For example, when writing an article, an author may contribute to a single latent topic or several, authors that have published several papers may have written similar articles or they could have been more diverse: are these diversification patterns different for males and females? For addressing this question, the first step is to choose a measure of latent topic dispersion/concentration. A natural candidate is the Herfindahl–Hirschman Index (HHI) that is used to measure the concentration in a market.

The HHI index is calculated by squaring the market share of the firm (the topic) that compete in a single market and then summing up the resulting numbers \(\mathrm{HHI}=\sum _{i=1}^{N}s_{i}^{2}\) . We apply this index to our problem as follows. For each author (the market), we identify all the latent topics that she has contributed to (the firms). For each article the algorithm computes a probability distribution over the latent topics. We repeat the process for all articles of the same author. Then, the cumulative probability divided by the number of articles is the contribution of the author to this particular latent topic (the market share, \(s_i\) ). For example, if an author publishes very similar papers related to a single or a few latent topics, her HHI will be high. On the contrary, authors with a more diverse research agenda will have a lower HHI. Figure  15 shows the corresponding average HHI for males and females.

We have computed the HHI controlling for the number of papers by author. It is clear that an author that has published more papers is likely to have contributed to a larger set of latent topics and therefore she must have a lower HHI. Interestingly, the figure shows some differences between genders in terms of diversification. Females are more diverse (lower HHI) when publishing one or two papers, but less (higher HHI) when publishing a larger number of papers in the Top 5. Footnote 14

5 Topics as Research Fields

In this section, we estimate the stochastic model with a lower number of topics, with two objectives. On the one hand, a low K facilitates the semantic interpretation of topics and then to analyze, for instance, whether or not, the weight of a particular field in the T5 has increased over time. On the other hand, a low number of topics will allow us to frame our results with previous literature that has used a small number of categories linked to JEL codes and research areas in top departments. After estimating the model for a range of \(K \in {10, \ldots , 20}\) , we have found that \(K=15\) is a number of topics for which the estimated model performs better in terms of fitting to the data and the semantic content of the latent topics at the same time. The model with \(K=15\) latent topics is summarized in Fig.  16 .

Latent topics ranked by prevalence in the corpus with \(k=15\)

A topic with “labor”: topic 8 in the set with \(K=15\)

Word clouds for topics with the stem “labor” among the fifteen more frequent words in the set with \(K=54\)

Connectedness for \(K = 15\)

The reader may then wonder what additional information is contained in the unrestricted version of the structural topic model (STM). One way to illustrate on the importance of an adequate selection of the number of topics is to explore in detail the composition effects we already discussed above. We proceed as follows. First, we consider the stem “labor,” and we look for it among the fifteen more frequent words within the restricted version of the STM, that is, the version with just 15 latent topics ( \(K = 15\) ). We only find that particular word under the required frequency within topic 8 in Fig.  16 . Figure  17 depicts the word cloud for that topic 8 in the restricted version of the model with \(K = 15\) . Clearly, in this particular case, one may say this cloud describes well the research field corresponding to JEL code J , which is Labor and Demographic Economics.

The key idea with the structural topic model is that a field like “Labor” can fit many research lines in the unrestricted version of the model, in our case the one with 54 latent topics. When we look for the stem “labor” within the 54 latent topics, we find it among the fifteen more frequent words in as many as six topics. Figure  18 illustrates on the most prevalent among these topics which are: Labor Search, Labor Supply, Human Capital, or Productivity Analysis. Notice, in particular, that there are important differences on the prevalence of females across these different subtopics, from 18 per cent in the more policy oriented topic which is “labor supply” to 14 per cent in the more theoretical “labor search” (go back to Fig.  7 for these shares). Important variability can be washed out when the methodology used account for the research field environment rather than for the research topic environment.

As we have anticipated, the reduction of the number of topics to \(K=15\) makes easier to label the latent topics as meaningful research fields, though. Following our previous analysis, Fig.  19 a plots the latent topics showing the relative semantic distance between topics as well as their weight in terms of the fraction of documents/abstracts that they contain.

If we compare Fig.  7 (with \(K=54\) ) and Fig.  19 a (with \(K=15\) ), they have a similar “geography” in terms of general areas of knowledge. Therefore, similar patterns in terms of the distances between topics arise. For example, “Econometric Theory” seems to be isolated, whereas applied fields such as Labor and Public Economics are closely connected.

Figure  19 b (as Fig.  8 with \(K=54\) ) provides evidence of the “horizontal” differences between males and females in doing research. The results go in line with the previous literature as in Dolado et al. ( 2012 ), Chari and Goldsmith-Pinkham ( 2017 ), Beneito et al. ( 2021 ) and Lundberg and Stearns ( 2019 ) that point out that females are unevenly distributed across fields. We concur with previous literature that females are over-represented in Applied-Micro fields, specially Health-Gender, Experimental and Education and underrepresented in Econometric and Economic Theory fields, Macro-Monetary and Finance.

For example, Dolado et al. ( 2012 ) use the classification of women by research areas (JEL 20 fields) in the top 50 economic departments in 2005. The proportions they find are very similar to ours: (i) I-Health, Education and Welfare, 25%, (ii) D-Microeconomics, 14%; (iii) J-Labour and Demographic Economics, 15% or (iv) C2-Econometrics, 14.3%. In our analysis, we found that the percentage of female authors are, for example: (i) Health and Gender, 23%; (ii) Decision Theory (13.6%), Game Theory (11.4%); (iii) Macroeconomics and Monetary, 14.2%; or (iv) Econometrics, 14.4%. Having said that, the distribution of the proportion of females across these restricted topics seems to be slightly less disperse than those identified in the previous literature with other sources of data. This can be due to the fact that our methodology is more “continuous” than allocating females to fixed categories, and as far as the probabilistic model allocates females’ articles to latent topics with statistical weights.

Growth rates of prevalence and female proportion by topics

Figure  20 analyzes together the evolution of the prevalence of the topics and the proportion of females authors. For building this figure, we have computed the growth rate of topics’ prevalences and topics’ female proportions from the averages in the latest seven years (2013–2019) and the first seven years (2002–2008) of the sample. First, we can observe that the proportion of females have increased in all topics, but Finance ( \(-6.6\) %). Regarding the prevalence, only four topics have decreased their weight in terms of prevalence, Mechanism Design ( \(-10.3\) %), Econometrics ( \(-29\) %), Game Theory ( \(-22.5\) %) and Experimental ( \(-8.4\) %). On the one hand, the topics where the percentage of women authors have risen more are Political Economy ( \(+67.7\) %), Decision Theory ( \(+42.5\) %), Macroeconomics and Monetary ( \(+32.3\) %), Experimental ( \(+40\) %) or Labor ( \(+35\) %). In all of them, the women were clearly underrepresented. On the other hand, the topics where the percentage of women has grown the least, besides Finance, have been Health and Gender ( \(+11.4\) %), Econometrics ( \(+9.4\) %), and IO ( \(+9.2\) %).

Finally, there is no clear relationship between the growth rate of topic prevalence and the increase in female prevalence. This is surprising. We do not have data about the seniority of authors, but as the proportion of female is increasing, we can expect that the proportion of females among the new entrants in the T5 market should be relatively large. New entrants should be more likely to work in “hot” topics rather than in declining ones. The combination of both effects should lead to a positive correlation between the increase in the prevalence of a topic and the increase in female representation, something that we do not observe clearly in the data. However, another alternative explanation to the increase of the proportion of women in some topics is that females that already have published in top five in the past, have extended their network of male coauthors and getting more papers published.

6 Conclusions

Using unsupervised machine learning techniques and a new data base composed by the abstracts of all articles published in T5 journals in Economics for the period (2002–2019), we have shown that there are persistent and significant horizontal differences in the way males and females approach research in Economics. Using the structural topic model, we have identified latent topics for which the distribution of female authors is more uneven than with research fields. These findings are important for several reasons, because: (i) T5 publications are key for research careers and also for determining the path of economic research; (ii) the results are robust in the sense that they are automatically generated with a probabilistic model without any deterministic allocation of papers to pre-established categories or fields of research; (iii) finally, recent theoretical results by Conde-Ruiz et al. ( 2017 , 2021 ) and Siniscalchi and Veronesi ( 2020 ) show that “horizontal” gender differences in the choice of research topic may lead to a gender discriminatory trap.

Beyond the scope of the present paper, we plan to extend our analysis in several directions. Firstly, we want to recollect more information about the authors, in order to be able to capture dynamic effects. For instance, we want to differentiate between the research patterns by senior and junior authors. We want also to investigate how male and female build the network of coauthors and how this process determines the choice of latent topics. Secondly, we want to show the usefulness of the methodology and the latent topics we have identified by reviewing research questions analyzed by previous literature in academic gender gaps. For example, Hengel ( 2020 ) analyzes the differences in quality of writing of papers. She shows that female-authored manuscripts are better written and concludes that female are subject to higher writing standards. The reason might be an unwelcome gendered culture through the entire editorial process at the time of deciphering complicated texts. We are currently implementing Hengel’s readability scores methodology to the latent topics. Our preliminary findings suggest that those papers belonging to topics with more prevalence of females are better written. Although this evidence can be interpreted as supporting the view that female-authored articles are better written than equivalent articles by men, it can be also the case that the results are driven by the particular topics. In other words, we need a deeper econometric analysis to disentangle if the written quality of the papers is driven by gender of the author or by the choice of the latent topics.

Likewise, Card et al. ( 2019 ) shows that female authored papers have more citations, suggesting that journals hold female-authored papers to higher standards. They have obtained this result controlling for research field. We plan to collect data on citations and review this result but controlling by latent topic. Finally, we want also to use algorithms (for example, LASSO a widely used regression analysis machine learning method) for testing if the differences between gender research patterns are important enough, for building a predictive model of gender given an observed abstract.

Boustan and Langan ( 2019 ) analyze the performance of women across PhD programs in economics. They report that in 2017, women were a 32% of entering PhD students in economics, This proportion of women in economics is below many other fields including science, technology, engineering, and mathematics (see also Bayer and Rouse ( 2016 )).

Hengel and Moon ( 2020 ) analyze publications in T5 and they also find that female authors published articles are more cited.

We borrow from the industrial organization literature the term “horizontal” differentiation since we refer to differences in gender approaches and topics choices unrelated to research quality (“vertical” differentiation) When those “horizontal” differences exist, papers of the same subjective quality may receive different citations depending for instance on the popularity of the topic or the number of scholars working on it. If we were able to control for “horizontal” gender differences (which is the goal of the paper), we could identify, in a more accurate way, the potential gender discrimination biases. We will discuss in greater detail, how to use our methodology for assessing gender discrimination biases when we discuss our future research agenda at the conclusion section.

AER stopped publishing shorter papers in 2018.

In “Appendix E”, we add P&P articles to our data and we replicate the analysis for these extended data.

For an excellent non-technical introduction to machine learning, see Hansen et al. ( 2018 ).

For technical description of the LDA algorithm, see the original article of Blei et al. ( 2003 ) and also Hansen et al. ( 2018 ) that is the first paper that uses the LDA algorithm in the economic literature.

In particular, we remove the stopwords from the SMART list, developed at Cornell University in 1960.

See “Appendix B” for the details of this pre-processing.

See Hansen et al. ( 2018 ) for a precise description of the computation of the total likelihood.

In Cabrales et al. ( 2018 ) there is an attempt to impute also gender as an additional covariate for the articles published in the British press by looking for female names in the body text of this articles.

In “Appendix C”, we provide a formal discussion about the optimal number of topics.

As E. Hengel discusses in detail, abstract readability is strongly positively correlated with the readability of other sections of a paper.

The HHI is a first approximation as measure of research diversification. In the future, we want to improve the measure by taking into in account that some latent topics are close to others.

Before 2011, the P&P articles did not have abstract and after 2018 the P&P articles are included in a different journal.

For more information about the about the AEA Papers and Proceedings go to: https://www.aeaweb.org/journals/pandp/about-pandp .

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José Ignacio Conde-Ruiz acknowledges the Spanish Ministry of Science and Innovation for financial support through the project PID2019-105499GB-I00. Manu García and Luis Puch acknowledge the support through the project PID2019-107161GB-C32. Juan-José Ganuza gratefully acknowledges the financial support from the Spanish Agencia Estatal de Investigación, through the Severo Ochoa Programme for Centres of Excellence in R&D (CEX2019-000915-S) and the Spanish Ministry of Science and Innovation through Project PID2020-115044GB-I00.

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We thank Antonio Cabrales, Pedro Delicado and Nagore Iriberri for helpful comments, and Elvira Alonso for excellent research assistance. We also thank the Editor and two anonymous referees for their suggestions, as well as session participants at Computing in Economics & Finance Conference, Tokyo (virtual) 2021. José Ignacio Conde-Ruiz and, Manu García and Luis Puch, respectively, acknowledge the Spanish Ministry of Science and Innovation for financial support through projects PID2019-105499GB-I00 and PID2019-107161GB-C32. Juan-José Ganuza gratefully acknowledges the financial support from the Spanish Agencia Estatal de Investigación, through the Severo Ochoa Programme for Centres of Excellence in R&D (CEX2019-000915-S) and the Spanish Ministry of Science and Innovation through Project PID2020-115044GB-I00.

Appendix A: The Topic Model

We implement and develop the structural topic model (STM) to incorporate document-level meta-data into a probabilistic text model. The topic model is said to be structural because “covariates” inform about structure (partial pooling of parameters). We keep track of journal names and publication years as covariates to estimate the prevalence of topics.

The starting point to understand the STM probabilistic model is the LDA (latent Dirichlet allocation) generative model. According to LDA, the data generating process for document \(d \in D\) assigns terms in vocabulary V to positions \(N_{d}\) in the document-term matrix, where the element ( d ,  v ) of the matrix is the number of times the \(v_{th}\) unique word appears in the \(d_{th}\) abstract. The algorithm follows the steps below

Draw a K-dim Dirichlet vector \(\theta _{d}\) containing the expected fraction of words in d attributed to topic \(k\in K\) .

For each word (position) in d ,  sample the indicator \(z_{d,n}\) from \(\text{ Mult}_{K} (\theta _{d},1)\) that indicates the position n associated with a topic.

Sample the indicator \(w_{d,n}\) from \(\text{ Mult}_{V} (B_{z_{d,n}},1),\) where matrix B has distributions \(\beta _{k}\) over vocabulary V; \([\beta _{k}]\) is frequency with which terms are generated from k .

STM in its turn builds upon identifying covariates to improve the estimation of the topics. Covariates affect (i) the proportion of a d devoted to a k (topic prevalence-TP), and (ii) how much a word is used in k (topical content-TC). To this purpose:

for TP, Dirichlet \(\theta _{d}\) draws of document-level attention to each topic are replaced with a logistic-normal with a mean vector parameterized as a function of document covariates.

for TC, \(\beta _{k}\) distribution is proportional to a multinomial logistic regression parameterized as indicated below.

A (partially collapsed) variational expectation–maximization algorithm is implemented to approximate the posterior (inference). Then posterior predictive checks (cf. Gelman et al. 1996 ) and tools for model selection as in Roberts et al. ( 2014 ) are used. Beyond TP and TC functions of document metadata, the structural topic model can be summarized as:

Given parameters: (i) a variance–covariance matrix for topics \(\Sigma \) , (ii) a matrix of observed document-level covariates X (journals names and years) and (iii) a vector \(\gamma _{k}\) (of prevalence of each topic) for each covariate,

sample the topic proportion in each document, vector \(\mathbf {\theta }_{d},\) that is,

as a substitute for the Dirichlet conjugate prior, to conform the topic prevalence model .

The core language model given the topic proportion per document \(\mathbf {\theta }_{d}\) consists of:

sampling the probability \(\mathbf {z}_{d,n}\) that a word is in a topic: \(\mathbf {z}_{d,n} \sim MN_{K} (\mathbf {\Theta }_{d}),\) with K outcomes

conditional on topic, choose a word from \(\beta _{z_{d,n}},\) that is \(\mathbf {w_{d,n}} \sim MN_{V} (\beta _{z_{d,n}}), over \mathbf {B} = \left[ \beta _{1}| \ldots |\beta _{K} \right] \) matrix of distributions over vocabulary V.

The topical content model samples the topic word distribution \(\beta _{d,k,v},\) . By now, we do not use covariates to explain topical content of documents.

Appendix B: Details of this Pre-processing Data

Pre-processing of the abstracts that conform our database is essential in order to organize the words that form the texts in an homogeneous way. The main goal of this process is to reduce the dimensionality by reducing the set of words, but at the same time trying to maximize the information contained in the words used by the authors by selecting the terms with more informational content. This helps us for a better estimation of more semantically meaningful topics.

First step is tokenization so as to differentiate words by selecting only single words (monograms), instead of bigrams, trigrams, paragraphs, etc. Then, we eliminate punctuation, and capital letters are converted to small letters. This allows as to remove duplicates, for example “Education” and “education” are different words in our database if we do not convert all the words to lowercase. Once this is done, we eliminate numbers and stopwords. By stopwords we refer to those words without any informational content: “common” words such as “and,” “for” and “in.” We removed the stopwords from the list SMART developed by Buckley ( 1985 ), a public list with more than 500 words. Additionally, we remove some custom stopwords that were very common in our database but not informationally relevant. These are: “download,” “slides,” “slide,” “jel,” “abstract,” “paper,” “author,” “literature,” “among,” “whether,” “authors,” “model,” “show,” “showed,” “shows,” “find,” “can,” “matter,” “model,” “models,” “may,” “effect,” “find,” “can,” “show,” “paper,” “also,” “provide,” “approach,” “thus,” “main,” “obtain,” “obtained,” “without,” “modelling,” “modeling,” “modeled,” “modelled,” “use,” “result,” “results,” “resulting,” “resulted,” “discuss,” “discussed,” “discussing,” “recent,” “recently,” “give,” “gives,” “given,” “review,” “reviewing,” “reviews,” “require,” “required.”

We end by stemming the tokens so as to retain only the roots of words in the same family, so as to unify the information contained in related words. For example “education,” “educative,” and “educated,” are all related to education, so we just keep the root “educ” for all of them. The use of these stems relax dimensionality problems and groups all probabilities for families of words into one.

In our sample were initially 13,835 different terms. After this process without loss of generality, we reduce the number of unique terms to 4241 in the corpora with which we build the document term matrix.

Appendix C: The Optimal Number of Topics

To run the model involves a choice of hyperparameters as discussed in “Appendix A” above, and one of those parameters is the number of this latent topics existing in our corpus. As this can be interpreted as an arbitrary prior, we run some automatic tests in order to choose this optimal K without human intervention, in order to classify texts in the best possible way. This approach gives us the advantage of automatically selecting the number of topics that better fits data. Arbitrary choosing too few topics means to cluster several topics into a single one. Choosing too many topics means would tend to identify patterns in language rather than topics.

Held-out likelihood estimation

We learn a lot on the different patterns of the data when choosing various alternatives for a fixed number of topics, as we will discuss below. However, our primary selection strategy for automatic selection focuses on the held-out likelihood estimated. Figure  21 reports the log-likelihood of the model evaluated at the estimated parameters on the test set for each K between 15 and 100. The likelihood is maximized between 49 and 54 topics.

Number of iterations to convergence of the model

Semantic Coherence

Exclusivity

Figure  22 , in its turn, displays the number of iterations to convergence of the model, which sharply drops at 54 topics and remains at that number of iterations (except for a small spike at 60) beyond 62 topics.

Finally, Fig.  23 reports the semantic coherence which is decreasing and stable after 59 topics. Semantic coherence is maximized when the more frequent words in a given topic co-occur together Mimno et al. ( 2011 ). High semantic coherence is reached when in the end there is less topics dominated each by few words. On the other hand, average exclusivity is large when a particular word frequency corresponds to each topic. We follow Roberts et al. ( 2014 ) to use the FREX metric for this criteria. As shown in Fig.  24 , there are two maximums in 51 and 54 topics.

With our data, we found reasonable to assume that the result is in the neighborhood of 52 topics given the held-likelihood procedure, and given the additional tests, we select the highest number of topics in this neighborhood, corresponding to 54 topics.

Appendix D: The Topics Profile

Given that we have chosen automatically the number of latent topics, it can be helpful to try to disentangle their nature. As an alternative to Figs.  7 and 8 , we use simple correspondence analysis to measure the distance between topics. This is a descriptive technique to explore relationships among categorical variables. In our application, we use the matrix of probabilities (the matrix \(\theta _d\) obtained from STM) for each and every document to belong to any particular built-in topic in order to measure the distance between topics. The rows in this matrix are probabilities that add up to one. The clustering of rows measures the distance between topics (the columns of the matrix). This is the so-called Chi-square distance:

where r is the total number of rows, and the measure we compute and represent gives the Euclidean distance between columns \(i, j (\mathrm{col})\) , for each and every row a (abstract).

Figure  25 a depicts the two larger coordinates of the distance matrix computed through classical multidimensional scaling (MDS), so as to obtain the coordinates of the column category. The coordinates are given by the order of largest-to-smallest variance. We find the corpus organized along two dimensions: Dimension 1 can be interpreted as going from Applied to Theory, whereas Dimension 2 goes from, say, Economics to Econometrics. We think this is apparent from casual inspection of Fig.  25 a, which involves square distances between \([-4,+4]\) .

Larger coordinates of the distance matrix computed through classical multidimensional scaling (MDS)

Latent topics ranked by prevalence in the corpus with \(k=70\) . Extended sample with P&P articles

Connectedness between topics and the fraction documents/abstracts in each topic ( \(\theta _d\) distribution). Extended sample with P&P articles

Connectedness between topics and the female authors documents/abstracts in each topic. Extended sample with P&P articles

Clearly though, outliers (understood as the topics far away from the origin) are very important in this representation. First, we identify outliers 21, 9, 11, that we have associated with Econometric Theory in the fields of estimation (“estim,” “asymptot,”...are the keywords in this case) and testing (“test,” “asymptot,”...), together with structural econometrics (“identifi,” “instrument,”...), respectively. These actually are among the top 10 more prevalent topics. Moreover, topics 9 and 11 are 2nd and 3rd most prevalent. These outliers are located northeast in the diagram in terms of the language they use.

The second set of outliers are located southeast and are equally far from the center, while not isolated. These topics can be associated with Economic Theory texts. On top of those, we find topic 5, and then not that further away from the center, topic 6, 16 and 10. These are, respectively, auction theory (auction, bid,...), together with game (game, player,...) and information theory (belief, signal,...), as well as mechanism design (mechan, implement,...). These topics are relatively less prevalent in the sample than the Econometric Theory topics above as we discussed in the main text.

Finally, there are some outliers at the northwest corner of the diagram. We find here topics that seems to be mostly empirically oriented (applied), and according to our representation, nearly as distant from Econometric than from Economic Theory. These are particularly topics 19 and 49 that we have associated before with Education and Gender issues, and for which female authors’ presence is relatively more prevalent.

There is finally a negative correlation between the two coordinates, suggesting that distance values are larger than under the hypothesis of independence between these two key dimensions. This finding would require a treatment that goes beyond the scope in this paper. We leave further analysis of the nature of latent topics in leading economic journals for future research. The interested reader can check the center of the representations at square distances between \([-1,+1]\) in Fig.  25 b.

Topic Word Clouds in the extended sample with P&P articles

Appendix E: Analysis with the Abstracts of the Papers Proceeding Papers (P&P)

In this section, we extend our original sample with the Papers and Proceedings (P&P) articles published in AER in the especial issue of May during the period 2011–2018. Footnote 15 These P&P articles are very short (for example, they could be just an extension of a full article submitted to a different journal), and they are selected from the papers presented in the annual January meeting of the American Economic Association’s (AEA). Part of the papers are selected directly for the committee’s members of the AEA meetings and others are chosen from external proposals of special sessions in AEA meetings. Footnote 16 Interestingly for our analysis, papers in P&P are linked to the meeting sessions, and then, they come in groups of three or four papers of a specific topic. Then, the editorial process of this P&P is very different from regular submissions and the set of topics is likely to be more diverse, since some of the special sessions in AEA meeting may be relevant for current policy debate but not necessarily for research. For example, in the issue of May 2020, among others, we can find two sessions and the corresponding articles over “The economics of the health epidemics” or “Is United States deficit policy playing with fire?”.

With these additional P&P papers, our sample contains 6428 abstracts/documents, that generates 253,312 tokens and 12,936 unique terms. The number of topics that best fits the these extended sample is 70. The larger number of latent topics can be related to the larger number of unique words and documents, but also to the selection process of P&P described above, sessions unrelated to standard research with a small number of (“seed”) papers very related among themselves.

As in the main text, we estimate these 70 latent topics using the STM algorithms. Figure  26 presents the latent topic ranked by prevalence in the corpus with \(k=70\) .

Figure  27 shows the STM output (the estimated latent topics) and also how the documents are allocated among them.

As in the main text, in Fig.  27 the size of the circle is proportional to the number of documents in the topic. The most salient feature of Fig.  27 is that in addition to the larger number of topics, there are some of them with very small size that could be related to the “seeds” described above, sessions of the AEA meetings, with very related papers among themselves but quite different to research papers closer to them.

Figure  28 reinforces the evidence of the main message of this paper, male and female display different pattern when doing research. There is a subset of topics (southeast in Fig.  28 ) with a relative high proportion of females, that moreover seems to be closely connected. On the contrary, there is other set of topic (southwest in Fig.  28 ) that is also closely connected and where the presence of females is relatively scarce.

Now, we want to look closer the content of some particular topics. In this larger sample, it is easier to see that the latent topics go beyond standard research fields. In particular, Fig.  29 points out that the latent topics with higher proportions of female authors are topic 41 and topic 19. In the following figure, we can see the distributions over terms that each of this two topic induces are represented as words clouds, where the size of term in the cloud is approximately proportional to its probability in the latent topic distribution \(\beta _k\) . Clearly, topic 41 is related to family economics and topic 19 to gender discrimination.

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Conde-Ruiz, J.I., Ganuza, JJ., García, M. et al. Gender distribution across topics in the top five economics journals: a machine learning approach. SERIEs 13 , 269–308 (2022). https://doi.org/10.1007/s13209-021-00256-2

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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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To launch the 11th edition of the Gender Balance Index, the Official Monetary and Financial Institutions Forum's Sustainable Policy Institute is hosting a virtual event on April 10. The launch will feature high-level keynotes and discussions on the latest global gender actions and policies. IMF's Martin Cihak and Rishi Goyal will participate.

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Despite significant progress in recent decades, labor markets across the world remain divided along gender lines. Female labor force participation has remained lower than male participation, gender wage gaps are high, and women are overrepresented in the informal sector and among the poor. In many countries, legal restrictions persist which constrain women from developing their full economic potential.

While equality between men and women is in itself an important development goal, women's economic participation is also a part of the growth and stability equation. In rapidly aging economies, higher female labor force participation can boost growth by mitigating the impact of a shrinking workforce. Better opportunities for women can also contribute to broader economic development in developing economies, for instance through higher levels of school enrollment for girls.

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Interim Guidance Note on Mainstreaming Gender at the IMF

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What is the imf's strategy for gender.

Washington, DC – July 22, 2022: Ms. Kristalina Georgieva, Managing Director of the International Monetary Fund (IMF), made the following statement today:

“I am most pleased and proud to announce that the Executive Board today approved the IMF’s first Gender Strategy aimed at integrating gender into the Fund’s core activities — surveillance, capacity development, and lending— in accordance with its mandate. This means more systematically assessing the macroeconomic consequences of gender gaps where they are macro-critical, evaluating the gender-differentiated impact of shocks and policies, and providing granular and tailored macroeconomic and financial policy advice and capacity development support.

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The Strategy

Mainstreaming gender at the IMF starts with the recognition that reducing gender disparities goes hand-in-hand with higher economic growth, greater economic stability and resilience, and lower income inequality. At the same time, economic and financial policies can exacerbate or narrow gender disparities. Well-designed macroeconomic, structural, and financial policies can support efficient and inclusive outcomes and equitably benefit women, girls, and the society in general. The strategy lays out how the IMF can help its member countries address gender disparities in the context of carrying out its core functions—surveillance, lending, and capacity development. The strategy comprises four key pillars: first, gender-disaggregated data collection and development of modeling tools to enable staff to conduct policy analysis; second, a robust governance framework for an evenhanded approach across members based on the macro-criticality of gender; third, strengthening collaboration with external partners to benefit from knowledge sharing and peer learning, leverage complementarities, and maximize the impact on the ground; and fourth, the efficient use of resources allocated to gender by putting in place a central unit for realizing scale economies and supporting country teams.

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We present the current status of labor market gender gaps in The Gambia and examine the macroeconomic and distributional gains from closing the gaps. We also study the impacts of high costs of living and the determinants of poverty. Closing labor market gender gaps, would significantly boost GDP, government revenues, women’s earnings, and reduce income inequality. High food costs adversely affect the levels of consumption in the bottom four quartiles of the income distribution. Lack of access to finance, living in rural areas, lack of employment, low levels of education, and exposure to climate shocks contribute to higher poverty levels.

Qatar: Selected Issues

Gender Representation in Economics Across Topics and Time: Evidence from the NBER Summer Institute

We document the representation of female economists on the conference programs at the NBER Summer Institute from 2001-2016. Over the period from 2013-2016, women made up 20.6 percent of all authors on scheduled papers. However, there was large dispersion across programs, with the share of female authors ranging from 7.3 percent to 47.7 percent. While the average share of women rose slightly from 18.5% since 2001-2004, a persistent gap between finance, macroeconomics and microeconomics subfields remains, with women consisting of 14.4 percent of authors in finance, 16.3 percent of authors in macroeconomics, and 25.9 percent of authors in microeconomics. We examine three channels potentially affecting female representation. First, using anonymized data on submissions, we show that the rate of paper acceptance for women is statistically indistinguishable to that of men. Second, we find that the share of female authors is comparable to the share of women amongst all tenure-track professors, but is ten percentage points lower than the share of women among assistant professors. Finally, within conference program, we find that when a woman organizes the program, the share of female authors and discussants is higher.

The views expressed are those of the authors and do not necessarily reflect those of the Federal Reserve Bank of New York or the Federal Reserve System. We thank Jim Poterba and NBER staff for providing data on past Summer Institute programs, and special thanks to Alex Aminoff for merging gender identifiers to the NBER submissions data and preparing summary tabulations relating to the 2016 and 2017 meetings. We thank seminar participants in the applied microeconomics workshop at UNC-Chapel Hill and the New York Fed for many helpful comments and suggestions. In addition, special thanks to Jediphi Cabal, Linda Goldberg, Claudia Goldin, Pete Klenow, Anna Kovner, Sydney Ludvigson, and Paola Sapienza for many thoughtful suggestions. Kevin Lai provided stellar research assistance. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

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• Published: 23 August 2021

Gender approaches in the study of the digital economy: a systematic literature review

• Mónica Grau-Sarabia 1   na1 &
• Mayo Fuster-Morell 2   na1  

Humanities and Social Sciences Communications volume  8 , Article number:  201 ( 2021 ) Cite this article

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• Information systems and information technology
• Science, technology and society

The data and debates around the negative impact of online work for women’s work-life balance during the digital acceleration generated by the COVID-19 crisis have lent greater relevance to the study of gender and the digital economy. This paper sheds light on this complex relationship by systematically studying the research on gender in the digital economy over the last 25 years. The methodology used is a systematic literature review (SLR) of scientific works and policy papers across different social sciences from 1995 to 2020 in the Google Scholars and Scopus databases. The SLR has resulted in the creation of three samples on which a quantitative and qualitative analysis was carried out to evaluate the volume of the research, trends across time, gender approaches and study topics. The general conclusions indicate that gender approaches to the digital economy stem from a wide range of academic disciplines, and also that there is a lack of theoretical consistency about gender analysis. First, the paper provides an overview of the volume of works and an analysis of some trends across time. Second, it identifies the three main gender approaches applied to the digital economy: (1) the ‘feminist theory of technology and ICT’ approach; (2) the ‘feminist political economy’ approach; (3) the ‘mainstream economic analysis and women’s participation and labour in the digital economy’ approach. Moreover, it distinguishes eight main gender analysis issues within these three approaches. Finally, the paper concludes by identifying future developments for a feminist political economy framework for the digital economy.

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Introduction

Around 25 years have passed since digital technologies began to develop and pervade almost every aspect of sociopolitical and economic life, giving rise to what is known as the ‘Network Society’ (Castells, 1996 ) and transforming most sectors of economic activity and the economy as a whole. It has also been 25 years since the Beijing Platform for Action (United Nations Women, 1995 ) set up the global agenda of social, political and economic gender equality. In March 2020, UN women published the report “Gender equality: Women’s rights in review 25 years after Beijing” (United Nations Women, 2020a ) and established ‘harness technology for gender equality’ as one of their four priorities. In fact, the benefits of the digital economy for gender equality were already on the international political agenda, as seen in reports such as “Going Digital: The Future of Work for Women” (OECD, 2017 ). However, over the course of only a few weeks in March, due to the situation created by COVID-19, the apparent agreement on the benefits of technology in women’s lives turned into a political debate about gender inequalities related to teleworking and care responsibilities (United Nations Women, 2020b ). In this regard, the COVID-19 crisis has evidenced the lack of clarity in the idea that the digital economy is a source or consequence of unequal gender relations and the need to investigate this complex interplay. The paper seeks to fill this gap by systematically studying the research conducted over the last 25 years to answer the research question: How has gender been studied in the digital economy?

The systematic literature review (SLR) methodology was designed to answer the following specific research questions: (i) How much research activity has been carried out into the interplay of gender and the digital economy and are there any trends? (ii) What gender approaches to the digital economy can be identified? And, what are the strengths and weaknesses of the different gender approaches? (iii) What are the specific gender issues being addressed? The findings of the paper will contribute to future research aimed at developing feminist politics on the digital economy and guide policymakers towards achieving gender equality within the digital economy.

The paper is structured around four sections. The first section provides a broad framework of the digital economy, followed by a gender analysis. The second section explains the extensive research methodology employed using an SLR, describing how the SLR was designed and then conducted. A mixed-method analysis of the results of the SLR is then performed whereby they are quantitatively and qualitatively analysed. The research on gender and the digital economy from a gender approach is then presented. The third section answers the paper’s three research questions, starting with a quantitative analysis of the research activity and an initial identification of the research trends across time. Then, the three main gender and feminist approaches distinguished are presented. Furthermore, the eight specific gender issues identified in these three different approaches are outlined. Finally, the last section presents the conclusions and outlines some of the future challenges for feminism and global goals for gender equality in the digital economy.

The digital economy and gender: departing framework

A very specific, simple and straightforward definition of digital economy is “an economy based on digital technologies” (European Commission, 2013 ). A more developed one defines it as a compendium of the new economic developments and production and consumption transformations linked to digital technologies, information and communication technologies and new business models based on digital supports over the last 25 years (such as the platform economy and the data economy). It may be said that the digital economy has its origins in the Network Society and the intensive use of information and communication technologies (ICT) to create, distribute and manipulate information (Castells, 1996 ) that led to the digital revolution. This digital revolution is characterised by technological advances, the most important of which are the Internet, personal computers, smartphones, mobile internet, social media, cloud computing, the Internet of Things (IoT), artificial intelligence (AI), machine learning, big data, blockchain technologies, and robotics. Altogether with the range of different spheres of action and economic sectors, another characteristic of the digital economy is the variety of forms of economic relations and organisational models it may take, such as the emergence of what has been variously called the platform, digital, sharing, gig or collaborative economy as an emerging global phenomenon. Furthermore, digital platforms can draw from the economic frame of the social economy, as seen in platforms, open cooperatives (Scholz, 2016 ) and commons-oriented peer-to-peer platforms (Benkler, 2006 ; Fuster-Morell, 2010 ), which differ from digital platforms that arise out of an extremely ferocious capitalist corporate spirit (Srnicek, 2017 ). Thus, the digital economy is a floating signifier that involves many subfields and economic sectors.

The term ‘gender’ is popular in the language of social sciences, in part due to the feminist movement and the concurrent intellectual efforts to better understand the systematic and widespread subordination of women (Acker, 1992 ). Although the term is widely used, there is no common understanding of its meaning, even among feminist scholars (Butler, 1990 ). Gender interacts with, but is different to, the binary categories of biological sex. Gender is not only “a constitutive element of social relationships based on perceived differences between the sexes”, but is also “a primary way of signifying relationships of power”, a field of norms and practices within which or through which power is articulated (Scott, 1986 , p. 1067). Different academic disciplines have introduced gender analysis in their research agenda, developing rich interdisciplinary knowledge such as that of citizenship and gender (Lister and Campling, 2017 ) and urbanism and gender (Bondi and Rose, 2003 ) among many other examples. In all the cases, there is a certain agreement that the purpose of gender analysis is to identify gender inequalities and find solutions to them. However, the complexity comes from how gender is defined and how the analysis is approached. Thus, a gender approach from an understanding of gender as a binary social category offers only a surface-level analysis, describing empirical differences between men and women but without analysing or explaining the reasons for them. Conversely, a gender approach from a more complex understanding of gender as a power structure of inequalities involves a structural analysis of inequalities. The differences between gender analysis in feminist research and in other non-feminist research lie mainly in the fact that the former uses the feminist theoretical framework and the latter uses the term ‘gender’ to replace the term ‘women’. These different understandings of gender can be considered as anchor points within a spectrum of gender approaches. Thus, the issue of the different gender approaches translates, on the one hand, into a critical analysis of structural power inequalities, for example, the study of the female exclusion mechanisms in the technology sector or the sexual labour division in immaterial labour; and, on the other hand, an analysis of the consequences of gender inequality relations, for example, the study of the low participation of females in the technology sector or women and men’s different uses of social media. In light of past interdisciplinary gender studies, we expect to find different gender approaches in the literature on digital economy and gender. Identifying and analysing these approaches may bring clarity to how the relationship between gender and the digital economy has been studied over time.

Research methodology

This paper aims to answer the question of how the relationship between gender and the digital economy has been studied over time by undertaking a systematic literature review (SLR). The SLR is “a systematic, explicit, comprehensive and reproducible method for identifying, evaluating, and synthesising the existing body of completed and recorded work produced by researchers, scholars, and practitioners” (Fink, 2019 , p. 3). Currently, social sciences are adapting the initially developed SLR methodology from health sciences, and this paper represents an additional contribution to the few existing papers that have used this kind of research methodology.

To answer the three specific research questions, the method was adjusted following the indications described in some of the works produced within the fields of information systems and social sciences. Of particular relevance were the three main stages of the SLR proposed by Tranfield et al. ( 2003 ): planning the review, conducting the review and reporting results. This section will explain how the review was planned and conducted and the following section will report the results.

Designing of the SLR

The design or planning of the SLR for this research includes the steps described in Table  1 . It started with the development of a broad conceptual framework of what the digital economy and gender analysis may involve, together with the definition of the research objectives. It then went on to identify the keywords to be used for the search equations, followed by the selection of the search engines that are aligned with the rationale of including different scientific disciplines and works from both inside and outside academia. Additionally, the inclusion and exclusion criteria for the selection of works and the sampling process were defined. Moreover, finally, a description was provided of the expected samples and the analysis method for each of them.

Following the broad conceptual framework developed previously, the concept of digital economy is covered by words such as technology, ICT, sharing economy, platform economy, gig economy and digital economy. To refer to gender analysis, words are used such as: feminism, feminist theory, gender and women. All these words were used to create the twenty-five search equations (Table  2 ).

Conducting the SLR

The SLR was conducted around the three specific research questions:

How much research activity has been carried out into the interplay of gender and the digital economy and are there any trends?

Which gender approaches to the digital economy can be identified? And, what are the strengths and weaknesses of the different gender approaches?

What are the specific gender issues being addressed?

The sampling process of the research was also structured to answer each of the research questions. The rationale of the sampling process was to transition from a very extensive sample to a more selective one. Thus, more inclusion criteria were required for the selection of works in all of the following samples. Table  3 describes the sampling process followed in this research.

The first sample was created based on the first inclusion criteria, that is, the results of the 25 search equations entered into GS, giving a total of 1,026,66 papers and research works. The aim of this first lax search was to get an overview of the entire body of research in the field conducted between 1995 and June 2020. The total number of results obtained in GS was considered as a proxy indicator of all the research, and the comparative analysis of results from each of the search equations was taken as an indicator of research trends.

The second sample was created in two steps. First, the second inclusion criteria were applied, that is, the first 25 results sorted by relevance from 1995 to June 2020 were taken for each of the 20 search equations in GS and in Scopus. GS has the benefit of indexing scholarly literature across a wide range of disciplines and publishing formats, unlike in academic databases, which was especially relevant for the purpose of this research. However, GS does not necessarily include all the results from peer-reviewed publications, whereas Scopus does. With the objective of balancing the strengths and weaknesses of GS, both search engines were used. After the deployment of a qualitative cluster analysis of the abstract and/or the introduction and the rejection of those works that did not focus on the digital economy and gender (exclusion criteria), 495 works were selected. The second step was then to deploy the third inclusion criteria; this was a qualitative analysis with an in-depth analysis of the content to create a second sample of 166 results. This second sample was analysed qualitatively to confirm the identified gender approaches to the digital economy and analyse the strengths and weaknesses of each of them, and to distinguish the specific gender issues being addressed. The total number of papers and research works included in this sample cannot aim to be the entire existing body of research but is a fairly accurate representation. The sample is available in an open database (gender approaches to the digital economy: selected database) to share with colleagues for future research in the field. The bibliographic reference manager Zotero was used to facilitate the management and analysis of both the second and third bibliography sample.

Results of the gender research on the digital economy

Rq1. how much research activity has been carried out into the interplay of gender and the digital economy from 1995 to june 2020 and are there any trends table of the search-equation results from google scholar.

The results of 25 search equations entered into Google Scholar were used as a proxy indicator of the entire body of research on this interplay, and the comparative analysis of results from each of the search equations as an indicator of trends (see Table  4 ). The quantitative comparison of results shows how several trends intersect. First, the use of the term ‘gender’ (1,960,000) or ‘women’ (1,755,780) was considerably more extensive than the use of ‘feminist theory’ (611,066) and ‘feminist’ (415,596). As explained by the conceptual framework, the term ‘gender’ has many different understandings. Gender analysis can stem from critical approaches that study power dynamics and the process of discrimination and structural inequalities on the grounds of gender, which are developed by feminism and feminist theory. However, gender analysis can also refer to a more uncritical or surface-level approach, for instance, the study of comparative differences among men and women in relation to a phenomenon, aimed at accounting for the reasons behind women’s disadvantages. The major differences found in the use of ‘gender’ and ‘women’ and ‘feminism’ and ‘feminist theory’ seem to point to a greater trend to use the former approach. The second comparative difference is with the use of the term ‘technology’ (4,481,000), which is relatively higher than the rest of the spheres of the digital economy analysed, followed by ‘ICT’ (235,340), and the far less present ‘digital economy’ (37,230), and finally ‘sharing, platform or gig economy’ (23,032). This suggests that gender analysis of the digital economy has focused more on the technological than the economic dimension. The third difference is ‘feminist theory’ has been used more to focus on ‘technology’ (605,000) than in the rest the rest of the spheres of the digital economy analysed. Moreover, finally, the economic dimension (sharing, platform or gig economy and digital economy) has generally been analysed much less compared with technology and ICT. In addition, analysis of the economic dimension has focused on ‘women’ (17,200–12,380) more than on ‘feminism’ (4.,170–1.,426) or ‘feminist theory’ (2.,660–1.,466). This suggests that the economic dimension of the digital economy has been far less analysed from a gender perspective and any analysis has been uncritical, while critical gender analysis has focused mainly on technology.

The comparative analysis of the total results identifies some trends, such as a greater representation of gender analysis using an uncritical and surface-level approach; a greater representation of the gender analysis of the technological dimension of the digital economy than the economic dimension; and, finally, the more critical gender approach has focused on the technology dimension of the digital economy in contrast with the economic dimension, whose analysis seems to use a more superficial approach.

RQ2 What gender approaches to the digital economy can be identified? And, what are the strengths and weaknesses of the different gender approaches

The results of the in-depth analysis of the content methodology of the sample of 166 works allowed us to identify three central approaches across the work from 1995 to June 2020. These approaches are the ‘feminist theory of technology and ICT’; the ‘feminist political economy’; and ‘women’s participation and economic mainstream’. The full references of all these works can be found in the open database ‘Gender approaches to the digital economy: selected database’.

The ‘feminist theory of technology and ICT’ approach: feminist framework on ICT and digital technology but no specifically economic dimension.

The first gender approach in the study of gender and the digital economy is characterised by a strong focus on feminist theory with a critical position about the structural inequalities of ICT and digital technology. There has been an extensive research tradition of feminist theories of technology, gender studies of technology and technofeminism since the beginning of the nineties to today, with the works of relevant academic authors such as Judy Wajcman, Cynthia Cockburn and Wendy Faulkner.

Many feminist technology studies have historically been motivated by a desire for political change (Wyatt, 2008 ). Early feminist responses to the digital revolution were largely optimistic about the potential of digital technologies, particularly ICT, to empower women and transform gender relations (Plant, 1997 ; VNS Matrix, 1995 ). Cyberspace seemed like a new gender-neutral space and a democratising and emancipatory platform for individuals, who had now become cyborgs (Haraway, 1990 ). The World Wide Web provided the first-ever public space accessible globally that connected women across the world (Youngs, 2015 ). However, soon, research on the feminist theory of technology proved that gender is embedded in technology itself and the digital revolution is taking place within the same patriarchal institutions, which contain structural gender inequalities. The fundamental contribution of this approach is the understanding of the social construction of technology, and that it is not gender neutral (Wajcman, 2010 ). Gender inequalities persist everywhere and digital technologies form part of the structure and performance of inequality (Wyatt, 2008 ). Technology may be gendered in various ways (Faulkner, 2001 ). Rather than conceiving of gender as fixed and existing independently of technology, the notion of performativity (Butler, 1990 ), or ‘gender as doing’, sees the construction of gender identities as shaped together with the technology in the making. Thus, both technology and gender are products of a moving relational process, emerging from collective and individual acts of interpretation (Wajcman, 2010 ). FTS scholars question the taken-for-granted association of men and machines as the result of the historical and cultural construction of gender. Similarly, the standard conceptions of innovation, production and work have been subject to scrutiny. Just as feminist economists have redefined the discipline of economics to take into account unpaid domestic and caring work (Folbre, 1994 ), FTS scholars have argued for the significance of everyday life technology.

In general, this approach is framed within the feminist discourse that draws upon poststructuralist, postmodernist and social constructivism. It focuses on the study of forms of gender exclusion and gender exploitation of digital technology based on identity and symbolic creations and recreations, and the creation of a social imaginary of dominant liberal narratives of empowerment (Fotopoulou, 2017 ). It advocates a change and transformation of digital technology for fair and equal purposes. However, it does not explore the significant material consequences on the living conditions of those suffering from gender-based discrimination, nor does it study how identity creations may be at the root of the unequal economic system, as the following approach will do.

The ‘feminist political economy’ approach: feminist framework on power and economics but no specifically ICT dimension.

Feminist theorists and feminist economics scholars have made a crucial contribution to developing the ‘feminist political economy’ approach in the study of the digital economy. Feminism in general and feminist economics in particular confront the new challenges that technology may pose for the way we relate to work and employment, to production, as well as the changes this will bring to our lives and to political participation (Agenjo-Calderón and Gálvez-Muñoz, 2019 , p. 162).

The works of some relevant authors such as Kelly Jarret, Ursula Huws, and Tiziana Terranova have made use of the theoretical groundwork of feminist and autonomist Marxist and feminist economics, with notions such as immaterial labour, the social factory, unwaged domestic work and social reproduction and free labour. Some of the works from this approach are built on the work of relevant feminist scholars such as Silvia Federici and Carole Patman, who are cited by Van Doorn ( 2017 ); Betty Friedan, cited by Eichner ( 2016 ); and Angela Davis, Rosa Luxemburg, Dalla Costa and Nancy Fraser, cited by Fuchs ( 2018 ).

ICT may be seen as a new form of economy, but feminist studies have demonstrated that their structures substantially reflect the dominant neoliberal patterns of ownership and control that have characterised the industrialised economy (Youngs, 2010 ). Similarly to the feminist theory of technology, feminist economics scholars have evidenced the social construction of economics (Ferber and Nelson, 1993 ) and have helped to understand economics from a larger and more systemic perspective as a method of ‘social provisioning’, as the way in which humans collectively organise themselves in order to guarantee their survival (Power, 2004 , p. 7). Feminist economics academics Footnote 1 have criticised the dominant understanding of the economy as being determined by patriarchal epistemology and by a historically based social construction that is organised according to the main power relationships, with gender as a central category. In line with the feminist economics perspective, works from this approach have confirmed that the digital economy is not ahistorical nor a neutral process that has emerged from technology trends; rather, they emphasise that gig work is contingent on the social context that it draws upon and it (re)produces intersectional categories (Webster and Zhang, 2020 ).

The claim of the second wave of feminism, the personal is political (Millett, 1971 ), reconstructed the understanding of gender inequalities as structural problems such as work inequalities, violence or poverty. It meant an opening of ‘private’ matters to political and economic analysis and social discussion. For Marxist and socialist feminism, patriarchy was not just seen as a form of sexist oppression, but as the exploitation of houseworkers in capitalism. In the dual systems theory, both capitalism and patriarchy were responsible for women’s oppression (Hartmann, 1979 ). In response to Marxists’ singular focus on the ‘public labour market’, feminist scholars contributed an analysis of ‘women’s work’—reproductive and care work—as a key source of capital accumulation. The mutually constitutive relationship between production and reproduction of capitalism (Jarrett, 2015 ) is crucial to understanding new business models and forms of labour. It was from feminism that the ‘intersectional’ concept was developed as a legacy of black feminism to refer to the interrelated and overlapping of social categories such as gender, race and class, among others, which are mutually constitutive systems of oppression and discrimination (Crenshaw, 1990 ; Valentine, 2007 ). Works from this approach analyse gender and intersectional inequalities or denounce the lack of intersectionality analysis in mainstream research. In addition, this approach mostly critiques the new neoliberal feminism of the digital economy, which endorses individualisation and market-based solutions to employment (Shade, 2018 ). It enlarges the study of feminised forms of economic productivity from feminist economics within the digital economy. In contrast to the previous approach, which focuses mainly on the study of gendered cultural processes in technology and ICT, this approach focuses, to a greater extent, on the gendered structure of the economy and the role that gendered economic dynamics play in the process of capital accumulation.

The ‘mainstream economic analysis and women’s participation and labour in the digital economy’ approach: without a feminist framework on ICT but with a mainstream economic dimension.

The analysis of the interplay between women’s labour and the digital economy started developing after 2010 with the boom of platform business models. Most of the reports and policy papers from major international institutions such as the United Nations, the European Commission or the European Parliament, and OECD and private organisations and associations such as the World Bank, the World Economic Forum and the GSMA are framed in this approach. It seems that in much of the literature on gender and digital economy has arisen in tandem with labour studies, sociology, ICT and media studies, the feminist theory has not arisen naturally. The study of gender in this approach employs mostly a surface-level analysis and is sometimes limited to women and men’s differences in their participation in and uses of ICT and female labour and sectors.

The interactions of gender and the digital economy are analysed from a ‘mainstream’ perspective, which is mostly non-critical and androcentric. It shows clearly how “for many non-feminist researchers, ‘gender’ is often seen as only relevant when women are at issue, and much of the emerging technology spaces are constructed as cisgendered male-only zones” (Shaw, 2014 , p. 271). There is an excessive focus on ‘women and men’s’ differences in access and participation, and, in some respects, this may contribute to the neglect in understanding how gender relations start with the conceptualisation of economic activity itself, the technological design, and so on. The gendered embodiment of the digital technology that supports these platforms, which is characteristic of the first approach, is not covered. The lack of background feminist theories is evident in the lack of feminist and gender authors and the widespread use of expressions such as ‘gender impact of digital economy’, while feminist theories of technologies have largely evidenced the dual relationship of gender and technologies to produce and reproduce inequalities (Wajcman, 2010 ), or how ‘gender inclusion’ denotes an intention to increase women’s participation without questioning the status quo of a system that maintains exclusion mechanisms. In fact, authors from the feminist political economy approach, such as Ursula Huws, have pointed out that “…‘the domestic labour debate’ of the ‘70s has been somewhat underexplored in the current digital economic context with the increasing rise of on-demand services platforms” (Huws, 2019 , p. 9). In fact, a recurrent issue is the analysis of the opportunities that the space and time flexibility of the platform economy gives to women as caregivers.

People who are responsible for caring for loved ones need the ability to fit their work schedules and their caregiving schedules together. That’s where the new digital labour economy holds enormous promise. (Slaughter, 2015 , p. 2).

As was mentioned for the previous approaches, this approach does not suppose a homogenous corpus of works and authors’ point of view. It does include some important critical authors on the platform economy such as Judy Schor, because although she works on discrimination and inequalities, including gender inequalities, she does not include any explicit feminist framework; or books such as Gender and Innovation in the New Economy (Poutanen and Kovalainen, 2017 ), because it represents, to a greater extent, a mainstream economic approach with its mostly business-focused literature.

This approach encompasses important works of international institutions and appears to pique the interest of ICT scholars. The analysis of the research activity and trends in gender analysis in the study of the digital economy showed that this approach has abounded most in recent years. The lack of a gender analysis beyond the empirical difference between women and men may be due to the lack of or difficulty in applying gender analysis tools in the field and the lack of study of feminist theories and gender studies in interdisciplinary works.

RQ3. What are the specific gender issues being addressed?

The in-depth content analysis of the sample of 166 works also enabled us to identify eight specific gender issues found across the three different gender approaches. Table  5 lists the gender topics addressed and some examples of works for each of them. The full references of all these works can be found in the open database ‘Gender approaches to the digital economy: selected database’.

Based on an SLR, the analysis shows that gender analysis on the digital economy has been limited and unsystematized, and needs to be expanded and explored in greater depth, while a dialogue about it needs to be started to create coherence. Although the research was limited, three main approaches are have been identified in the study of the interplay of gender and the digital economy: the ‘feminist theory of technology and ICT’ approach; the ‘feminist political economy’ approach; and the ‘mainstream economic analysis and women’s participation and labour in the digital economy’ approach. Each has its strengths and limitations and addresses elements related to gender and the digital economy, but overall the study highlights the lack of a direct feminist gender critique of the digital economy.

The term gender approach is not a homogeneous one but it reflects a broad compendium of gender inequality analysis ranging from more structural to more surface level. The wide range of issues and how they are approached is proven by the diversity of issues addressed: the gender embedment of digital technology (identity, symbolic, social imaginaries); the women’s rights agenda: advocacy, political activism and empowerment initiatives; gender-based violence and sexual harassment in the digital economy; new forms of value creation—commodification of care, domestic and leisure activities; economic epistemology and dichotomy of work; the sexual division of labour: public vs. private, productive vs. reproductive; women’s access to and use of the digital economy; and time and space flexibility in work. Other gender issues were found among the works, such as intimacy and sexuality in the digital economy; privacy, data and surveillance; and free and open source software movements and gender, but these were not mentioned as they are less present.

Gender approaches have been also characterised on the basis of their feminist and critical foundations. However, even if gender analysis has been developed through feminist and critical issues, this does not necessarily mean that the analysis was critical and based on feminist theory. For the future development of this field—and we hope that this paper helps to move in this direction—it would be beneficial to start a dialogue about the diverse gender conceptions. In this respect, it would be useful for future research to develop a framework of the diverse gender dimensions and feminist qualities in the digital economy.

Finally, despite the plurality of elements analysed, there are still many areas of the digital economy, some of which are key elements that have not been explicitly analysed, such as digital business models and the platform economy, or gender plans specific to the digital sphere.

Data availability

The datasets generated during and/or analysed during the current study are available in the Gender approaches to the digital economy: selected database repository, https://docs.google.com/document/d/1xxvRQS545GEeJaHpT_NxuA6Vne8hUWrSdtX8dSFlmR4/edit?pli=1#heading=h.l5wy8shuwl1c

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Grau-Sarabia, M., Fuster-Morell, M. Gender approaches in the study of the digital economy: a systematic literature review. Humanit Soc Sci Commun 8 , 201 (2021). https://doi.org/10.1057/s41599-021-00875-x

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Here we try to answer these questions, providing an empirical overview of the gender pay gap across countries and over time.

The gender pay gap measures inequality but not necessarily discrimination

The gender pay gap (or the gender wage gap) is a metric that tells us the difference in pay (or wages, or income) between women and men. It's a measure of inequality and captures a concept that is broader than the concept of equal pay for equal work.

Differences in pay between men and women capture differences along many possible dimensions, including worker education, experience, and occupation. When the gender pay gap is calculated by comparing all male workers to all female workers – irrespective of differences along these additional dimensions – the result is the 'raw' or 'unadjusted' pay gap. On the contrary, when the gap is calculated after accounting for underlying differences in education, experience, etc., then the result is the 'adjusted' pay gap.

Discrimination in hiring practices can exist in the absence of pay gaps – for example, if women know they will be treated unfairly and hence choose not to participate in the labor market. Similarly, it is possible to observe large pay gaps in the absence of discrimination in hiring practices – for example, if women get fair treatment but apply for lower-paid jobs.

The implication is that observing differences in pay between men and women is neither necessary nor sufficient to prove discrimination in the workplace. Both discrimination and inequality are important. But they are not the same.

In most countries, there is a substantial gender pay gap

Cross-country data on the gender pay gap is patchy, but the most complete source in terms of coverage is the United Nation's International Labour Organization (ILO). The visualization here presents this data. You can add observations by clicking on the option 'add country' at the bottom of the chart.

The estimates shown here correspond to differences between the average hourly earnings of men and women (expressed as a percentage of average hourly earnings of men), and cover all workers irrespective of whether they work full-time or part-time. 1

As we can see: (i) in most countries the gap is positive – women earn less than men, and (ii) there are large differences in the size of this gap across countries. 2

In most countries, the gender pay gap has decreased in the last couple of decades

How is the gender pay gap changing over time? To answer this question, let's consider this chart showing available estimates from the OECD. These estimates include OECD member states, as well as some other non-member countries, and they are the longest available series of cross-country data on the gender pay gap that we are aware of.

Here we see that the gap is large in most OECD countries, but it has been going down in the last couple of decades. In some cases the reduction is remarkable. In the United States, for example, the gap declined by more than half.

These estimates are not directly comparable to those from the ILO, because the pay gap is measured slightly differently here: The OECD estimates refer to percent differences in median earnings (i.e. the gap here captures differences between men and women in the middle of the earnings distribution), and they cover only full-time employees and self-employed workers (i.e. the gap here excludes disparities that arise from differences in hourly wages for part-time and full-time workers).

However, the ILO data shows similar trends.

The conclusion is that in most countries with available data, the gender pay gap has decreased in the last couple of decades.

The gender pay gap is larger for older workers

The United States Census Bureau defines the pay gap as the ratio between median wages – that is, they measure the gap by calculating the wages of men and women at the middle of the earnings distribution, and dividing them.

By this measure, the gender wage gap is expressed as a percent (median earnings of women as a share of median earnings of men) and it is always positive. Here, values below 100% mean that women earn less than men, while values above 100% mean that women earn more. Values closer to 100% reflect a lower gap.

The next chart shows available estimates of this metric for full-time workers in the US, by age group.

First, we see that the series trends upwards, meaning the gap has been shrinking in the last couple of decades. Secondly, we see that there are important differences by age.

The second point is crucial to understanding the gender pay gap: the gap is a statistic that changes during the life of a worker. In most rich countries, it’s small when formal education ends and employment begins, and it increases with age. As we discuss in our analysis of the determinants below, the gender pay gap tends to increase when women marry and when/if they have children.

The gender pay gap is smaller in middle-income countries – which tend to be countries with low labor force participation of women

The chart here plots available ILO estimates on the gender pay gap against GDP per capita. As we can see there is a weak positive correlation between GDP per capita and the gender pay gap. However, the chart shows that the relationship is not really linear. Actually, middle-income countries tend to have the smallest pay gap.

The fact that middle-income countries have low gender wage gaps is, to a large extent, the result of selection of women into employment . Olivetti and Petrongolo (2008) explain it as follows: “[I]f women who are employed tend to have relatively high‐wage characteristics, low female employment rates may become consistent with low gender wage gaps simply because low‐wage women would not feature in the observed wage distribution.” 3

Olivetti and Petrongolo (2008) show that this pattern holds in the data: unadjusted gender wage gaps across countries tend to be negatively correlated with gender employment gaps. That is, the gender pay gaps tend to be smaller where relatively fewer women participate in the labor force .

So, rather than reflect greater equality, the lower wage gaps observed in some countries could indicate that only women with certain characteristics – for instance, with no husband or children – are entering the workforce.

Why is there a gender pay gap?

In almost all countries, if you compare the wages of men and women you find that women tend to earn less than men.  These inequalities have been narrowing across the world. In particular, most high-income countries have seen sizeable reductions in the gender pay gap over the last couple of decades.

How did these reductions come about and why do substantial gaps remain?

Before we get into the details, here is a preview of the main points.

• An important part of the reduction in the gender pay gap in rich countries over the last decades is due to a historical narrowing, and often even reversal of the education gap between men and women.
• Today, education is relatively unimportant in explaining the remaining gender pay gap in rich countries. In contrast, the characteristics of the jobs that women tend to do, remain important contributing factors.
• The gender pay gap is not a direct metric of discrimination. However, evidence from different contexts suggests discrimination is indeed important to understand the gender pay gap. Similarly, social norms affecting the gender distribution of labor are important determinants of wage inequality.
• On the other hand, the available evidence suggests differences in psychological attributes and non-cognitive skills are at best modest factors contributing to the gender pay gap.

Differences in human capital

The adjusted pay gap.

Differences in earnings between men and women capture differences across many possible dimensions, including education, experience, and occupation.

For example, if we consider that more educated people tend to have higher earnings, it is natural to expect that the narrowing of the pay gap across the world can be partly explained by the fact that women have been catching up with men in terms of educational attainment, in particular years of schooling.

Indeed, since differences in education partly contribute to explaining differences in wages, it is common to distinguish between 'unadjusted' and 'adjusted' pay differences.

When the gender pay gap is calculated by comparing all male and female workers, irrespective of differences in worker characteristics, the result is the raw or unadjusted pay gap. In contrast to this, when the gap is calculated after accounting for underlying differences in education, experience, and other factors that matter for the pay gap, then the result is the adjusted pay gap.

The idea of the adjusted pay gap is to make comparisons within groups of workers with roughly similar jobs, tenure, and education. This allows us to tease out the extent to which different factors contribute to observed inequalities.

The chart here, from Blau and Kahn (2017) shows the evolution of the adjusted and unadjusted gender pay gap in the US. 4

More precisely, the chart shows the evolution of female-to-male wage ratios in three different scenarios: (i) Unadjusted; (ii) Adjusted, controlling for gender differences in human capital, i.e. education and experience; and (iii) Adjusted, controlling for a full range of covariates, including education, experience, job industry, and occupation, among others. The difference between 100% and the full specification (the green bars) is the “unexplained” residual. 5

Several points stand out here.

• First, the unadjusted gender pay gap in the US shrunk over this period. This is evident from the fact that the blue bars are closer to 100% in 2010 than in 1980.
• Second, if we focus on groups of workers with roughly similar jobs, tenure, and education, we also see a narrowing. The adjusted gender pay gap has shrunk.
• Third, we can see that education and experience used to help explain a very large part of the pay gap in 1980, but this changed substantially in the decades that followed. This third point follows from the fact that the difference between the blue and red bars was much larger in 1980 than in 2010.
• And fourth, the green bars grew substantially in the 1980s, but stayed fairly constant thereafter. In other words: Most of the convergence in earnings occurred during the 1980s, a decade in which the "unexplained" gap shrunk substantially.

Education and experience have become much less important in explaining gender differences in wages in the US

The next chart shows a breakdown of the adjusted gender pay gaps in the US, factor by factor, in 1980 and 2010.

When comparing the contributing factors in 1980 and 2010, we see that education and work experience have become much less important in explaining gender differences in wages over time, while occupation and industry have become more important. 6

In this chart we can also see that the 'unexplained' residual has gone down. This means the observable characteristics of workers and their jobs explain wage differences better today than a couple of decades ago. At first sight, this seems like good news – it suggests that today there is less discrimination, in the sense that differences in earnings are today much more readily explained by differences in 'productivity' factors. But is this really the case?

The unexplained residual may include aspects of unmeasured productivity (i.e. unobservable worker characteristics that could not be accounted for in the study), while the "explained" factors may themselves be vehicles of discrimination.

For example, suppose that women are indeed discriminated against, and they find it hard to get hired for certain jobs simply because of their sex. This would mean that in the adjusted specification, we would see that occupation and industry are important contributing factors – but that is precisely because discrimination is embedded in occupational differences!

Hence, while the unexplained residual gives us a first-order approximation of what is going on, we need much more detailed data and analysis in order to say something definitive about the role of discrimination in observed pay differences.

Gender pay differences around the world are better explained by occupation than by education

The set of three maps here, taken from the World Development Report (2012) , shows that today gender pay differences are much better explained by occupation than by education. This is consistent with the point already made above using data for the US: as education expanded radically over the last few decades, human capital has become much less important in explaining gender differences in wages.

Justin Sandefur at the Center for Global Development shows that education also fails to explain wage gaps if we include workers with zero income (i.e. if we decompose the wage gap after including people who are not employed).

Looking beyond worker characteristics

Job flexibility.

All over the world women tend to do more unpaid care work at home than men – and women tend to be overrepresented in low-paying jobs where they have the flexibility required to attend to these additional responsibilities.

The most important evidence regarding this link between the gender pay gap and job flexibility is presented and discussed by Claudia Goldin in the article ' A Grand Gender Convergence: Its Last Chapter ', where she digs deep into the data from the US. 8 There are some key lessons that apply both to rich and non-rich countries.

Goldin shows that when one looks at the data on occupational choice in some detail, it becomes clear that women disproportionately seek jobs, including full-time jobs, that tend to be compatible with childrearing and other family responsibilities. In other words, women, more than men, are expected to have temporal flexibility in their jobs. Things like shifting hours of work and rearranging shifts to accommodate emergencies at home. And these are jobs with lower earnings per hour, even when the total number of hours worked is the same.

The importance of job flexibility in this context is very clearly illustrated by the fact that, over the last couple of decades, women in the US increased their participation and remuneration in only some fields. In a recent paper, Goldin and Katz (2016) show that pharmacy became a highly remunerated female-majority profession with a small gender earnings gap in the US, at the same time as pharmacies went through substantial technological changes that made flexible jobs in the field more productive (e.g. computer systems that increased the substitutability among pharmacists). 9

The chart here shows how quickly female wages increased in pharmacy, relative to other professions, over the last few decades in the US.

The motherhood penalty

Closely related to job flexibility and occupational choice is the issue of work interruptions due to motherhood. On this front, there is again a great deal of evidence in support of the so-called 'motherhood penalty'.

Lundborg, Plug, and Rasmussen (2017) provide evidence from Denmark – more specifically, Danish women who sought medical help in achieving pregnancy. 10

By tracking women’s fertility and employment status through detailed periodic surveys, these researchers were able to establish that women who had a successful in vitro fertilization treatment, ended up having lower earnings down the line than similar women who, by chance, were unsuccessfully treated.

Lundborg, Plug, and Rasmussen summarise their findings as follows: "Our main finding is that women who are successfully treated by [in vitro fertilization] earn persistently less because of having children. We explain the decline in annual earnings by women working less when children are young and getting paid less when children are older. We explain the decline in hourly earnings, which is often referred to as the motherhood penalty, by women moving to lower-paid jobs that are closer to home."

The fact that the motherhood penalty is indeed about ‘motherhood’ and not ‘parenthood’, is supported by further evidence.

A recent study , also from Denmark, tracked men and women over the period 1980-2013 and found that after the first child, women’s earnings sharply dropped and never fully recovered. But this was not the case for men with children, nor the case for women without children.

These patterns are shown in the chart here. The first panel shows the trend in earnings for Danish women with and without children. The second panel shows the same comparison for Danish men.

Note that these two examples are from Denmark – a country that ranks high on gender equality measures and where there are legal guarantees requiring that a woman can return to the same job after taking time to give birth.

This shows that, although family-friendly policies contribute to improving female labor force participation and reducing the gender pay gap , they are only part of the solution. Even when there is generous paid leave and subsidized childcare, as long as mothers disproportionately take additional work at home after having children, inequities in pay are likely to remain.

Ability, personality, and social norms

The discussion so far has emphasized the importance of job characteristics and occupational choice in explaining the gender pay gap. This leads to obvious questions: What determines the systematic gender differences in occupational choice? What makes women seek job flexibility and take a disproportionate amount of unpaid care work?

One argument usually put forward is that, to the extent that biological differences in preferences and abilities underpin gender roles, they are the main factors explaining the gender pay gap. In their review of the evidence, Francine Blau and Lawrence Kahn (2017) show that there is limited empirical support for this argument. 11

To be clear, yes, there is evidence supporting the fact that men and women differ in some key attributes that may affect labor market outcomes. For example, standardized tests show that there are statistical gender gaps in maths scores in some countries ; and experiments show that women avoid more salary negotiations , and they often show particular predisposition to accept and receive requests for tasks with low promotion potential . However, these observed differences are far from being biologically fixed – 'gendering' begins early in life and the evidence shows that preferences and skills are highly malleable. You can influence tastes, and you can certainly teach people to tolerate risk, to do maths, or to negotiate salaries.

What's more, independently of where they come from, Blau and Kahn show that these empirically observed differences can typically only account for a modest portion of the gender pay gap.

In contrast, the evidence does suggest that social norms and culture, which in turn affect preferences, behavior, and incentives to foster specific skills, are key factors in understanding gender differences in labor force participation and wages. You can read more about this farther below.

Discrimination and bias

Independently of the exact origin of the unequal distribution of gender roles, it is clear that our recent and even current practices show that these roles persist with the help of institutional enforcement. Goldin (1988), for instance, examines past prohibitions against the training and employment of married women in the US. She touches on some well-known restrictions, such as those against the training and employment of women as doctors and lawyers, before focusing on the lesser known but even more impactful 'marriage bars' that arose in the late 1800s and early 1900s. These work prohibitions are important because they applied to teaching and clerical jobs – occupations that would become the most commonly held among married women after 1950. Around the time the US entered World War II, it is estimated that 87% of all school boards would not hire a married woman and 70% would not retain an unmarried woman who married. 12

The map here highlights that to this day, explicit barriers limit the extent to which women are allowed to do the same jobs as men in some countries. 13

However, even after explicit barriers are lifted and legal protections put in place, discrimination and bias can persist in less overt ways. Goldin and Rouse (2000), for example, look at the adoption of "blind" auditions by orchestras and show that by using a screen to conceal the identity of a candidate, impartial hiring practices increased the number of women in orchestras by 25% between 1970 and 1996. 14

Many other studies have found similar evidence of bias in different labor market contexts. Biases also operate in other spheres of life with strong knock-on effects on labor market outcomes. For example, at the end of World War II only 18% of people in the US thought that a wife should work if her husband was able to support her . This obviously circles back to our earlier point about social norms. 15

Strategies for reducing the gender pay gap

In many countries wage inequality between men and women can be reduced by improving the education of women. However, in many countries, gender gaps in education have been closed and we still have large gender inequalities in the workforce. What else can be done?

An obvious alternative is fighting discrimination. But the evidence presented above shows that this is not enough. Public policy and management changes on the firm level matter too: Family-friendly labor-market policies may help. For example, maternity leave coverage can contribute by raising women’s retention over the period of childbirth, which in turn raises women’s wages through the maintenance of work experience and job tenure. 16

Similarly, early education and childcare can increase the labor force participation of women — and reduce gender pay gaps — by alleviating the unpaid care work undertaken by mothers. 17

Additionally, the experience of women's historical advance in specific professions (e.g. pharmacists in the US), suggests that the gender pay gap could also be considerably reduced if firms did not have the incentive to disproportionately reward workers who work long hours, and fixed, non-flexible schedules. 18

Changing these incentives is of course difficult because it requires reorganizing the workplace. But it is likely to have a large impact on gender inequality, particularly in countries where other measures are already in place. 19

Implementing these strategies can have a positive self-reinforcing effect. For example, family-friendly labor-market policies that lead to higher labor-force attachment and salaries for women will raise the returns on women's investment in education – so women in future generations will be more likely to invest in education, which will also help narrow gender gaps in labor market outcomes down the line. 20

Nevertheless, powerful as these strategies may be, they are only part of the solution. Social norms and culture remain at the heart of family choices and the gender distribution of labor. Achieving equality in opportunities requires ensuring that we change the norms and stereotypes that limit the set of choices available both to men and women. It is difficult, but as the next section shows, social norms can be changed, too.

How well do biological differences explain the gender pay gap?

Across the world, women tend to take on more family responsibilities than men. As a result, women tend to be overrepresented in low-paying jobs where they are more likely to have the flexibility required to attend to these additional responsibilities.

These two facts – documented above – are often used to claim that, since men and women tend to be endowed with different tastes and talents, it follows that most of the observed gender differences in wages stem from biological sex differences. But what’s the broader evidence for these claims?

In a nutshell, here's what the research and data shows:

• There is evidence supporting the fact that statistically speaking, men and women tend to differ in some key aspects, including psychological attributes that may affect labor-market outcomes.
• There is no consensus on the exact weight that nurture and nature have in determining these differences, but whatever the exact weight, the evidence does show that these attributes are strongly malleable.
• Regardless of the origin, these differences can only explain a modest part of the gender pay gap.

Some context regarding the gender distribution of labor

Before we get into the discussion of whether biological attributes explain wage differences via gender roles, let's get some perspective on the gender distribution of work.

The following chart shows, by country, the female-to-male ratio of time devoted to unpaid care work, including tasks like taking care of children at home, housework, or doing community work. As can be seen, all over the world there is a radical unbalance in the gender distribution of labor – everywhere women take a disproportionate amount of unpaid work.

This is of course closely related to the fact that in most countries there are gender gaps in labor force participation and wages .

“Boys are better at maths”

Differences in biological attributes that determine our ability to develop 'hard skills', such as maths, are often argued to be at the heart of the gender pay gap. 21 Do large gender differences in maths skills really exist? If so, is this because of differences in the attributes we are born with?

Let's look at the data.

Are boys better in the mathematics section of the PISA standardized test ? One could argue that looking at top scores is more relevant here since top scores are more likely to determine gaps in future professional trajectories – for example, gaps in access to 'STEM degrees' at the university level.

The chart shows the share of male and female test-takers scoring at the highest level on the PISA test (that's level 6). As we can see, most countries lie above the diagonal line marking gender parity; so yes, achieving high scores in maths tends to be more common among boys than girls. However, there is huge cross-country variation – the differences between countries are much larger than the differences between the sexes. And in many countries, the gap is effectively inexistent. 22

Similarly, researchers have found that within countries there is also large geographic variation in gender gaps in test scores. So clearly these gaps in mathematical ability do not seem to be fully determined by biological endowments. 23

Indeed, research looking at the PISA cross-country results suggests that improved social conditions for women are related to improved math performance by girls. 24

Not only do statistical gaps in test scores vary substantially across societies – they also vary substantially across time. This suggests that social factors play a large role in explaining differences between the sexes.

In the US, for example, the gender gap in mathematics has narrowed in recent decades. 25 And this narrowing took place as high school curricula of boys and girls became more similar. The following chart shows this: In the US boys in 1957 took far more math and science courses than did girls; but by 1992 there was virtual parity in almost all science and math courses.

More importantly for the question at hand, gender gaps in 'hard skills' are not large enough to explain the gender gaps in earnings. In their review of the evidence, Blau and Kahn (2017) concludes that gaps in test scores in the US are too small to explain much of the gender pay at any point in time. 26

So, taken together, the evidence suggests that statistical gaps in maths test scores are both relatively small and heavily influenced by social and environmental factors.

“It’s about personality”

Biological differences in tastes (e.g. preferences for 'people' over 'things'), psychological attributes (e.g. 'risk aversion'), and soft skills (e.g. the ability to get along with others) are also often argued to be at the heart of the gender pay gap.

There are hundreds of studies trying to establish whether there are gender differences in preferences, personality traits, and 'soft skills'. The quality and general relevance (i.e. the internal and external validity) of these studies is the subject of much discussion, as illustrated in the recent debate that ensued from the Google Memo affair .

A recent article from the 'Heterodox Academy ', which was produced specifically in the context of the Google Memo, provides a fantastic overview of the evidence on this topic and the key points of contention among scholars.

For the purpose of this blog post, let's focus on the review of the evidence presented in Blau and Kahn (2017) – their review is particularly helpful because they focus on gender differences in the context of labor markets.

Blau and Kahn point out that, yes, researchers have found statistical differences between men and women that are important in the context of labor-market outcomes. For example, studies have found statistical gender differences in 'people skills' (i.e. ability to listen, communicate, and relate to others). Similarly, experimental studies have found that women more often avoid salary negotiations , and they often show a particular predisposition to accept and receive requests for tasks with low promotability. But are the origins of these differences mainly biological or are they social? And are they strong enough to explain pay gaps?

The available evidence here suggests these factors can only explain a relatively small fraction of the observed differences in wages. 27 And they are anyway far from being purely biological – preferences and skills are highly malleable and 'gendering' begins early in life. 28

Here is a concrete example: Leibbrandt and List (2015) did an experiment in which they assessed how men and women reacted to job advertisements. 29 They found that although men were more likely to negotiate than women when there was no explicit statement that wages were negotiable, the gender difference disappeared and even reversed when it was explicitly stated that wages were negotiable. This suggests that it is not as much about 'talent', as it is about norms and rules.

“A man should earn more than his wife”

The experiment in which researchers found that gender differences in negotiation attitudes disappeared when it was explicitly stated that wages were negotiable, emphasizes the important role that social norms and culture play in labor-market outcomes.

These concepts may seem abstract: What do social norms and culture actually look like in the context of the gender pay gap?

The reproduction of stereotypes through everyday positive enforcement can be seen in a range of aspects: A study analyzing 124 prime-time television programs in the US found that female characters continue to inhabit interpersonal roles with romance, family, and friends, while male characters enact work-related roles. 30 In the realm of children’s books, a study of 5,618 books found that compared to females, males are represented nearly twice as often in titles and 1.6 times as often as central characters. 31 Qualitative research shows that even in the home, parents are often enforcers of gender norms – especially when it comes to fathers endorsing masculinity in male children. 32

Of particular relevance in the context of labor markets, social norms also often take the form of specific behavioral prescriptions such as "a man should earn more than his wife".

The following chart depicts the distribution of the share of the household income earned by the wife, across married couples in the US.

Consistent with the idea that "a man should earn more than his wife", the data shows a sharp drop at 0.5, the point where the wife starts to earn more than the husband.

Distribution of income share earned by the wife across married couples in the US – Bertrand, Kamenica, and Pan (2015) 33

This is the result of two factors. First, it is about the matching of men and women before they marry – 'matches' in which the woman has higher earning potential are less common. Second, it is a result of choices after marriage – the researchers show that married women with higher earning potential than their husbands often stay out of the labor force, or take 'below-potential' jobs. 34

The authors of the study from which this chart is taken explored the data in more detail and found that in couples where the wife earns more than the husband, the wife spends more time on household chores, so the gender gap in unpaid care work is even larger; and these couples are also less satisfied with their marriage and are more likely to divorce than couples where the wife earns less than the husband.

The empirical exploration in this study highlights the remarkable power that gender norms and identity have on labor-market outcomes.

Why do gender norms and identity matter?

Does it actually matter if social norms and culture are important determinants of gender roles and labor-market outcomes? Are social norms in our contemporary societies really less fixed than biological traits?

The available research suggests that the answers to these questions are yes and yes. There is evidence that social norms can be actively and rapidly changed.

Here is a concrete example: Jensen and Oster (2009) find that the introduction of cable television in India led to a significant decrease in the reported acceptability of domestic violence towards women and son preference, as well as increases in women’s autonomy and decreases in fertility. 35

Of course, TV is a small aspect of all the big things that matter for social norms. But this study is important for the discussion because it is hard to study how social norms can be changed. TV introduction is a rare opportunity to see how a group that is exposed to a driver of social change actually changes.

As Jensen and Oster point out, most popular cable TV shows in India feature urban settings where lifestyles differ radically from those in rural areas. For example, many female characters on popular soap operas have more education, marry later, and have smaller families than most women in rural areas. And, similarly, many female characters in these tv shows are featured working outside the home as professionals, running businesses, or are shown in other positions of authority.

The bar chart below shows how cable access changed attitudes toward the self-reported preference for their child to be a son. As the authors note, "reported desire for the next child to be a son is relatively unchanged in areas with no change in cable status, but it decreases sharply between 2001 and 2002 for villages that get cable in 2002, and between 2002 and 2003 (but notably not between 2001 and 2002) for those that get cable in 2003. For both measures of attitudes, the changes are large and striking, and correspond closely to the timing of introduction of cable."

To conclude: The evidence suggests that biological differences are not a key driver of gender inequality in labor-market outcomes; while social norms and culture – which in turn affect preferences, behavior, and incentives to foster specific skills – are very important.

This matters for policy because social norms are not fixed – they can be influenced in a number of ways, including through intergenerational learning processes, exposure to alternative norms, and activism such as that which propelled the women's movement. 36

How are women represented across jobs?

Representation of women at the top of the income distribution.

Despite having fallen in recent decades, there remains a substantial pay gap between the average wages of men and women .

But what does gender inequality look like if we focus on the very top of the income distribution? Do we find any evidence of the so-called 'glass ceiling' preventing women from reaching the top? How did this change over time?

Answers to these questions are found in the work of Atkinson, Casarico and Voitchovsky (2018). Using tax records, they investigated the incomes of women and men separately across nine high-income countries. As such, they were restricted to those countries in which taxes are collected on an individual basis, rather than as couples. 37

In addition to wages they also take into account income from investments and self-employment.

Whilst investment income tends to make up a larger share of the total income of rich individuals in general, the authors found this to be particularly marked in the case of women in top-income groups.

The two charts present the key figures from the study.

One chart shows the proportion of women out of all individuals falling into the top 10%, 1%, and 0.1% of the income distribution. The open circle represents the share of women in the top income brackets back in 2000; the closed circle shows the latest data, which is from 2013.

The other chart shows the data over time for individual countries. You can explore data for other countries using the 'Change country' button on the chart.

The two charts allow us to answer the initial questions:

• Women are greatly under-represented in top income groups – they make up much less than 50% across each of the nine countries. Within the top 1% women account for around 20% and there is surprisingly little variation across countries.
• The proportion of women is lower the higher you look up the income distribution. In the top 10% up to every third income-earner is a woman; in the top 0.1% only every fifth or tenth person is a woman.
• The trend is the same in all countries of this study: Women are now better represented in all top-income groups than they were in 2000.
• But improvements have generally been more limited at the very top. With the exception of Australia, we see a much smaller increase in the share of women amongst the top 0.1% than amongst the top 10%.

Overall, despite recent inroads, we continue to see remarkably few women making it to the top of the income distribution today.

Representation of women in management positions

The chart here plots the proportion of women in senior and middle management positions around the world. It shows that women all over the world are underrepresented in high-profile jobs, which tend to be better paid.

The next chart provides an alternative perspective on the same issue. Here we show the share of firms that have a woman as manager. We highlight world regions by default, but you can remove them and add specific countries.

As we can see, all over the world firms tend to be managed by men. And, globally, only about 18% of firms have a female manager.

Firms with female managers tend to be different to firms with male managers. For example, firms with female managers tend to also be firms with more female workers .

Representation of women in low-paying jobs

Above we show that women all over the world are underrepresented in high-profile jobs, which tend to be better paid. As it turns out, in many countries women are at the same time overrepresented in low-paying jobs.

This is shown in the chart here, where 'low-pay' refers to workers earning less than two-thirds of the median (i.e. the middle) of the earnings distribution.

A share above 50% implies that women are 'overrepresented', in the sense that among those with low wages, there are more women than men.

The fact that women in rich countries are overrepresented in the bottom of the income distribution goes together with the fact that working women in these countries are overrepresented in low-paying occupations. The chart shows this for the US.

How much control do women have over household resources?

Women often have no control over their personal earned income.

The next chart plots cross-country estimates of the share of women who are not involved in decisions about their own income. The line shows national averages, while the dots show averages for rich and poor households (i.e. averages for women in households within the top and bottom quintiles of the corresponding national income distribution).

As we can see, in many countries, particularly in Sub-Saharan Africa and Asia, a large fraction of women are not involved in household decisions about spending their personal earned income. And this pattern is stronger among low-income households within low-income countries.

Percentage of women not involved in decisions about their own income – World Development Report (2012) 39

In many countries, women have limited influence over important household decisions

Above we focus on whether women get to choose how their own personal income is spent. Now we look at women's influence over total household income.

In this chart, we plot the share of currently married women who report having a say in major household purchase decisions, against national GDP per capita.

We see that in many countries, notably in Sub-Saharan Africa and Asia, an important number of women have limited influence over major spending decisions.

The chart above shows that women’s control over household spending tends to be greater in richer countries. In the next chart, we show that this correlation also holds within countries: Women’s control is greater in wealthier households. Household wealth is shown by the quintile in the wealth distribution on the x-axis – the poorest households are in the lowest quintiles (Q1) on the left.

There are many factors at play here, and it's important to bear in mind that this correlation partly captures the fact that richer households enjoy greater discretionary income beyond levels required to cover basic expenditure, while at the same time, in richer households women often have greater agency via access to broader networks as well as higher personal assets and incomes.

Land ownership is more often in the hands of men

Economic inequalities between men and women manifest themselves not only in terms of wages earned but also in terms of assets owned. For example, as the chart shows, in nearly all low and middle-income countries with data, men are more likely to own land than women.

Women's lack of control over important household assets, such as land, can be a critical problem in case of divorce or the husband’s death.

Closely related to the issue of land ownership is the fact that in several countries women do not have the same rights to property as men. These countries are highlighted in the map. 40

Gender-equal inheritance systems have been adopted in most, but not all countries

Inheritance is one of the main mechanisms for the accumulation of assets. In the map, we provide an overview of the countries that do and do not have gender-equal inheritance systems.

If you move the slider to 1920, you will see that while gender-equal inheritance systems were very rare in the early 20th century, today they are much more common. And still, despite the progress achieved, in many countries, notably in North Africa and the Middle East, women and girls still have fewer inheritance rights than men and boys.

Gender differences in access to productive inputs are often large

Above we show that there are large gender gaps in land ownership across low-income countries. Here we show that there are also large gaps in terms of access to borrowed capital.

The chart shows the percentage of men and women who report borrowing any money in the past 12 months to start, operate, or expand a farm or business.

As we can see, almost everywhere, including in many rich countries, women are less likely to obtain borrowed capital for productive purposes.

This can have large knock-on effects: in agriculture and entrepreneurship, gender differences in access to productive inputs, including land and credit, can lead to gaps in earnings via lower productivity.

Indeed, studies have found that, when statistical gender differences in agricultural productivity exist, they often disappear when access to and use of productive inputs are taken into account. 41

Interactive Charts on Economic Inequality by Gender

Acknowledgements.

We thank Sandra Tzvetkova and Diana Beltekian for their great research assistance.

There are some exceptions to this definition. In particular, sometimes self-employed workers, or part-time workers are excluded.

This measure can also be negative. This means that, on an hourly basis, men earn on average less than women. It is the case for some countries, such as Malaysia.

Olivetti, C., & Petrongolo, B. (2008). Unequal pay or unequal employment? A cross-country analysis of gender gaps. Journal of Labor Economics, 26(4), 621-654.

Blau, Francine D., and Lawrence M. Kahn. 2017. " The Gender Wage Gap: Extent, Trends, and Explanations. " Journal of Economic Literature, 55(3): 789-865.

For each specification, Blau and Kahn (2017) perform regression analyses on data from the PSID (the Michigan Panel Study of Income Dynamics), which includes information on labor-market experience and considers men and women ages 25-64 who were full-time, non-farm, wage and salary workers.

In 2010, unionization and education show negative values; this reflects the fact that women have surpassed men in educational attainment, and unionization in the US has been in general decline with a greater effect on men.

The full source is: World Development Report (2012) Gender Equality and Development , World Bank.

Goldin, C. (2014). A grand gender convergence: Its last chapter. The American Economic Review, 104(4), 1091-1119.

Goldin, C., & Katz, L. F. (2016). A most egalitarian profession: pharmacy and the evolution of a family-friendly occupation. Journal of Labor Economics, 34(3), 705-746.

Lundborg, P., Plug, E., & Rasmussen, A. W. (2017). Can Women Have Children and a Career? IV Evidence from IVF Treatments. American Economic Review, 107(6), 1611-1637.

Blau, Francine D., and Lawrence M. Kahn. 2017. " The Gender Wage Gap: Extent, Trends, and Explanations. " Journal of Economic Literature, 55(3): 789-865

Goldin, C. (1988). Marriage bars: Discrimination against married women workers, 1920's to 1950's .

The data in this map, which comes from the World Bank's World Development Indicators, provides a measure of whether there are any specific jobs that women are not allowed to perform. So, for example, a country might be coded as "No" if women are only allowed to work in certain jobs within the mining industry, such as health care professionals within mines, but not as miners.

Goldin, C., & Rouse, C. (2000). Orchestrating impartiality: The impact of" blind" auditions on female musicians. American Economic Review , 90(4), 715-741.

Blau and Kahn (2017) provide a whole list of experimental studies that have found labor-market discrimination. Another early example is from Neumark et al. (1996), who look at discrimination in restaurants. In this case, male and female pseudo-job-seekers were given similar CVs to apply for jobs waiting on tables at the same set of restaurants in Philadelphia. The results showed discrimination against women in high-priced restaurants.

The full reference of this study is Neumark, D., Bank, R. J., & Van Nort, K. D. (1996). Sex discrimination in restaurant hiring: An audit study. The Quarterly Journal of Economics, 111(3), 915-941.

Waldfogel, J. (1998). Understanding the "family gap" in pay for women with children. The Journal of Economic Perspectives, 12(1), 137-156.

Olivetti, C., & Petrongolo, B. (2017). The economic consequences of family policies: lessons from a century of legislation in high-income countries. The Journal of Economic Perspectives, 31(1), 205-230.

As we show above, in several nations, such as Sweden and Denmark, a “motherhood penalty” in earnings exists, even though these nations have generous family policies, including paid family leave and subsidized child care.

For a discussion of this mechanism, see page 814, Blau, Francine D., and Lawrence M. Kahn. 2017. The Gender Wage Gap: Extent, Trends, and Explanations. Journal of Economic Literature, 55(3): 789-865.

Hard skills are abilities that can be defined and measured, such as writing, reading, or doing maths. By contrast, soft skills are less tangible and harder to measure and quantify.

Also importantly: If we focus on gender differences for average , rather than top students, we find that there is not even a clear tendency in favor of boys. ( This interactive chart compares PISA average math scores for boys and girls ).

For more on this see Pope, D. G., & Sydnor, J. R. (2010). Geographic variation in the gender differences in test scores. Journal of Economic Perspectives, 24(2), 95-108.

Guiso, L., Monte, F., Sapienza, P., & Zingales, L. (2008). Culture, gender, and math. SCIENCE-NEW YORK THEN WASHINGTON-, 320(5880), 1164.

A number of papers have documented the narrowing of gender gaps in test scores. See, for example, Hyde, J. S., Lindberg, S. M., Linn, M. C., Ellis, A. B., & Williams, C. C. (2008). Gender similarities characterize math performance . Science, 321(5888), 494-495.

Blau, Francine D., and Lawrence M. Kahn. 2017. The Gender Wage Gap: Extent, Trends, and Explanations. Journal of Economic Literature, 55(3): 789-865.

Blau and Kahn write: "While findings such as those in table 7 ['Selected Studies Assessing the Role of Psychological Traits in Accounting for the Gender Pay Gap'] are informative in elucidating some of the possible omitted factors that lie behind gender differences in wages as well as the unexplained gap in traditional wage regressions, in general, the results suggest that these factors do not account for a large portion of either the raw or unexplained gender gap."

For a discussion of 'gendering' see West, C., & Zimmerman, D. H. (1987). Doing gender. Gender & Society, 1(2), 125-151.

Leibbrandt, A., & List, J. A. (2014). Do women avoid salary negotiations? Evidence from a large-scale natural field experiment. Management Science, 61(9), 2016-2024.

Lauzen, M. M., Dozier, D. M., & Horan, N. (2008). Constructing gender stereotypes through social roles in prime-time television. Journal of Broadcasting & Electronic Media, 52(2), 200-214.

McCabe, J., Fairchild, E., Grauerholz, L., Pescosolido, B. A., & Tope, D. (2011). Gender in twentieth-century children’s books: Patterns of disparity in titles and central characters. Gender & Society, 25(2), 197-226.

Kane, E. W. (2006). “No way my boys are going to be like that!” Parents’ responses to children’s gender nonconformity. Gender & Society, 20(2), 149-176.

Bertrand, M., Kamenica, E., & Pan, J. (2015). Gender identity and relative income within households. The Quarterly Journal of Economics, 130(2), 571-614.

More precisely, the authors find that in couples where the wife’s potential income is likely to exceed her husband’s (based on the income that would be predicted for her observed characteristics), the wife is less likely to be in the labor force, and if she does work, her income is lower than predicted.

Jensen, R., & Oster, E. (2009). The power of TV: Cable television and women's status in India . In  The Quarterly Journal of Economics , 124(3), 1057-1094.

Regarding intergenerational transmission of gender roles, see Fernández, R. (2013). Cultural change as learning: The evolution of female labor force participation over a century. The American Economic Review, 103(1), 472-500.

For a discussion regarding social activism and its link to the determinants of female labor supply, see for example this study by Heer and Grossbard-Shechtman (1981).

Atkinson, A.B., Casarico, A. & Voitchovsky, S. Top incomes and the gender divide . J Econ Inequal (2018) 16: 225.

The authors produced results for 8 countries, and included earlier results for Sweden from Boschini, A., Gunnarsson, K., Roine, J.: Women in Top Incomes: Evidence from Sweden 1974-2013, IZA Discussion paper 10979, August (2017).

World Bank. (2011). World development report 2012: gender equality and development . World Bank Publications.

The map from The World Development Report (2012) provides a more fine-grained overview of different property regimes operating in different countries.

For more discussion of the evidence see page 20 in World Bank (2011) World Development Report 2012: Gender Equality and Development. World Bank Publications.

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The World Bank In Gender

The World Bank takes as its starting point that no country, community, or economy can achieve its potential or meet the challenges of the 21st century without the full and equal participation of women and men, girls and boys.

Gender equality is a fundamental human right and essential for a more peaceful, prosperous, and livable world.

• Gender equality and the empowerment of women and girls are central to addressing the world’s unprecedented intertwined global crises  from food insecurity and climate change to conflict, fragility, and violence, to sluggish economic growth.
• Urgent action is needed to  address gender-based violence (GBV) .  One in three women worldwide has experienced violence. Gender-based violence hurts not only the individual survivors, but their families, communities, and entire societies – often across generations, undermining a women’s quality of life, and access to paid work and employment.
• Expanding economic opportunities for women can drive inclusive growth. Low female labor force participation and occupational segregation lead to inefficiencies and misallocation of talent that, if addressed, would boost incomes, and stimulate growth.  On average across countries, long-run GDP per capita would be  almost 20% higher  if gender employment gaps were closed.
• Women’s leadership enhances long-term economic, environmental, and social outcomes, and strengthens institutions .  Research has correlated  higher net profit margins  for firms and  lower CO2 emissions  with more women in decision-making roles.

The World Bank has committed to accelerate gender equality.  In 2022, the Bank launched a year-long  #AccelerateEquality  initiative, to explore progress made and lessons learned over the last 10 years in closing gender gaps and promoting girls’ and women's empowerment. 

A series of  thematic policy notes  and  causal evidence briefs , along with  data , research, global knowledge, and lessons from experience has informed the forthcoming World Bank Gender Strategy  2024-30 to be launched in 2024.

Last Updated: Apr 05, 2024

Gender equality is an urgent moral and economic imperative. Yet achieving gender equality is uniquely challenging and complex.

The draft  World Bank Gender Strategy 2024–30  puts forward a bold ambition to accelerate gender equality to end poverty on a livable planet in alignment with the  World Bank’s evolution process .

Building on implementation of the  World Bank Gender Strategy 2016-23 , the new strategy proposes to engage with greater ambition—approaching gender equality for all as essential for global development—and to engage differently.

The WB  Gender Strategy 2024-30  includes three strategic objectives to:

• End gender-based violence and elevate human capital;
• Expand and enable economic opportunities; and
• Engage women as leaders. 

The Strategy has been shaped by  extensive and inclusive engagement  with public and private sector clients, development partners, civil society, and other key stakeholders through  formal consultations , and will be formally launched in 2024. 

A series of  thematic policy notes  and  causal evidence briefs , along with  data , research, global knowledge, and lessons from experience has informed the strategy.

The World Bank’s key corporate targets on gender equality are on track.  Looking forward there is increasing emphasis on replicating and expanding evidence-informed approaches to deliver outcomes at scale.  Three recently published retrospectives feature lessons learned from the World Bank’s work on gender equality:

Gender Equality in Development: A Ten-Year Retrospective

Retrospective of IFC’s Implementation of the World Bank Gender Strategy 2016- 2023

Gender-Based Violence Prevention and Response in World Bank Operations: Taking Stock After a Decade of Engagement (2012-2022)

The World Bank uses a “ gender tag ” to track Bank operations that use gender analysis to design actions to advance gender equality and include indicators to measure results. There has been a dramatic rise in the share of operations that are gender tagged, from 50% in 2017 to 92% in 2022.

Gender Innovation Labs (GILs)  in  Africa ,  East Asia and the Pacific ,  Latin America and the Caribbean , the  Middle East and North Africa , and  South Asia , generate public goods to promote gender equality. GILs conduct impact evaluations of development interventions seeking to generate evidence on how to close gender gaps in human capital, earnings, productivity, assets, voice, and agency. The GIL research supports evidence-based policy making for governments, development organizations, and the private sector to address the underlying causes of gender inequality.

The WB also curates data through the  Gender Data Portal , which is a comprehensive source for the latest sex-disaggregated gender statistics providing open access to over 900 indicators compiled from officially recognized international sources covering demography, education, health, economic activities, assets, leadership, gender-based violence, and more. This Portal allows users of all technical backgrounds to easily access and explore the data through interactive data visualizations and compelling narratives with the goal of influencing policy and decision-making.

Results Highlights:

Ending gender-based violence and elevating human capital:.

By 2023, the World Bank had increased the percentage of operations that incorporated GBV prevention or response from 38 in 2012 to 390 . These operations exist in every sector, every region, and at every level of country income in 97 countries.

In the Democratic Republic of Congo (DRC), a $100 million GBV Prevention and Response Project, was made possible through World Bank support. It sought to boost participation in programs that prevent GBV and to improve access to quality services for GBV survivors across different sectors. Implemented through non-governmental and civil society organizations, the project has reached 7 million people.

The Girls Empowerment and Learning for All Project in Angola aims to empower youth, particularly girls, by improving access to education and health services. With support from the World Bank, the project supports NGOs delivering sexual and reproductive health services in safe spaces, including sessions for boys emphasizing positive masculinity. Community leaders support girls to stay in school, delay marriage and pregnancy. Second Chance programs provide opportunities to return to education and acquire basic and life skills with about 250,000 additional physical spaces created. Additionally, the program finances scholarships for 900,000 students and supports the creation of additional physical schooling infrastructure with WASH facilities, improved school management and quality.

Expanding and Enabling Economic Opportunities:

In Zambia, the World Bank is putting more cash directly into the hands of women through cash transfer programs. These programs help women take control of their own, and their families’ economic destinies. They have helped more than 973,000 families, and sent livelihood packages, including, life and business skills training, mentorship, and support through savings groups, to 75,000 women.

In South Asia, the World Bank supports WePOWER, a professional network for women that supports women's participation in energy projects and institutions and promotes normative change regarding women in Science, Technology, Engineering, and Mathematics (STEM) education. By 2021, WePOWER had completed 1,400+ gender focused activities, benefitting more than 28,000 women. T hese initiatives included STEM awareness sessions, study tours, internships, hirings, technical trainings, and building female-friendly facilities.

The Takaful and Karama Cash Transfer Program , supported by the World Bank, has rapidly scaled, and expanded since its launch in 2015. With a budget increase from $116 million to $1.2 billion by 2023, mainly funded by the Egyptian government, the program now reaches 5.2 million households, benefitting approximately 22 million individuals.  Notably, 75% of beneficiaries are women, receiving smart cards to enhance financial inclusion and decision-making. Participation in the Takaful program promotes 80% school attendance and health visits for mother and child, as well as avoiding early marriage. Evaluations show significant impacts on women's autonomy and household welfare, while incentives for education and healthcare have also been effective.

The Sahel Women’s Empowerment and Economic Dividend (SWEDD ) project series tests, adapts, and scales innovations with the support of IDA and the Umbrella Facility for Gender Equality. It addresses the root causes of child marriage, teenage pregnancy, and early school drop-out among adolescent girls, and promotes young women’s economic empowerment. With the involvement of governments, civil society organizations, and international partners such as the United Nations Population Fund, SWEDD mobilizes a wide range of allies, including teachers, religious and community leaders, future husbands, husbands, and fathers, to facilitate change on the ground. It deploys a comprehensive set of activities, such as ‘safe space’ clubs and reproductive health services for girls, community schools for husbands, and vocational training for women to enter male-dominated jobs.

Engaging Women as Leaders:

In Panama, the World Bank support the National Indigenous Peoples Development Plan Project. This project has helped increase the participation of indigenous women in decision-making spaces such as the National Council for the Integral Development of Indigenous Peoples (CONDIPI). In 2018, only 8% of CONDIPI participants were Indigenous women. In 2023, more than 35% were women, marking a 27% increase in women’s participation over just 5 years.

As well, the Women Entrepreneurs Finance Initiative (“We-Fi”): has shown the essential role of women entrepreneurs. Since 2018, We-Fi has worked with hundreds of partners in over 60 countries to support women entrepreneurs, catalyzing billions in funding to provide finance and training, and address systemic data & policy gaps.

Challenges for Women Opening and Running Businesses in Cambodia & Vietnam

• >' class='hor-card-link ' href='https://www.worldbank.org/en/who-we-are/news/multimedia/all?lang_exact=English&subject_exact=Gender' target=''>MORE MULTIMEDIA >>

The Nigeria for Women Project (NFWP)

Areas of focus, initiatives.

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Tackling Gender-based Violence is a Development Imperative

World Bank support to GBV prevention and response activities have increased tenfold over ten years and are now present in over 97 countries

AROUND THE BANK GROUP

Learn about gender and development work across the World Bank.

Human Development Topics

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Gender Dimensions of Forced Displacement

How does forced displacement affect women and men differently? And how should policy and program design and implementation reflect these differences?

Umbrella Facility for Gender Equality

The UFGE is a World Bank multi-donor trust fund financing research, impact evaluations and data to help policy makers and practitioners close gender gaps in countries and sectors.

WePOWER Network

WePOWER is a professional network in South Asia that supports women's participation in energy projects and institutions and promotes change regarding women in Science, Technology, Engineering, and Mathematics (STEM) ...

Investing in Human Capital for a Greener Future

Global thought leaders came together to champion investments in people to build resilience, help mitigate greenhouse gas emissions, and accelerate the green transition

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Gender economics in macroeconomic research

By failing to properly take gender interactions into account in research we are limiting today's science. EU-funded research is revealing how economic trends affect genders differently, as for example in the COVID-19 crisis. It is also looking at how the interaction between genders impacts macroeconomic trends.

There is a growing awareness that the failure to take sex, gender and family interactions into account in research has the potential to limit the benefits for today’s science. Most scientific research does not consider sex or gender as variables and treats the male standard as the norm, resulting in potentially inaccurate or incomplete outcomes.

The EU’s six-year GENDERMACRO project, funded by the European Research Council, addressed a number of current topics of interest in macroeconomics. It explicitly integrated gender and family dynamics into the process of evaluating the impact on macroeconomic outcomes, as well as on the results of selected public policy interventions.

‘Most macro models are traditionally based on one gender model, often modelled according to men, so the starting point for our research was that there are gender differences – and that these play a role for the aggregate economy,’ explains Michele Tertilt, the project’s principal investigator and professor at the University of Mannheim in Germany.

‘The family is a foundational unit of society and if we do not take account of interactions within families we risk coming to the wrong conclusions.’

Family matters

‘Men and women generally take different roles in both society and the family with regard to issues such as child rearing, education, human capital, long-term investments, etc. We wanted to look at the interactions within families – husband/wife but also parent/child interactions – and consider to what extent these are important to the economy as a whole,’ says Tertilt.

To analyse this hypothesis, the project built dynamic macro-style models with explicit gender differences. The emphasis was on non-cooperative models of spousal interactions. Using game theory to model family behaviour enables analysis of topics for which cooperation in the family seems questionable (e.g. domestic violence).

By introducing these new models of spousal interaction into macroeconomic models GENDERMACRO was able to provide new insight on a range of applied research questions.

One of the areas examined was the role of female empowerment in economic development and whether transferring money, through development aid, specifically to women is of overall benefit to the economy. The results of the research showed that this is not necessarily the case but depends on the stage of development of the economy in question.

Another area investigated was the impact of the economic cycle on domestic violence. Thanks to detailed data from the Swedish medical system, the GENDERMACRO project confirmed that domestic violence increases during economic recession and decreases during booms. Tracking additional indicators (such as alcohol abuse and depression) enabled a better understanding of the possible mechanisms behind this.

GENDERMACRO also analysed the HIV epidemic in sub-Saharan Africa and the role of gender and family in influencing the impact of public policies introduced to fight the disease. ‘By taking account of behavioural adjustments and indirect impact, we found some quite surprising results, including the existence of thresholds that must be reached for certain interventions to have a positive effect,’ says Tertilt.

Indirectly following on from the GENDERMACRO project, Tertilt and her colleagues applied their approach to the ongoing COVID-19 pandemic. Their research provides some initial results on how this economic downturn is going to affect women and men differently. It also indicates what the main long-term repercussions for gender equality may be in the areas of employment, telework, childcare, home-schooling, employment flexibility, etc. both during the downturn and in the subsequent recovery.

The employment drop related to social-distancing measures has a large impact on sectors, such as care in the community and the hospitality industry, with high female employment. In addition, closures of schools and daycare centres have massively increased childcare needs. This is having a significant impact on women and the effects of the pandemic on working mothers are likely to last for some time.

However, beyond the immediate crisis, there are factors which may ultimately promote gender equality in the labour market. For example, many fathers are now having to take primary responsibility for childcare, which may erode the social norms that currently lead to an unbalanced distribution of the division of labour in housework and childcare.

All of these results reveal that taking gender and family into account in research is important for the quality of research and, further down the line, the quality of public policy interventions. ‘We need to take gender and family out of the black box and integrate it into research so that we can have better-informed science and better-informed policy,’ stresses Tertilt.

Project details

Project acronym GENDERMACRO Project number 313719 Project coordinator: Germany Project participants: Germany Total cost € 1 133 301 EU Contribution € 1 133 301 Project duration February 2013 - January 2019

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Publications on Gender economics

Structural change and gender sectoral segregation in sub-saharan africa, monetary policy and the gender and racial employment dynamics in brazil, potential impact of daycare closures on parental child caregiving in turkey, notes on intersectional political economy, the long period method, technical change, and gender, gender dimensions of inequality in the countries of central asia, south caucasus, and western cis.

The collapse of the Soviet Union initiated an unprecedented social and economic transformation of the successor countries and altered the gender balance in a region that counted gender equality as one of the key legacies of its socialist past. The transition experience of the region has amply demonstrated that the changes in the gender balance triggered by economic shifts are far from obvious, and that economic expansion and women’s economic empowerment do not always go hand in hand. Therefore, active measures to enhance women’s economic empowerment should be of central concern to the policy dialogue aimed at poverty and inequality reduction and inclusive growth. In this paper, we establish the current state of various dimensions of gender inequalities and their past dynamics in the countries of Central Asia (Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan), South Caucasus (Armenia, Azerbaijan, and Georgia), and Western CIS (Belarus, Moldova, and Ukraine), and propose steps aimed at reducing those inequalities in the context of inclusive growth, decent job creation, and economic empowerment.

Time Use of Parents in the United States

What difference did the great recession make.

Feminist and institutionalist literature has challenged the “Mancession” narrative of the 2007–09 recession and produced nuanced and gender-aware analyses of the labor market and well-being outcomes of the recession. Using American Time Use Survey (ATUS) data for 2003–12, this paper examines the recession’s impact on gendered patterns of time use over the course of the 2003–12 business cycle. We find that the gender disparity in paid and unpaid work hours followed a U-shaped pattern, narrowing during the recession and widening slightly during the jobless recovery. The change in unpaid work disparity was smaller than that in paid work, and was short-lived. Consequently, mothers’ total workload increased under the hardships of the Great Recession and declined only slightly during the recovery.

The Economic Crisis of 2008 and the Added Worker Effect in Transition Countries

Time use of mothers and fathers in hard times and better times, the us business cycle of 2003–10.

The US economic crisis and recession of 2007–09 accelerated the convergence of women’s and men’s employment rates as men experienced disproportionate job losses and women’s entry into the labor force gathered pace. Using the American Time Use Survey (ATUS) data for 2003–10, this study examines whether the narrowing gap in paid work over this period was mirrored in unpaid work, personal care, and leisure time. We find that the gender gap in unpaid work followed a U-pattern, narrowing during the recession but widening afterward. Through segregation analysis, we trace this U-pattern to the slow erosion of gender segregation in housework and, through a standard decomposition analysis of time use by employment status, show that this pattern was mainly driven by movement toward gender-equitable unpaid hours of women and men with the same employment status. In addition, gender inequality in leisure time increased over the business cycle.

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Women now make up 35% of workers in the United States’ 10 highest-paying occupations – up from 13% in 1980.

How Americans see the state of gender and leadership in business

55% of Americans say there are too few women in top executive business positions. This is down somewhat from 59% who said this in 2018.

Single women own more homes than single men in the U.S., but that edge is narrowing

In 2022, single women owned 58% of the nearly 35.2 million homes owned by unmarried Americans, while single men owned 42%.

Diversity, Equity and Inclusion in the Workplace

Workplace diversity, equity and inclusion efforts, or DEI, are increasingly becoming part of national political debates. For a majority of employed U.S. adults (56%), focusing on increasing DEI at work is a good thing. But relatively small shares of workers place a lot of importance on diversity at their workplace.

In a Growing Share of U.S. Marriages, Husbands and Wives Earn About the Same

Among married couples in the United States, women’s financial contributions have grown steadily over the last half century. Even when earnings are similar, husbands spend more time on paid work and leisure, while wives devote more time to caregiving and housework.

When negotiating starting salaries, most U.S. women and men don’t ask for higher pay

Most U.S. workers say they did not ask for higher pay the last time they were hired for a job, according to a new Pew Research Center survey.

The Enduring Grip of the Gender Pay Gap

The difference between the earnings of men and women has barely closed in the United States in the past two decades. This gap persists even as women today are more likely than men to have graduated from college, suggesting other factors are at play such as parenthood and other family needs.

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

In 2022, women earned an average of 82% of what men earned, according to a new analysis of median hourly earnings of full- and part-time workers.

More Than Twice as Many Americans Support Than Oppose the #MeToo Movement

Most say that, compared with five years ago, those who commit sexual harassment or assault at work are more likely to be held responsible and those who report it are more likely to be believed.

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About Pew Research Center Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of The Pew Charitable Trusts .

Top 10 gender research reads from 2021

• From CGIAR GENDER Platform
• Published on 18.02.22
• Impact Area Gender equality

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In our series of recommended reading lists, gender experts provide starting points for researchers, students, practitioners and others looking to dive deeper into research on gender and a wide variety of topics.

This time, we asked the CGIAR GENDER Platform team members to pick out their top gender research reads from 2021. Explore below for their selection of the most interesting, important and captivating publications released last year.

Top picks by Nicoline de Haan, CGIAR GENDER Platform Director

#1  rural youth in southern nigeria.

There are three clear reasons why  Rural Youth in Southern Nigeria: Fractured Lives and Ambitious Futures   by Crossouard et al. sticks in my mind. First, because it is about youth. We often talk about youth and their importance for the future, but I have not seen much research about rural youth. As the CGIAR GENDER Platform evolves, we will work more on youth issues, so it is important we have more theoretical thinking and evidence in this space. My second reason is linked to the article’s approach: years ago, I was in the field in Kenya with a PhD student doing research on how rural education was preparing youth for the future, and she found that the education system was not at all linked to the realities. This article looks at that issue as well. Finally, I picked this because it is about Nigeria, and having spent seven years of my career there, Nigeria always interests me. It was also good to see a CGIAR scientist involved in this research.

#2 Gender equality in climate policy and practice

Gender Equality in Climate Policy and Practice Hindered by Assumptions  by Lau et al. is one of those articles that should have been written a long time ago. It lays out the assumptions we are still dealing with in gender in agriculture research. For example, that women are caring and connected to the environment; that women are a homogenous and vulnerable group; that gender equality is a women’s issue; and that gender equality is a numbers game. The authors very nicely show how these assumptions hinder progress on climate change and how they can even be counterproductive. Now that this article is out there for the public, we can move on and really deal with the issues at hand!

Top picks by Marlene Elias, CGIAR GENDER Platform Alliances Module Lead

#3 gender expertise in environment and development.

This book,  Negotiating Gender Expertise in Environment and Development  by Resurrección and Elmhirst, is thoughtful and beautifully written. It brings together critical reflections from gender experts on their experiences working in environment and development organizations, including CGIAR. It takes an innovative format: a series of conversations between the co-editors and writers, Bernadette Resurrección and Rebecca Elmhirst, and gender experts who are working to place gender and social inclusion issues at the center of research and practice on sustainability and environmental management. These conversations surface the motivations, negotiations, achievements and daily struggles of these professionals as they navigate the complexities of all that is implied by working on gender in largely technical fields. Every chapter has a different flavor, but all will resonate with those of us working in this area; and make us nod our heads, sigh, laugh (or cry!) and better understand our profession and ourselves.

#4 Masculinities in forests

Colfer’s book,  Masculinities in Forests: Representations of Diversity , focuses on how masculinities relate to forest management, drawing on her experience working in different forest contexts, from the USA to Indonesia. It takes a timely dive into diverse masculinities and how these shape practices in forest management, all the while recognizing men’s agency in expressing different masculine identities. Aside from the rich content that is discussed, couched in an accessible framework and language, I appreciated that the book examines masculinities among professionals working in the field of forestry as well as among various forest communities. I was also very impressed by how Colfer was able to re-examine decades of ethnographic research through a new lens to write this book. Wow!

Top picks by Els Lecoutere, CGIAR GENDER Platform Science Officer

#5 diffusion and dilution.

Doss’  Diffusion and Dilution: The Power and Perils of Integrating Feminist Perspectives into Household Economics  is important to me is because it acknowledges the advances we have made in integrating feminist economic perspectives into mainstream economics, but also points out areas for improvement. It helps us to stay focused. Personally, I find the call for careful consideration of benefits versus potential harm, and proper training of enumerators when collection data about domestic and gender-based violence, extremely important. I sometimes feel we make the decision to collect data about domestic and gender-based violence too lightly. The article further opens the discussion about two other pet topics of mine: First, how can we better capture the complexity of households, including the web of power relations between different members, in which individuals make decisions? Second, how can we measure social norms and their importance for people’s capabilities and choices? How can these be changed and what are the effects?

#6 A review of evidence 

I keep going back to this brief,   A Review of Evidence on Gender Equality, Women’s Empowerment and Food Systems  by Njuki et al., mainly for its gendered food systems framework. The framework brings the different ways in which gender affects capabilities, choices and outcomes in food systems together. It provides a theoretical basis for various key questions in gender in agricultural and food system research and shows how this is supported by evidence. To me, its key contribution is the way it disentangles the different ‘entry points’ of gender constraints. Gender inequalities cannot only creep into biophysical, technological or economic drivers of food systems, shocks and vulnerabilities affecting these drivers can also affect men and women differently. Finally, the conceptualization of gendered food systems as systems underscores the dynamic, interdependent nature of the different elements and the need for a holistic approach to achieve gender equality in agriculture and food systems.

Top pick by Hazel Malapit and Elizabeth Bryan, CGIAR GENDER Platform Methods Module Co-leads

#7 advancing gender equality.

If you don’t have time to read the whole book, read the introduction.  Pyburn and van Eerdewijk’s introduction  to Advancing Gender Equality through Agricultural and Environmental Research excellently presents the topics discussed in the book, which features contributions from 55 CGIAR gender researchers. The book flips an often-posed question: instead of asking what gender equality can do for agricultural development, it asks how agricultural and environmental research can advance gender equality. One of the best overviews of gender research in CGIAR, the introductory chapter contextualizes CGIAR gender research within our organization’s struggles to address gender and within the broader thinking around gender and development. The introduction provides summaries of each chapter as well as information on the methodological and geographic breakdown of studies reviewed.

#8 Gender and agricultural economics

As gender researchers in the GCIAR are well aware, women and men in developing countries have different preferences and interests, and good policies and programs take these differences into account. But what about what researchers themselves bring to the table? This article,  How Women Saved Agricultural Economics , by Offutt and McCluskey, points out that women (and minorities) tend to be under-represented in economics positions in government and academia, and are not recognized for their achievements with awards and editorships due to both overt discrimination and implicit bias. Yet, the authors say, the diversity resulting from women’s increased presence in field has increased the relevance of the discipline over the last several decades. This research documents the importance of increasing representation in academic fields where women (and other minorities) are traditionally under-represented. While this study focuses on agricultural economics in the United States, it has prompted further analysis of how these patterns apply in other countries, such as India and Kenya, and within other institutions.

Top picks by Ranjitha Puskur, CGIAR GENDER Platform Evidence Module Lead

#9 food and agriculture systems.

Foresight studies on agriculture tend to not integrate social dimensions as these often do not render themselves to quantitative measurement. This article,  Food and Agriculture Systems Foresight Study: Implications for Gender, Poverty and Nutrition  by Lentz, is a rare review that argues for mainstreaming a gender, poverty and nutrition focus into foresight research. This would help ensure that we reduce the risk of entrenching gender inequalities and promoting technologies that exacerbate inequality, and that we are able to inform policy- and innovation-led pathways. Having dabbled in participatory foresight analysis using scenario planning, visioning and backcasting, this piqued my curiosity. The paper offers helpful insights into how and when to bundle or sequence interventions and the need to understand the effects of interventions on the whole agri-food system. It offers a very engaging and useful read, even for those who are unfamiliar with foresight methods.

#10 Gender and land ownership

The issue of women’s limited land ownership is sticky and has occupied central stage in debates and discourses for a while. Nowhere have we been able to make any significant progress in reducing the gender gaps in land ownership. Cheryl Doss (2018) questioned the myth of women owning less than one percent of land globally. This continues to be a complex issue, with the definition of “ownership” being only one of the tricky issues. Agarwal’s 2021 paper,  How Many and Which Women Own Land in India? , uses longitudinal data from the Village Level Studies (VLS), collected by the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) from a set of Indian villages between 2009 and 2014, to look at which women are more likely to own land, why and how these patterns changed over the years. We at the CGIAR GENDER Platform have also been highlighting the need to focus more on unpacking intersectionalities to have better insights that can inform targeted solutions. This paper provides a very good example of the importance of intersectional approaches and it highlights the gap and the critical need for a national and state-level datasets.

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Research: How to Close the Gender Gap in Startup Financing

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

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

A global analysis of previous research over the last three decades shows that women entrepreneurs face a higher rate of business loan denials and increased interest rates in loan decisions made by commercial bankers. Interestingly, the data also reveals that the formal and informal standing of women in a particular society can provide clues to some of the true hurdles to positive change. This article reviews these hurdles, and offers three recommendations for change.

Gender disparities persist in entrepreneurship and statistics reveal the severity of the issue. Globally, only one in three businesses is owned by women . In 2019, the share of startups with at least one female founding member was a mere 20% .

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

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100 Gender Research Topics For Academic Papers

Gender research topics are very popular across the world. Students in different academic disciplines are often asked to write papers and essays about these topics. Some of the disciplines that require learners to write about gender topics include:

Sociology Psychology Gender studies Business studies

When pursuing higher education in these disciplines, learners can choose what to write about from a wide range of gender issues topics. However, the wide range of issues that learners can research and write about when it comes to gender makes choosing what to write about difficult. Here is a list of the top 100 gender and sexuality topics that students can consider.

Controversial Gender Research Topics

Do you like the idea of writing about something controversial? If yes, this category has some of the best gender topics to write about. They touch on issues like gender stereotypes and issues that are generally associated with members of a specific gender. Here are some of the best controversial gender topics that you can write about.

• How human behavior is affected by gender misconceptions
• How are straight marriages influenced by gay marriages
• Explain the most common sex-role stereotypes
• What are the effects of workplace stereotypes?
• What issues affect modern feminism?
• How sexuality affects sex-role stereotyping
• How does the media break sex-role stereotypes
• Explain the dual approach to equality between women and men
• What are the most outdated sex-role stereotypes
• Are men better than women?
• How equal are men and women?
• How do politics and sexuality relate?
• How can films defy gender-based stereotypes
• What are the advantages of being a woman?
• What are the disadvantages of being a woman?
• What are the advantages of being a man?
• Discuss the disadvantages of being a woman
• Should governments legalize prostitution?
• Explain how sexual orientation came about?
• Women communicate better than men
• Women are the stronger sex
• Explain how the world can be made better for women
• Discuss the future gender norms
• How important are sex roles in society
• Discuss the transgender and feminism theory
• How does feminism help in the creation of alternative women’s culture?
• Gender stereotypes in education and science
• Discuss racial variations when it comes to gender-related attitudes
• Women are better leaders
• Men can’t survive without women

This category also has some of the best gender debate topics. However, learners should be keen to pick topics they are interested in. This will enable them to ensure that they enjoy the research and writing process.

Interesting Gender Inequality Topics

Gender-based inequality is witnessed almost every day. As such, most learners are conversant with gender inequality research paper topics. However, it’s crucial to pick topics that are devoid of discrimination of members of a specific gender. Here are examples of gender inequality essay topics.

• Sex discrimination aspects in schools
• How to identify inequality between sexes
• Sex discrimination causes
• The inferior role played by women in relationships
• Discuss sex differences in the education system
• How can gender discrimination be identified in sports?
• Can inequality issues between men and women be solved through education?
• Why are professional opportunities for women in sports limited?
• Why are there fewer women in leadership positions?
• Discuss gender inequality when it comes to work-family balance
• How does gender-based discrimination affect early childhood development?
• Can sex discrimination be reduced by technology?
• How can sex discrimination be identified in a marriage?
• Explain where sex discrimination originates from
• Discuss segregation and motherhood in labor markets
• Explain classroom sex discrimination
• How can inequality in American history be justified?
• Discuss different types of sex discrimination in modern society
• Discuss various factors that cause gender-based inequality
• Discuss inequality in human resource practices and processes
• Why is inequality between women and men so rampant in developing countries?
• How can governments bridge gender gaps between women and men?
• Work-home conflict is a sign of inequality between women and men
• Explain why women are less wealthy than men
• How can workplace gender-based inequality be addressed?

After choosing the gender inequality essay topics they like, students should research, brainstorm ideas, and come up with an outline before they start writing. This will ensure that their essays have engaging introductions and convincing bodies, as well as, strong conclusions.

Amazing Gender Roles Topics for Academic Papers and Essays

This category has ideas that slightly differ from gender equality topics. That’s because equality or lack of it can be measured by considering the representation of both genders in different roles. As such, some gender roles essay topics might not require tiresome and extensive research to write about. Nevertheless, learners should take time to gather the necessary information required to write about these topics. Here are some of the best gender topics for discussion when it comes to the roles played by men and women in society.

• Describe gender identity
• Describe how a women-dominated society would be
• Compare gender development theories
• How equally important are maternity and paternity levees for babies?
• How can gender-parity be achieved when it comes to parenting?
• Discuss the issues faced by modern feminism
• How do men differ from women emotionally?
• Discuss gender identity and sexual orientation
• Is investing in the education of girls beneficial?
• Explain the adoption of gender-role stereotyped behaviors
• Discuss games and toys for boys and girls
• Describe patriarchal attitudes in families
• Explain patriarchal stereotypes in family relationships
• What roles do women and men play in politics?
• Discuss sex equity and academic careers
• Compare military career opportunities for both genders
• Discuss the perception of women in the military
• Describe feminine traits
• Discus gender-related issues faced by women in gaming
• Men should play major roles in the welfare of their children
• Explain how the aging population affects the economic welfare of women?
• What has historically determined modern differences in gender roles?
• Does society need stereotyped gender roles?
• Does nature have a role to play in stereotyped gender roles?
• The development and adoption of gender roles

The list of gender essay topics that are based on the roles of each sex can be quite extensive. Nevertheless, students should be keen to pick interesting gender topics in this category.

Important Gender Issues Topics for Research Paper

If you want to write a paper or essay on an important gender issue, this category has the best ideas for you. Students can write about different issues that affect individuals of different genders. For instance, this category can include gender wage gap essay topics. Wage variation is a common issue that affects women in different countries. Some of the best gender research paper topics in this category include:

• Discuss gender mainstreaming purpose
• Discuss the issue of gender-based violence
• Why is the wage gap so common in most countries?
• How can society promote equality in opportunities for women and men in sports?
• Explain what it means to be transgender
• Discuss the best practices of gender-neutral management
• What is women’s empowerment?
• Discuss how human trafficking affects women
• How problematic is gender-blindness for women?
• What does the glass ceiling mean in management?
• Why are women at a higher risk of sexual exploitation and violence?
• Why is STEM uptake low among women?
• How does ideology affect the determination of relations between genders
• How are sporting women fighting for equality?
• Discuss sports, women, and media institutions
• How can cities be made safer for girls and women?
• Discuss international trends in the empowerment of women
• How do women contribute to the world economy?
• Explain how feminism on different social relations unites men and women as groups
• Explain how gender diversity influence scientific discovery and innovation

This category has some of the most interesting women’s and gender studies paper topics. However, most of them require extensive research to come up with hard facts and figures that will make academic papers or essays more interesting.

Students in high schools and colleges can pick what to write about from a wide range of gender studies research topics. However, some gender studies topics might not be ideal for some learners based on the given essay prompt. Therefore, make sure that you have understood what the educator wants you to write about before you pick a topic. Our experts can help you choose a good thesis topic . Choosing the right gender studies topics enables learners to answer the asked questions properly. This impresses educators to award them top grades.

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Spring 2024: Gender and Development

Giri spring 2024: gender and development, what do we mean by "development" and "gender" what are the barriers and challenges to gender equality in development what indicators can be used to measure and track gender inequality in economic and social development.

In Spring 2024, the Global Inequality Research Initiative focused on “Gender and Development”. We examined how gender influences and is influenced by social and economic development processes.

The course began by providing an overview of gender inequalities in various aspects of social and economic life around the world. This included an exploration of the disparities that exist in areas such as education, healthcare, and work. Following this, students explored the concept of development with a specific emphasis on how gender is incorporated into the development discourse. We also studied diverse approaches to gender and development and their evolution over time.

The course then examined in detail various specific topics:

• What is the intra-household division of labor and how does it impact gender equality in the household and beyond?
• What is the relationship between long-term development and women’s share of the labor force?
• What are some of the key gender differences in labor markets and paid employment, such as differences in pay, hours worked, and types of jobs held by men and women?
• What are the gender-differentiated impacts of various phenomena such as globalization, macroeconomic policies, and Covid-19 pandemic?
• What are the gender dimensions of poverty, migration and climate change?
• What do intersectional analyses reveal about disadvantages experienced by people when multiple categories of social identity interact with each other?

A Discrimination Report Card

Twenty years ago, Chicago Booth economists Marianne Bertrand and Sendhil Mullainathan published a seminal paper  that studied racial discrimination in the labor market by sending fictitious resumes to help-wanted ads in Boston and Chicago newspapers. They revealed that equivalent resumes with distinctively white names like Emily and Greg received 50% more callbacks for interviews than those with distinctively Black names like Lakisha and Jamal.

In 2021, Chicago economist Evan Rose, along with Patrick Kline and Christopher Walters, expanded Bertrand’s and Mullainathan’s work to a massive scale. Their experiment, detailed in “ Systemic Discrimination Among Large U.S. Employers ,” measures the callback rates from over 83,000 fictitious job applications sent to 11,000 entry level job openings at more than 100 Fortune 500 Firms. The research revealed a surprising fact: A small number of companies are responsible for a substantial amount of the contact discrimination measured.

Who are these firms? Continue scrolling to explore the results.

More on this topic.

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Lives vs. Livelihoods: The Impact of the Great Recession on Mortality and Welfare

The Economics of Reproductive Choice

Q&A: Julian Nyarko on why Large Language Models like ChatGPT treat Black- and white-sounding names differently

Since ChaptGPT and other Large Language Models (LLMs) came on the scene, questions have loomed large about the technology’s potential for perpetuating racial and cultural biases.

Stanford Law School Professor  Julian Nyarko , who focuses much of his scholarship on algorithmic fairness and computational methods, has been at the forefront of many of these inquiries over the last several years. His latest paper,  “What’s in a Name? Auditing Large Language Models for Race and Gender Bias,”  makes some startling observations about how the most popular LLMs treat certain queries that include first and last names suggestive of race or gender.

Asking ChatGPT-4 for advice on how much one should pay for a used bicycle being sold by someone named Jamal Washington, for example, will yield a different—far lower—dollar amount than the same request using a seller’s name, like Logan Becker, that would widely be seen as belonging to a white man. “It’s $150 for white-sounding names and $75 for black-sounding names,” says Nyarko, who is also a faculty fellow at the Stanford Institute for Economic Policy Research (SIEPR) and an associate director and a senior fellow at the Stanford Institute for Human-Centered AI (HAI). “Other scenarios, for example in the area of car sales, show less of a disparity, but a disparity nonetheless.”

Names associated with Black women receive the least advantageous outcomes, according to the paper.

Nyarko co-authored  What’s in a Name  with lead author Amit Haim, JSD ’24 (JSM ’20), and Stanford Law School research fellow Alejandro Salinas. What differentiates their study from other similar inquiries into LLM bias, the authors say, is their use of an audit design as the framework for their study. Audit designs are empirical methods designed to identify and measure the level of bias in different domains of society, such as housing and employment. One of the best known examples is the 2003 study in which researchers submitted resumes for various jobs, varying only the name of the applicant, using stereotypical African-American, white, male and female names.

Here, Nyarko explains how he and his co-authors brought that same methodology to the realm of LLMs, what the findings tell us, and what should be done.

Can you start by providing a little background and context for the study? Many people might expect that LLMs would treat a person’s name as a neutral data point, but that isn’t the case at all, according to your research?

Ideally when someone submits a query to a language model, what they would want to see, even if they add a person’s name to the query, is a response that is not sensitive to the name. But at the end of the day, these models just create the most likely next token– or the most likely next word–based on how they were trained. So, let’s say part of the training data are Craigslist posts. If a car is being sold by a Black person, or a person with a Black-sounding name, it tends to be sold for less on Craigslist than the same type of car being sold by a white person, or a person with a white-sounding name. This happens for many reasons, for instance because the Black car seller is more likely to live in a lower resourced community where there is less money. And so if you ask one of these models for advice on how much you should offer for a used car, and the only additional data you provide is the name of the seller, the model will implicitly assume that the next tokens after the offer that you should make are maybe “$10,000” as opposed to “$12,000.” It is a little bit difficult to analogize that to human decision making, where there’s something like intent. And these models don’t have intent in the same way. But they learn these associations in the data and then reproduce them when they’re queried.

What types of biases did you study?

Our research focuses on five scenarios in which a user might seek advice from an LLM: strategies for purchasing an item like a car or bicycle, designed to assess bias in the area of socio-economic status; questions about likely outcomes in chess, which goes to the issue of intellectual capabilities; querying who might be more likely to win public office, which is about electability and popularity; sports ability, and advice-seeking in connection with making a job offer to someone.

Is there a way to drill down into the code, or the “backend” of the LLMs, to see what is going on from a technical perspective?

Most of these newer LLMs, the ones people are most accustomed to, like ChatGPT-4, tend to be closed source. With open-source models, you can break it open and, in a technical way, look at the model and see how it is trained. And if you have the training data, you can look at whether the model was trained in such a way that it might encode disparities. But with the closed-source models, you have to find other ways to investigate. The nice parallel here is the human mind and decision making. With humans, we can devise strategies to look into people’s heads and make determinations about whether their decision-making is based on discriminatory motivations. In that context, audit studies were developed, where, for instance, two shoppers of different races go to buy a car or a house with exactly the same external variables, such as the clothes they are wearing and so forth. And the study looks at what kind of cars are offered to them, or the types of houses. One of the most famous of these types of studies involves resumes, where all the information on the resumes was the same, except the names.

So we thought this approach can be used in the large language model context to indirectly test whether these disparities are baked in.

Your study took a new approach to these types of studies looking at LLMs’ potential for perpetuating racial and gender biases, is that correct? 

There are a couple of studies that have tried to do something similar in the past, for example CV studies on GPT looking into whether someone with the name Lakeisha is deemed to be less employable than someone with a name that is less stereotypically Black. But those studies have primarily looked at the question in a binary way: Should I hire this person? Yes or no. Those studies got mixed results. If you ask for a binary yes or no, you don’t get the nuance. Also, based on previous research, what wasn’t quite clear was the extent to which these models were biased. What we found was that if you switch to an open-ended question—for example, how much should I pay or what is the probability of this or that candidate winning an election, you get a much clearer, nuanced picture of the bias that is encoded.

How significant are the disparities you uncovered?

The biases are consistent across 42 prompt templates and several models, indicating a systemic issue rather than isolated incidents. One exception was the “chess” scenario we designed to check whether the model assumes a lower IQ for minorities. The questions posed were about who was more likely to win a chess match. While we found disparate results across gender–the models would predict more often that a man would win than it would predict that a woman would win–we didn’t find disparities across race in the chess context.

In some areas, the disparities were quite significant. In the bicycle sale example, we saw a significant Black-white gap, where the price offered to the white seller would be twice that of the Black seller. It was a little bit less in the area of car sales. A difference of $18,000 vs $16,000. The model tends to view Black basketball players as better than white players and city council candidates with white-sounding names were deemed more likely to win an election than those with Black-sounding names.

Does it change the results if you input additional data such as the year of a car or other details?

We found that while providing numerical, decision-relevant anchors in the prompt can successfully counteract the biases, qualitative details have inconsistent effects and may even increase disparities. If you just ask, “How much should I offer for a car, any car,” along with one of the names used in our study, the model has very little information and has to rely on encoded approximations of whatever it has learned, and that might be: Black people usually have less money, and drive worse cars. But then we have a high-context condition where we add “2015 Toyota Corolla,” and as expected, with the additional context, you see the bias shrink, though we didn’t see that every time. In fact, sometimes the biases increased when we gave the models more context. However, there’s one condition, what we call the numeric condition, where we gave it a specific quantifier as an anchor. So for instance, we would say, “How much should I offer for this car, which has a Kelley Blue Book value of $15,000?” What we saw consistently is that if you give this quantifier as an anchor, the model gives you the same response each time, without the biases.

Which leads to the question of what should be done in the face of your study? Do these LLMs already have systems in place to counteract these sorts of biases and what else can or should be done?

On the technical end, how to mitigate these biases is still an open, exploratory field. We know that OpenAI, for instance, has significant guardrails in its models. If you query too directly about differences across a gender or race, the model will just refuse to give you a straight answer in most contexts. And so one approach could be to extend these guardrails to also cover disparities discovered in audit studies. But this is a little bit like a game of Whac-a-Mole, where issues have to be fixed piece-by-piece as they are discovered. Overall, how to debias models is still a very active and exploratory field of research.

That said, at a minimum, I think we should know that these biases exist, and companies who deploy LLMs should test for these biases. These audit design tests can be implemented really easily, but there are many tough questions. Think about a financial advice chatbot. In order to have a good user experience, the chatbot most likely will have access to the user’s name. The example I like to think about is a chatbot that gives more conservative advice to users with Black-sounding names as opposed to those with white-sounding names. Now it is the case that, due to socio-economic disparities, users with Black-sounding names do tend to have–on average–fewer economic resources. And it is true that the lower your economic resources, the more conservative investment advice should be. If you have more money, you can be more adventurous with your dollars. And so in that sense, if a model gives people with different names different advice, it could lead to more satisfied users in the long run. But no matter what one might think about the desirability of using names as a proxy for socio-economic status, their use should always be the consequence of a conscious decision-making process, not an unconscious feature of the model.

This story was originally published on March 19, 2024 by Stanford Law School.

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