Gender Differences in Faculty CVs in Leading US Economics Departments

Gender Differences in Faculty CVs in Leading US Economics Departments | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Gender Differences in Faculty CVs in Leading US Economics Departments Tolga Yuret This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8440876/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract There are notable gender differences in the distribution of academic tasks. In this paper, we match 351 female faculty members from leading US economics departments with male counterparts from the same departments who have comparable post-PhD experience. The presence of specific sections in CVs - as well as the number of words and activities listed within them - is used as a proxy for involvement in particular academic tasks. Our findings are partly consistent with the existing literature: for example, women tend to have longer “Service” sections than men. However, we also identify patterns that deviate from prior research; notably, women have longer “Conference Participation” sections. In addition, women are more likely to disclose private information, such as marital status and phone numbers. Finally, we find that the number of publications listed on women's CVs exceeds their Scopus-indexed publications by a greater margin than for men. Curriculum Vitae academic tasks gender economics departments 1. Introduction Academics are often generous in making their CVs publicly available. These CVs contain objective information that has been used in studies analyzing educational backgrounds and career mobility. However, CVs also exhibit heterogeneous structures. For example, some academics selectively report conference participation. As a result, CVs cannot always be treated as fully objective data sources. There are gender differences in the distribution of academic tasks. For example, survey-based studies have found that women tend to take on more service work in academia (Guarino & Borden, 2017 ; Misra et al., 2012 ). Alternatively, the prominence of service work in CVs can serve as a proxy for such involvement. While CVs are not fully objective sources of information, neither are surveys. Therefore, analyzing CVs provides an additional, albeit subjective, perspective on gender differences in academic responsibilities. CVs also offer valuable insights into how academics choose to present their academic output. For instance, an academic may include an extensive list of publications, such as working papers and policy notes, beyond peer-reviewed articles. Similarly, the service section can be expanded to include relatively minor tasks, such as participation in commencement ceremonies. Therefore, analyzing the content and structure of CVs is important for understanding how academics construct and communicate their professional identity. In this paper, we analyze the CVs of male and female faculty members from leading economics departments, focusing on gender differences across all sections, including publications and service work. The data obtained from CVs can be interpreted along three dimensions. First, CVs reflect reality to some extent; for example, one cannot list conferences they did not attend. Second, they reveal how academics perceive and present their professional identity. A faculty member who does not view herself as involved in administrative tasks may choose to omit certain service roles from her CV. Third, CVs may be strategically crafted - for instance, listing numerous working papers can signal a strong publication pipeline. While we cannot disentangle these three dimensions, the observed gender differences in CV content offer valuable insight. 2. Related Literature Cañibano and Bozeman ( 2009 ) identify four key limitations of using data extracted from CVs. First, there is an issue of availability, as some academics choose not to make their CVs publicly accessible. Second, CVs are heterogeneous in both content and level of detail; for instance, some list only selected publications while others are more comprehensive. Third, CVs often contain missing or incomplete information. Finally, extracting data from CVs is labor-intensive, requiring manual coding and interpretation. Macfarlane ( 2020 ), analyzing CVs of academics at different career stages, observes notable variation in CV styles. Similarly, Texeira et al. (2020) highlight the absence of institutional oversight over CV content, which can result in misleading or even fabricated information. These authors also propose principles for institutional regulation of academic CVs. While this paper presents a comprehensive analysis of academic CVs, it is not immune to the aforementioned limitations. Nevertheless, we take several steps to mitigate these challenges. First, we focus on the field of economics, which is characterized by a relatively high rate of publicly available CVs, reducing concerns about availability. Second, our analysis is limited to faculty members at leading economics departments, where the risk of data fabrication is presumably low. Third, by comparing male and female faculty with similar post-PhD experience, we minimize potential generational differences in CV style. However, two major concerns affect our study. First, although we compare the CVs of men and women from the same institutions, we observe considerable heterogeneity in CV styles. Second, some CVs contain missing information. Therefore, CV data should not be interpreted as an objective record of academic output, but rather as a reflection of how individuals choose to present their academic achievements. Numerous studies have utilized data extracted from academic CVs. Youtie et al. ( 2013 ), for instance, match academics from North America and Europe with comparable academic achievements and examine differences in their CVs. They find that grant income and awards from professional associations are more prominently featured in the CVs of North American academics. Our approach is similar in that we also match academics based on academic success; however, our focus is on gender differences rather than geographic ones. CV analysis has also been employed to investigate gender disparities in academic promotion. Sabatier et al. ( 2006 ) extract multiple variables from the CVs of French academics and demonstrate that the criteria for promotion differ by gender. Their findings suggest that women face higher standards and longer timelines for promotion. Other studies focus on specific features or sections of CVs. Bi et al. (2020), for example, examine the fonts used in CVs as a proxy for self-esteem and correlate this with academic productivity. Yuret ( 2017 ) uses publicly available CVs and online sources to retrieve undergraduate information for successful academics. Cañibano et al. ( 2008 ) and Sandström ( 2009 ) analyze the career mobility of Spanish and Swedish academics, respectively, using CV-based data. Numerous survey-based studies provide substantial evidence that women in academia perform more service work than men (Guarino & Borden, 2017 ; Misra et al., 2012 ). This is particularly concerning because service work tends to carry less weight in promotion decisions. Consequently, gender disparities in service responsibilities may contribute to differences in promotion outcomes. In this paper, we investigate whether a gender difference in reported service work appears in CVs. As noted earlier, CV data are neither fully objective nor complete, so the presence and length of service sections should be interpreted as proxies for actual service contributions. Lundberg and Stearns ( 2019 ) highlight that women are underrepresented in prestigious economics departments and generally publish less than men. Ghosh and Liu ( 2020 ) compare untenured male and female economists in top departments, noting that women publish less in top five economics journals partly because they are more likely to be placed in lower-ranked institutions and thus collaborate with less productive coauthors. Similarly, Hilmer and Hilmer ( 2007 ) find that women who graduate from the same PhD programs as men tend to be less productive in terms of publication output. This paper addresses gender differences in research productivity in two ways. First, we match male and female faculty members within the same departments to assess whether gender disparities persist when institutional context is held constant. Second, we supplement bibliometric data with total publication counts from CVs, which include non-indexed outputs such as working papers and policy papers. This allows us to examine whether gender gaps in top-tier publications also appear in less competitive academic outputs. The COVID-19 pandemic has served as a natural experiment, highlighting gendered constraints in academic participation. Bierman (2024) and Olechnicka et al. ( 2025 ) find that women were more likely to participate in virtual conferences than in in-person ones. In this paper, we investigate whether conference participation mentioned in CVs differs by gender. 3. Data There are 1,786 faculty members in the top 50 economics departments, as ranked by US News & World Report (2022). Of these, 372 (21%) are women. For our analysis, we matched each female faculty member with a male faculty member from the same department whose post-PhD experience differed by no more than five years. If multiple male faculty members had the same absolute difference in post-PhD experience, one was randomly selected. We successfully created 351 matched pairs. Nine women were excluded because their CVs were not publicly available, and twelve could not be matched due to the absence of suitable male counterparts within the specified experience range. Although men outnumber women in all departments, suitable matches were not always possible because many women faculty members have similar post-PhD experience. For instance, in some departments, most newly hired faculty members are women. In total, we analyzed 702 CVs − 351 from women and 351 from their matched male counterparts. First, we categorized CVs into standardized sections across all faculty members. Second, we manually coded data from the CVs to quantify entries such as the number of advisees. Additionally, we collected bibliometric data for all 702 faculty members from Scopus, including the total number of publications and the number of articles published in the top five economics journals. 4. Analysis Table 1 outlines the standardized sections of the CVs used in our analysis. Given the heterogeneous structure of the original CVs, we undertook a standardization process to ensure consistency across cases. For instance, editorial roles appear under “Appointments” in some CVs and under “Refereeing” in others. In such cases, we created a distinct “Editorial” section to capture these positions consistently. Similarly, while advising can be considered a form of service work, we chose to separate it into its own “Advising” section to maintain exclusive, non-overlapping categories. Private information - including address, citizenship, and marital status - is grouped under an “Introduction” section, even though this information is rarely labeled as such and may appear at the end of some CVs. Smaller categories were consolidated into broader sections; for example, organizing a workshop was classified under “Conference Organization.” Nearly all CVs include sections on “Appointments,” “Publications,” and “Education.” Most sections appear in the majority of CVs, with the exceptions of “Field,” “Media,” and “Review.” We also observe gender-based differences in section availability. For instance, 234 female CVs include a “Service” section compared to 207 male CVs - indicating that 13% more women than men report service work. In fact, women’s CVs show equal or greater representation across all sections except for “Advising”, “Editorial”, and “Field”. To further analyze CV content, we counted the number of words in each section as a proxy for the amount of activity reported. We then compared these word counts between matched pairs. For example, in 194 pairs, the female faculty member had more words in the “Conference Participation” section than her male counterpart; in 143 pairs, the opposite was true; and in 14 pairs, the word counts were identical. The final column of Table 1 presents the ratio of pairs in which women have more words to those in which men do. For “Conference Participation,” for example, 194 women had more words compared to 143 men—yielding a 36% higher incidence among women. Interestingly, while prior research suggests that women attend fewer conferences than men (Bierman, 2024; Olechnicka et al., 2025 ), the “Conference Participation” sections of women’s CVs in our sample tend to be longer. Two explanations are plausible: first, since our sample pairs men and women from the same departments, there may be little actual difference in participation rates. Second, women may be more inclined to provide comprehensive documentation of their conference activity. Consistent with prior research indicating that women take on more service responsibilities in academia (Guarino & Borden, 2017 ; Misra et al., 2012 ), we find that women are more likely than men to report a higher word count in the “Service” sections of their CVs. Conversely, men are more likely to report greater detail in other service-related categories, such as “Refereeing” and “Review.” Table 1 Sections in CVs: Women (W) vs. Men (M) Exists in % # of words in sections % Section W M W over M W higher M higher W over M Advising 171 184 93 113 134 84 Appointments 349 350 100 198 152 130 Awards 305 302 101 188 153 123 Conference Organization 204 180 113 150 124 121 Conference Participation 298 291 102 194 143 136 Editorial 199 205 97 119 120 99 Education 351 350 100 169 173 98 Field 149 154 97 94 116 81 Grants 258 249 104 170 138 123 Introduction 351 351 100 151 191 79 Media 43 36 119 37 35 106 Publications 351 351 100 146 204 72 Referee 276 277 100 150 168 89 Review 147 147 100 99 113 88 Service 234 207 113 170 120 142 Teaching 274 258 106 158 161 98 All Sections: 351 351 100 177 173 102 Table 2 Sections in CVs: Women (W) and Men (M) (only closely mathed pairs) Exists in % # of words in sections % Section W M W over M W higher M higher W over M Advising 121 124 98 84 84 100 Appointments 254 253 100 147 106 139 Awards 221 220 100 135 111 122 Conference Organization 151 130 116 109 87 125 Conference Participation 216 203 106 150 91 165 Editorial 148 140 106 92 79 116 Education 254 253 100 125 123 102 Field 107 120 89 64 89 72 Grants 189 178 106 128 96 133 Introduction 254 254 100 102 144 71 Media 36 27 133 32 26 123 Publications 254 254 100 110 143 77 Referee 200 197 102 114 115 99 Review 106 102 104 69 80 86 Service 178 150 119 130 83 157 Teaching 200 177 113 123 103 119 All Sections 254 254 100 142 112 127 Women are known to publish less frequently in prestigious journals (Lundberg & Stearns, 2019 ). Our analysis introduces two key innovations. First, we compare male and female faculty members within the same institutions, which helps control for differences in faculty quality. Second, we use CV data that include all publications - indexed and non-indexed alike - such as working papers, policy briefs, and book chapters. From Table 1 , we observe that more men than women report longer “Publications” sections in their CVs, measured by word count. However, despite our efforts to match faculty members closely by post-PhD experience, there is still a systematic bias: female faculty in our sample tend to be younger, and as a result, they were more often matched with slightly more senior male counterparts. To address this bias, we conducted a secondary analysis on a more tightly matched subsample, which we refer to as the closely matched sample. This group includes only faculty pairs whose difference in post-PhD experience is less than one year, yielding 254 matched pairs. Table 2 presents results from this closely matched sample. The gender differences observed in the full sample are even more pronounced in this subset. For instance, in the full sample (Table 1 ), 2% more women than men have longer overall CVs; in the closely matched sample, this figure rises to 27%. Similarly, 57% more women than men report more service work in their CVs, compared to a 42% difference in the full sample. For “Conference Participation,” the corresponding figures are 65% in the closely matched sample versus 36% in the full sample. Part of the reason women appear to have shorter “Publications” sections in the full sample is that we did not control for experience level. Once we restrict the analysis to closely matched faculty, the gender gap narrows. In fact, 77% more women than men have longer “Publications” sections in the closely matched sample, compared to 72% in the full sample. While word count serves as a useful proxy, a more direct measure of activity in each CV section is the number of individual entries. Accordingly, we counted the number of advisees, conference participations, editorial roles, grants, publications, and journals refereed. The results, shown in Table 3 , are consistent with the patterns in Tables 1 and 2 . Women report more conference participations, editorial positions, and grant involvement, while men report more advising, publications, and referee work. Table 3 Number of activities in CV sections: Women (W) vs. Men (M) All Closely Matched # of activies % # of activies % Section W higher M higher W over M W higher M higher W over M Advising 108 136 79 79 86 92 Conference Participation 184 149 123 138 99 139 Editorial 112 111 101 88 72 122 Grants 166 128 130 124 91 136 Publications 128 209 61 104 140 74 Referee 148 168 88 110 116 95 As shown in Table 4 , there are gender differences in the disclosure of private information. Male faculty members are more likely to provide their address, email, and personal or professional homepages, while female faculty members are more likely to list their phone and fax numbers. Women are also more likely to report their marital status and number of children. Very few faculty members - regardless of gender - include information about their gender identity or date of birth in their CVs. Table 4 Private Information: Women (W) vs. Men (M) Exists in % W M W over M Address 295 317 93 E-mail 330 339 97 Home Page 258 265 97 Phone 188 178 106 Fax 45 38 118 Citizenship 92 95 97 Language 58 42 138 Date of Birth 19 27 70 Gender 6 2 300 Children 23 17 135 Marriage 24 19 126 We compare publication performance based on CVs and Scopus records in Table 5 . The first row reproduces data from Table 3 , showing the total number of publications listed in CVs. Since CVs often include working papers, policy briefs, and other non-indexed outputs, they typically contain more publications than Scopus profiles. Only eight male and eight female faculty members list fewer publications in their CVs than are found in their Scopus profiles. Table 5 confirms that women have fewer publications than men, both in their CVs and in Scopus. This aligns with findings from earlier studies on gender disparities in academic publishing. Our contribution builds on this literature by controlling for institutional context and post-PhD experience, comparing men and women within the same departments. Moreover, we show that women publish less not only in top-tier journals, but also in terms of the broader range of publications they choose to include in their CVs. In the closely matched sample, women appear to have a slight advantage in CV-listed publications. Among the closely matched pairs, 97 women report more Scopus-indexed publications than their male counterparts, while 104 women list more total publications in their CVs. This difference is reflected in the final row of Table 5 , which compares the ratio of CV-listed to Scopus-indexed publications. We find that 34% more women than men have a higher CV-to-Scopus publication ratio compared to their matched counterparts. We were unable to classify publication types in the CVs, as many faculty members list all outputs - such as journal articles, working papers, and book chapters - under a single heading. Therefore, we cannot determine whether women are overrepresented in a specific type of publication. Nevertheless, the results suggest that women tend to include more non-indexed or alternative forms of scholarly output in their CVs, relative to what appears in Scopus. Table 5 Number of Publications in CVs and Scopus: Women (W) vs. Men (M) All Closely Matched % % Section W higher M higher W over M W higher M higher W over M Publications (CV) 128 209 61 104 140 74 Publications (Scopus) 125 202 62 97 141 69 Top 5 Journal Pub. (Scopus) 90 150 60 70 106 66 % Publications (CV over Scopus) 192 151 121 142 106 134 5. Conclusion We compare the CVs of male and female faculty members from the same departments and with similar post-PhD experience. Our findings show that women tend to have longer CVs, as measured by word count, and are more likely to report extensive service work. They also disclose more private information, such as marital status and number of children. Although women have fewer total publications overall, they report more publications in their CVs relative to their Scopus-indexed records. CVs offer a valuable source of academic data, particularly for variables that are difficult to measure through bibliometric databases - such as service work and certain forms of academic engagement. However, CVs must be used with caution. They are heterogeneous in format, often contain missing or selective information, and reflect individual choices in self-presentation. Despite these limitations, our analysis demonstrates that CVs can reveal meaningful gender differences in academic roles and responsibilities. Importantly, CVs also provide complementary information to formal bibliometric records. They capture types of academic output not indexed in standard databases, including working papers, policy briefs, and teaching or service contributions. In this way, CV analysis contributes to a more holistic understanding of the academic production process—highlighting not only what academics produce, but also how they present and prioritize their work. Declarations Author Contribution I am the sole author and conducted all aspects of the research for this study. Ethics declarations Funding and/or Conflicts of interests/Competing interests No funding was received for conducting this study References Bi, W., Chan, H. F., & Torgler, B. (2019). Self-esteem, self-symbolizing, and academic recognition: behavioral evidence from curricula vitae. Scientometrics , 119 , 495-525. Biermann, M. (2024). Remote talks: Changes to economics seminars during COVID-19. European Economic Review , 163, 104677. Cañibano, C., & Bozeman, B. (2009). Curriculum vitae method in science policy and research evaluation: the state-of-the-art. Research Evaluation , 18(2), 86-94. Cañibano, C., Otamendi, J., & Andújar, I. (2008). Measuring and assessing researcher mobility from CV analysis: the case of the Ramón y Cajal programme in Spain. Research Evaluation , 17(1), 17-31. Ghosh, P., & Liu, Z. (2020). Coauthorship and the gender gap in top economics journal publications . Applied Economics Letters , 27(7), 580-590. Guarino, C. M., & Borden, V. M. (2017). Faculty service loads and gender: Are women taking care of the academic family?. Research in higher education , 58, 672-694. Hilmer, C., & Hilmer, M. (2007). Women helping women, men helping women? Same-gender mentoring, initial job placements, and early career publishing success for economics PhDs. American Economic Review , 97(2), 422-426. Lundberg, S., & Stearns, J. (2019). Women in economics: Stalled progress. Journal of Economic Perspectives , 33(1), 3-22. Macfarlane, B. (2020). The CV as a symbol of the changing nature of academic life: performativity, prestige and self-presentation. Studies in Higher Education , 45 (4), 796-807. Misra, J., Lundquist, J. H., & Templer, A. (2012). Gender, work time, and care responsibilities among faculty 1. Sociological forum, 27(2), 300-323. Olechnicka, A., Ploszaj, A., & Zegler-Poleska, E. (2025). The impact of the virtualization of scholarly conferences on the gender structure of conference contributors. Scientometrics , 130(1), 423-445. Sabatier, M., Carrere, M., & Mangematin, V. (2006). Profiles of academic activities and careers: Does gender matter? An analysis based on French life scientist CVs. The Journal of Technology Transfer , 31, 311-324. Sandström, U. (2009). Combining curriculum vitae and bibliometric analysis: mobility, gender and research performance. Research Evaluation , 18(2), 135-142. Teixeira da Silva, J. A., Dobránszki, J., Al-Khatib, A., & Tsigaris, P. (2020). Curriculum vitae: challenges and potential solutions. KOME , 8(2), 109-127. US News & World Report (2022). Best economics schools . Youtie, J., Rogers, J., Heinze, T., Shapira, P., & Tang, L. (2013). Career-based influences on scientific recognition in the United States and Europe: Longitudinal evidence from curriculum vitae data. Research Policy , 42(8), 1341-1355. Yuret, T. (2017). An analysis of the foreign-educated elite academics in the United States. Journal of Informetrics , 11(2), 358-370. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8440876","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":581134642,"identity":"0481f5a9-de00-46e2-9500-f480ee2702b9","order_by":0,"name":"Tolga Yuret","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAr0lEQVRIiWNgGAWjYPACGyBmbDxAlFoeMJmQBtLSQJKWw2CKOC32EjlmHz7+OG+3tv0w0JYam2jCtkjkGM+ckXA7eduZRKCWY2m5DcRoYeYBajE7ANTC2HCYSC1/Es4lm51/SIoWhoQDdmY3iLblzLNixp605ASzG0BbEojxC3t78maGHzZ29mbn0x8++FBjQ1gLg0ACmEoEq0wgqBwE+A+AKXuiFI+CUTAKRsHIBABCMEPDD4bWewAAAABJRU5ErkJggg==","orcid":"","institution":"Istanbul Technical University","correspondingAuthor":true,"prefix":"","firstName":"Tolga","middleName":"","lastName":"Yuret","suffix":""}],"badges":[],"createdAt":"2025-12-24 08:53:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8440876/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8440876/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101942810,"identity":"7af24992-a630-4722-8e1c-2662970db0fb","added_by":"auto","created_at":"2026-02-05 09:38:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":598199,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8440876/v1/5c0928e4-ec84-4ee5-9ca4-284ad757a9a7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Gender Differences in Faculty CVs in Leading US Economics Departments","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAcademics are often generous in making their CVs publicly available. These CVs contain objective information that has been used in studies analyzing educational backgrounds and career mobility. However, CVs also exhibit heterogeneous structures. For example, some academics selectively report conference participation. As a result, CVs cannot always be treated as fully objective data sources.\u003c/p\u003e \u003cp\u003eThere are gender differences in the distribution of academic tasks. For example, survey-based studies have found that women tend to take on more service work in academia (Guarino \u0026amp; Borden, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Misra et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Alternatively, the prominence of service work in CVs can serve as a proxy for such involvement. While CVs are not fully objective sources of information, neither are surveys. Therefore, analyzing CVs provides an additional, albeit subjective, perspective on gender differences in academic responsibilities.\u003c/p\u003e \u003cp\u003eCVs also offer valuable insights into how academics choose to present their academic output. For instance, an academic may include an extensive list of publications, such as working papers and policy notes, beyond peer-reviewed articles. Similarly, the service section can be expanded to include relatively minor tasks, such as participation in commencement ceremonies. Therefore, analyzing the content and structure of CVs is important for understanding how academics construct and communicate their professional identity.\u003c/p\u003e \u003cp\u003eIn this paper, we analyze the CVs of male and female faculty members from leading economics departments, focusing on gender differences across all sections, including publications and service work. The data obtained from CVs can be interpreted along three dimensions. First, CVs reflect reality to some extent; for example, one cannot list conferences they did not attend. Second, they reveal how academics perceive and present their professional identity. A faculty member who does not view herself as involved in administrative tasks may choose to omit certain service roles from her CV. Third, CVs may be strategically crafted - for instance, listing numerous working papers can signal a strong publication pipeline. While we cannot disentangle these three dimensions, the observed gender differences in CV content offer valuable insight.\u003c/p\u003e"},{"header":"2. Related Literature","content":"\u003cp\u003eCa\u0026ntilde;ibano and Bozeman (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) identify four key limitations of using data extracted from CVs. First, there is an issue of availability, as some academics choose not to make their CVs publicly accessible. Second, CVs are heterogeneous in both content and level of detail; for instance, some list only selected publications while others are more comprehensive. Third, CVs often contain missing or incomplete information. Finally, extracting data from CVs is labor-intensive, requiring manual coding and interpretation.\u003c/p\u003e \u003cp\u003eMacfarlane (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), analyzing CVs of academics at different career stages, observes notable variation in CV styles. Similarly, Texeira et al. (2020) highlight the absence of institutional oversight over CV content, which can result in misleading or even fabricated information. These authors also propose principles for institutional regulation of academic CVs.\u003c/p\u003e \u003cp\u003eWhile this paper presents a comprehensive analysis of academic CVs, it is not immune to the aforementioned limitations. Nevertheless, we take several steps to mitigate these challenges. First, we focus on the field of economics, which is characterized by a relatively high rate of publicly available CVs, reducing concerns about availability. Second, our analysis is limited to faculty members at leading economics departments, where the risk of data fabrication is presumably low. Third, by comparing male and female faculty with similar post-PhD experience, we minimize potential generational differences in CV style.\u003c/p\u003e \u003cp\u003eHowever, two major concerns affect our study. First, although we compare the CVs of men and women from the same institutions, we observe considerable heterogeneity in CV styles. Second, some CVs contain missing information. Therefore, CV data should not be interpreted as an objective record of academic output, but rather as a reflection of how individuals choose to present their academic achievements.\u003c/p\u003e \u003cp\u003eNumerous studies have utilized data extracted from academic CVs. Youtie et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), for instance, match academics from North America and Europe with comparable academic achievements and examine differences in their CVs. They find that grant income and awards from professional associations are more prominently featured in the CVs of North American academics. Our approach is similar in that we also match academics based on academic success; however, our focus is on gender differences rather than geographic ones.\u003c/p\u003e \u003cp\u003eCV analysis has also been employed to investigate gender disparities in academic promotion. Sabatier et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) extract multiple variables from the CVs of French academics and demonstrate that the criteria for promotion differ by gender. Their findings suggest that women face higher standards and longer timelines for promotion.\u003c/p\u003e \u003cp\u003eOther studies focus on specific features or sections of CVs. Bi et al. (2020), for example, examine the fonts used in CVs as a proxy for self-esteem and correlate this with academic productivity. Yuret (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) uses publicly available CVs and online sources to retrieve undergraduate information for successful academics. Ca\u0026ntilde;ibano et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) and Sandstr\u0026ouml;m (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) analyze the career mobility of Spanish and Swedish academics, respectively, using CV-based data.\u003c/p\u003e \u003cp\u003eNumerous survey-based studies provide substantial evidence that women in academia perform more service work than men (Guarino \u0026amp; Borden, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Misra et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). This is particularly concerning because service work tends to carry less weight in promotion decisions. Consequently, gender disparities in service responsibilities may contribute to differences in promotion outcomes. In this paper, we investigate whether a gender difference in reported service work appears in CVs. As noted earlier, CV data are neither fully objective nor complete, so the presence and length of service sections should be interpreted as proxies for actual service contributions.\u003c/p\u003e \u003cp\u003eLundberg and Stearns (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) highlight that women are underrepresented in prestigious economics departments and generally publish less than men. Ghosh and Liu (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) compare untenured male and female economists in top departments, noting that women publish less in top five economics journals partly because they are more likely to be placed in lower-ranked institutions and thus collaborate with less productive coauthors. Similarly, Hilmer and Hilmer (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) find that women who graduate from the same PhD programs as men tend to be less productive in terms of publication output.\u003c/p\u003e \u003cp\u003eThis paper addresses gender differences in research productivity in two ways. First, we match male and female faculty members within the same departments to assess whether gender disparities persist when institutional context is held constant. Second, we supplement bibliometric data with total publication counts from CVs, which include non-indexed outputs such as working papers and policy papers. This allows us to examine whether gender gaps in top-tier publications also appear in less competitive academic outputs.\u003c/p\u003e \u003cp\u003eThe COVID-19 pandemic has served as a natural experiment, highlighting gendered constraints in academic participation. Bierman (2024) and Olechnicka et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) find that women were more likely to participate in virtual conferences than in in-person ones. In this paper, we investigate whether conference participation mentioned in CVs differs by gender.\u003c/p\u003e"},{"header":"3. Data","content":"\u003cp\u003eThere are 1,786 faculty members in the top 50 economics departments, as ranked by US News \u0026amp; World Report (2022). Of these, 372 (21%) are women. For our analysis, we matched each female faculty member with a male faculty member from the same department whose post-PhD experience differed by no more than five years. If multiple male faculty members had the same absolute difference in post-PhD experience, one was randomly selected.\u003c/p\u003e \u003cp\u003eWe successfully created 351 matched pairs. Nine women were excluded because their CVs were not publicly available, and twelve could not be matched due to the absence of suitable male counterparts within the specified experience range. Although men outnumber women in all departments, suitable matches were not always possible because many women faculty members have similar post-PhD experience. For instance, in some departments, most newly hired faculty members are women.\u003c/p\u003e \u003cp\u003eIn total, we analyzed 702 CVs \u0026minus;\u0026thinsp;351 from women and 351 from their matched male counterparts. First, we categorized CVs into standardized sections across all faculty members. Second, we manually coded data from the CVs to quantify entries such as the number of advisees.\u003c/p\u003e \u003cp\u003eAdditionally, we collected bibliometric data for all 702 faculty members from Scopus, including the total number of publications and the number of articles published in the top five economics journals.\u003c/p\u003e"},{"header":"4. Analysis","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e outlines the standardized sections of the CVs used in our analysis. Given the heterogeneous structure of the original CVs, we undertook a standardization process to ensure consistency across cases. For instance, editorial roles appear under \u0026ldquo;Appointments\u0026rdquo; in some CVs and under \u0026ldquo;Refereeing\u0026rdquo; in others. In such cases, we created a distinct \u0026ldquo;Editorial\u0026rdquo; section to capture these positions consistently. Similarly, while advising can be considered a form of service work, we chose to separate it into its own \u0026ldquo;Advising\u0026rdquo; section to maintain exclusive, non-overlapping categories.\u003c/p\u003e \u003cp\u003ePrivate information - including address, citizenship, and marital status - is grouped under an \u0026ldquo;Introduction\u0026rdquo; section, even though this information is rarely labeled as such and may appear at the end of some CVs. Smaller categories were consolidated into broader sections; for example, organizing a workshop was classified under \u0026ldquo;Conference Organization.\u0026rdquo;\u003c/p\u003e \u003cp\u003eNearly all CVs include sections on \u0026ldquo;Appointments,\u0026rdquo; \u0026ldquo;Publications,\u0026rdquo; and \u0026ldquo;Education.\u0026rdquo; Most sections appear in the majority of CVs, with the exceptions of \u0026ldquo;Field,\u0026rdquo; \u0026ldquo;Media,\u0026rdquo; and \u0026ldquo;Review.\u0026rdquo; We also observe gender-based differences in section availability. For instance, 234 female CVs include a \u0026ldquo;Service\u0026rdquo; section compared to 207 male CVs - indicating that 13% more women than men report service work. In fact, women\u0026rsquo;s CVs show equal or greater representation across all sections except for \u0026ldquo;Advising\u0026rdquo;, \u0026ldquo;Editorial\u0026rdquo;, and \u0026ldquo;Field\u0026rdquo;.\u003c/p\u003e \u003cp\u003eTo further analyze CV content, we counted the number of words in each section as a proxy for the amount of activity reported. We then compared these word counts between matched pairs. For example, in 194 pairs, the female faculty member had more words in the \u0026ldquo;Conference Participation\u0026rdquo; section than her male counterpart; in 143 pairs, the opposite was true; and in 14 pairs, the word counts were identical.\u003c/p\u003e \u003cp\u003eThe final column of Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the ratio of pairs in which women have more words to those in which men do. For \u0026ldquo;Conference Participation,\u0026rdquo; for example, 194 women had more words compared to 143 men\u0026mdash;yielding a 36% higher incidence among women.\u003c/p\u003e \u003cp\u003eInterestingly, while prior research suggests that women attend fewer conferences than men (Bierman, 2024; Olechnicka et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), the \u0026ldquo;Conference Participation\u0026rdquo; sections of women\u0026rsquo;s CVs in our sample tend to be longer. Two explanations are plausible: first, since our sample pairs men and women from the same departments, there may be little actual difference in participation rates. Second, women may be more inclined to provide comprehensive documentation of their conference activity.\u003c/p\u003e \u003cp\u003eConsistent with prior research indicating that women take on more service responsibilities in academia (Guarino \u0026amp; Borden, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Misra et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), we find that women are more likely than men to report a higher word count in the \u0026ldquo;Service\u0026rdquo; sections of their CVs. Conversely, men are more likely to report greater detail in other service-related categories, such as \u0026ldquo;Refereeing\u0026rdquo; and \u0026ldquo;Review.\u0026rdquo;\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSections in CVs: Women (W) vs. Men (M)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExists in\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e# of words in sections\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eW over M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eW higher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eM higher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eW over M\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdvising\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAppointments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAwards\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConference Organization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e121\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConference Participation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e136\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEditorial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eField\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntroduction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePublications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReferee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReview\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eService\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e142\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTeaching\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll Sections:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSections in CVs: Women (W) and Men (M) (only closely mathed pairs)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExists in\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e# of words in sections\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eW over M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eW higher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eM higher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eW over M\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdvising\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAppointments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e139\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAwards\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConference Organization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConference Participation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e165\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEditorial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e116\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eField\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e133\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntroduction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePublications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReferee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReview\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eService\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e157\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTeaching\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e119\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll Sections\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e127\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWomen are known to publish less frequently in prestigious journals (Lundberg \u0026amp; Stearns, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Our analysis introduces two key innovations. First, we compare male and female faculty members within the same institutions, which helps control for differences in faculty quality. Second, we use CV data that include all publications - indexed and non-indexed alike - such as working papers, policy briefs, and book chapters.\u003c/p\u003e \u003cp\u003eFrom Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, we observe that more men than women report longer \u0026ldquo;Publications\u0026rdquo; sections in their CVs, measured by word count. However, despite our efforts to match faculty members closely by post-PhD experience, there is still a systematic bias: female faculty in our sample tend to be younger, and as a result, they were more often matched with slightly more senior male counterparts.\u003c/p\u003e \u003cp\u003eTo address this bias, we conducted a secondary analysis on a more tightly matched subsample, which we refer to as the closely matched sample. This group includes only faculty pairs whose difference in post-PhD experience is less than one year, yielding 254 matched pairs.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents results from this closely matched sample. The gender differences observed in the full sample are even more pronounced in this subset. For instance, in the full sample (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), 2% more women than men have longer overall CVs; in the closely matched sample, this figure rises to 27%. Similarly, 57% more women than men report more service work in their CVs, compared to a 42% difference in the full sample. For \u0026ldquo;Conference Participation,\u0026rdquo; the corresponding figures are 65% in the closely matched sample versus 36% in the full sample.\u003c/p\u003e \u003cp\u003ePart of the reason women appear to have shorter \u0026ldquo;Publications\u0026rdquo; sections in the full sample is that we did not control for experience level. Once we restrict the analysis to closely matched faculty, the gender gap narrows. In fact, 77% more women than men have longer \u0026ldquo;Publications\u0026rdquo; sections in the closely matched sample, compared to 72% in the full sample.\u003c/p\u003e \u003cp\u003eWhile word count serves as a useful proxy, a more direct measure of activity in each CV section is the number of individual entries. Accordingly, we counted the number of advisees, conference participations, editorial roles, grants, publications, and journals refereed. The results, shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, are consistent with the patterns in Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Women report more conference participations, editorial positions, and grant involvement, while men report more advising, publications, and referee work.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNumber of activities in CV sections: Women (W) vs. Men (M)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eClosely Matched\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e# of activies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e# of activies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eW higher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM higher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eW over M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eW higher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eM higher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eW over M\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdvising\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConference Participation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e139\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEditorial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e136\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePublications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReferee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, there are gender differences in the disclosure of private information. Male faculty members are more likely to provide their address, email, and personal or professional homepages, while female faculty members are more likely to list their phone and fax numbers. Women are also more likely to report their marital status and number of children. Very few faculty members - regardless of gender - include information about their gender identity or date of birth in their CVs.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrivate Information: Women (W) vs. Men (M)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExists in\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eW over M\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAddress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE-mail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHome Page\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCitizenship\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLanguage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDate of Birth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChildren\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarriage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e126\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWe compare publication performance based on CVs and Scopus records in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The first row reproduces data from Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, showing the total number of publications listed in CVs. Since CVs often include working papers, policy briefs, and other non-indexed outputs, they typically contain more publications than Scopus profiles. Only eight male and eight female faculty members list fewer publications in their CVs than are found in their Scopus profiles.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e confirms that women have fewer publications than men, both in their CVs and in Scopus. This aligns with findings from earlier studies on gender disparities in academic publishing. Our contribution builds on this literature by controlling for institutional context and post-PhD experience, comparing men and women within the same departments. Moreover, we show that women publish less not only in top-tier journals, but also in terms of the broader range of publications they choose to include in their CVs.\u003c/p\u003e \u003cp\u003eIn the closely matched sample, women appear to have a slight advantage in CV-listed publications. Among the closely matched pairs, 97 women report more Scopus-indexed publications than their male counterparts, while 104 women list more total publications in their CVs. This difference is reflected in the final row of Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, which compares the ratio of CV-listed to Scopus-indexed publications. We find that 34% more women than men have a higher CV-to-Scopus publication ratio compared to their matched counterparts.\u003c/p\u003e \u003cp\u003eWe were unable to classify publication types in the CVs, as many faculty members list all outputs - such as journal articles, working papers, and book chapters - under a single heading. Therefore, we cannot determine whether women are overrepresented in a specific type of publication. Nevertheless, the results suggest that women tend to include more non-indexed or alternative forms of scholarly output in their CVs, relative to what appears in Scopus.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNumber of Publications in CVs and Scopus: Women (W) vs. Men (M)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eClosely Matched\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eW higher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM higher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eW over M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eW higher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eM higher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eW over M\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePublications (CV)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePublications (Scopus)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTop 5 Journal Pub. (Scopus)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% Publications (CV over Scopus)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e134\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eWe compare the CVs of male and female faculty members from the same departments and with similar post-PhD experience. Our findings show that women tend to have longer CVs, as measured by word count, and are more likely to report extensive service work. They also disclose more private information, such as marital status and number of children. Although women have fewer total publications overall, they report more publications in their CVs relative to their Scopus-indexed records.\u003c/p\u003e\n\u003cp\u003eCVs offer a valuable source of academic data, particularly for variables that are difficult to measure through bibliometric databases - such as service work and certain forms of academic engagement. However, CVs must be used with caution. They are heterogeneous in format, often contain missing or selective information, and reflect individual choices in self-presentation. Despite these limitations, our analysis demonstrates that CVs can reveal meaningful gender differences in academic roles and responsibilities.\u003c/p\u003e\n\u003cp\u003eImportantly, CVs also provide complementary information to formal bibliometric records. They capture types of academic output not indexed in standard databases, including working papers, policy briefs, and teaching or service contributions. In this way, CV analysis contributes to a more holistic understanding of the academic production process—highlighting not only what academics produce, but also how they present and prioritize their work.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eI am the sole author and conducted all aspects of the research for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding and/or Conflicts of interests/Competing interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received for conducting this study\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBi, W., Chan, H. F., \u0026amp; Torgler, B. (2019). 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An analysis of the foreign-educated elite academics in the United States. \u003cem\u003eJournal of Informetrics\u003c/em\u003e, 11(2), 358-370.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Curriculum Vitae, academic tasks, gender, economics departments","lastPublishedDoi":"10.21203/rs.3.rs-8440876/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8440876/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"There are notable gender differences in the distribution of academic tasks. In this paper, we match 351 female faculty members from leading US economics departments with male counterparts from the same departments who have comparable post-PhD experience. The presence of specific sections in CVs - as well as the number of words and activities listed within them - is used as a proxy for involvement in particular academic tasks. Our findings are partly consistent with the existing literature: for example, women tend to have longer “Service” sections than men. However, we also identify patterns that deviate from prior research; notably, women have longer “Conference Participation” sections. In addition, women are more likely to disclose private information, such as marital status and phone numbers. Finally, we find that the number of publications listed on women's CVs exceeds their Scopus-indexed publications by a greater margin than for men.","manuscriptTitle":"Gender Differences in Faculty CVs in Leading US Economics Departments","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-29 10:04:09","doi":"10.21203/rs.3.rs-8440876/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ea641cd1-8171-44d8-b4f0-1b44869ec07a","owner":[],"postedDate":"January 29th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-29T10:04:09+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-29 10:04:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8440876","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8440876","identity":"rs-8440876","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}