Construction of a Publication Database of the Scientific Journal Demography : How to Identify Author s Gender and Field of Publication

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1 Max-Planck-Institut für demografische Forschung Max Planck Institute for Demographic Research Konrad-Zuse-Strasse 1 D Rostock GERMANY Tel +49 (0) ; Fax +49 (0) ; MPIDR TECHNICAL REPORT OCTOBER 2014 Construction of a Publication Database of the Scientific Journal Demography : How to Identify Author s Gender and Field of Publication Valeria Nieberg Sandra Krapf (krapf@demogr.mpg.de) Michaela Kreyenfeld (kreyenfeld@demogr.mpg.de) Katharina Wolf (wolf@demogr.mpg.de) For additional material see Copyright is held by the authors. Technical reports of the Max Planck Institute for Demographic Research receive only limited review. Views or opinions expressed in technical reports are attributable to the authors and do not necessarily reflect those of the Institute.

2 Construction of a Publication Database of the Scientific Journal Demography : How to Identify Author s Gender and Field of Publication Valeria Nieberg, Sandra Krapf, Michaela Kreyenfeld und Katharina Wolf Abstract This Technical Report describes a database that has been established for the research project Gendered authorship and demographic research An analysis of 50 years of Demography. In this project, we have investigated whether gender determines field of publication. Our analysis was based on all papers published in Demography which is the flagship journal of the Population of America Association (PAA) and currently celebrates its 50 th anniversary. We scrutinized all issues of Demography and compiled information on title, topic as well as gender of all authors. This Technical Report describes how we set up the resulting database. The Report is accompanied by the respective database (in STATA-format, version 13.0). Keywords: Demography, gender, publication Accompanying file: Datasets in STATA-format (GENDEMO.dta) 1

3 1 Introduction Demography is the flagship journal of the Population of America Association (PAA) and currently celebrates its 50 th anniversary. With 41.9% of all authorships from 1990 to 2011 being female (West et al., 2013, p. 2), demography is among the most gender equal disciplines with regard to gender composition of scientific output. However, in recent studies on gender dissimilarities in publication rates, critical voices call for a more accentuated look into gender and scientific authorship, as for example, West et al. (2013) report that where raw publication counts seem to be equal between genders, close inspection reveals that, in certain fields, men predominate in the prestigious first and last author positions and that even in fields with a gender composition near parity, men and women are unequally distributed in subfields. In due consideration of these findings, our project Gendered authorship and demographic research An analysis of 50 years of Demography looks at the development of gender dissimilarities in scholarly authorship within subfields of demography over time. This Technical Report reconstructs the process in which the data was obtained and processed. We also provide the dataset that is the basis of our analyses. The data is available in STATAformat (version 13.0). 2 How We Compiled the Data Citation information for all publications in Demography were drawn from the online Jstor-database and Web-of-Science-database which covers the Science Citation Index Expanded, the Social Sciences Citation Index and the Arts and Humanities Citation 2

4 Index. Jstor at present only covers Demography publications until volume 50 while we were interested in the Volumes 1 to 51 to cover all publications from 1964 to Web of Sciences only provides initials for the first names of authors until Volume 42. On account of this, both online libraries had to be used: Jstor for those articles published from 1964 to 2005 in Volume 1 to 42, Web of Science for those published from 2006 to 2014 in Volumes 43 to 51. Citation information was then imported into Endnote (Version X5). In Endnote, we deleted the front matters and back matters, editors information, doubles in order to focus on scientific articles. Moreover, the search algorithm captured a small number of articles published in the Southern African Journal of Demography by mistake, and these papers were also dropped. The total sample size then was 2,335 articles. From Endnote, relevant information, i.e. year, volume, issue, pages, names of all authors as well as title and abstract, was exported into Excel and preprocessed. In particular, the data on names of authors of a publication was formatted. The conditions on how information was extracted from Endnote into a csv-file were specified in Endnote. Different authors were separated by a semicolon and the given name of an author was the first name mentioned. Information on every author was split into two cells. One cell contains information on the given name and the second cell contains all following names (middle and last names). Because the highest number of authors per one paper was seven, every publication was assigned with seven fields for names of authors. These fields were coded missing if there was no author in the respective position. 3

5 The main dependent variable in the models applied in this research project is gender of the first author. However, citation information obtained from Web of Science and Jstor gives no account of author s gender. Therefore, the given name was analyzed to assign gender to author. In order to do this, the data was in a next step reshaped from a wide into a long format. Each author became a single data row and each publication was identified via a sequence number. gender.c is a program for determining the gender on basis of fist names. 1 The program contains a name database and the referring gender of each first name. The dictionary file nam\_dict.txt contains a list of more than 42,000 first names and the respective gender, plus some 700 pairs of "equivalent" names, i.e. names that are used for both men and women. According to the developer, the list covers the vast majority of first names in all European countries and in some overseas countries (e.g. China, India, Japan, U.S.A.) as well. The function "get\_gender" is used to check first names and determine their gender. This function has the following return values, which are defined in the file "gen\_ext.h": IS\_FEMALE, IS\_MOSTLY\_FEMALE, IS\_MALE, IS\_MOSTLY\_MALE, UNISEX\_NAME, NAME\_NOT\_FOUND, ERROR\_IN\_NAME, ERROR\_IN\_DATAFILE. We merged the gender information of the names database to the names in our author sample using STATA. Only names that had been assigned a gender with certainty, i.e. 1 The copyright ( ) for this program lies with Jörg Michael, Adalbert-Stifter-Str. 11, Hannover, Germany. The program is subject to the GNU Lesser General Public License (LGPL) as published by the Free Software Foundation. 4

6 IS\_FEMALE and IS\_MALE were coded. Names that were either identified as IS\_MOSTLY\_FEMALE, IS\_MOSTLY\_MALE, UNISEX\_NAME or NAME\_NOT\_FOUND, or those that were assigned to multiple options (resulting from the fact that typical gender for name varies among countries) were left blank and coded manually by identification of an individual through internet research. Authors which could not be found and therefore could not be assigned a gender were coded missing. In addition, a variable was created that specifies whether gender was assigned automatically or manually. The process of gender identification held a number of potential problems. Some given names were abbreviated. In one case, the first word drawn from the database was not the given name, but a title. These cases were not assigned a gender automatically because they did not match any name from the database. They were coded manually given that the author s gender could be identified by internet research with information on initial of given name and last name. Another problem which occurred a few times was that given name and last name were interchanged. This resulted from the fact that they were in interchanged order in Web of Science or Jstor and in that order drawn from the databases. These cases are not problematic if the author s last name did not match any name from the database. Then, gender could be assigned manually. However, if an author s name was Maralinda Simon and indexed in wrong order in the database, the observation would be coded male automatically. We checked for these possible errors in our data, and, if necessary, corrected for it. Table 1 provides the outcome of the classification procedure. In our database, we have 4,382 authors. We were able to manually identify roughly 30 percent of the gender of all 5

7 authors relying on the program gender.c. The gender of the other authors were assessed by internet search. Only in 185 cases (4 percent), we were unable to retrieve the gender of the author. Table 1: Categorization of author s gender male female unknown automatically assigned 2,095 72% % - manually assigned % % 185 total 2, % 1, % Assigning Articles to Categories An important aspect in our analyses was the categorization of articles to the single research areas of demography. West et al. (2013) who were interested in the publication activities in different scientific fields used computerized tools (hierarchical map equation) 2 in order to automatically analyze texts and group them into research fields. Others could draw on existing data, like in the case of Abramo et al. (2009) who studied natural scientists and engineers in Italy (in the Italian academic system all professors are classified in so called Scientific Disciplinary Sectors, there are 370 of them grouped in 14 University Disciplinary Areas). However, with our narrow focus on 2 West et al. (2013) base their method on Rosvall & Bergstrom (2011). 6

8 demographic topics and the strong overlap among the subfields, we decided to do the categorization manually. 3 We identified (1) fertility, (2) mortality and (3) migration as classic fields of population studies. Moreover, we distinguished the fields (4) family & households, (5) reproductive health, (6) health, (7) education, labor market, income & wealth and (8) formal demography, data & methods. These topics are similar to those identified by Rößger (2015) and are in line with the research articles published in Demography. A ninth category ( others ) included related topics that are occasionally studied such as environmental issues, domestic violence or general papers on demography as a scientific discipline. Also unclear cases were assigned to others. We developed a comprehensive list of keywords and a number of decision rules that were used to identify these nine categories. The categorization procedure was refined in several pretests (see Appendix). Based on a content analysis of titles and abstracts, each article of 50 volumes of Demography was manually classified according to these nine categories. In order to identify differences in the authorship in single subfields, it was necessary to assign each study to only one category. As there are many overlaps between the chosen topics, we established several rules that guided a clear assignment to a single 3 A simple string analyses based on keywords resulted in only a limited number of meaningfully classified articles. This might be related to the fact that it is difficult to automatically recognize the direction of causal relationships in an article based on a keyword search. 7

9 class. We decided to take the perspective of the dependent variable. If a paper investigated the effect of maternal employment on second birth behavior, for instance, the article had been grouped in the category fertility. In cases with more than one dependent variable, say disability ( health ) and survival ( mortality ), we prioritized the classic field of demography, in this example mortality. Some studies analyzed both fertility and mortality (or migration processes), i.e. more than one classic field. We considered these cases as unclassifiable; they fell in the others -category. The same procedure was applied to those studies with two (or more) dependent variables that each belonged to categories 4 to 9. Some papers focus not on a cause-effect relationship between variables but rather on methodological aspects such as improved measurement or data issues. Independent of the content, we assigned these studies to methods & data. The migration -category was narrowed down to those papers that focus on the explicit process of migration while related topics such as demographic behavior of ethnic minorities, integration, segregation etc. were categorized based on the dependent variable in the studies. These instructions for classification also increased the reproducibility of our classification (see Table A1 in Appendix for details on classification rules). To assess the quality of our procedure, we were interested in the degree of agreement of different raters measured by the coefficient kappa. Kappa gives the degree of agreement of the rating of different raters. The measure varies between 0 and 1 and agreement is perfect when kappa takes a value of 1. The referring values of kappa for our classification of articles are given in Table 2 and reached acceptable levels (Cohen 1960). 8

10 Table 2: Kappa Ratings of Classification Scheme Person 1 Person 2 Person 3 (t 1 ) Person Person Person 3 (t 2 ) Note: Three persons independently classified the articles. One of the persons reclassified the articles after a certain period (t 1 ;t 2 ) to evaluate also the repeatability. 4 The Database Table A3 in the Appendix provides a list of the variables that are included in this database. Apart from gender and year of publication, the field of publication is our main variable of interest. Due to the small sample sizes articles published in the subfield health were re-categorized into mortality, reproductive health into fertility and education & income into others. As can be seen from Figure 1 the demographic subfields evolved differently over time. Panel 1 shows the general development for both female and male authors combined. In the period 1964 to 1979 most authors published in the field of fertility, followed by formal demography and migration. Both subfields lost their relative importance and other subfields, like family and household and mortality became more prominent. Panel 2 and 3 display the development of the subfields for male and female authors. The most striking difference between the two is the relative greater importance of formal demography among males. Women more often publish in the field of family & household. 9

11 Figure 1: Evolvement of publications by subfield over time. 100% 75% 50% 25% 0% 100% 75% 50% 25% 0% Total Men Others Formal demography Migration Mortality Family & household Fertility Others Formal demography Migration Mortality Family & household Fertility 100% 75% 50% 25% 0% Women Others Formal demography Migration Mortality Family & household Fertility 10

12 5 Outlook The compiled database is used for the project Gendered authorship and demographic research An analysis of 50 years of Demography. In this project, we study how publication in the demographic subfields has changed over time and how it developed by gender. 6 References Abramo, G., D Angelo, C. A., Caprasecca, A. (2009). "Gender differences in research productivity: A bibliometric analysis of the Italian academic system." Scientometrics 79(3): Cohen, J. (1960): A coefficient of agreement for nominal scales. Educational and Psychological Measurement. 20(1): Rößger, F. (2015): Demographic Perspectives on International Migration. Rostock University (forthcoming). Rosvall, M., Bergstrom, C. T. (2011). Multilevel compression of random walks on networks reveals hierarchical organization in large integrated systems. PLoS one 6(4). e West, J. D., Jacquet J., King, M. M., Corell, S. J., Bergstrom, C. T. (2013). "The role of Gender in scholarly authorship." PLoS one 8(7). 7 Software ENDNOTE (2011): Thomson Reuters Endnote X5. STATA (2013): gender.c ( ): STATA/SE 13.0 for Windows. College Station, TX. 11

13 8 Appendix Table A1: Classification Rules I. We take the perspective of the dependent variable (e.g. an article on the effect of female labor force participation on first birth risks would be categorized to subfield fertility ). a. If there is more than one dependent variable, we classify according to the one that belongs to one of our classic fields fertility, mortality and migration. b. If two or more of the dependent variables belong to the classic fields, we assign the referring article to others. c. If there are two (or more) dependent variables that both belong not to the classic fields (but to more than one of the other categories), the study is grouped in others. II. The category formal demography, methods &data includes more general population issues such as population growth and population projection, survey methods and data issues but also formal papers (independent of the specific application). a. If in a methodological or formal paper a method is applied to one of our 7 topics, the paper is grouped into one of our 7 topics, e.g. tempo adjusted fertility rates (or the projection of mortality rates), it is categorized to fertility (or mortality, respectively). b. If in a methodological paper, more than one of our 7 topics are in the focus, we classify according to the one that belongs to one of our classic fields fertility, mortality and migration. c. If in a methodological paper, more than one construct is in the focus that both belong not to the classic fields (but to more than one of the other categories), the study is grouped in others. III. The category migration includes articles that focus on the process of migration itself. When it comes to ethnic minorities, integration, segregation etc. we categorize the study based on the dependent variable. 12

14 Table A2: List of Keywords for Classification Scheme Subfield Keywords/ special articles Category Fertility Family & Household Mortality Migration Health Keyword births birth rate childbearing childbearing desires/preferences pregnancy spacing of births birth interval family partnerships marriage household leaving home institutional child care/ parental leave elderly care (gendered) division of labor child support child well-being intermarriage kinship homogamy mortality survival causes of death infant/neonatal mortality life expectancy migration processes residential mobility internal/ international migration health aging disability transition to disability/ care need healthy life expectancy healthy behavior/ risk behavior birth weight BMI/obesity Height Code

15 Reproductive Health Formal Demography, Data & Methods Education, Labor Market, Income & Wealth reproductive health sexual health/ HIV family planning (programs) contraception sterility abortion infanticide sex-ratio fecundity miscarriage fetal loss abortion breastfeeding postpartum amenorrhea methods statistics calculus dispersion Bayesian forecasting modeling / models stable population /stationary population measures/ measurement population composition population projections population growth reporting errors survey methodology data collection procedures education language/ cognitive abilities developmental outcomes labor market transition to retirement pension policy wealth/poverty income income mobility neighborhood residential distribution ethnic inequality integration/ segregation socio-economic status

16 Others environment urbanization domestic violence social networks crime demography as a discipline review articles replies/comments unclassifiable unclear 9 Table A3: Codebook Variable Variable name Label Author's ID aid Publication ID pid Author's position in publication, aposnr 1-7 numerical Author's position in publication, aposcat 1 - Single Authorship categorical 2 - First Author, Multiple Authorship 3 - Middle First Author, Multiple Authorship 4 - Last Author, Multiple Authorship Author's gender agender 0 - Male 1 - Female Flag: Gender coded manually or automatically fgender 1 - Automatically 2 - Manually Author's name aname Author's surname asurname Publication subfield, broad pfieldbr 1 - Fertility (including reproductive health) 2 - Family & Household 3 - Mortality (including health) 4 - Migration 7 - Data & Methods, Formal Demography 9 - Others 15

17 Publication subfield, pfieldde 1- Fertility detailed 2 - Family & Household 3 - Mortality 4 - Migration 5 - Health 6 - Reproductive Health 7 - Data & Methods, Formal Demography 8 - Education, Labor Market, Income & Wealth 9 - Others Publication title ptitle Publication issue pissue Publication volume pvol Publication pages ppage Gender of the editor edgender 0 - Male 1 - Female 2 - Multiple editorship, mixed gender Period period