Gender, Family Characteristics, and Publication ...

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Gender, Family Characteristics, and Publication Productivity among Scientists Mary Frank Fox Social Studies of Science 2005 35: 131 DOI: 10.1177/0306312705046630 The online version of this article can be found at: http://sss.sagepub.com/content/35/1/131

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ABSTRACT This paper concentrates upon the relationship between marriage, parental status, and publication productivity for women in academic science, with comparisons to men. Findings indicate that gender, family characteristics, and productivity are complex considerations that go beyond being married or not married, and the presence or absence of children. For women particularly, the relationship between marriage and productivity varies by type of marriage: first compared with subsequent marriage, and occupation of spouse (in scientific compared with non-scientific occupation). Further, type of family composition is important: women with preschool children have higher productivity than women without children or with school-age children. Women with preschool children are found to be a socially selective group in their characteristics, particularly in their allocations of time. Keywords

children, family, gender, household, productivity, scientists

Gender, Family Characteristics, and Publication Productivity among Scientists Mary Frank Fox In the study of gender and science, publication productivity is important for two (related) reasons. First, publication productivity is a central social process of science. It is through publications that research findings are communicated and verified, and that scientific priority is established (Price, 1963; Merton, 1973; Mullins, 1973).1 Second and accordingly, until we understand factors that are associated with productivity, and variation in productivity by gender, we can neither assess nor correct inequities in rewards, including rank, promotion, and salary. This is because publication productivity operates as both cause and effect of status in science. Publication productivity reflects women’s depressed rank and status, and partially accounts for it. ‘Partially’ is a key term: comparable levels of publication do not produce the same rewards for women and men. This is particularly conspicuous in advancement in academic rank for women compared with men (see Sonnert & Holton, 1995). Numbers of studies indicate gender disparity in publication productivity.2 In recent years, the disparity has narrowed somewhat in life sciences, but has persisted in scientific fields outside life sciences (Blackburn & Lawrence, 1996). Further, data indicate that while women and men publish at different rates, the publication of both is positively skewed – Social Studies of Science 35/1(February 2005) 131–150 © SSS and SAGE Publications (London, Thousand Oaks CA, New Delhi) ISSN 0306-3127 DOI: 10.1177/0306312705046630 www.sagepublications.com


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so that most published work is produced by a minority of scientists among both women and men (Cole & Zuckerman, 1984). Documenting gender variation in productivity is one thing, and accounting for it another. The variation in publication may be clear, the explanations are not. This paper concentrates specifically upon the relationship between marriage, children, and publication productivity for women, with comparison with men, in academic science. Much has been made about the impact of marriage and children upon women’s scientific productivity and performance. The mythology of science (Bruer, 1984) has it that good scientists are either men with wives, or women without husbands and children. Evidence, however, tends to contradict this conventional wisdom. Studies across scientific disciplines indicate that married women publish as much as or more than unmarried women (Helmreich et al., 1980; Astin & Davis, 1985; Cole & Zuckerman, 1987; Kyvik, 1990). Further, the presence of children has no effect on women’s productivity (Cole & Zuckerman, 1987), a slightly negative, nonsignificant effect (Reskin, 1978; Long, 1990), or a positive effect (Astin & Davis, 1985; Fox & Faver, 1985). These patterns remain puzzling and somewhat counter-intuitive. This paper goes beyond the limits of current findings that emphasize the effects of being married or not married, or the presence or nonpresence of children. It takes a new, extended approach to the study of gender, productivity, and family characteristics among scientists, examining: (1) types of household and marriage patterns; (2) types of family composition in relationship to productivity of women and men. It then addresses factors that may account for the patterns through: (1) advantages of marriage by first or subsequent marriage, and type of occupation of spouse; (2) the association between productivity and parenthood of young children, as this relates to seven sets of conditions. In the study of gender, status, and science, analysis of the complexities of the relationships between household/marriage patterns, family composition, and publication productivity is important. This is because emphasis upon motherhood alone can operate as an ‘intrinsic’ (or deterministic) explanation of the status of women in science, with implications that gender and status are governed by factors that are very difficult to alter, socially and organizationally (Luukkonen-Gronow & Stolte-Heiskanen, 1983).

Method Data The data are from a national mail survey, addressing aspects of research and graduate-level teaching as well as background characteristics. The survey, conducted in 1993–94, was of 1215 full-time, tenured, or tenuretrack faculty in doctoral-granting departments in computer science, chemistry, electrical engineering, microbiology, and physics. These fields represent a range of the scientific classifications of the National Research


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Council (NRC) and National Science Foundation (NSF), including specifically, physical, life, computing, and engineering sciences (the only two NRC/NSF scientific categories3 not represented in the survey are earth and atmospheric sciences, and mathematics). The study is distinguished by sampling of faculty from known populations. Details on sampling design are given in the Appendix. The sampling design of faculty from departments that are low, high, and improved in proportions of doctoral degrees awarded to women reflects the interest in issues of departmental context for students and faculty, including diversity of participants in science – and in turn, this represents a key concern of national science policy (Pearson & Fechter, 1994; National Academy of Sciences, 1995). Of the 1215 faculty sent questionnaires, 26 were ineligible because of the faculty member’s departure from the department or being deceased. The response rate was 65% (removing ineligible from the base). Those faculty in computer science (58%) and electrical engineering (63%) had lower, and those in chemistry (69%) and physics (65%) higher, rates than average; and women’s response rate (71%) was higher than men’s (64%).

Variables Independent Variables Household composition is assessed through respondents’ reports of being married, never married, cohabiting with partner, divorced or separated, or widowed. Marriage pattern is assessed through two sets of responses for those married. One set is an indication of present marriage as a ‘first’ or ‘subsequent’ marriage. The second is an indication of occupation of spouse. Spousal occupations reported by respondents are then classified into categories of: (1) academic professor in same scientific field as respondent; (2) academic professor in different scientific field than respondent; (3) academic professor outside of scientific fields; (4) science occupation outside of academia (non-academic engineer, mathematical specialist, life and physical scientist, scientific researcher); (5) traditional professional (attorney, judge, physician, dentist, or architect); (6) other, nontraditional professional (accountant, nurse/dietitian/physical therapist, health technologist, social worker, teacher in other than college, other technician, writer/artist, computer specialist, librarian); (7) not scientific or professional occupation, but employed; (8) homemaker, retired, or not employed. Type of family composition is assessed through respondents’ reports of the presence of children and their ages. The responses are then categorized as: no children (childless); preschool children only; some elementary and/ or secondary school children; and only college and/or adult children. These categories, in turn, reflect blocks of time and scheduling for children, with consequences for schedules of parents. Preschool children constitute a separate category because they have special needs in blocks of


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time for care, since the USA does not have universal ‘day care’ for young children. Elementary and/or secondary school children constitute a combined category because schedules of children in both of these groups are governed by universal schooling in USA, with consequences, in turn, for parents’ schedules of ‘time away from home’. Further, it is not feasible or desirable to cut the categories more ‘thinly’, because the resulting numbers of women, in particular, would become very small.4 Dependent variable Publication productivity is the number of papers published or accepted for publication in refereed journals within the 3 years before the survey. This measure takes into account (1) types of publications; (2) time lags; (3) period of time; (4) self-reported data. First, the survey instrument asks respondents to list separately the number of items that were published or accepted for publication within categories (papers in refereed journals; papers in other publications; conference proceedings; critiques, responses, and comments; abstracts; book reviews; chapters, papers in newspapers/newsletters; and other). This itemization helps eliminate mis-categorizing (for example, categorizing critiques, responses, and comments or abstracts as papers) and thus helps provide more valid counts of papers in referred journals, which represent the core of publication productivity in scientific fields. Second, the inclusion of both papers published and accepted for publication within the period helps take into account the time lags between research, submission, and publication.5 Third, the specification of data for the 3-year period controls for the effects of seniority levels of scientists, and thus the available publishing period. Fourth, with respect to the validity of self-reported publications, self-reports correlate highly (0.94–0.85) with those listed in indices of abstracts (Clark & Centra, 1985; Creswell, 1985). In the (multivariate) regression analyses of productivity, publication productivity takes the form 1 + log (number of published papers + number of accepted papers + 0.5). For regression analyses, the logarithmic form normalizes the skewed distribution of productivity. The addition of 0.5 (equivalent of half a paper) avoids the quantity of the log of zero (minus infinity) and thus allows one to deal with those with no papers published or accepted for publication in the period. The addition of a constant of 1 avoids negative scores and their inconvenience in interpretation (see also Pelz & Andrews, 1976).

Findings and Discussion Gender and Publication Productivity To begin, let us look at general features of women’s and men’s publication productivity in this study. These scientists are all full-time, tenured, or



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tenure-track academics in doctoral-granting, university departments. In the 3-year period before the survey, women published or had accepted for publication 8.9 papers; this compares with 11.4 for men (Figure 1). This is a significant (p = .008)6 gender difference. However, the gender difference in publication is not as large as for samples that include women and men scientists across a broader range of institutional types, both research and non-research oriented colleges and universities (see Blackburn & Lawrence, 1996), or as large as for women and men over a longer span of time, especially over the entire career (Cole & Singer, 1991). In addition, two other features of the distributions of productivity are notable (Figure 1). First, the overall difference between women’s and men’s productivity owes especially to gender disparity at both extremes of productivity: women are almost twice as likely as men to publish zero or one paper7 in this period (women 18.8%, men 10.5%); men are twice as likely as women to be at the other extreme, publishing 20 or more papers in the period (men 15.8%, women 8.4%). Gender differences are not as marked in the middle categories, for those publishing 2–9 papers (men 44.2%, women 51.4%), or even 10–19 papers (men 29.4%, women 21.0%). Second, the productivity of both men and women is strongly positively skewed: 14.5% of women account for one-half (50.8%) of papers published by women; 19.8% of men account for one-half (49.3%) of papers published by men. The pattern of highly variable and strongly skewed publication was documented more than 75 years ago in Lotka’s (1926) analysis of papers published in physics journals – and is characteristic of both the men and women scientists in this study.

FIGURE 1 Number of refereed journal papers (accepted + published) by gender


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FIGURE 2 Household and marital status and productivity by gender

Gender, Marital/Household Status, and Productivity Family characteristics are assessed through marital/household status, as well as parental status. Women and men differ significantly in their marital/ household statuses (p = .0000). We find (see bars at bottom of Figure 2) that most men are married (86%); this compares with 62% of the women. Women are twice as likely as men to be never married (16 vs 8%); almost three times as likely to be divorced or separated (11.1 vs 4.2%); and five times more likely to be cohabiting but not married (11.1 vs 1.8%). How does marital/household status relate to productivity (Figure 2)? The publication productivity of married women (and cohabiting) exceeds that of never married women and of divorced and separated women. Among men, the never married have the lowest productivity. Men of all other marital/household statuses, except the never married, have higher productivity than women of comparable status. The gender difference in productivity is most significant among married scientists (p = .096) and among divorced scientists (p = .05). In the data collected, categories were provided for current marriage as a first marriage or subsequent marriage, that is, re-married. Previous studies have put the married into one category. In data separated by first and subsequent marriage, women in subsequent marriages have productivity that is nearly twice that of women in a first marriage (15.43 vs 8.08 papers; p = .033) (Figure 3). For men, productivity varies little for those in subsequent compared with first marriage (10.55 vs 11.87 papers).



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FIGURE 3 Household and marital status and productivity by gender, noting marriage

This then leads another question: to whom are these scientists married, and how does that relate to productivity? Specifically, what is the occupation of spouse for men and women who are married, and for those in first or subsequent marriages? How does occupation of spouse relate to productivity? For married scientists across first and subsequent marriages (that is, combining the two categories), the most common pattern among women is marriage to another scientist (see bars at bottom of Figure 4). Most of the women (59%) are married to another scientist (this combines occupations of academic professor in same field as respondent, academic position in another science field, and scientific occupation outside academia). This contrasts with 17% of men who are married to another scientist (combining the same three science occupations). For women, the highest productivity is among those married to a scientist outside academia or to someone in one of the non-traditional professions (such as accountant, social worker, librarian, physical therapist, writer/artist). Among men, productivity varies less than women’s by the occupation of their spouse (see the graph lines in Figure 4). What about the relationship between spousal occupation and productivity for those in first marriages compared with subsequent marriages? Does the higher productivity of women in subsequent marriages relate to the occupations of spouses? It is the case that women in subsequent, compared with first, marriages are somewhat more likely to be married to another scientist; 63% of the women in subsequent marriages, compared with 56% of those in first marriages, have a spouse employed in science


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FIGURE 4 Spouse’s occupation and productivity by gender

(Figure 5).8 Further, for the group of women in subsequent marriages, productivity is highest among those married to another scientist who is an academic in their field or is a nonacademic scientist. In sum, women in subsequent compared with first marriages have higher productivity overall; and for women in subsequent marriages, productivity is particularly high among those married to an academic scientist in their own field or a scientist outside of academia. Men in subsequent marriages are also more likely than those in first marriages to have a spouse employed in science (32% compared with 14%) (Figure 6). For men in subsequent marriages, productivity is highest for those married to an academic in the same field. Overall, then, subsequent compared with first marriages among scientists are marriages that are more ‘synchronized’ in matching the occupations of spouses. In subsequent marriages, especially, scientists may have chosen spousal circumstances that support their productivity and performance. It may also be the case that for those in subsequent marriages with ‘matched occupations’ (two scientists), science is especially focal and ‘outside interests’ are limited. Gender, Family Composition, and Productivity Children form more complex circumstances than marriage. This study looks beyond having or not having children to the current family composi-


Fox: Gender, Family Characteristics, and Publication Productivity FIGURE 5 Spouse’s occupation and productivity by marriage, for women

FIGURE 6 Spouse’s occupation and productivity by marriage, for men


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tion: no children (childless); preschool children only; some elementary and/or secondary school children; and only college and/or adult children. Women and men differ significantly in these family compositions (p = .0000). Most noteworthy is that most women (52%) have no children; this contrasts with 21% of the men (Figure 7). Men are 2.5 times more likely than women to have college or adult children (men 33.3%, women 13.2%), and 1.5 times more likely to have elementary/secondary children (men 31.4%, women 22%). However, the same proportions of men and women have preschool children only (men 13.4%, women 13.2%). Considering the relationship between family composition and productivity, especially notable is the high productivity of women with preschool children only, in comparison with women without children or women with school children. The productivity of men varies less by family composition than does women’s. Given the demands of young children upon time, energy, and attention, how do we account for this anomaly of women with preschool children having higher productivity than women without children or women with school-aged children? Who are these women, and what are their circumstances? I address seven sets of considerations. The first consideration is the presence of children as a possible artifact in relationship to productivity, reflecting the effect of age and stage of life. The question is: are women bearing and caring for young children at a stage of already established productivity? For this group of women scientists, the relationship between family composition and productivity does FIGURE 7 Family composition and productivity by gender



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not owe to life stage.9 In analysis of covariance (with covariates of age, age2, time since PhD, and time since PhD2), family composition does remain a significant determinant of productivity for women. A second consideration is number of children. The question is: do women with preschool children only have fewer children than other groups? Women with preschool children do have fewer children (1.4) than do women with elementary and secondary school children (2.0) or only adult/college children (2.2) (Figure 8). A third consideration is the occupation of spouse. The question is: does the productivity of women with preschool children reflect the occupation of spouse – particularly, marriage to a scientist? Marriage to another scientist can provide women access to mainstream networks of information, funding, and resources for research (Astin & Davis, 1985; Astin & Milem, 1997; Creamer, 1999). Figure 8 shows that women with preschool children are the group most likely to be married to another scientist. A fourth consideration is type of marriage, specifically a subsequent (compared with first) marriage, which, as discussed earlier, is associated with higher publication productivity among the women in this study. The question is: compared with women with other family compositions, are women with preschool children especially likely to be in subsequent, compared with first, marriages? To the contrary, women with preschool children are less likely to be in subsequent marriages; 12.5% of women with preschool children, compared with 21% of women in other family compositions, are in subsequent marriages (Figure 8). FIGURE 8 Factors by family composition and gender


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A fifth consideration is social selectivity: women who marry, and those who currently have preschool children, may have especially strong stamina for and commitment to research – with implications for their productivity (Fox & Faver, 1985). These women with preschool children are holding full-time academic appointments in science. They have not phased out of, or scaled down in, scientific careers at this particular parental-stage, and they may then be a ‘super-select’ group in stamina and commitment. Accordingly, we find that compared with women without children and women with older children, the women in this study with preschool children are the group with the highest proportion (50%) reporting that their work interest is ‘heavily in research’ (compared with the other categories of work interest: ‘heavily in teaching’ ‘both, but leaning toward teaching’ or ‘both, but leaning toward research’) (Figure 8). A sixth consideration comes from interviews with scientists (Cole & Zuckerman, 1987), which suggest that women with children make especially ‘disciplined allocations’ of time – allocating time and attention to work and children, but little else. One way to assess this is with data collected in the study on reported hours per week in work activities (Figure 9). These activities are: research and writing; advising graduate students; preparation and administration of grants; editorial and review boards; preparation for instruction; instruction; advising undergraduates; and serving on departmental/university committees. The first four activities are more research-related; the second four, less research-related. (In Figure 9, FIGURE 9 Work activity by family composition and gender



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horizontal lines separate the more compared with less research related activities. These data do not include time in non-work [for example, domestic] activities.) Interestingly and with implications for ‘disciplined allocation of time’, compared with women in other family compositions, women with preschool children spend more time advising graduate students, and spend somewhat less time advising undergraduates and serving on departmental and university committees. In Figure 9, we see that it is not differences in time in research and writing that are notable among women in different family compositions; rather, what is more notable is time in activities related to research, particularly more time advising graduate students and less time advising undergraduates. A final, seventh consideration is that women with preschool children may be located in departments that have climates more attuned to the participation of women. One limited way to assess this consideration, with data that are available, is to see whether women with preschool children are especially likely to be located in departments that are high or improved in doctoral degrees awarded to women (the variable of the sampling design). Figure 10 shows that women with preschool children are, in fact, the group who are least likely to be in departments that are high or improved, without support for this consideration. To assess further the association for women between productivity, family composition of preschool children, and family composition of school-age children, the relationships are expressed in regression models in two stages (Table 1). The first stage represents the relationship between FIGURE 10 Departmental context by family composition and gender


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TABLE 1 Regression of publication productivity on family composition variables (stage I) and family composition variables plus factors (stage II) for women Stage I Variable Family composition: Preschool children School-age children No. of children Married to scientist Interest: heavily in research No. of hours per week: Advising graduate students Advising undergraduates Committees Constant R2 N SE of estimate

Stage II

Coefficent

SE

Significance Coefficient SE

Significance

.657 –.345

.294 .241

.027** .155

2.664 .063 131 1.107

.381 –.440 .063 .214 .086

.319 .287 .105 .210 .218

.234 .128 .549 .310 .696

.040

.022

.069*

–.092

.041

.027**

–.012 2.532 .140 131 1.086

.034

.715

* significant at < .05; **significant at < .01.

productivity and family composition of preschool children only (compared with other family compositions = no children, school-aged children, or adult/college-aged children only) and school-age children (compared with other family compositions = no children, preschool children only, or adult/college-aged children only). This is the baseline model. The second stage represents the relationship between productivity, family composition of preschool children, family composition of school-age children, and factors found here to be related to family composition,10 specifically: number of children, marriage to another scientist (compared with marriage to person outside of science or not married), heavy interest in research (compared with interest ‘heavily in teaching’, ‘both, but leaning toward teaching’, or ‘both, but leaning toward research’), and number of hours per week in activities. This is the model with factors included, simultaneously. In stage/model 1, we see among women scientists the positive, significant effect (p = .027) for productivity of the presence of preschool children and the negative effect (p = .155) of the presence of school-age children. In stage/model 2, with factors considered simultaneously, we see that the family composition variables are no longer significant predictors of productivity. In model 2, the variables that emerge as significant predictors are number of hours per week advising graduate students (positive effect) and number of hours advising undergraduates (negative effect). This suggests



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that, among the factors considered here as they account for the relationship between productivity and family composition, hours in activities related to research (advising graduate students) compared with hours in non-research (advising undergraduates) are most notable. The pattern of investments of time in research-related activities and in teaching-related activities, as related to productivity, has been found in analyses among social scientists as well (Fox, 1992). The finding here suggests that time investments in research-related and teaching-related activities operate in opposite directions in relationship to productivity. These time investments account, in larger part than other factors here, for the relationship between family composition and productivity among women scientists in doctoralgranting departments – insofar as they emerge, in model 2, as the significant predictors of productivity and the family composition of preschool children is no longer significant. At the same time, factors beyond the range considered here should be the subject of further analyses of family composition and productivity, as total variation (R2 ) explained in model 2 is 15% (.140).

Summary and Conclusions In summary, gender, productivity, and family characteristics are complex considerations that go beyond being married or not married, and the presence or absence of children. For women, particularly, the relationship between marriage and productivity varies by type of marriage: that is, subsequent compared with first marriage, and occupation of spouse. Women in subsequent marriages have higher productivity than women in first marriages. This relates to their greater likelihood to be married to another scientist; and when married to a scientist, the effects for productivity are positive. In family composition, the predominant pattern for women scientists is that of ‘no children’, found among 52% of women (compared with 21% of men). Among types of family compositions, however, the productivity of women with preschool children is higher than that of women without children or those with school-aged children. In pursuing factors that may be associated with this anomalous pattern, women scientists who have preschool children show signs of being a socially selective group in marriage and family patterns, research interests, and allocations of time. In a multivariate model of productivity for women, allocations of more time in research-related activity and less in non-research-related activity are the most significant factors, among those considered. The data do not indicate particular policies and practices in the work environments of the women scientists. At issue, for example, are the implications for productivity of flexible-time policies on campus or programs of parental leave and child-care. It may – or may not – be the case that women with preschool children are apt to be in settings with such policies or programs.


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In addition, it is important to emphasize that these data do not indicate that marriage and young children have no effect upon women in science. Marriage and young children have a multitude of effects in personal sacrifices as well as rewards, and extraordinary arrangements of accommodation (Grant et al., 2000). What these data show is that marriage and young children are not associated with depressed publication productivity among women who do hold academic positions in science. In the interpretation of the data on marriage, parenthood, and productivity, it is important to point out this: these data are based upon women who have survived a rigorous and demanding process of scrutiny, selection, and evaluation in science. Family demands may take their toll along the way, through graduate school and early career, so that a proportion of women are eliminated from scientific careers and do not even fall into such cross-sectional data of professional, employed scientists (see Long, 1987). Thus in continuing steps, we need to understand more about the way that productivity and productivity differences unfold over time and in relationship to family and household characteristics – with implications for sustained participation and performance in science.

Notes The research reported here was supported by grants from the National Science Foundation (SED-9153994 ad SBE-0123532). For his reading of and comments on an earlier version of this paper, I thank J. Scott Long. 1. Unpublished work and informal exchange are also consequential for the development and communication of knowledge. But informal communication is haphazard, and unpublished work cannot be widely evaluated. 2. See reviews in Creamer (1998), Long & Fox (1995), and Zuckerman et al. (1991). When one considers citations, however, which are not at focus in this paper, women’s papers may receive as many citations as men’s on a per paper basis (Long, 1992). 3. ‘Scientific categories’ as considered here include those fields outside of social sciences. 4. The numbers of women in these family composition groups are: 70 with no children; 18 with preschool children only; 30 with some elementary and/or secondary school children; 18 with college or adult children only. 5. Inclusion of accepted and published papers also reduces a ‘floor’ effect of publications in analyses. 6. Probability levels of significance appear within the text of the paper. The tests of significance are these: for two groups, t-test; for more than two groups, analysis of variance; and for categorical variables, χ2-test. Because of small sizes of groups of women, particularly when divided by variables, differences among women may be notable but not statistically significant. They are indicated and treated as such. 7. 8.7% of women and 7.1% of men published zero papers in this time span; 10.1% of women and 3.3% of men published one. 8. Note that for women the percentage ‘married to another scientist’ is relatively high (56%) among those in a first marriage. It is yet higher (63%) among women in subsequent marriages. However, this is against a comparative point that is already high (56%), such that a ‘ceiling’ effect may be operating here. 9. Relatedly, although the publication productivity of women with preschool children is higher than that of women with no children or elementary and/or secondary aged



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school children, this does not appear to be a function of women with preschool children being an untenured group, prompted to publish by this status. The clear (61%) majority of women with preschool children only are tenured; this compares with 41% of women without children and 73% of those women with elementary and/or secondary aged school children. 10. As discussed, family composition remained a significant determinant of productivity in the presence of age, age2, time since PhD, and time since PhD2; age and time since PhD are not then included in the model. As discussed also, women with preschool children, compared with women with other family compositions, are not more likely to be in a subsequent (compared with first) marriage or to be in a department that is high or improved (compared with low) in doctoral degrees awarded to women. Thus, neither of these variables is included in the model.

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Kyvik, Svein (1990) ‘Motherhood and Scientific Productivity’, Social Studies of Science 20: 149–60. Long, J. Scott (1987) ‘Discussion: Problems and Prospects for Research on Sex Differences’, in L.S. Dix (ed.), Women: Their Underrepresentation and Career Differentials in Science and Engineering (Washington, DC: National Research Council): 157–69. Long, J. Scott (1990) ‘The Origins of Sex Differences in Science’, Social Forces 68: 1297–315. Long, J. Scott (1992) ‘Measures of Sex Differences in Science’, Social Forces 71: 159–78. Long, J. Scott & Mary Frank Fox (1995) ‘Scientific Careers: Universalism and Particularism’, Annual Review of Sociology 21: 45–71. Lotka, A.J. (1926) ‘The Frequency Distribution of Scientific Productivity’, Journal of the Washington Academy of Sciences 26: 317-23. Luukkonen-Gronow, Terttu & Veronica Stolte-Heiskanen (1983) ‘Myths and Realities of Role Incompatibility of Women Scientists’, Acta Sociologica 26: 267–80. Merton, Robert K. (1973) ‘Priorities in Scientific Discovery’, in N.W. Storer (ed.), The Sociology of Science (Chicago, IL: The University of Chicago Press). Mullins, Nicholas C. (1973) Science: Some Sociological Perspectives (Indianapolis, IN: BobbsMerrill). National Academy of Sciences (1995) Reshaping the Graduate Education of Scientists and Engineers (Washington, DC: National Academy Press). Pearson, Willie & Alan Fechter (1994) Who Will Do Science? Educating the Next Generation (Baltimore, MD: Johns Hopkins University Press). Pelz, Donald & Frank M. Andrews (1976) Scientists in Organizations (Ann Arbor, MI: Institute for Social Research, The University of Michigan). Price, Derek (1963) Little Science, Big Science (New York: Columbia University Press). Reskin, Barbara (1978) ‘Scientific Productivity, Sex, and Location in the Institution of Science’, American Journal of Sociology 83: 1235–43. Sonnert, Gerhard & Gerald Holton (1995) Gender Differences in Science Careers (New Brunswick, NJ: Rutgers University Press). Zuckerman, Harriet, Jonathan Cole & John Bruer (1991) The Outer Circle: Women in the Scientific Community (New York: W.W. Norton).

Mary Frank Fox is NSF Advance Professor in the School of Public Policy and Co-director of the Center for the Study of Women, Science, and Technology, at Georgia Institute of Technology. Her research focuses upon gender and scientific and academic organizations and occupations. Her current research includes a Study of Programs for Women in Science and Engineering, supported by the National Science Foundation. Her publications appear in more than 40 different scholarly and scientific journals, collections, and books. She has been a member of the National Research Council/National Academy of Sciences (NRC/NAS) study panel on Trends in the Early Careers of Life Scientists, and consultant to the NRC/NAS study panel on Gender Differences in Science and Engineering. She was awarded the WEPAN (Women in Engineering Programs) Betty Vetter Research Award, 2002, for notable achievement in research on women in engineering, and the SWS Feminist Lecturer Award, 2000, for a prominent feminist scholar who has made a commitment to social change. Address: School of Public Policy, Georgia Institute of Technology, Atlanta, Georgia, GA 30332-0345, USA; fax +1 404 371 8811; email: [email protected]



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Appendix Sampling Design For each of the fields (except microbiology, discussed subsequently), the doctoral-granting departments sampled were those, identified on the basis of data from the National Research Council (NRC), Survey of Doctoral Recipients, as being: (1) consistently low; (2) consistently high; or (3) most improved in proportions of doctoral degrees awarded to women over a 17-year period. For chemistry, computer science, electrical engineering, and physics, the rate of degrees awarded to women was computed for the first 5 years and the last 5 years of the period for which data were available. The first 5 years were 1974–78 except for computer science (1978–82) and electrical engineering (1977–81); the last 5 years were 1986–90. Within each field, the departments were ranked based upon the total number of PhDs produced during the period. The largest PhD departments – those producing 70% of all doctorates in the field – were selected as the ‘population of interest’, except for computer science where a 50% cutoff was used. Within each of the four fields, departments were then ranked, based on the difference between the ending rate of women PhDs (that is, the rate over the last 5 years) and the beginning rate (that is, the rate over the first 5 years). For chemistry, the ‘most improved’ departments were those with an increase of 15% or more in the rate of women PhDs. For computer science and engineering, the most improved were 8% or more, and for physics, 9% or more. For each of the four fields, the ‘consistently low’ and ‘consistently high’ departments were those for which the difference in the rate of women PhDs was within a change of not more than ±5%. Within each field, the consistent departments were then ranked, based on the average of the beginning and ending rates. Depending upon the field, cutoffs were selected by ‘low’ consistent rate and ‘high’ consistent rate. By field, the cutoff points are: chemistry, high = > 15%, low = < 13%; computer science, high = > 10%, low = < 8%; electrical engineering, high = > 5%, low = < 1%; physics, high = > 8%, low = < 4%. The study is distinguished by sampling of faculty from known populations. To accomplish this, I obtained rosters of faculty from the respective departments determined through the NRC data. Because of the low proportions of women compared with men faculty in these four science and engineering fields, and the aim for sufficient numbers to allow gender comparisons, sampling fractions were applied separately for male and female faculty. For the departments in these four fields, all women and 40% of men were sampled. Likewise, because the study included focus upon differences in organizational and outcome variables among departmental categories (low, high, improved), it is desirable to put those categories in the design and not leave sample outcome to randomness of departmental categories. Microbiology cannot be sampled with the same design (of departments that have been low, high, or improved in proportions of degrees


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awarded to women). That is because for microbiology, the field in which students identify degree (in NRC Survey of Doctoral Recipients) corresponds more loosely with department. For example, a degree listed as field of biology or microbiology may be from variable departments, such as molecular genetics, neurobiology, or other units. Thus, for microbiology, rosters of faculty and students were obtained from 69 responding departments of the 103 US doctoral-granting departments of microbiology in the listing of ‘Colleges and Universities Granting Degrees in the Microbiological Sciences’, American Society of Microbiology. The sample of faculty was drawn from the 19 departments granting 50% of all microbiology degrees. Sampling fractions were applied separately for women and men faculty, with 40% of male faculty and 50% of female faculty sampled. Departments were then classified as ‘high’ if more than 50% of the doctoral students in the department were women (based upon the rosters of doctoral students collected from the departments).
