Returns to Science, Engineering and Technology Careers' (DTI 2002). .... The Longitudinal Study (LS) contains linked census and event information for.
Women's Scientific Employment and Family Formation: A Longitudinal Perspective Louisa Blackwell a, Office for National Statistics, UK and Judith Glover, Roehampton University, UK Word count: 6753 Published in Gender, Work and Organization 15, 6, 579-599, 2008
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This work was carried out at the University of London Institute of Education and funded by the Economic and Social Research Council R000223190
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Dr Louisa Blackwell currently works at the Office for National Statistics where she is the Head of the Longitudinal Study Development Team. Recent publications include Blackwell, L. and Guinea-Martin, D. (2005) 'Occupational segregation by sex and ethnicity in England and Wales, 1991 to 2001', Labour Market Trends, Vol 114 and Blackwell, L., Akinwale, B., Antonatos, A. and Haskey, J. (2005) 'Opportunities for new research using the post-2001 ONS Longitudinal Study', Population Trends, No 121. Dr Judith Glover is Professor of Employment Studies in the School of Business and Social Sciences at Roehampton University. Her books include Women and Scientific Employment (Macmillan, 2000) and (with G Kirton) Women, Employment and Organizations (Routledge, 2006).
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Abstract: We focus here on the issue of the retention of highly qualified women scientists in science-based employment in England and Wales. Using linked census records from the Longitudinal Study 1971-1991, we show that women's education and employment rates in science, engineering and technology increased somewhat, although some fields show persistently low representation. We then compare the retention in employment of women with health-related degrees with that of women with degrees in science, engineering and technology, showing that the latter group has markedly lower retention rates. Those who stay on in science-based employment have children later than other types of graduate and their rates of non-motherhood are higher. Four-fifths of women in health-related occupations were mothers, compared to only two-fifths in science, engineering and technology. Our findings have implications for policy-makers who wish to make best use of the knowledge base: attention should be paid to retention, as well as the more usual focus on qualifications and recruitment. The findings also suggest the potential for institutionally-based theories in terms to explain why highly qualified women have such low retention rates in science-based employment.
Keywords: women’s employment; highly qualified women; women scientists; science, engineering and technology; retention and advancement in employment; fertility rates
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WOMEN’S REPRESENTATION IN THE SCIENTIFIC LABOUR FORCE There is a history in the UK of government concern about the historically low representation of women in science, engineering and technology (SET). The Dainton Report (1968) referred to girls and women as an ‘untapped pool of ability’ in science. The 1980 Finniston Report likewise saw women as a resource, stating that there was a need to reverse the under-utilisation of women. As Dainton had said, women were a potential labour force at a time when an insufficient number of men were coming forward. In the early 1990s, the link between science and economic growth was reinforced through the appointment of a Cabinet Minister with responsibility for science and technology in the newly created Office for Science and Technology (OST). The 1993 White Paper 'Realising Our Potential' (HMSO, 1993) followed. It noted that high calibre students in particular continued to be in short supply and acknowledged, in rather similar terms to Finniston in 1980, that women remain an underutilised source of potential scientific and technological expertise. The government set up a working party composed of eminent women scientists, who produced a report 'The Rising Tide' (Committee of Women in SET 1994). The 2000 Science and Innovation White Paper ‘Excellence and Opportunity’ (DTI 2000) set out a number of targets regarding women and their participation in SET, including the commissioning of a report on women returners to science. The difficulty of returning to scientific employment after a break was highlighted in the Department for Trade and Industry (DTI) report ‘Maximising Returns to Science, Engineering and Technology Careers’ (DTI 2002). This showed that at any one time 50,000 women with science, engineering and technology degrees were not in paid work; only half could be expected to return to paid work and only a third of these to SET occupations. The Greenfield Report (2002), commissioned also
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by the DTI, signalled a change in perspective in two major ways. First, it acknowledged the importance of focusing on retention as well as recruitment. Using the term ‘institutional sexism’, it moved beyond the ‘blaming women’ approach (see Wajcman 1991) to an acknowledgement that the institutions of science – its culture, expectations, perceptions, habits and so forth – might form a major reason for women’s apparent disengagement with scientific education and employment. Research to investigate ‘institutional sexism’ in science-based workplaces remains elusive, however. The main thrust of government activity is at the level of giving advice from a supply-side perspective to employers and employees about recruitment and retention issues in science, engineering and technology. Up to the Greenfield Report (2002), much of the UK’s past policy and rhetoric on women and science was underpinned by the view that the ‘problem’ will be resolved if more women enter scientific education and employment. This can be seen as the ‘critical mass perspective’ (Glover 2000). In all industrialised countries, there is attrition at successive phases of the academic pipeline. Whilst women account for a steadily growing percentage of undergraduate science in most scientific fields (although not in physics, see Glover 2000), there is a marked drop-off in their representation at the doctoral and especially post-doctoral levels, the point at which the academic career track begins. This process is remarkably similar across Europe (European Commission 2000). This implies that the ‘women and science issue’ is one of retention, as well as recruitment. A growing literature is focusing on the retention issue and this article seeks to add to this; the latter part of this article focuses on the issue of retention, having first contextualised the issue by establishing trends over time in the representation of women in scientific employment.
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One factor that is likely to affect retention is family formation. It has been argued that industrial and technological change, the expansion of higher education and fertility decline are inextricably linked (Folbre 1994). Previous research shows that education levels influence both the age at which women have children and whether or not they have children (Dale and Egerton 1997). Highly qualified women, (defined as having degree level qualifications) are less likely than other less qualified women to have formed partnerships and to have had children at an early age (see also Rendall et al, 2005). Women who begin childbearing at a later age are less likely than younger mothers to have another child (Rendall and Smallwood, 2003).1 There are several perspectives that underpin the concern about low numbers of women in scientific education and employment. Women will be penalised if they are not involved in an increasingly knowledge-driven society, particularly when the acquisition of knowledge enhances control over use (Cockburn and Fürst-Dilic 1994). It is furthermore argued that science is a social activity that is structured by the shared practices of specialists; if this community does not reflect the diversity of society as a whole, questions and interpretations are likely to be narrow (Rosser 2000; Wyer et al 2001). A more diverse scientific workforce is seen by some commentators as the most likely way to change the content of science and technology (Rose 1994). Women's detachment from science both as citizens and professional scientists is increasingly seen to be an issue for the sometimes uncomfortable relationship between science and the general public (European Commission 2002). Furthermore, the educational investment both by individuals and by governments is arguably wasted if women scientists are not retained in knowledge-related employment (Glover 2000), especially in the context of the economically important ‘triple helix’ of government-industry-university relations (Etzkowitz and Leydesdorff 1998). Finally, the perceived need for in excess of 600,000 new Research and Development (R&D) specialists in the European Research Area by
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2010 sharpens the drive to identify untapped pools of labour, of which women constitute the major one (European Commission 2003).
Women’s representation in scientific education and employment shows relative stability in two major ways. First, it is largely stable over time, unlike other fields such as law and health, where women's representation has increased markedly in numerical terms, particularly in the second half of the 20th century; second, cross national comparison indicates a largely uniform picture, with most advanced industrialised countries showing similar patterns of both horizontal and vertical segregation (European Commission 2000, Rees 2002). Most analyses have used a time series of cross-sectional labour force data to show these trends (for example DTI 2002). However, we add a further dimension by using large-scale longitudinal data to show the representation of women in different fields.
Most discussions of women and science have excluded the health-related disciplines. Here we compare the experiences of science graduates with those graduating in health-related subjects and working as graduates in health-related occupations for several reasons. Health-related studies demand aptitude in science and so we are not comparing groups that are strongly differentiated in terms of educational background. Nevertheless, health professions have tended to be less impenetrable for women than have science and technology. Lastly, we follow the European definition of ‘scientist’, which reflects the wissenschaft model, using a broad definition of science that includes health-related graduates. The fields of study included in our definition are therefore broader than the conventional UK government’s definition of science-based occupations. As well as health, we include engineering and technology, natural sciences (biology, chemistry and physics) architecture and surveying, and
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mathematics. When we are using this definition, we take care to point out that this is the broad definition. Appendix 1 gives more detailed information. Our definition of health-related graduates includes nurses as well as other sorts of health professionals. Because nursing is a highly feminised occupation, we need to note that this will have the effect of reducing the size of the sex-related differentials that are found in other more narrowly based analyses. The analysis described below documents developments in women's participation in scientific education and their employment outcomes up to 1991. Further analysis that includes 2001 Census data is planned, but will take the form of a follow-up of the graduates identified in this analysis rather than rolling forward this analysis to identify those newly qualified in 2001. This is because the 2001 Census did not collect information on degree subject studied. The following analysis should therefore be viewed as casting an historical eye over developments in women's scientific education and employment towards the end of the 20th Century. Our analyses use data from the Longitudinal Study; the following section describes the data set.
THE ONS LONGITUDINAL STUDY The Longitudinal Study (LS) contains linked census and event information for 1% sample of the population of England and Wales. The original LS sample was drawn from the 1971 Census using four dates of birth. Information from subsequent census has been linked, together with event information such as births, deaths, cancer registrations and migration. The sample is replenished with births and immigrations, using the same birth dates, so that it remains representative of the population of England and Wales. The LS includes around 500,000 members at each Census and its
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size and the fact that it (so far) spans more than three decades makes the LS unique in permitting both cohort and period comparisons of employment transitions at different stages of the life course. Its size permits research into relatively small sub-groups2. It has nonetheless some disadvantages: because of the 10-yearly interval between observations, important inter-censal employment transitions are invisible; the data tell us very little about work histories; graduates are a minority within the population as a whole and disaggregating this group according to field of qualification can create the analytical problem of small numbers. Estimates and patterns in the data that are based on small numbers can have large sampling errors and will not necessarily be representative, which provides an additional imperative for the aggregation of different subject areas and different types of scientific occupation in the following analysis.
ACQUIRING QUALIFICATIONS IN SCIENCE, ENGINEERING AND TECHNOLOGY Figure 1 presents an overall picture of the growth in the numbers of graduates and of SET graduates in particular. Numbers represented in this graph are approximately one per cent of the population of graduates. The whole 10 year cohort, including both graduates and non-graduates, is around 3 million (30,000 LS members) of each sex. The richness of the LS data is clear here: we are able to give information on the cohort born around 1910. Two time points are taken: 1971 and 1991. Most LS members born around 1930 (1927-36) and 1940 (1937-46) are represented in both 1971 and 1991 data. Thus, for example, women LS members born around 1910 who acquired SET degrees were present in the 1971 Census in extremely small numbers; but so too were women graduates more generally. There is a clear expansion in the
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potential size of the highly qualified broadly-defined SET workforce, which would form a major part of what has become known as the knowledge economy. In terms of the European Commission’s targets, referred to earlier, the particularly important cohorts are those born around 1960, since they would be at a roughly mid-career point. However, whilst an overall expansion is evident, the sex differences are clear.
Figure 1 about here
Arnot, David and Weiner (1999) identify the growth of a ‘female graduate elite’ in post-war British society, drawn disproportionately from wealthier backgrounds, and armed with the educational credentials that allowed some to pursue occupations previously reserved for men: engineering, management, pure science and medicine. This growth in female involvement in higher education is supported empirically by the LS, showing the scale of growth in the graduate population between 1971 and 1991. The expansion of higher education in the 1970s and 80s particularly benefited women born around 1950 and 1960 and the overall numbers of women graduates have risen dramatically. However, in comparison to this, the rise in the numbers of SET-qualified women graduates is lower. In the 1991 Census, there were only around 1000 SET-qualified women LS members born around 1960. If we were using the narrow definition of SET, these figures would of course be considerably smaller. Policy-makers might conclude that their efforts to increase the number of scientifically-qualified women, described above, are yielding rather little in terms of a concrete outcome. By 1991, there were more than four times as many SET-qualified women graduates among the cohort born around 1960, compared to that born around 1930.
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However, despite this increase, men born around 1960 were more than twice as likely to have SET qualifications than women of the same vintage. Moving from raw numbers to the representation of women, expressed as a percentage of the entire cohort of graduates, Figure 2 examines the representation of women graduates in the various sectors that make up our definition of SET.
Figures 2 and 2a about here
In 1971 the representation of women varied between the different subject areas, with the technology subjects being the most male-dominated. Within each subject, women's representation was similar between the different cohorts . For example, in all the four cohorts shown, women were fewer than 30 per cent of health graduates and fewer than 10 per cent of physics graduates. We have distinguished physics from the other natural sciences, because other work shows its persistently low representation of women (see Glover 2000). In physics, the representation of women has decreased, as indeed it has in the other natural sciences. In general the 1971 picture is of rather little difference between cohorts, showing little to enthuse policymakers who are keen on tapping relatively untapped sources of labour. The 1991 picture (Figure 2a) is rather different, with women’s representation in successive cohorts generally rising. In some fields – health is the obvious example - the increase is marked. The 1991 data show a new field that was not distinguishable in the 1971 census data: computing. The analysis shows a marked rise in successive cohorts, although from a very low base. There is however recent concern that the representation of women in the general field of Information and Communication Technologies has plateaued and may indeed be decreasing (Losh 2003, Varma 2003).
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As in 1971, the representation of women in physics is persistently low, but in the other natural sciences, representation is increasing quite markedly. The analyses discussed up to this point do not tell us anything about whether these people remained in employment. This is clearly a crucial point, for the various reasons discussed earlier. To get a picture of retention, we need to exploit the longitudinal nature of the data more fully; this is the focus of the second half of the paper. Here we compare two sub-groups within our broad population of SETqualified graduates: a narrow definition of science, engineering and technology graduates are compared with those who graduated in health-related subjects.
RETENTION OF HIGHLY QUALIFIED KNOWLEDGE WORKERS Analysis of UK scientists in their twenties and early thirties in the National Child Development Study (NCDS) 3 indicates that women's scientific employment is more short-term and discontinuous than men's (Fielding and Glover 1999). Acknowledging the problem of rather small numbers in the analyses, Fielding and Glover concluded that women are considerably more likely than men to exit from professional scientific jobs in the first two years of employment. Furthermore, the median tenure for men in their early thirties in professional scientific occupations in the National Child Development Study was shown to be just under ten years, compared to just under four years for women. Exit from the labour force does not appear to be easily reversible: in 2000, almost 40% of women SET graduates had been out of employment for five years or more (DTI 2002). There is growing evidence that return to science-based employment can be particularly hard to achieve, as Greenfield (2002) noted.
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Labour force participation is affected by women’s status as mothers/nonmothers, as a very wide body of literature has shown for many years. Fielding and Glover (1999) showed that women science graduates in their early thirties who are mothers primarily work part-time or are out of the labour market. Findings from the UK Labour Force Survey confirm that women science and engineering graduates are less likely to return to work after starting a family than women with other high level qualifications (DTI 2002). Retention appears low and return more difficult in science-based employment than in other fields. In order to explore further a link with family formation, we now examine the employment outcomes of women SET graduates in two different fields: those women with qualifications in the narrow definition of SET (natural science, engineering and technology) are compared with those with health-related qualifications. Employment outcome is defined very broadly and is undifferentiated for each group: both are examined in terms of their presence in the ‘SET occupations’ listed in Appendix 2. These include occupations within groups that we classify as the natural scientists, engineers and technologists, architects and surveyors, health professionals, health associate professionals, SET associate professions and computing professions. It is also deliberately broad in the sense that it includes Professional occupations and as well as Associate Professional occupations; Fielding and Glover (1999) showed that women with science qualifications are more likely to be employed in Associate Professional jobs than their male counterparts. . Those described as being in ‘non-SET occupations’ include any occupations not in the definition of ‘SET occupation’.
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The definition of non-SET includes teachers. We would agree that teaching SET is a particularly important use of SET qualifications. Nevertheless, it is not possible to detect from the data whether people with these qualifications teach science or include science in their teaching. Furthermore, Fielding and Glover (1999) showed that the majority of women with SET qualification who enter teaching are in primary teaching. They may have been involved in some element of science teaching, but the broad category of teacher used in the Standard Occupational Classification does not allow for this to be established. Table 1 shows these two groups, disaggregated into 4 cohorts, born around 1930, 1940, 1950 and 1960; both women and men are shown.
Table 1 about here
There are some particularly marked differences in Table 1 in the occupational outcomes of these two groups of graduates. For both women and men with healthrelated degrees, remaining in a SET occupation is the usual pattern. Remembering that this analysis relates to the state of play in 1991, the high figures for women not in employment (34%) amongst the cohort born around 1930 can be explained by retirement. The rather lower figure for men who were not in employment (SET or otherwise) in this cohort (19%) can be explained by the different retirement ages. The other three cohorts show consistently low figures for ‘non-SET occupations’. We can conclude that the retention of health graduates is high. However the picture shown in Table 1 for natural science graduates is very different. A first point to make is that both women and men show high levels of ‘nonSET employment’. This is probably explained by the fact that many become teachers,
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as Fielding and Glover (1999) showed.4 The definition of SET employment used here excludes teaching, as Appendix 2 shows. Having said that, there are clear sex differences. Setting aside the 1930 cohort for the reasons explained in the above paragraph, women in the other three cohorts show high levels of ‘non-SET employment’ and of being out of the labour force completely. For example, in the 1940 cohort, who would be aged around 50 in the 1991 census, 60% are in non-SET employment and 22% are out of the labour force. Of particular concern for policymakers who wish to develop capacity in this economically crucial area, the 1960 cohort, aged around 30 in the 1991 census, show figures of 44% in non-SET employment and 17% out of the labour force. It needs to be noted, nevertheless, that men in this cohort show an even higher level of non-SET employment: 50%. Science-based employment appears therefore to be unpopular for both sexes but, with the exception of the 1960 cohort, particularly so for women. However the proportion of men who were are out of the labour force is low, unlike that for women. It looks as though many men with science, engineering and technology qualifications are employed in a wide range of jobs. It is quite possible that they are applying their scientific qualifications to these jobs and thereby enriching those jobs and the economy as a whole. Their location outside of our definition of science-based jobs is therefore not necessarily to be seen as 'wastage' and a similar point could be made about teaching, as discussed above. The point is that men with SET qualifications are still in the labour force, unlike many of their female counterparts. Table 2 compares those aged 25-34 in 1981 in SET occupations and those in health-related occupations, in terms of their occupational position in 1991. Here a major advantage of the Longitudinal Study is apparent: it is possible to establish how
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many people were in the same occupational position in both 1981 and 1991. Thus, Table 2 examines what we can conceive of as occupational stability over a 10-year period. Essentially, the data allow us to answer the question: are these people still in the same position 10 years on? In order to throw light on the question of whether mothers are being retained, it also asks what percentage of those women who are present in the same occupation at the two time-points are mothers. It therefore goes a long way toward addressing both the issue of retention and the relationship to this of motherhood. The data do not of course make visible any occupational change during the years between censuses.
Table 2 about here
Table 2 shows that there is considerably less occupational stability amongst the graduates in the SET occupations than those in the health occupations. As in Table 1, this is the case for both women and men, but particularly for women. Focusing on the women, 87% of SET graduates aged 25-34 who were employed in health occupations in 1981 were still there in 1991. By contrast, only 51% of the SET women graduates in natural science and technology occupations in 1981 were still there in 1991. In the health occupations, 81% of this group were mothers but the equivalent figure for the natural science and technology occupations was only 39%.
MOTHERHOOD AND SCIENTIFIC EMPLOYMENT: A CONTRADICTION IN TERMS?
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One explanation for the persistently low representation of women in sciencebased employment is that it is not family-friendly. Several major studies of the scientific workplace and organisations have not focused on gender (see for example Glaser, 1964; Latour & Woolgar, 1986; Knorr-Cetina 1999). However, some in-depth studies of gender and the scientific workplace do exist (McRae, Devine and Lakey 1991; Evetts 1994; Jones and Causer 1995; Ellis 2003). McRae et al found a consensus between employees and managers that childcare responsibilities were a major impediment to women's careers. Although measures such as the formalisation of recruitment procedures aimed to halt discrimination in the recruitment of women, stereotypical assumptions were widespread among managers and employees. These assumptions about future childbearing affected not only mothers but also unmarried women and those without children. Promotion demanded a demonstration of willingness to sacrifice family life for the organisation. A study of women in electronic and aerospace engineering suggested that motherhood was seen as a disqualifying condition for advancement, to be hidden from view; many had been asked about their long-term commitment to employment and their childbearing plans (Jones and Causer 1995). In the science-based organisations studied by Ellis (2003), scientific employment was seen as something that required full-time commitment. Ellis notes that the senior women in her study were less likely than unpromoted women to have let their child rearing and child caring responsibilities touch their working lives. They had not taken career breaks and they had not reduced their working hours for any significant period. Their reputation within the organisation remained largely based upon the male model of long hours and unbroken employment; their decisions about how to deal with childrearing had thus defined their employment prospects. In academic science, the ages of about 25 to 35 are the
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peak times for active research productivity (Warrior, 1997). Since this period is also the peak time for childbearing, women scientists have to take far-reaching decisions about having children and many had concluded that one way to avoid the difficulties of trying to combine their paid work and motherhood was to choose to be child-free (Evetts 1994). We stated earlier that the Longitudinal Study does not make visible any occupational change between censuses, but significant inter-censal events such as childbirth are visible. Table 3 reveals that women who became mothers at some point between the censuses in 1981 and 1991 were more likely to still be in health occupations in 1991, whilst those in the science and technology occupations were both much less likely to be in science-related employment in 1991 and more likely to be out of the labour force entirely.
Table 3 about here
Previous research, briefly discussed above, suggested that highly qualified women may be delaying childbirth or not embarking on it. We investigate this in our final analyses of graduates. At the same time we put rather more flesh on the category of science, engineering and technology by disaggregating this category into those with degrees in natural science, engineering and technology comparing with health and introducing a further category of non-graduates.
Figure 3 about here
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The qualifications of women graduates are shown to be linked to their motherhood status. Figure 3 shows the proportions of different types of graduate aged 25-34 years that were child-free in 1991. Proportions are represented by small boxes and the vertical bars above and below the boxes show the size of the 95 per cent confidence interval around each estimated proportion. Among 25-34 year olds with different types of degree-level qualifications shown in Figure 3, there was no significant difference in the proportions of health, natural science and non-SET graduates who were child-free in 1991. However, those qualified in engineering and technology were the most likely to be without children and were twice as likely to be without children than women of the same age without degree-level qualifications. The possibility that highly qualified women are delaying having children was also mentioned above. We have used the technique of survival analysis to compare the ages of entering motherhood in different occupations. The technique (which has its origins in research into mortality rates and has retained the terminology associated with this) requires that a ‘hazard’ be established. Here, the ‘hazard’ is ‘becoming a mother’ and the analysis calculates the length of ‘survival’ up until this ‘hazard’, with age parameters of 15 and 49 years.5 The analysis produces a survival curve, which in this case shows how many women in each type of occupation remained child-free. Thus the cumulative survival curves shown in Figure 4 represent the proportions of graduates who had, after years of childlessness as indicated on the x-axis, not entered motherhood. It is important to note that not all women in the analysis described here had complete child-bearing histories. The same analysis based on complete fertility histories could show somewhat different results, as explained in note 4.
Figure 4 about here
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Figure 4 shows clear differences between occupations. 'Other occupations' includes all graduate occupations not counted as 'teachers', 'technologists', 'scientists' or 'health professions'. Those working in engineering and technology and in natural sciences were less likely to be mothers than those in ‘other occupations’, while those working in teaching and in health were more likely to be mothers. With ‘other occupations’ as the reference category, the hazard here is the relative probability of becoming a mother at each year of age. The hazards were 1.27 for ‘health professions’, 1.33 for ‘teachers’, 0.76 for ‘scientists’ and 0.84 for ‘technologists’. All differences (from the reference category) were statistically significant at the 5 per cent level.
CONCLUSION The longitudinal evidence presented here shows that women's employment rates in SET increased particularly between the early 1970s and 1990s. Most women and men with health-related degrees stayed in health occupations over the period of family formation. At this point science, engineering and technology graduates tended to leave both SET occupations and the labour force altogether. The 10-yearly intervals between major observations in the Longitudinal Study do not permit a detailed analysis of the relationship between employment and childbearing histories, but as far as we can tell attrition from science and technology occupations intensified during the period of family formation for women. Those remaining in science and technology occupations have been shown to have children later than other types of graduate, and their rates of non-motherhood are also higher.
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Our analyses do not allow us to investigate the reasons for this, nor can they make definitive statements about causality. Nevertheless, having established that there is a relationship, we suggest that women may be taking a decision about the familyfriendliness or otherwise of their future or present occupations and adjusting their actions accordingly. Furthermore, we need to recognise the exceptional aspects of women with degrees in science, engineering and technology, particularly the fields where the representation of women is particularly low, such as mathematics, physics, engineering and technology. They have survived the personal and social costs of gender-atypical subject choices in education. It is plausible that they are more likely than undergraduates to adjust their fertility behaviour rather than their career aspirations when confronted with employment practices that are largely incompatible with family life. Qualitative research, attitudinal research and/or quantitative research using work histories – or a combination of all of these – would be required to investigate the ‘why’ that is underpinning these results. Our analyses do not distinguish between the public and private sector, since this variable is not available in the Longitudinal Study. Work-life conflict, which appears to be less problematic for many health professionals, most of whom work in the public sector, could also be less intense than in the private sector for women scientists and technologists working in the public sector. Public/private sector contrasts are certainly worth investigation, as indeed would part-time employment patterns of the different types of professionals studied here.
We have placed emphasis on supply-side arguments. But demand-side arguments could also be pertinent when considering the greater growth of women in healthrelated occupations, compared to women in SET occupations. Reskin and Roos
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(1990) argued that an expansion in the number of jobs (as in the health sector in its growth in the second half of the 20th century) can lead to employers being more likely to select among women further down the gender-divided job queue. Because such an expansion did not take place in SET on the same scale, the same argument could not apply.
Implications for theory and policy A range of reasons was discussed early in this paper about why the underrepresentation of SET-qualified women matters. It appears from our findings that the returns to the individual and to the state are high for health-qualified women, but considerably lower for SET-qualified women. Whilst large numbers of most women and men with SET qualifications appear not to work in SET occupations, as defined here, we have shown that the men are not lost to the labour market in the way that the women are (Table 1). DTI (2002) showed major loss to the labour market of these women, and our findings confirm this. We are able to add weight to earlier findings by showing a relationship between withdrawal from both SET occupations and the labour force by SET-qualified mothers. There is a growing body of evidence that suggests that whilst the issue of recruitment of women to SET education and employment remains an issue, a more pressing issue is that of retaining these women in employment. This suggests that government policy should be concentrated in this area. The Greenfield Report for the DTI recommended the setting up of a parttime/job-sharing scheme, where employers would be offered incentives such as tax breaks if they made such forms of work available (Greenfield 2002). What such schemes may ignore, however, is the possibility that if such forms of working are
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largely the domain of one sex (women), they will leave untouched the norm of continuous, full-time, long-hours working. This could lead to an increase in vertical sex segregation and a persistence (and possibly increase) of the pay gap. The sciencebased organisations studied by Ellis (2003) had put in place flexible working policies in order to help retention. However Ellis's conclusion was that the most common form of flexible working, part-time working, was a double-edged sword, since such forms of working might help retention, but could act as a barrier to advancement. In an unchanged cultural context, therefore, policies that lead to the retention of women scientists may undermine promotion prospects. Monitoring of the possibly gendered take-up of such policies and their long-term effect is therefore essential. Glover (2000) argued that the ‘problem’ of women in science was three-fold: ‘getting in’, ‘staying on’ and ‘getting on’. Women could ‘stay on’ in scientific employment in larger numbers, but fail to ‘get on’. Qualification and recruitment are clearly fundamental, but retention and advancement (which apart from its intrinsic attraction could also be a factor in retaining women) both require much more attention from employers and policy-makers.
The distinctive patterns of family-building reported here may reflect the influence of the institutional contexts that SET-qualified women study and work in, as suggested by the qualitative research of several authors including Devine (1992), Ellis (2003), Evetts (1996), Jones and Causer (1995) and McRae et al (1991). The Greenfield Report brings in the concept of ‘institutional sexism’ as a factor that militates against women’s persistence in science (Greenfield 2002). ‘Unintentional practices’, says the report, ‘can create a culture that does not favour women’ (p 26). Institutional sexism is defined partly as the combination of structural factors (such as
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traditional family roles, responsibilities and expectations and unequal pay) with ‘intangible cultural factors’ that appear to exclude women from positions of influence and prestige (p 28). The culture of science has received some attention (for example Noble 1992; McIlwee and Robinson 1992; Traweek 1992; Rose 1994; Rossiter 1982, 1995; Wertheim 1997; Woodfield 2000). The general conclusion is that scientific employment is not woman-friendly or family-friendly and that the roots of this are of an institutional nature. Research that starts from this epistemological position is thus needed. But the reality may be that researchers’ access to scientific workplaces is particularly difficult, since this would require acceptance by the existent scientific community that there is indeed an issue here that needs researching – and such acceptance may be quite rare.
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Notes 1
However, at any given age of childbearing, mothers with higher qualifications are
more likely than those without to have another child.
2
For more details of the Longitudinal Study see Hattersley and Creeser (1995) and
Blackwell et al (2003).
3
The NCDS is a continuing longitudinal study that follows the lives of all those living
in Great Britain who were born in one week in March 1958. To date, six sweeps have taken place. The NCDS is currently administered by the Centre for Longitudinal Studies, Institute of Education, University of London. Data are available from the Economic and Social Data Service (www.esds.ac.uk). The use of the NCDS is limited for small sub-sets, however, because of its relatively small overall sample size. The definition of ‘scientists’ in the Fielding and Glover study was a narrow one, unlike the broader definition used in this article.
4
Fielding and Glover showed in their analyses of the NCDS that men in non-SET
occupations tended to be in a broad category of management, whilst women in nonSET occupations tended to be in teaching. However, the definition of SET occupations used in this paper include some managers (categories 110, 111 and 126) and thus the explanation used in the Fielding and Glover analysis is less powerful in this case.
5
These results must be interpreted as synthetic summaries for comparing women with
different qualifications, rather than as tools of prediction. Various technical problems
25
need to be pointed out. First, because of small numbers, the analysis combines two cohorts; it is possible that these have their own distinct childbearing histories. Second, we use birth data to 1998 only, since information for 1999-2001 was not available at the time of analysis. Thus the birth histories for those age less than 40 in 1991 are not complete. However, the statistical technique takes account of incomplete childbearing histories. Third, our use of birth history data mean that those with children that they did not give birth to (ie adopted or stepchildren) are counted as being without children.
26
References Arnot, M., David, M. and Weiner, G. 1999. Closing the gender gap: postwar education and social change, Cambridge: Polity. Blackwell, L., Lynch, K., Smith, J. and Goldblatt, P. (2003) Longitudinal Study 19711991: Completeness of Census Linkage, Series LS No.10, London: ONS. Cockburn, C. and Fürst-Dilic, R. 1994. 'Bringing Technology Home: Gender and Technology in a Changing Europe'. Buckingham: Open University Press. Committee of Women in SET. 1994. 'The Rising Tide: A Report on Women in Science, Engineering and Technology'. London: HMSO. Dainton Report. 1968. 'Enquiry into the flow of candidates in science and technology into higher education' . London: HMSO Dale, A. and Egerton, M. 1997. 'Highly Educated Women: Evidence from the National Child Development Study'. London: HMSO. DTI 2000. 'Excellence and Opportunity - a science and innovation policy for the 21st century' . London: Stationery Office Ltd. DTI 2002. ' Maximising returns to science, engineering and technology careers.' . London: DTI. Report URN 02/514 Devine, F. 1992. Gender Segregation in the engineering and science professions: a case of continuity and change. Work, Employment and Society, 6.4:557-575. Ellis, P. 2003. Persistence and progression for women in science-based employment. Unpublished PhD submitted to the University of Surrey. Etzkowitz, H. and Leydesdorff, L. 1998. 'The endless transition: A "triple helix" of university-industry-government relations'. MINERVA 36: 203-208. European Commission. 2000. 'Science policies in the European Union - Promoting excellence through mainstreaming gender equality. European Technology Assessment Network (ETAN) 'Women and Science' report.' Brussels: CEC. European Commission. 2002. Science and Society Action Plan. DG Research Science and Society Directorate. Brussels: CEC. European Commission. 2003. Communication "Investing in research : an action plan for Europe"' . Brussels: DG Research. COM2003 226(final) Evetts, J 1994. ‘Career and Motherhood in Engineering: Cultural Dilemmas and Individualistic Solutions’. Journal of Gender Studies. 3. 2 . 177-85 Fielding, J. and Glover, J. 1999. 'Women Science Graduates in Britain: the value of secondary analysis of large scale data sets'. Work, Employment and Society 13: 353-67. Finniston Report. 1980. 'Engineering our Future’. London: HMSO. Cmnd 7794 Folbre, N. 1994. Who Pays for the Kids? London and New York: Routledge. Glaser, B. 1964. Organizational Scientists: their Professional Careers. Indianopolis: Bobbs-Merrill. Glover, J. 2000. Women and Scientific Employment. Basingstoke: Macmillan. Greenfield, S. 2002. 'SET Fair: A Report on Women in Science, Engineering and Technology' . London: Department for Trade and Industry. Hattersley, L. and Creeser,R. 1995. ‘Longitudinal Study 1971-91, History, Organisation and Quality of Data’. London: HMSO Jones, C. and Causer, G. 1995. 'Men Don't Have Families': Equality and Motherhood in Technical Employment'. Gender, Work and Organization 2: 51-62. Knorr-Cetina, K. 1999. Epistemic Cultures: How the Sciences Make Knowledge. London: Harvard University Press
27
Latour, B. and Woolgar, S. 1986. Laboratory life : the construction of scientific facts. Princeton, N.J.: Princeton University Press Losh, A. C. 2003. Gender and Educational Digital Gaps: 1983-2000. IT & Society, 1(5): 56-71. McIlwee, J. and Robinson, J. 1992. Women in Engineering: Gender, Power and Workplace Culture. Albany: State University of New York Press. McRae, S., Devine, F. and Lakey, J. 1991. Women Into Engineering and Science, London: PSI Noble, D. 1992. A World Without Women: the Christian Clerical Culture of Western Science. New York: Knopf. Rees, T. 2002. 'National policies on Women and Science in Europe'. Brussels: European Commission, DG Research, Women in Science Unit, prepared for the Helsinki Group on Women and Science Rendall, M. and Smallwood, S (2003) ‘Higher qualifications, first-birth timing, and further childbearing in England and Wales’, Population Trends, Spring (111) 18-26. Rendall, M., Couet, C., Lappegard, T., Robert-Bobée, I., Rønsen, M. and Smallwood, S. (2005) ‘First births by age and education in Britain, France and Norway’, Population Trends, Autumn, 121: 27-34. Reskin, B., & Roos, P. (1990) Job Queues, Gender Queues; Explaining women's inroads into male occupations, Philadelphia: Temple University Press. Rose, H. 1994. Love, Power and Knowledge: Towards a Feminist Transformation of the Sciences. Cambridge: Polity. Rosser, S. 2000. Women, Science and Society: The Crucial Union. New York: Teachers College Press. Rossiter, M. 1982. Women Scientists in America: Struggles and Strategies to 1940. Baltimore: Johns Hopkins University Press. Rossiter, M. 1995. Women Scientists in America: Before Affirmative Action 19401972. Baltimore and London: Johns Hopkins University Press. Standard Occupational Classification (SOC90). 1990. London: Office for National Statistics Traweek, S. 1992. Beamtimes and Lifetimes: Harvard University Press. Varma, R. 2003. Guest Editorial: Special Issue on Women and Minorities in Information Technology. IEEE Technology and Society Magazine, Fall, 22(3):6-7. Wajcman, J. 1991. Feminism Confronts Technology. Cambridge: Polity Press. Warrior, J. 1997. Cracking it! Helping Women to Succeed in Science Engineering and Technology. Watford: Training Publications Limited Wertheim, M. 1997. Pythagoras' Trousers: God, Physics and the Gender Wars. London: Fourth Estate. Woodfield, R. 2000. Women, Work and Computing. Cambridge: Cambridge University Press is. Wyer, M. 2001. Ed. Women, Science and Technology. New York and London: Routledge.
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Figure 1
Graduates and SET graduates in 1971/91, by year of birth
6000 5000 1991 Male SET graduates 1991 Male graduates
4000
1991 Female SET graduates 1991 Female graduates
No. 3000
1971 Male SET graduates 1971 Male graduates
2000
1971 Female SET graduates 1971 Female graduates
1000 0 around 1910
around 1920
around 1930
around 1940
around 1950
around 1960
Year of birth
Source: ONS Longitudinal Study
29
Figure 2 Representation of women in each subject area in 1971, by cohort 60
50 non-SET graduates
40
health
%
engineering & tech'y natural science
30
architecture & surveying physics
20
mathematics
10
0 1907-16
1917-26
1927-36
1937-46
Year of birth
Source: ONS Longitudinal Study Figure 2a Representation of women in each subject area in 1991, by cohort 60
50 non-SET graduates health
40
%
engineering & tech'y natural science
30
architecture & surveying computing physics
20
mathematics
10
0 1927-36
1937-46
1947-56
1957-66
Year of birth
30
Source: ONS Longitudinal Study Figure 3 Women graduates without children, 1991, by subject, 25-34 years, with 95% Confidence Interval
1.0 .9 Proportion without children
.8 .7 .6 .5 .4 .3 N=
36419 322 574 208 3007 non-grads natural sci non-SET eng. & health technology
31
Figure 4
Age of entering motherhood, graduates aged 25-44 in 1991, by occupation Occupations
1.0
1. Scientists n=122
.8
2. Technologists n=297
Cum ulati ve survi val
.6
3. Other occ’s 1
.4
3 5
.2
n=122
2
4. Health profs
4
n=358 5. Teachers
0.0
n=2615
0
10
20
30
40
Years childless after 14 Source: Survival analysis of ONS LS
32
Table 1: Employment outcomes in 1991 of two major groups of SET graduates, by cohort, column percentages
Health graduates SET occupations Not in employment Non-SET occupations
Natural Science graduates SET occupations Not in employment Non-SET occupations
Women Cohort born around: 1930 1940 1950 1960 % % % %
Men Cohort born around: 1930 1940 1950 1960 % % % %
61
82
75
76
73
86
85
81
34
4
12
13
19
3
5
10
5
14
13
11
8
11
10
9
Women Cohort born around: 1930 1940 1950 1960 % % % %
Men Cohort born around: 1930 1940 1950 1960 % % % %
9
18
23
39
32
46
49
42
58
22
21
17
34
6
4
8
33
60
56
44
34
48
47
50
Source: ONS Longitudinal Study
33
Table 2: Occupational stability amongst SET graduates aged 25-34 in 1981 Percentage of those present in the 1981 occupational group who were in the same group in 1991 1981 Men Women occupations Health1 92% 87% Science, 68% 51% engineering and technology2 Source: ONS Longitudinal Study
Among women in the same occupation in 1981 and 1991, the percentage who were mothers 81% 39%
Notes 1. 'Health' occupations include the following Standard Occupational Classification 1990 (SOC 90) unit groups: Health professionals 220, 221, 222, 223, 224, 345 Health associate professions 340, 341, 342, 343, 346, 347, 348 2. 'Science, engineering and technology occupations include the following SOC 90 unit groups; Natural scientists 200, 201, 202, 209 Engineers & technologists 210 , 211, 212, 213, 215, 216, 219, 110 Architects & surveyors 260, 312, 313, 111 SET associate professions 300, 301, 302, 303, 304, 309 Computing professionals 126, 214, 320
Table 3: 1991 employment status of SET women graduates aged 25-44 who became mothers during the 1980s 1981 occupations Health Science, Engineering and Technology
1991 destinations In SET employment Out of the labour force (%) (%) 78 15 31 40
34
Source: ONS Longitudinal Study
35
36
