Job Mobility in 1990s Britain: Does Gender Matter? *

Transcription

1 Job Mobility in 1990s Britain: Does Gender Matter? * Alison L Booth and Marco Francesconi Institute for Social and Economic Research University of Essex Wivenhoe Park Colchester CO4 3SQ England Tel.: Fax: albooth@essex.ac.uk Institute for Social and Economic Research University of Essex Wivenhoe Park Colchester CO4 3SQ England Tel.: Fax: mfranc@essex.ac.uk Title as Running Head: Job Mobility in 1990s Britain: Does Gender Matter? Abstract The paper examines gender differences in intra-firm and inter-firm job changes, including worker-initiated and firm-initiated separations, for white full-time British workers over the period We document four main findings. First, job mobility is high for both men and women, with more than one quarter of the sample changing job each year. Second, the distinction between promotions, quits and layoffs is important, suggesting that studies that either aggregate worker-initiated and firm-initiated separations or neglect within-firm mobility may provide an inappropriate picture of career mobility. Third, we find that the average male and female quit and promotion probabilities are remarkably similar, but there are significant gender differences in layoff probabilities. Fourth, we find significant gender differences in the impact of variables such as union coverage, occupation and presence of young children. First version, January 1999 This version, September 1999 JEL Classification: J24, J41, J62 Keywords: Career mobility, gender, promotions, quits, layoffs * The support of the Economic and Social Research Council under Award No. L is gratefully acknowledged. Views expressed here are those of the authors and not necessarily those of the ESRC. We are grateful to an anonymous referee, Joe Altonji, Anne Preston, and seminar participants at the University of Amsterdam, University of Essex, and the American Economic Association meetings 1999 (New York) for useful comments, and to Jeff Frank for stimulating discussion.

2 NON-TECHNICAL SUMMARY It is well-known that women workers have a lower attachment to the labour force than men, with potentially important consequences for human capital accumulation, job mobility and occupational segregation by gender. But are men more likely than women to be promoted? How do career patterns differ by gender for workers who are strongly attached to the labour market? Does distinguishing between intra-firm and inter-firm job changes improve our understanding of gender differences in mobility? We address these questions using a sample of workers from the British Household Panel Survey (BHPS), collected annually over the period This sample comprises only full-time workers, and excludes from the analysis job-to-nonemployment transitions. Our data allow career (or job-to-job) mobility to be disaggregated into job changes involving a promotion within a firm and job changes involving movements across firms. With this disaggregation, the paper extends previous work in two directions. First, it distinguishes between internal and external job mobility, which has not been possible before using other British survey data. Second, within the career mobility literature, it extends the approach of most US empirical research by distinguishing between worker-initiated (quits) and firm-initiated (layoffs) job changes across firms. We find that, although women s promotion and quit rates are higher than men s in the raw data, such differences vanish once we control for standard individual and job characteristics. This contrasts with the popular view that women quit more often and are promoted less frequently than men. However, women s layoff rates remain significantly higher than men s. Our results also demonstrate that, although average job mobility rates of men and women in the sample are similar, the rates do respond differently to specific changes in their socio-economic environment. These findings emphasise the importance of distinguishing between different forms of job mobility. The turnover effects of certain variables, such as the presence of young children, union coverage and occupation, differ significantly by gender across all forms of job mobility. Thus, even with a reasonably homogenous sample of workers, as long as women s and men s career patterns differ in terms of their response to changes in individual or job-specific characteristics, analyses that focus simply on the overall separation rate and neglect intra-firm mobility may provide an incomplete picture of workers career development and reach potentially misleading conclusions. 1

3 1. Introduction Job mobility is a striking feature of the British labour market in the 1990s. According to new longitudinal data, each year more than a quarter of full-time workers can expect to change job, and another quarter will move again one year later. Yet relatively little is known about the incidence of various forms of mobility within and across firms and how they differ by gender. Are men more likely than women to be promoted? What are the major determinants of job changes for men and women? It is well-known that women workers have a lower attachment to the labour force than men, with potentially important consequences for human capital accumulation, job mobility and occupational segregation by gender (Mincer and Ofek, 1982; Royalty, 1998). But how do career patterns differ by gender for workers who are strongly attached to the labour market? Does distinguishing between intra-firm and interfirm job changes improve our understanding of gender differences in mobility? We address these questions using a sample of workers from the British Household Panel Survey (BHPS), collected annually over the period This sample comprises only full-time workers, and excludes from the analysis job-to-nonemployment transitions. Our data allow career (or job-to-job) mobility to be disaggregated into job changes involving a promotion within a firm and job changes involving movements across firms. With this disaggregation, the paper extends previous work in two directions. First, it distinguishes between internal and external job mobility, which has not been possible before using other British survey data. 1 This distinction is important, because promotions are an integral part of workers careers (Gibbons, 1998; Gibbons and Waldman, 1999; and references therein). Second, within the career mobility literature, it extends the approach of most US empirical research by distinguishing between worker-initiated (quits) and firm-initiated (layoffs) job changes across firms (Sicherman and Galor, 1990; McCue, 1996). This distinction is 2

4 important when there are informational asymmetries and costly renegotiation between workers and firms (Hall and Lazear, 1984). 2. Background Why do workers change job? From the supply side, workers leave their job (to a different employer or another job at the same employer) if the expected utility from so doing exceeds current utility less the costs of the change. To the extent that men and women differ in alternative opportunities and costs, there may be gender differences in job mobility. Voluntary job separation behaviour has been addressed most frequently in the context of human capital and job-matching theory (see among others Oi, 1962; McLaughlin, 1991; Harper, 1995). Gender implications for career mobility can be obtained from these models if there are (i) differences in male and female human capital acquisition that make men or women less highly valued by a firm (Blau and Kahn, 1981); or (ii) gender differences in job search costs (Meitzen, 1986); or (iii) differences in employers monopsony power, which arise if there is gender discrimination in hiring so that women must search for a gender match as well as the usual job match (Neumark, 1988). From the demand side, firms will terminate a job if the profits from so doing exceed expected profits from continuation, less any redundancy costs. Permanent exogenous shocks (through, for example, technical and structural change rendering skills obsolete) reduce expected profits by lowering marginal productivity. Nominal wages in Britain are typically downwardly rigid, and hence demand shocks may induce layoffs. To the extent that there is occupational or industrial segregation or sex-typing, women and men may be subject to differential demand shocks. Moreover, employers will want to dismiss a worker if the match quality is poor. Although the scope for dismissal may be circumscribed by labour laws, 3

5 dismissals may be packaged as redundancies for which there is a statutory procedure in Britain. If employers follow different strategies in their attempts to retain men and women (through promotions, wages and/or bonuses, targeted redundancy pay), there may be gender differences in involuntary job separation behaviour. The last fifteen years have witnessed a remarkable growth of studies of the organisation of labour within firms (for recent surveys, see Gibbons, 1996, and Gibbons and Waldman 1999). However, we currently have little systematic knowledge of the contribution of promotions to career mobility, and even less knowledge of sources of gender differences in career mobility after accounting for promotions. 2 Firms promotion policies are a means of increasing productivity within an organisation by increasing human capital acquisition, increasing effort, of inducing separating equilibria in terms of worker types (Chang and Wang, 1995), or even of constraining favouritism (Prendergast and Topel, 1996). Firms may backload compensation to elicit higher levels of effort, where effort may be proxied by hours of overtime work (Landers et al., 1996). If women are constrained by family factors from working long hours, this may lead to gender differences in promotion rates. There may also be gender differences in the way family responsibilities affect promotion and mobility; if women are more likely to quit, firms will be less likely to train and promote them. On the other hand, if women view promotion as unlikely due to discriminatory promotion practices, they may be less prone to put themselves forward for training programmes at the firm. Gender differences in job mobility rates are not only interesting in their own right, but may also suggest explanations of the gender pay gap. If, for example, women are more likely to quit their jobs than men, firms will be less willing to invest in their training, resulting in lower accumulation of human capital and ultimately lower rates of pay. Alternatively, if women are more likely to receive a promotion, and large wage jumps are observed upon 4

6 promotion, we may expect promotions to play an important role in reducing gender wage differentials The data The data are from the first six waves of the British Household Panel Survey (BHPS), a nationally representative survey collected annually since The BHPS provides information on the timing and type of job changes, including job changes at the same employer. For all jobs ending during the 12-month period between interviews, workers give the reason for stopping a job. Therefore we can identify job changes involving promotion (a change of duties or different job spell at the one employer), movement across employers, and other forms of job termination. We define a firm-initiated separation or layoff as when a worker is either made redundant or dismissed, or when a temporary job is terminated. All other movements across employers are defined as worker-initiated separations. 4 Our estimating sample comprises white men and women who: (i) were born after 1936; (ii) reported full interviews; (iii) have at least two years of labour market data; (iv) were in full-time employment at the time of the survey; and (v) were not self-employed, farmers, or in the armed forces. These restrictions primarily imposed to narrow the sample to those with a reasonably strong attachment to the labour market yield an unbalanced panel comprising 2,135 men and 1,475 women, with 9,697 and 6,210 person-year observations, respectively. 5 At the bottom of Tables 1 and 2, we show the distributions of the male and female samples by career states (the omitted state is staying with the same employer without promotion). For this sample of workers, the mobility rates by gender are very similar, with women being slightly (but significantly) more mobile over the sample period. About 12% of female person-year observations and 10.4% of male person-year observations were promoted 5

7 within the firm, while another 15-16% were observed to move across firms. Some 9.5% of women and just less than 9% of men quit voluntarily, while 7% and 6.3% of women and men respectively were laid-off layoffs. Thus we find evidence of considerable job mobility over the sample period, but gender differences are quantitatively small. The last columns of Tables 1 and 2 show the sample means of the variables used in the multivariate analysis to follow. Men have greater work experience and job tenure than women, work longer hours, are more concentrated in skilled-manual and managerial occupations, and in the private sector, and have more dependent children. But a larger fraction of women than men with dependent children are out of the labour force or in part-time jobs, and thus would not be included in our sample. 4. Results The first three columns of Tables 1 and 2 report the coefficients (and robust standard errors) 6 of a multinomial logit (MNL) regression for men and women, respectively. The Tables report the results for the mutually exclusive states of promotion, quit, and layoff, relative to the base of staying in the same job with the same employer. The explanatory variables include: tenure in the current job and its square, labour market experience and its square, highest educational qualification (4 dummy variables), usual hours of overtime work, 7 living in London, marital status, number of children by three age groups (aged 0 to 4; 5 to 11; and 12 to 16), union coverage, working in the public sector, 8 establishment size (2 dummy variables), occupation (5), occupation of origin (5), cohort of entry into the labour market (4), travel-to-work time (3), and local unemployment rate. The base for highest educational qualification is qualification below an Ordinary level. 9 The base for establishment size is more than 200 employees, while the base occupational group is semi-skilled and unskilled 6

8 workers. The base for date of labour market entry is the cohort entering by 1960, while for travel-to-work time the base is less than 20 minutes. TABLES 1 AND 2 ABOUT HERE We computed the test that Cramer and Ridder (1991) suggest for pooling career states over the entire sample of men and women, after including a gender dummy and all the interaction terms of gender with the initial regressors. 10 The test for pooling quits and layoffs yields a likelihood-ratio test statistic of 178.3, which is asymptotically distributed as χ 2 with 59 degrees of freedom. After distinguishing quits from layoffs, the Cramer-Ridder test for pooling promotions and stays yields a value of 963.9, and this is again χ 2 (59). These tests suggest that it is inappropriate to pool inter-firm transitions (quits and layoffs), and that promotion within a firm must be kept separate from staying in the same firm without promotion. 4.1 Similarities by gender We find some striking similarities by gender. The probability of being promoted is significantly higher for male and female workers who are married or cohabiting, and are in managerial occupations. It is also increasing in the number of hours worked overtime, ceteris paribus. These findings are consistent with theories viewing promotion as a reward for higher human capital embedded in higher occupational levels (Sicherman and Galor, 1990), or as a reward for effort or longer hours of work (Landers, et al., 1996), 11 or responsibility in higherpaying occupational positions (Manove, 1997). Tenure and experience have a statistically significant negative impact on promotion for both men and women. The fact that tenure has a significant negative effect on promotion for both men and women is not surprising (and does not contradict the predictions of human 7

9 capital theory), because tenure here measures tenure in the job rather than tenure with the employer. 12 The negative relationship between experience and promotion is probably primarily an age effect: the older (or more experienced) the worker, the less likely is he or she to be promoted, perhaps reflecting skills obsolescence (see Harper, 1995: Table 6, for a similar result). For both male and female workers, highest educational qualifications have no statistically significant effect on the promotion probability (with the exception of the vocational qualification for men, which significantly increases the male promotion probability). However, when we estimated the model excluding current occupation (because it might produce a spurious association with promotion), our main results remained virtually unaffected. 13 The only notable exception involved the estimated coefficients of the highest educational qualification variables, which become positively and significantly related to the promotion rate. 14 The quitting probability for both men and women is significantly reduced by the number of very young children, union coverage, longer job tenure, and being single. The result that union coverage reduces the likelihood of quitting is consistent with exit-voice theories, and is a common finding in the union literature (Booth, 1995:199). The negative correlation between quitting and job tenure is consistent with job matching theories and is also well established in the empirical literature (McLaughlin, 1991); while the negative relationship between quitting and number of young children aged up to 4 years may capture time constraints in job search when young children are present. Finally, for both men and women, the layoff probability is significantly reduced with longer job tenure, and managerial occupations. The finding about job tenure suggests that labour market institutions (e.g., firing costs) are important in regulating firm-initiated 8

10 separations. For example, many firms adopt a last-in-first-out rule. On the other hand, ceteris paribus, firms tend to keep on workers with greater levels of embodied human capital, such as men and women in managerial occupations. 4.2 Differences by gender Despite these similarities, there are some substantial gender differences in the way in which such variables affect individuals job mobility. To illustrate this point more starkly, we perform the same analysis for the pooled male and female subsamples, and include in the multinomal logit regression a gender dummy along with the interaction terms of gender with all the regressors of Tables 1 and 2. To stress the principal differences, Table 3 presents predicted probabilities of job mobility, which compare the overall distribution of job mobility at alternative values for selected variables evaluated at the sample distribution of the sample of male and female workers. 15 The third column of Table 3 shows the difference between female and male mobility rates as a percentage of the corresponding male rate. 16 Notice that, after controlling for all our explanatory variables, the baseline promotion and quit rates do not differ by gender, but women are still significantly more likely to be laid off than men. TABLE 3 ABOUT HERE An extension of union coverage to the entire population of workers is associated with an increase in men s and women s promotion probabilites from 11% to 12.7% and from 10.9% to 11.6% respectively. The increase in the promotion probability for men is 9.4 percentage points higher than that of women, and may offer some support for the longstanding hypothesis that unions typically look after men better than women (Glucksmann, 1990). Male and female promotion probabilities also increase if they work 5 more hours of overtime per week, but the female increase is far larger than the male, at about 19 percentage 9

11 points. Men work longer hours (on average 2.5 overtime hours more than women each week, see Tables 1 and 2), and a larger proportion of men work overtime (60% of men vs 45% of women). Thus, women may have a relative advantage in using overtime hours (a signal for effort) to elicit promotion at both the intensive and extensive margins. Alternatively, this gender difference may be picking up some unobserved heterogeneity. 17 A similar effect is found for an occupational shift into managerial jobs. The largest gender gap in promotion probabilities occurs when we modify the distribution of workers by firm size: moving workers into the smallest establishments would lead women s promotion probabilities to be 32% higher than men s. Larger organisations may be more likely to operate a well-defined internal labour market with institutionalised career ladders than smaller firms (Chang, 1996). The differential response by gender is primarily generated by a substantial reduction in the male promotion probability of about one-fifth of the baseline probability. This gap may then be explained by the fact that more than 70% of women in the smallest establishments are in higher occupational positions (professionals, managers and, particularly, skilled non-manual workers), while only 40% of men in the smallest establishments are in such occupations. The most sizable gender differences in quitting probabilities are associated with an additional child aged 0 to 4 years, and a change in the occupational composition of the labour force. The first change decreases women s quitting probability by 11 percentage points more than men s. As noted above, the presence of young children is likely to increase the costs of job search (because of time constraints) or reduce the moticvation to search for a new job (because of financial benefits such as maternity pay embedded in the current contract): mothers seem to be more affected than fathers. Moving all workers to the managerial 10

12 occupations decreases the firm-to-firm mobility of both men and women, but the decrease for female managers is 14% less than their male counterparts. Male and female layoff rates respond most differently to recent labour market entry, the presence of young children, and public sector employment. While a hypothetical shift of all workers into the public sector reduces the male layoff probability, it leaves the female virtually unchanged, making women 40 percentage points more likely to be laid-off than men, a very large effect. An additional child leaves the female layoff probability almost unaltered as compared to the baseline, but reduces the layoff probability of men by approximately onefourth: this generates the 30 percent gender differential shown in Table 3. A larger number of young children may induce a stronger job attachment or may simply provide better job protection for fathers. 5. Conclusions We have examined a special longitudinal sample of full-time British workers observed between 1991 and 1996 to study gender differences in job mobility, distinguishing between promotions, quits and layoffs. Our results show that, although women s promotion and quit rates are higher than men s in the raw data, such differences vanish once we control for standard individual and job characteristics. This contrasts with the popular view that women quit more often and are promoted less frequently than men. However, women s layoff rates remain significantly higher than men s. Our results also demonstrate that, although average job mobility rates of men and women in the sample are similar, the rates do respond differently to specific changes in their socio-economic environment. These findings emphasise the importance of distinguishing between different forms of job mobility. The turnover effects of certain variables, such as the presence of young children, 11

13 union coverage and occupation, differ significantly by gender across all forms of job mobility. Thus, even with a reasonably homogenous sample of workers, as long as women s and men s career patterns differ in terms of their response to changes in individual or job-specific characteristics, analyses that focus simply on the overall separation rate and neglect intra-firm mobility may provide an incomplete picture of workers career development and reach potentially misleading conclusions. 12

14 Endnotes 1 To our knowledge the only exception is Harper (1995), who uses the National Training Survey (NTS) to analyse male occupational mobility over the period Harper s study is not directly comparable to ours because it does not refer to female mobility, applies to a substantially different time period, and examines occupational mobility (defined to occur when individuals move from their 3-digit occupation to one of the other 395 occupations recorded in the NTS data) rather than job mobility, which is our primary interest. 2 While most of the literature on promotion incidence uses data from individual firms (e.g., Baker, Gibbs and Holmstrom, 1994), there are only a few studies of job mobility that use representative samples of workers (Sicherman and Galor, 1990; McCue, 1996; Groot and van den Brink 1996). 3 However, Booth, Francesconi, and Frank (1998) find a significant gender pay gap for promoted workers over the period , using BHPS data. They conclude that the promotion process does not systematically mitigate the general disadvantage women face in the labour market. 4 Booth, Francesconi, and Garcia-Serrano (1999) use the retrospective work history data from the BHPS to examine gender differences in job tenure and job mobility up to The work history data distinguish between voluntary and involuntary separations, but do not include any information on promotion. 5 Part-time work occurs when an individual claims to have usually worked less than 30 hours per week on his/her primary job in the past 12 months. A substantial proportion of British women works part-time. With restriction (iv), we exclude 797 women (for a total of 4,088 person-periods) and only 37 men (227 person-periods). However, part-time workers are likely to face different career profiles, with fewer promotion prospects. Therefore, our restriction to full-time employees is designed to make the male and female samples as comparable as possible. 6 Given the panel nature of our data, the estimated standard errors account for multiple observations on the same individual. Consequently, they are robust to arbitrary forms of correlation within each cluster or individual. 7 This variable is obtained from the following question: How many hours overtime do you usually work in a normal week?. It refers to normal circumstances in a usual week of work (rather than temporary or exceptional circumstances), and it includes both paid and unpaid hours of overtime work. 13

15 8 The definition of public sector includes civil servants and central government employees, local government and town hall employees, workers in the National Health Service, nationalised industries, higher education and nonprofit organisations. 9 O level takes the value unity if the individual s highest educational qualification was one or more Ordinary - level qualifications (later replaced by GCSE), usually taken at the end of compulsory schooling at age 16 years. A-level takes the value unity if the individual s highest educational qualification was one or more Advanced - level qualifications, representing university entrance-level qualification typically taken at age 18 years. Vocational qualification takes the value unity if the individual s highest educational qualification was a vocational qualification (such as Higher National Diploma (HND), Higher Natioanl Certificate (NHC), nursing, and teaching qualifications), while higher qualification takes the value unity if the individual obtained a university degree or above. 10 In the interests of brevity, we do not report the estimates for this model, but its principal findings are summarised in Table 3 below. 11 To provide further evidence on the finding that promotion is a reward for effort, we re-estimated the model with the same regressors as reported in Tables 1 and 2 but excluding current overtime hours, and including lagged overtime hours of work. For both men and women, the estimated coefficients of this new variable remain positive, at values of and 0.027, respectively. The male estimate, however, is much less precisely measured yielding a t-ratio of 1.364, while the female estimate remains highly significant with a t-ratio of These estimates should be interpreted with caution, because our sample sizes are reduced after lagging overtime hours one period. For the regressions with lagged overtime hours, most of the results for the other variables are unaltered. 12 Furthermore, specific human capital and job matching theories predict a negative effect of tenure on inter-firm mobility after an initial positive duration effect (see Mortensen, 1986). If different jobs with the same employer are viewed as different inspection-goods, standard job-matching models yield monotone-decreasing jobseparation hazard rates (Mortensen, 1978; Jovanovic, 1979 and 1984). If there is, however, gradual learning about the quality of the worker-firm match, such models would predict a non-monotonic separation hazard (that is, an initial rise followed by a decline in the hazard rate). Given the annual frequency of our observations, it is not surprising that our data can only detect a monotonically declining relationship of job tenure with promotion rates. 14

16 13 Of those who have received a promotion from one year to the next, the vast majority remained in the same occupational group. The proportions of promoted workers who moved across occupations are 23 percent for men and 16 percent for women. 14 For example, the promotion coefficients (and t-ratios) associated to Higher qualification are (tratio=2.967) and (t-ratio-3.125) for men and women respectively; while those associated to Vocational degree are (t-ratio=3.938) and (t-ratio=2.841) for men and women, respectively. 15 In particular, let p ij denote the predicted probability for individual i (i=1, N) in outcome j (j=1,2,3,4, our four mobility states). Because of the MNL assumption, p ij =(exp(b j X ij ))/(1+Σ j (exp(b j X ij ))), where b j is the MNL parameter estimate and X ij denotes the sample value of the corresponding regressor. Let p j =(1/N)Σ i p ij be the average predicted probability over individuals. The first two columns of Table 3 report p jm and p jw, that is, the average predicted probability for men and women respectively, evaluated at sample values of the variables X used in the estimation. 16 Formally, using the notation of the previous footnote, the values reported in this column are computed using the following expression (p jw -p jm ) 100/p jw. For example, the gender difference in the promotion rate due to union coverage is given by ( ) 100/0.116= /0.116=-9.4 (as Table 3 shows). Furthermore, from the pooled men-women regression that includes the entire set of gender interactions, the standard errors of each gender difference are given by the standard errors of each gender-interaction term. 17 For example, women who work longer hours have some unobserved characteristics (e.g., motivation) that make them more likely to receive a promotion than men working the same number of overtime hours. This should also be related to the finding that higher levels of past effort (that is, more lagged overtime hours of work) significantly increase the promotion rate for women but not for men. 15

17 References Baker, G., Gibbs, M., Holmstrom, B The Internal Economics of the Firm: Evidence from Personnel Data. Quarterly Journal of Economics, 109(4), pp Blau, F.D., Kahn, L.M Race and Sex Differences in Quits by Young Workers. Industrial and Labor Relations Review, 34, pp Booth, A.L The Economics of Trade Unions. Cambridge University Press, Cambridge. Booth, A.L., Francesconi, M., Frank, J Glass Ceilings or Sticky Floors? Centre for Economic Policy Research, Discussion Paper No. 1965, September. Booth, A.L., Francesconi, M., Garcia-Serrano, C. Job Tenure and Job Mobility in Britain, Industrial and Labor Relations Review, 53(1), October, pp Chang, C., Wang, Y A Framework for Understanding Differences in Labor Turnover and Human Capital Investment. Journal of Economic Behavior and Organization, 28(1), pp Cramer, J.S., Ridder, G Pooling States in the Multinomial Logit Model. Journal of Econometrics, 47, pp Gibbons, R Incentives in Organisations. Journal of Economic Perspectives, 12(4), pp Gibbons, R., Waldman, M. 1999, Careers in Organizations: Theory and Evidence. In Ashenfelter, O. and D. Card (eds.), Handbook of Labor Economics, vol. IIIB, Amsterdam: New Holland. Glucksmann, M Women Assemble: Women Workers and the New Industries in Interwar Britain. Routledge, London. Groot, W., van den Brink, H.M Glass Ceilings or Dead Ends: Job Promotion of Men and Women Compared. Economics Letters, 53, pp Hall, R.E., Lazear, E.P The Excess Sensitivity of Layoffs and Quits to Demand. Journal of Labor Economics, 1984, 2(2), pp Harper, B Male Occupational Mobility in Britain. Oxford Bulletin of Economics and Statistics, 57(3), pp

18 Jovanovic, B. (1979). Firm-Specific Capital and Turnover. Journal of Political Economy, 87(6), pp Jovanovic B. (1984). Wages and Turnover: A Parametrization of the Job-matching Model. In G.R. Neumann and N.C. Westergard-Nielsen (eds.), Studies in Labor Market Dynamics. Berlin: Springer-Verlag. Landers, R.M., Rebitzer, J.B., Taylor, L.J Rat Rate Redux: Adverse Selection in the Determination of Work Hours in Law Firms. American Economic Review, 86(3), pp Manove, M Job Responsibility, Pay and Promotion. Economic Journal, 107(1), pp McCue, K Promotions and Wage Growth. Journal of Labor Economics, 14(2), pp McLaughlin, K.J A Theory of Quits and Layoffs with Efficient Turnover. Journal of Political Economy, 99(1), pp Meitzen, M.E Differences in Male and Female Job-quitting Behavior." Journal of Labor Economics, 4(2), pp Mincer, J., Ofek, H Interrupted Work Careers: Depreciation and Restoration of Human Capital. Journal of Human Resources, 17(1), pp Mortensen, D.T Specific Capital and Labor Turnover. Bell Journal of Economics, 9(2), pp Mortensen, D.T Job Search and Labor Market Analysis. In O. Ashenfelter and R. Layard (eds.), Handobook of Labor Economics, vol. II, Amsterdam: New Holland. Neumark, D Employers Discriminatory Behavior and the Estimation of Wage Discrimination. Journal of Human Resources, 23(3), pp Oi, Walter Y (1962) Labor as a Quasi-fixed Factor, Journal of Political Economy, 70, Prendergast, C., Topel, R.H Favoritism in Organizations. Journal of Political Economy, 104(5), pp

19 Royalty, A.B Job-to-Job and Job-to-Nonemployment Turnover by Gender and Education Level. Journal of Labor Economics, 16 (2), pp Sicherman, N., Galor, O A Theory of Career Mobility. Journal of Political Economy, 98(1), pp

20 Table 1: Multinomial Logit Estimates of Job Mobility, Men Types of Job Mobility Variables Promotion Quit Layoff Means O level (0.136) (0.136) (0.137) A level (0.140) (0.144) (0.150) Vocational degree 0.399** (0.177) (0.181) (0.226) Higher qualification (0.168) (0.172) (0.194) Experience (years) *** *** * (0.016) (0.016) (0.019) Experience squared 0.003*** 0.001** (0.0004) (0.0004) (0.001) Tenure (years) *** *** *** (0.019) (0.045) (0.041) Tenure squared 0.002** 0.015*** 0.011*** (0.0007) (0.002) (0.002) Living in London (0.136) (0.147) (0.173) Number of children: Aged * *** *** (0.082) (0.089) (0.123) Aged * ** (0.065) (0.070) (0.082) Aged (0.105) (0.103) (0.105) Married or cohabiting 0.597*** 0.422*** 0.245** (0.104) (0.102) (0.121) Firm size: < 50 workers *** (0.093) (0.100) (0.112) workers * (0.093) (0.111) (0.128) Overtime (weekly hours) 0.007*** (0.002) (0.005) (0.007) Union coverage 0.268*** ** (0.088) (0.099) (0.103) Travel-to-work time: minutes * (0.091) (0.095) (0.108) minutes (0.119) (0.125) (0.146) > 60 minutes ** (0.187) (0.157) (0.217) Public sector *** (0.100) (0.116) (0.133)

21 Table 1: (continued) Current occupation: Professional 0.848*** (0.189) (0.195) (0.224) Managerial 1.275*** ** *** (0.156) (0.156) (0.177) Skilled non-manual 0.657*** (0.153) (0.142) (0.155) Skilled manual 0.449*** (0.144) (0.128) (0.129) Occupational of origin: Professional (0.188) (0.195) (0.249) Managerial *** (0.184) (0.199) (0.249) Skilled non-manual (0.130) (0.142) (0.151) Skilled manual (0.117) (0.127) (0.124) Entry cohort in labour market: ** (0.148) (0.172) (0.170) ** (0.164) (0.180) (0.195) ** ** (0.147) (0.173) (0.191) After ** (0.189) (0.197) (0.232) Local unemployment rate 3.203** ** (1.572) (1.691) (1.924) Constant *** * (0.247) (0.258) (0.282) Log likelihood Psuedo R Observed proportion by state (%) Number of observations 9697 Note: Estimated coefficients are relative to the state of staying with the same employer without promotion. Standard errors are shown in parentheses. * significant at 0.1 level ** significant at 0.05 level *** significant at 0.01 level

22 Table 2: Multinomial Logit Estimates of Job Mobility, Women Types of Job Mobility Variables Promotion Quit Layoff Means O level (0.160) (0.161) (0.169) A level (0.178) (0.184) (0.211) Vocational degree (0.220) (0.221) (0.286) Higher qualification (0.210) (0.234) (0.273) Experience (years) *** * (0.019) (0.021) (0.025) Experience squared 0.003*** (0.0005) 0.001) (0.001) Tenure (years) ** *** *** (0.028) (0.064) (0.051) Tenure squared *** 0.017*** (0.001) (0.003) (0.002) Living in London ** (0.143) (0.145) (0.178) Number of children: Aged *** (0.186) (0.193) (0.185) Aged ** (0.125) (0.108) (0.124) Aged (0.118) (0.120) (0.137) Married or cohabiting 0.635*** 0.458*** 0.287** (0.107) (0.107) (0.124) Firm size: < 50 workers (0.109) (0.127) (0.142) workers * (0.117) (0.138) (0.146) Overtime (weekly hours) 0.032*** ( (0.007) (0.010) (0.010) Union coverage *** * (0.111) (0.125) (0.130) Travel-to-work time: minutes (0.106) (0.111) (0.129) minutes (0.135) (0.149) (0.177) > 60 minutes * (0.214) (0.268) (0.262) Public sector (0.118) (0.137) (0.142)

23 Table 2: (continued) Current occupation: Professional * (0.309) (0.304) (0.335) Managerial 1.283*** *** (0.264) (0.239) (0.273) Skilled non-manual 0.636*** (0.249) (0.220) (0.225) Skilled manual (0.261) (0.227) (0.234) Occupational of origin: Professional (0.294) (0.312) (0.365) Managerial (0.292) (0.305) (0.360) Skilled non-manual 0.372* (0.224) (0.208) (0.218) Skilled manual (0.223) (0.205) (0.224) Entry cohort in labour market: *** (0.187) (0.208) (0.218) * (0.194) (0.215) (0.242) (0.178) (0.193) (0.208) After (0.215) (0.212) (0.264) Local unemployment rate * (1.886) (1.985) (2.131) Constant *** *** (0.361) (0.357) (0.385) Log likelihood Psuedo R Observed proportion by state (%) Number of observations 6210 Note: Estimated coefficients are relative to the state of staying with the same employer without promotion. Standard errors are shown in parentheses. * significant at 0.1 level ** significant at 0.05 level *** significant at 0.01 level

24 Table 3: Predicted Probability Distributions of Job Mobility, by Gender and Selected Characteristics Mobility Status and Variables Men Women (%) Promotion Baseline Changes: Number of children 0-4 (+1) ** Union coverage *** Public sector Weekly overtime hours (+5) *** Experience (+1 year) *** Job tenure (+1 year) *** Managerial occupation *** Entry after Firm size: < 50 workers *** Local unemployment rate (+1%) * Quit Baseline Changes: Number of children 0-4 (+1) *** Union coverage *** Public sector Weekly overtime hours (+5) Experience (+1 year) *** Job tenure (+1 year) *** Managerial occupation ** Entry after Firm size: < 50 workers Local unemployment rate (+1%) ** Layoff Baseline ** Changes: Number of children 0-4 (+1) *** Union coverage * Public sector *** Weekly overtime hours (+5) Experience (+1 year) * Job tenure (+1 year) *** Managerial occupation *** Entry after *** Firm size: < 50 workers Local unemployment rate (+1%) Note: Predicted probabilities are obtained from pooled (men and women) MNL estimates with full set of interactions, equivalent to those reported in Tables 1 and 2. (%) is the percentage change of the female predicted probability with respect to the male predicted probability relative to the female rate. See text for detailed explanation and example. Baseline values are computed at sample values. * 0.05 < p < 0.10 ** 0.01 < p < 0.05 *** p < 0.01