Pay, Job Rank and Job Satisfaction amongst Academic Economists in the UK.

Transcription

1 Pay, Job Rank and Job Satisfaction amongst Academic Economists in the UK. Karen Mumford,1,2 and Cristina Sechel 3 1 Department of Economics, University of York 2 IZA, Institute for the Study of Labour. 3 InstEAD, University of Sheffield. June Abstract. Combining a rich source of data from surveys of individual academic economists in 2016 with institutional information collected from the Royal Economic Society Womens Committee Surveys, the determinants of pay, job rank, and job satisfaction are explored for academic economists in the UK. We find the conditional gender pay gap is some 10 log percentage points and has not decreased between 1999 and The within job rank gender pay differentials have been alleviated for Lecturers but the relative ability of women to move into higher paid job ranks has diminished: the overall gender pay gap in 2016 is strongly influenced by the relative concentration of men in the higher paid job ranks (especially Professor). We also find that gender is not robustly related to reported job satisfaction, nor are relative or own wages. JEL A1, A11, A2, I3, J01, J31, J7 Key words: economics; gender; pay, satisfaction, gaps. Corresponding author: Prof. Karen Mumford, karen.mumford@york.ac.uk Department of Economics and Related Studies, University of York. Heslington York YO10 5DD, UK. (phone ). We would like to thank Denise Osborn and Peter Smith for helpful comments and advice. 1

2 The relative position of women in UK academic economics has changed dramatically since data was first collected in the late 1990s (Mumford, 1997; Booth et al., 2000). Comparing balanced samples 1 for 1996 (Mumford, 1997) and 2016 (Tenreyro, 2017) shows that the proportion of the workforce that is female increased substantially over the twenty years, from less than one-in-six in 1996, to more than one-in-four in Women improved their relative representation in all of the academic job ranks. In 1996 women made up 16.6% of the Lecturers (34.6% in 2016), 9.6% of the Readers/Senior Lecturers/Associate Professors (25.8% in 2016) and 4.2% of the Professors (15.5% in 2016). There has also been substantial job rank inflation across the discipline with the numbers of Professors (male and female) amongst all staff doubling over the time period (from 13% of all staff to 25.7%). In 2016, 19% of the women working full-time were Professors, 35% of the males were. In 1996, 6% of the women were Professors and 22% of the men. The movement of women into academic economics is not unique to the UK; Canada, America, and Italy have seen similar trends. 2 There have been a series of reports investigating the status of women faculty in high prestige institutions following the seminal study at MIT (MIT, 1999). Whilst these studies included discussion of reforms to improve gender equality in academia they did not typically include formal analysis of gender earnings differentials or pay gaps 3. A notable example is provided by Princeton where an unconditional gender pay gap at the mean of 18% was found across all faculty in 2002, with a conditional gap of 8% once measures of experience and accomplishment were included (Tilghman, 2003; page 22). The conditional gaps for different discipline groups (including Social Sciences) were found to be across, rather than within, job ranks (Girgus, 2005). The Royal Economic Society (RES) commissioned a survey of pay and possible discrimination in The survey was carried out by the Royal Economic Society Working Party on the Representation of Ethnic and Other Minorities in the Economic Profession (see Blackaby and Frank, 2000). The data are subsequently used in Blackaby et 1 There have been considerable institutional changes during this time period, balanced sample analysis is consequently far from perfect. Such analysis has been carried out biennially by the Royal Economic Society (RES) Womens Committee and published in their reports, the regular comparison of these findings provides some support for the balanced sampling (see ). 2 In Canada: 5.4% of Professors were female in 1999, by 2014 this has risen to 14%; 10.9% of Associate Professors were female in 1999 and 27.5% were in 2015 (CWEN, 2015; table 3). In Italy: 5% of Professors were female in 1999, by 2016 this has risen to 16%; 22% of Associate Professors were female in 1999 and 33% were in 2016 (Corsi et al., 2017). In America: 6.6% of Professors were female in 1999, by 2016 this has risen to 13.1%; 13.5% of Associate Professors were female in 1999 and 25.6% were in 2015 (CSWEP, 2017; table 1). 3 For example, MIT (MIT, 2012); Duke (Keohane, 2003); and Caltech (Sargent, 2001). An influential study of gender pay gaps amongst US academics in the humanities, between the 1970s and the 1990s, is provided by Ginther and Hayes (2003). 2

3 al., (2005) to explore gender pay gaps; they found an unconditional gender pay gap of 17.7 log percentage points (lpp) and a conditional pay gap of 9.8 lpp, with a substantial component of this conditional gap occurring within job-ranks. The UK has seen substantial decreases in unconditional gender pay gaps across the entire workforce in recent years: the mean full-time unconditional gender pay gap has fallen from 21% in 2004 to 18.7% in 2011; and further to 16% in 2015 (Butcher et al., 2016; page 36). We might expect to see a fall in the gender pay gap amongst academic economists in the UK as well. A recent study of salaries at the London School of Economics (LSE) found an unconditional gender pay gap for academics of 16.5 log percentage points (lpp) and a gap of 10.5 lpp after controlling for age, tenure and research productivity (Bandiera, 2016). Within job-rank gender pay differentials accounted for some 25% of the conditional gap and the within job-rank pay gap increased with seniority. Bandiera (2016) also found that the gender pay gap conditional on age and experience had actually risen at the LSE from 3 lpp in 1998 to 9 lpp in In this paper, we return to consider gender pay differentials for academic economists across institutions in the UK. A particularly rich source of data generated from surveying individual academic economists in 2016 is combined with institutional information collected from the Royal Economic Society Womens Committee Surveys to explore the current determinants of pay, job rank, and job satisfaction for academic economists in the UK. The data are discussed in section 1 of the paper, section 2 discusses the estimation of the earnings function, decomposition analysis is presented in section 3, the probability of occurring in different job ranks is considered in section 4, job satisfaction is considered in section 5, and section 6 presents conclusions. 1. Data. Information was gathered from an online survey ed to individual academic staff members via their Heads of Department or similar department contact between February 26 and March 28, The individual staff member s responses were collated automatically 4 Contact details for the Heads of Departments were obtained from CHUDE (the Conference of Heads of University Departments of Economics). CHUDE was set up by the Royal Economic Society (RES) in 1987 in 3

4 via the survey software (Qualtrics) in an anonymised manner. Hard copies of the survey were also circulated at the 2016 Royal Economic Society Conference (March 21-23, 2016), 46 hard copies were returned completed. Interestingly just over half of these were repeats of online responses with additional information included (usually wage information). In total, we had 668 responses, however, many had little or no information and may have been accessed to simply look at the questionnaire rather than to participate in the survey. There were 543 responses providing us with information on their job rank. Given missing information on other variables of interest this allowed for the estimation of ordered probits for job ranking with 525 observations. There was a substantial fall off in the number of respondents who provided salary information; only some 383 did, allowing for the estimation of wage regressions with 367 observations. Finally, and perhaps surprisingly, respondents were also cautious when it came to revealing their job satisfaction in combination with other potentially identifying information; 443 respondents told us about job satisfaction but given missing information on the other explanatory variables, the probability of being satisfied with job estimation covers a sample of 306 individuals. Supplementary institutional information is taken from the Royal Economics Society Womens Committee Survey (Mitka et al., 2015; Tenreyro 2017). The Womens Committee Survey harvests information from CHUDE listed university department webpages on the individual academic staff by grade of employment and gender. These survey entries are then ed biennially to respective institutions for verification. Their overall verified survey response rate was high in 2015 with some 84% of the institutions responding, it was 57% in The Womens Committee Survey 2016 (Tenreyro, 2017) suggests there were 2077 workers across the entire UK academic economics workforce in This would imply a total response rate for our survey of around 32.7% (668/2077) with a useable response rate of some 26.1% (543/2077), falling to 18.4% for those providing salary information. A similar survey was carried out by the Royal Economic Society Working Party on the Representation of Ethnic and Other Minorities in the Economic Profession in 1999 (see Blackaby and Frank, 2000) when CHUDE listed Heads of Departments were asked to distribute a hard copy survey to full-time academic staff in their departments, 516 individuals completed the survey collaboration with the Association of University Teachers of Economics. Additional information on the survey distribution and invitation is provided in the Appendix, Section A1. 4

5 from 1600 distributed, a very similar response rate of 32%. Blackaby and Frank also encountered difficulties with missing data; their job rank analysis is for 452 individuals (implying a response rate of 28.3%), and their earnings estimations are for 405 (25.3%). The data are subsequently used in Blackaby et al., 2005 to explore gender pay gaps more fully; they include information on 351 individuals (or 21.9%) in their earnings estimations. Summary statistics for the main sample are provided in Table 1 (columns 1 to 3 for the pooled sample, columns 4 to 6 for men, and columns 7 to 9 for women). 5 There are concerns that the sample does not fully reflect the population. This concern is obvious in two main places. Firstly, females make up some 43% of our total sample; however, Tenreyro (2017) found the proportion of the UK academic workforce that is female is only 28%. Secondly, a little over a third of our sample are Professors (Table 1), 28.7% of the women and 42.3% of the men. Tenreyro (2017; table 1) found 25.5% of the workforce were Professors; 29.9% of the men and only 14.2% of the women. There is clearly an overrepresentation of Professors, especially female, in our sample and subsequent discussion of the results needs to include this caveat. One compensation of the over representation of female professors in the sample is the inclusion of a reasonable number of observations in the analysis as there are 45 female professors in the sample. Blackaby et al., (2005) could include only 7 female professors in their analysis. Our sample also has noticeable underrepresentation of Lecturers amongst the women (Tenreyro found 31.8% of women were Lecturers, we have 24.2%) and an underrepresentation of Researchers amongst the men (12.2% in Tenreyro, 8.6% in our sample). [TABLE 1 AROUND HERE] Variable definitions are provided in Appendix Table A1, most of these are selfexplanatory but some are worthy of more discussion here. The measure of annual salary is the current gross full-time equivalent wage for the main job. On average, the UK academic economists in our sample earn a full-time equivalent salary of 55,389 at the median in their main job: males average 60,000 and females average 52,000. This implies a raw gender 5 Comparable summary statistics for the samples used in the job ranking analysis are provided in the Appendix Tables A2 and Table A3 for the job satisfaction analysis. 5

6 pay gap of 15.4% at the median. 6 Only 2% of men believe they have ever suffered from gender discrimination, in striking contrast to the 24% of women. Furthermore, men perceive the average professorial gender pay gap to be 9%, with women predicting this gap to be 26%. In our sample the professorial gender pay gap is 14.3% at the mean. It would appear that these academics do not have a good understanding of their wage relative to their fellow employees, male or female, unlike most employees (Hampton and Heywood, 1993). If we consider only the predictions made by the professors, the predicted gaps are less biased but there is still a substantial gender difference. The male professors predict the gender gap to be 11% and the female professors predict it to be 18%; the women are clearly unduly pessimistic and the men unduly optimistic. Most authors adopt the human capital model as the theoretical basis for the earnings function. This approach will also be used here. At the individual employee level, it is assumed that wages increase with measures related to their own education, research output and funding, and teaching excellence. We cluster these variables into a category broadly referred to as individual productivity. The earnings function is augmented with the inclusion of further categories of explanatory variables such as demographic variables which may constrain an individual s choice of jobs (including having children, marital status, ethnic identification, and age); their workplace characteristics (working in stronger research department, regional location, the percentage of women in the department, workplace network available, mentoring, if the workplace is perceived to be cooperative, or if the workplace is perceived to be competitive ); and a range of variables loosely reflecting the individuals response to the labour market reflecting the individual seeking control over their own work experience, where control is commonly defined as the power to influence or direct people's behaviour or the course of events. 7 These measures reflecting control include moving between institutions, taking a career break, engagement with the promotion process, and attracting outside job offers. Beginning with the demographic variables, the great majority of this workforce classifies themselves as white (86%), they are relative young with close to half aged between 6 Unsurprisingly, the gender pay gap at the mean is higher; the mean male wage is 72,936 and the mean female wage is 60,301, implying a raw gender pay gap at the mean of 21%. 7 Definition taken from Google on June 1 st, ie=utf-8 6

7 35 and 49, three quarters are married and more than half have children (see Table 1). The women are less likely to be married and less likely to have children. There is a noticeable difference in the age distribution for men and women, with women being considerably more likely to be aged below 50 (75% of the women compared to 62% of the men). Considering the measures expected to be positively associated with individual productivity and earnings, a little over one in five of the total sample has a UK first class undergraduate degree (slightly more males than females). Males are considerably more likely to have a PhD (92% compared to 82% of the women). However, females are more likely to have been awarded more than 100,000 in research grants in the previous 5 years (35% relative to 24% for the men), and to consider themselves to be better teachers (45% of women consider themselves to be excellent teachers, whilst 34% of the men do). Individuals were asked to provide a REF (Research Excellence Framework) 8 style publication score for each of their career best three publications (ranging from zero to four), these were averaged into a single mean value. Women report a slightly lower average REF style score (self-reported over their three publications) than men, but not significantly so. Finally, one in ten of the workforce currently works part-time, and this is much the same for men and women. Whilst we do not have strong priors on the relationship between part-time employment and earnings in this sector, previous studies suggest a negative relationship across the economy (Mumford and Smith, 2009). Of the workplace characteristics, a similar proportion of both genders work in a top 6 (on REF ranking) department, although more of the men than women work in London. Women are more likely to work in the old universities. 9 We might expect the top 6 research departments and the old institutions to provide more facilities conducive to academic performance and for earnings to be higher on this basis. Women are also more likely to work with other women (or, alternatively, in a more feminised work-place) than are men. The percentage of females in the workplace is taken from the RES Womens Committee survey data for 2014 (Mitka et al., 2015), this avoids potential difficulties extrapolating from our 8 The Research Excellence Framework (REF) is an ongoing exercise judging the research quality of academic institutions in the UK. REF incorporates a range of measures but focusses largely on the quality of publications (subject to submission rules) of individual staff members. There is not an official list of journal quality used across economics departments, nor is there a commonly accepted list. 9 Old universities are those that were awarded their charter prior to the substantial movement of former Poytechnic and Central Institutions into the university sector in 1992 (with the Further and Higher Education Act, 1992). 7

8 relatively small (and overly female) sample when calculating this measure. Using this measure, the men in our sample are typically working in a workforce which is 23% female whilst for women this value is 28%. Working in a more feminised workplace is typically associated with lower salaries (Groshen, 1991; Bayard, 2004) and is often argued to be linked to over-crowding and a decrease in bargaining power. Most of the UK academic economists work in England (60.5% excluding London, and 79% including it). Women are more likely to work in London and in Scotland than are men. A positive relationship between London and earnings is expected as universities provide a London weighting (an additional salary component to partially compensate for the higher costs associated with living in London). Men are more likely to report that there are networks in their workplace they feel they can use for advice concerning professional advancement (62% of the men compared to 55% of the women). Although, one in five of either genders have never had a formal mentor they could turn to for work related advice. It would seem that there are unofficial support processes in these workplaces that, whilst relatively commonly available, are operating more effectively for men than women. It may also be that women are less willing to seek out these networks if they lack confidence in their relative abilities and/or fear being judged. Women in our sample are considerably more likely to report that they feel their workplace is competitive or very competitive (49% relative to 39% for the men) in Table 1. Neiderle and Versterlund (2007) argue that, even with equal ability and productivity, women are more likely to shy away from competition than men. They argue this gender difference is due to men being overconfident in their own abilities and women preferring non-competitive work environments. Interestingly, both genders in our sample report a similar average for feeling their workplace is cooperative or very cooperative (40% of the men and 38% of the women). Cooperation, or active recognition of mutual advantage, has long been associated positively with productivity in the labour economics literature (Mas and Moretti, 2009) and increasingly so amongst behavioural economists (Bruni and Sugden, 2013). Turning to the remaining variables loosely grouped together as labour market control related, some 60% of both men and women received an outside job offer in the previous five years. However, men are more likely to have been appointed from an external position. Men also less likely to have applied for promotion in the previous 10 years, they are less likely to have been rejected in this promotion process, and are less likely to have made a job application in the last 5 years. These outcomes may be due to many factors, however, 8

9 including the males being on average older and in more senior ranks (Artz et al., 2016), we will return to consider this more fully in the analysis below. Finally, some three quarters of the sample report that they are satisfied with their job, men being a little more likely to do so (79%) than women (73%), although not significantly different at standard confidence levels. It should be noted that the means for men and women of the characteristics discussed above are very rarely significantly different from each other at standard confidence levels. The summary statistics are also very similar for the samples used to analyse the determinants of salary (Table 1), job rankings (Appendix table A2) and job satisfaction (table A3). 2. Estimating the Earnings Function There is an enormous literature examining the gender wage differential in the context of the human capital model developed by Becker (1975) and Mincer (1974). Following in this literature, using semi-logarithmic wage equations, we estimate the earnings equation as: W i = + X G X + i (1) i i i where W i is the natural log of the wage for individual i; α is an intercept term; X i is a vector of regressors capturing the individual characteristics expected to impact on wages; and i is a residual term. An indicator variable G identifies males in the dataset and the coefficients measure where the coefficients on any of the variables X i differ between men and women. In the pooled model, the parameters are common parameters for men and women on individual characteristics. The parameters measure differences in the impact on male earnings only of a given characteristic. The fully pooled model sets all =0 whereas separate models for male and female earnings would allow all to be non-zero. We base our analysis on pooled wage equations for men and women rather than on separate estimates for the two groups (Neumark, 1988). We estimate the earnings function using ordinary least squares, the results are presented in Table 2. [TABLE 2 AROUND HERE] 9

10 Column 1 of Table 2 shows the unconditional gender wage gap to be log percentage points (lpp), there are 367 observations in the regression, and the goodness of fit (in this case, the adjusted R-squared) measure is low at The models become increasingly richer in explanatory variables as we move from column 1 to column 4 of Table 2. Model 2, presented in column 2, introduces the demographic measures (ethnicity, being married and having children) which may constrain an individual s choice of jobs. Interestingly, no significant relationship is found with any of these three demographic characteristics and salary. Age is also included in column 2, a strong relationship is found between older age bands and the omitted year olds. As job rank is not included in these regressions and a strong positive relationship would be expected between rank and age, this might be predicted. Furthermore, the great majority of this workforce is on a seniority based incremental pay structure within job ranks. We will return to discuss these issues further when we explicitly consider job rank in the next section of the paper. The conditional gender gap is 9.74% in column 2 and the overall goodness of fit increases to 29.5%. Model 3 adds the characteristics associated with productivity and the workplace, the results (column 3 of Table 2) are consistent with prior expectations. Salary is positively and significantly related to the variables measuring productivity. In particular having: a first class UK undergraduate degree; a PhD; higher REF style publication score; more than 100,000 of research income in the previous 5 years; and excellent teaching are all associated with higher earnings. We do not find a part-time pay penalty, however, this may be partly due to the dichotomous nature of the job ranks using part-time employment amongst academic economists; part-time employment is much more common amongst Researchers (21%) but also amongst the Professors (13.4%). Amongst the workplace variables, working in a top 6 REF ranked department and working in an old university are both positively associated with higher earnings as expected. The strong regional effect associated with working in London relative to the omitted other England, is also not surprising as these universities provide a London weighting (a salary 10

11 component to partially compensate for the additional costs associated with living in London). The top 6 University indicator includes the three most prestigious of the London universities (UCL, Imperial and the LSE). The percentage of the departmental workforce female is strongly and negatively associated with salary (Groshen, 1991; Bayard, 2004). This is a finding consistent with the traditional industrial relations and more recent gender literature and is often argued to be associated with gender segregation, over-crowding and a decrease in bargaining power. This effect can be represented separately in the estimation equation: W (2) % k i = + X i Gi X i + Fi i where k is the workplace; and %F is the percent proportion of females in the workplace. We return to the interpretation of this finding below. A negative and significant relationship is found between having a professional network available in the workplace and earnings and no relationship is found between having had a mentor and earnings. The gender pay gap is estimated to be 9.4 lpp and the measure of fit has increased substantially to 54.4% in Model C. Model D (column 4) presents our full model including variables associated with labour market control as reflected in the work environment. The gender pay gap is estimated to be 9.9 lpp and the measure of fit is 58.4%. Very few of the variables measuring control are found to be significantly related to salary, however, receiving an outside job offer (positive) and applying for an outside job (negative) are both found to be strongly significantly related to pay. Interestingly, we do not find a gender difference in the relationship between receiving an outside offer and salary. Including a male-interacted-with-outside-job-offer term in model D is found to have a low coefficient of with a standard error of These results do not suggest women face lower earnings because employers do not respond to them having an outside job offer as fully as they do with men. Instead, we find a strong positive relationship between outside job offers and salary for both men and women. Similarly, we do not find a gender difference in applying for promotion in the last 10 years nor in being 11

12 rejected in the promotion process. 10 Our results may be compared with previous studies. Model C, reported in column 3 of Table 2, is similar to the final model presented by Blackaby et al., 2005 (see Table 1, Model iii, column 3), where they find a conditional gender pay gap of 9.8 lpp and capture 64% of the total variation. Whilst there are differences in the variable specification, and Model C includes additional workplace variables, there is general consistency in the qualitative and quantitative results for the inclusion of (what we call) the demographic, productivity and workplace characteristics. It is worth stressing how striking this finding is, between 1999 and 2016 there is no evidence that the gender pay gap has fallen for UK academic economists. 3. Decomposing the Earnings Gap An unconditional earnings gap between men and women in these data of or lpp and a conditional gap of 9.88 lpp is found in section 2 above. Further insight into these gaps may be provided by decomposition analysis (Oaxaca, 1973). The approach we adopt to apportion the gap in the mean earnings of men and women here is that proposed by Neumark (1988) and discussed further in Oaxaca and Ransom (1994) where the reference set of parameters is given by the pooled estimates presented in Table 2. The decomposition of the mean earnings gap is calculated as: W W ( X X ) ˆ X ˆ (3) m f m f f Where ( m f X X ) ˆ captures the difference in the productive characteristics across the genders; and X ˆ is the remaining gender gap. f The decomposition results present calculations of the total unexplained gap as well as the part of the unexplained gap that can be attributed to individual or groups of variables. As discussed by Jann (2008) amongst others, there may be a problem in interpreting these results for categorical variables where the results may depend on the choice of omitted category. We 10 The coefficient on this male interaction with promotion application term if added to model 4 is with a standard error of , analogously the coefficient on the male interacted with promotion rejection is with a standard error of

13 adopt the solution proposed by Jann, and implemented in Stata, that takes the standard coefficient estimates and computes the elasticities for all categories including the omitted category by reweighting. [TABLE 3 AROUND HERE] Aggregate decompositions for the earnings function of Model D (the full model) are presented in the first panel Table 3. Comparing male with female academic economists, the total earnings gap is log per cent in favour of the males. Of this gap, 5.17 log percentage points is due to this group of females having more of those characteristics associated with lower earnings than do the corresponding males. This explained portion makes up just over a third (34.35%) of the total raw gap (of lpp) and is significant at a 99% confidence level. The explained component can be further decomposed, see panel 2, in this case into the clusters of explanatory variables discussed in section 1. There are only two significant components, or groupings of characteristics, in the explained portion of the earnings gap. The component associated with demographic characteristics and that related to the feminisation of the workplace. We can see that the demographic variables make up some log points of the lpp explained gap, clearly the largest component of the explained pay gap. The demographic component can be further decomposed into the portion associated with age ( lpp) and other (ethnicity, having children, and being married) which is only lpp. Thus, of the explained portion of the gender pay gap, the great majority (79.2%) is associated with demographic characteristics; gender age differences are associated with 96.3% of the total demographic component and 76.3% of the total explained pay gap. The age component can be further decomposed into the portions associated with each of the age bands included in the earnings function (also reported in panel 2 of Table 2). Each age band is associated with a negative component of the total negative age association; in accordance to what we saw in the summary statistics presented in Table 1, women are more likely to occur in the younger age bands associated with lower pay and less likely to occur in 13

14 the older age bands that are linked to higher pay. The youngest age band (20 to 34 year olds) is associated with lpp or 45.5% of the total age component; or 34.7% of the total explained gender pay gap. The relationships with the other age bands, the total age component, 11 and thereby the explained gender pay gap, are more modest and are not significant at standard confidence levels. The component of the decomposition related to feminisation at the workplace is some lpp or 40.1% of the explained pay gap. 12 The other components are not significant, at standard confidence levels, but are generally offsetting. Women on average have lower mean levels of those characteristics associated with decreased productivity ( lpp or 42.1% of the total explained pay gap) and greater mean levels of workplace characteristics associated with lower pay ( lpp or 86.97%) but show some sign of higher average labour market control characteristics (0.765 or 14.8%) that are related to higher pay. The remaining 9.88 log percentage points (clearly the major component of the gap at 65.6%) is unexplained and is due to the characteristics (as estimated in the full model) being in aggregate rewarded at a lower rate for females than for males (differences in the coefficients). To reiterate, the model does not explain why they are being rewarded differently (hence the term unexplained ). 11 The 35 to 49 age band is associated with 15.5% of the total age component, the 50 to 64 is associated with 22.6% and the above 65 age band is related to 16.4%. 12 It constitutes lpp (standard error ) of the lpp explained gap and lpp (standard error ) of the unexplained gap. The relevant decomposition of the mean earnings gap is calculated as: ^ ˆ k k W W ( X X ) X ˆ (% F % F ), where X X ) ˆ captures the difference in m f m f f m f ( m f the productive characteristics across the genders; and ^ k k [ X ˆ (% F % F )] is the remaining gender f m f gap. 14

15 4. Considering gender differences in job ranks. It is reasonable to expect there may be within-job rank gender differences that are also part of the picture of the relative status of academic economists. Table 4 presents ordered probit estimates for the full model, Model D above, for the pooled job rank sample of 525 individuals. The overall goodness of fit measure is not high (pseudo R 2 of 0.235, nevertheless this is consistent with estimations of this type and the model presented in Table 4 is both well specified and consistent with prior expectations. [TABLE 4 AROUND HERE] The estimation results reported in Table 4, row 1, shows that gender is clearly significantly related to job rank for academic economists in the UK. Males are less likely to be employed as Lecturers, Researchers or Teaching Fellows; and are more likely to be Readers/Senior Lecturers 13 and especially Professors. The other explanatory variables are consistently associated with higher job ranks. Being older, married, having a first class degree, having a PhD, higher publications score, receiving more than 100,000 in research income, working full-time and having an outside offer in the last 5 years are all associated with higher job ranks. It is perhaps surprising given the strength of these relationships that the conditional gender effect is so strong. Compared to Blackaby et al., (2005) we find a weaker negative male gender association for the Lecturer (-6.9 lpp relative to -13.8) and a stronger male association for the Professorial rank (10.5 lpp relative to 7.4), all measured at the 99% confidence level. It would appear that men are more likely to occur amongst the Lecturers and the Professors in 2016 than they were in Whilst there is a gender difference in the probability of being employed at a higher rank, there may not necessarily be a within job rank gender pay gap. Re-estimating Model D (the full model) earnings equation in section 2 for each job rank does not suggest gender pay gaps occur in any of the job ranks at standard confidence levels, 14 as is seen by the conditional estimated relationship between being male and earnings presented in Table 5. It is worth noting, however, that a 6.2 lpp gender pay gap occurs amongst Senior 13 Whilst many departments have moved away from having Readerships, there are still some institutions (such as York) that have two separate (but partially overlapping) pay scales for Senior Lecturers and Readers, implying that combining the job ranks may allow for a true within rank estimate. 14 The Teaching Fellow rank could not be included in this exercise as the number of observations were too few to allow for the estimation procedure. 15

16 Lecturer/Readers (significant at the 85% confidence level) and a 7.98 lpp gap amongst the Professors (with 80% confidence). These pay gaps are not very precisely measured but there appears to be some evidence of within rank gender pays gaps at the higher job ranks. [TABLE 5 AROUND HERE] Blackaby et al., (2005) found evidence of a within rank gender pay gap amongst Lecturers of 6.3 lpp (with 99% confidence) but a clearly insignificant gap of 3.4 lpp (standard error of , implying a t-stat of 0.44) amongst Professors in It would appear that the within rank gender pay gap has changed in nature over time: falling away for Lecturers but increasing amongst Professors. This finding is broadly consistent with those of Bandiera (2016) for the LSE, she finds: at the Assistant Professor (Lecturer) level the within rank gender gap is small at around 2% and insignificantly different from zero; with significant gaps of 7% for Associate Professors (Senior Lecturer/Reader) and 11% for Professors. 5. Job satisfaction. There is a small, but growing, literature that seeks to address the common finding that whilst women typically earn less than men, they usually report higher levels of job satisfaction (Card, 2012; Clarke and Oswald, 1996; Clarke and Senik, 2010; Clarke et al., 2009; Mumford and Smith, 2014). Interestingly, in our sample the males are more likely to report being satisfied with their job (79%) than the women (74%) although the difference is not significant at standard confidence levels. Following Clark and Senik (2010) we model the probability of reporting job satisfaction S of worker i in institution k (S ik ) as a function of the following vector of covariates: S ( W, W W, X ) ik ik ik i ik where W ik is the wage of that academic; (4) Wi is the average wage of his/her reference (or comparator) group and X ik is a vector of observable individual and institutional characteristics correlated with job satisfaction. The inclusion of a relative wage measure is argued to be important in this literature, as discussed in Mumford and Smith (2014). Card et al., (2012) 16

17 highlight alternative explanations where: an employee values their own relative utility and may be dissatisfied if their wage is lower than other workers (see also Zizzo and Oswald, 2001); or a model of co-worker wage as a signal of their own future wages (see also Clark et al., 2009). It is important to find the true comparator wage when setting the average wage of the reference group. Ideally, we would want the within job rank, within institution, average wage for both men and women to use as relevant comparator averages. Unfortunately, our sample is not rich enough to generate such a measure. Comparators group averages are instead set at the male or female wage average in either the old or new universities according to the characteristics of the individual. A series of probit regressions are estimated, with the unobserved latent dependent variable, S ik set equal to 1 if the individual lecturer i records above 5 to the survey question Overall how satisfied are you with your job these days, the scale of potential answers ranged from 1 (completely unsatisfied) to 10 (completely satisfied); and zero otherwise. The estimation results are presented in Table 6, marginal effects at the mean of the explanatory variables are reported with differential effects for binary variables (estimated coefficients are available from the authors upon request). In general, the models are not particularly well defined. The overall fit is also not high in absolute terms: the pseudo R-squared measures suggest the models are explaining some third of the reported job satisfaction amongst academic economists (summary statistics for the sample of interest are provided in Appendix Table A3). [TABLE 6 AROUND HERE} Considering the results in more detail, column 1 reports the unconditional relationship between relative wage and job satisfaction, academic economists are more satisfied when they earn more than their comparator group although the size of the effect is not large. This is consistent with a model of relative utility (Card et al., 2012). Column 2 adds the demographic variables, none of these are found to be significantly related to satisfaction (except that year olds are less likely to report job satisfaction relative to the omitted year old age group). It is particularly noteworthy that whilst the relationship between being male and reporting job satisfaction is positive, it not significant at standard confidence levels. 17

18 Results for the estimation of the full model, consistent with Model D in section 2, are reported in column 3 of Table 6. Of the individual characteristics associated with productivity only having a UK first class undergraduate degree is found to be significant related with job satisfaction. Amongst the workplace characteristics there are only significant results for: regional differences (with the English, excluding London, being least happy second only to the Welsh); working in more feminine departments (negatively related to job satisfaction); and working in a cooperative environment (positively related). Having had a career break, being rejected from promotion in last 10 years, and having made a job application are all negatively associated with the propensity to report job satisfaction. Applying for promotion in the last 10 years, and being a Researcher are all positively associated with job satisfaction. The relationship between relative wage and job satisfaction is smaller than the relationship established in columns 1 or 2 and is no longer significantly different to zero. The finding of an insignificant relationship between relative wage and job satisfaction in Model D may be due to a poor comparator group measure. Column 4 presents results for the estimation of the full model but including own-wage rather than relative wage. Interestingly, none of the results change significantly. A significant relationship between job satisfaction and own wage is also not found. To summarise, job satisfaction amongst the academic economists in 2016 is positively related to living in London or Scotland; having a first class UK undergraduate degree; applying for promotion; not being rejected for promotion; and not applying for an outside job. Job satisfaction is also positively related to working in a cooperative environment; working with fewer females; and being a Researcher. It is worth noting that the demographic characteristics of these academics are not strongly related to their job satisfaction. We also consistently found no gender difference in reported job satisfaction. 15 There are some obvious concerns with the inclusion of variables expected to impact on earnings in a job satisfaction model. For example, applying for an outside job might reasonably be interpreted as an outcome from being unhappy at work rather than a cause of 15 We also tried running the model reported in column 6 separately by gender, the model is not well specified and the sample sizes are small. 18

19 that unhappiness. 16 Excluding this variable from the full models presented in columns 3 or 4 does not change any of the other results either qualitatively or quantitatively. As discussed above, it has been argued that women prefer less competitive work environments (Neiderle and Vesterlund, 2007). We considered the inclusion of a gender interacted with competition measure in the full model with relative salary (column 3) or with own salary (column 4), the interacted measure was not found to be significant in any of these models, nor did its inclusion lead to changes in the other variables Conclusions Combining a particularly rich source of data from surveys of individual academic economists in 2016 with institutional information collected from the Royal Economic Society Womens Committee Surveys, we explore the determinants of pay, job rank, and job satisfaction for academic economists in the UK. Taken together, our results suggest there is a substantial and significant unconditional gender pay gap of some 15.4% at the median amongst our sample of UK based academic economists. Decomposition analysis reveals that there is a substantial and significant unconditional gender pay gap of some log percentage points, just over a third of this gap can be explained by gender differences in characteristics associated with higher wages (5.17 lpp or 34.4%) and some three quarters by unexplained differences in the rewards given to these characteristics (9.88 lpp or 65.6%). Of the explained component, the great majority is associated with demographic characteristics, particular a surge of women amongst the younger aged bands compared to the typically older men. Analysis of job ranking reveals gender is clearly significantly related to job rank for 16 We do not claim to establish causality; as Ferrer-i-Carbonell and Frijters (2004; pages ) discuss in detail, such a claim would require all unobserved factors to be either orthogonal to the observables or suitably controlled for (e.g. via an assumed structure) and would require a cardinality assumption for the ranking of measured job satisfaction requiring exceptionally rich data and simultaneous estimation with suitable instruments. 17 The results do not support this premise: the estimated coefficient for reporting a competitive environment in Model D is (standard error ) in female only estimates of job satisfaction; and (0.3123) in male only estimates. 19

20 academic economists in the UK. Males are less likely to be employed as Lecturers, Researchers or Teaching Fellows; and are considerable more likely to be Readers/Senior Lecturers and especially Professors. We also find some evidence (at the 80% confidence level) of within job rank gender pay gaps for Senior Lecturers/Readers and for Professors, suggesting that the overall gender pay gap is strongly influenced by the concentration of women into the lower paid job ranks (and vice versa for men). Strikingly, between 1999 and 2016 the size of the conditional gender pay gap in UK academic economics has shown no notable change. The within job rank gender pay differential has fallen amongst Lecturers as has the relative segregation of women into this job rank. The relative ability of women to move into the higher paid job ranks (especially Professorial) has diminished, however, and the within rank pay gaps in the top ranks appear to have increased. The overall gender pay gap in 2016 is strongly influenced by the relative concentration of men in the higher paid job ranks (especially Professor). We do not find a gender difference in reported job satisfaction. Whilst males are some 8% more likely to report they were satisfied with their job in the full model, the relationship is not significant at standard confidence levels. The relationship with relative wage and job satisfaction is positive, consistent with a model of relative utility; however, the significance of this result is not robust across specifications. Furthermore, a significant relationship between job satisfaction and own wage is not found, suggesting that wage is not a primary factor in the explanation of reported job satisfaction amongst academic economists in the UK. References. Akerlof, G. A. and Kranton, R. E. (2000). Economics and identity, Quarterly Journal of Economics, vol. 115, pp Arrow, K. (1973). The theory of discrimination, in O. A. Ashenfelter & A. Rees, (Eds.), Discrimination in Labor Markets, 3-33: Princeton, NJ: Princeton University Press. Babcock, L. and Laschever, S. (2003). Women Don t Ask: Negotiation and the Gender Divide, Princeton: Princeton University Press. Bagues, M., Sylos-Labini M., N. Zinovyeva (2017), Does the Gender Composition of Scientific Committees Matter?, American Economic Review, doi: /aer

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