Job Satisfaction, Mobility Decisions and Wage Gains by Gender

Job Satisfaction, Mobility Decisions and Wage Gains by Gender

Theodora Xenogiani

LSE, CEP

October 2003

Abstract

Workers tend to change jobs several times during their working life, generally towards better

career prospects. Most individuals face at some point of their working lives, the choice whether

to remain in the same job or change to a di¤erent job, in a di¤erent …rm etc. Their decision is

determined by the expected bene…ts of a job change (both monetary and non pecuniary payo¤s)

and the cost of moving.

If there are di¤erences in tastes as well as cost functions between men and women and women

are more constrained by household and family responsibilities, this should have an impact on their

job mobility behaviour. Furthermore, if women are less motivated by pecuniary aspects of the job

than men, then this could partly explain their lower returns to job mobility found in the literature.

Fist we investigate the determinants of job quitting for men and women distinguishing between

pecuniary and non pecuniary aspects of the job. Second we estimate the returns to job mobility by

gender. Although the link between the two has been extensively investigated in theory, there is very

limited empirical evidence, especially when it comes to gender issues.

We use the British Household Panel Data (BHPS) to undertake this investigation. The special

feature of the BHPS is that it permits a distinction between voluntary and involuntary job separa-

tions. It also contains a large number of variables for job attributes and personal characteristics.

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JEL: J60, J63, J28, J16

Keywords: Job mobility, wage growth, gender, job satisfaction

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1 Introduction

Workers tend to change jobs voluntarily several times during their working life, generally

towards better career prospects. Most individuals face at some point of their working lives the

choice whether to remain in the same job or change to a di¤erent job, in a di¤erent …rm etc.

This decision is determined by the expected bene…ts of a job change (monetary payo¤s and non

pecuniary payo¤s) and the cost of moving. It depends crucially on the type and quantity of

their human capital and the degree of satisfaction in their current job. Each individual chooses

an optimal career path depending on her own preferences as expressed by the utility she derives

from every possible outcome and her characteristics, especially education and human capital

investment1 . If the expected bene…ts in the current job are greater than those in potential

alternative jobs, the worker will decide not to move. This expected value is partly formed

according to workers’ past experience which determines their valuation and expectations. In

this paper we argue that women and men make di¤erent job mobility choices and thus their

career paths di¤er. These di¤erences may steam both from heterogeneity in preferences (tastes)

and the cost functions.

In this project we use British Household Panel Data (BHPS) to investigate …rst the deter-

minants of job mobility, separately for men and women. Moreover we look at job satisfaction

in order to gain a better understanding of the di¤erences in job preferences between men and

women. Second we are interested in the consequences of job mobility in terms of wage gains. The

special feature of the BHPS is that it permits a distinction between voluntary and involuntary

job separations. It also contains information on job attributes. In particular the BHPS dis-

criminates between di¤erent types of training i.e. general further education, job related training

and training undertaken to facilitate the transition to a new job. Moreover it provides informa-

tion on other job attributes such as distance to work, usual hours of work, over time and rich

information on overall job satisfaction and detailed aspects of job satisfaction.

In the …rst part we look at the factors determining job satisfaction. We want to investigate

whether these parameters di¤er between men and women. One would expect them to be the same

if tastes and preferences were the same for the two genders. The same holds for job constraints

1 Preferences may have both a direct and an indirect e¤ect on career decisions. The indirect e¤ect operates viaeducational choices and previous working experience (work choice decisions in the past).

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and the cost functions. Next we look at the determinants of voluntary job mobility, namely

quits. Although we also look at the determinants of promotions, this is not the main focus of

the paper. We distinguish between quits originating for family reasons and those for non family

reasons, but this is not part of the estimation because of the very small number of observations.

The decision to leave a job is determined by a broad set of characteristics which include both

personal characteristics of the worker and features of the job. We are also interested in the e¤ect

of job satisfaction on the decision to leave a job2 . We conduct the analysis separately for men

and women. This is because women are likely to make di¤erent career choices from men because

their time opportunity cost may be di¤erent in terms of family obligations. Opportunity cost

may a¤ect job mobility directly or indirectly through job search intensity. This could imply that

women search for new and better jobs less intensively than men, and thus they move less, or in

a less strategic way than men. In addition the constraints can di¤er between men and women.

In the …nal part we are interested in the consequences of job change in terms of earnings and

thus we attempt to estimate the returns to job mobility. In particular we want to investigate the

existence of di¤erent wage gains from job mobility for men and women. If this is the case, we want

to investigatewhether there is a part of thewage gap which can be attributed to di¤erent mobility

patterns. It has been shown in the literature that women’s decisions are motivated less by money

(pecuniary, monetary payo¤s) than men. In contrast, it is non pecuniary characteristics of the

job that play a more important role for them, such as hours ‡exibility, distance from home etc.

2 Related Literature

Empirical evidence suggest that men and women are di¤erent in terms of job search con-

straints as well as tastes, which partly induce di¤erent mobility patterns. Women are more

constrained by household and family responsibilities and pay is found to play a more important

role in job switching for men than for women. As a consequence, there are also gender di¤erences

in the returns to job mobility. These di¤erences imply di¤erent preferences for the two groups

as well as di¤erences in the degree of labour market attachment.

2 Subjective measures of satisfaction have been widely used in sociology but only recently have economistsstarted using them in economic research (Clark 1997, 1998, 2001, Ward and Sloanne 2000, Oswald, Levy- Garbouaet al 1999 etc).

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There is evidence (Manning 2003 for the UK, Sicherman 1996, Keith and McWilliams 1999

for the US3) suggesting that although overall job mobility rates are similar for the two genders,

the reasons for this mobility may di¤er (see Booth et al 1999). Women are more likely to leave

their jobs for non market reasons. Job to job changes are likely to be less responsive to wages for

women than for men and thus wage gains from mobility may be higher for men than for women.

Manning (2003) estimates separation elasticities with respect to the wage and he …nds that the

elasticity of separations to non employment is higher for women than men but the elasticity of

separations to other jobs is only weakly higher for men.

In this paper we are particularly interested in the impact of job mobility on wage growth.

The relationship between the two has been investigated in theory but there is very limited

empirical evidence. There is evidence that voluntary job mobility generates wage gains, relative

to non mobility or involuntary job change. (Bartel and Borjas 1981, Mincer 1986). Murphy and

Welch (1990) …nd strong evidence of rapid wage growth at the early stages of workers’ careers.

Topel and Ward (1992) …nd that a substantial part of the wage growth can be attributed to job

change.

Human capital theory4 suggests that job shifts are not necessary to increase earnings, which

can only take place with the accumulation of human capital. Newly recruited workers are more

likely to undertake investment in …rm speci…c human capital, which implies rather ‡at earnings

tenure pro…les after a certain period in the same job. This type of model predicts steeper

earnings pro…les following a job change, although there may be a downward shift of the pro…le

just after the job change. High tenure workers are likely to move to a new …rm which will o¤er

them the option of investment in speci…c human capital and thus experience higher wages and

wage growth than if they remain in their current job.

Other models which again explain the positive cross-section relationship between earnings

and job tenure are those of search and matching. A worker’s productivity is assumed to be

constant while employed in the same job. According to the matching models (Jovanovic 1979,

Burdett 1978), in the beginning the match is characterised by uncertainty regarding the pro-

3 They …nd that although the returns to di¤erent types of job mobility are similar for men and women, theincidence of these moves are di¤erent. Women are found to be more likely to quit for family related reasons andless likely to have a job to job voluntary change. In their 1999 paper they …nd that the returns to job search aresimilar for men and women but women undertake less on the job search.

4 See Neal (1995), Topel (1986, 1991) and Farber (1994).

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ductivity of the worker. After a certain period of time the true productivity is revealed and the

match can be either extended or ended. Workers will decide to leave their job if they believe

this is not a good match and they hope to switch to one which will be of better quality. If a

good match is found workers will be less likely to leave it. Search models predict an increase in

earnings following a job change. Workers engage in job search and will decide to move to a new

job if this o¤ers a higher wage than his current job. In addition search models suggest that the

probability of a job change falls with labour market experience. First because search intensity

is likely to fall and second because more experienced workers are more likely to be already in

a high paying job, given that they have been engaged in job search for a longer period. This

implies lower job mobility for more experienced workers since mobility costs are higher.

We will analyse the role of job mobility on wage growth and investigate whether this is

di¤erent for men than for women. It may be di¤erent in the same way that their career paths

di¤er signi…cantly. Job preferences di¤erences between men and women may imply di¤erent

trade o¤s between wages and non pecuniary job aspects across genders. For example women

may prefer a job with more ‡exible hours so that they can also take care of their families and

spend time in home work. Some women (especially in the old cohorts) are likely to choose jobs

with these desirable non pecuniary aspects although they may involve lower wages or lower wage

growth. Women are particularly interested in working hours and occupational characteristics

of their job. Discrimination may be one more reason for their di¤erent career paths in the

sense that certain high paying jobs may not be open to women. If women are more likely to

leave the labour market (in short periods) then employers would be less willing to hire them in

positions that require training and are associated with higher earnings. Both the di¤erences in

work tastes and gender discrimination in the labour market may result in lower wage growth for

women which further widens the gender wage gap.

Women do not simply follow di¤erent career paths than men. Even for those with stronger

attachment to the labour market, there are persistent di¤erences in the returns to job mobility.

According to the human capital model, there are gender di¤erences in productivity which lead to

higher wages for men than for women. One possible reason for these di¤erences is suggested to

be the weaker labour force attachment of women, due to household and family responsibilities.

To test the predictions of the human capital approach one can distinguish between actual and

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potential work experience. Di¤erences in the returns to potential experience can be explained

in terms of gender di¤erences in experience levels. In order to test for common returns to

experience we need data on actual labour market experience (see Light and Ulreta, 1995). The

returns to tenure are supposed to capture the returns to investment in job (…rm) speci…c human

capital. If women are more likely to leave the …rm, then the incentives to investment in job

speci…c human capital are lower than those for men. Thus we would expect women to invest

less in job speci…c human capital. This in turn would imply lower returns to tenure for women.

For the UK there is no strong evidence of that5.

The model of monopsony (see Manning 2003) suggests that labour market transition rates

and the reservation wage of men and women may be important in explaining the gender wage

gap. It has been found that women are more concerned with non pecuniary aspects of their job,

that is work hours (hours ‡exibility: Altonji and Paxson, 1988, 1992), job location etc. In that

sense they might accept or decide to move to a new job which is more ‡exible in terms of hours

worked but o¤ers lower pay.

Keith and McWilliams (1997) …nd that women have fewer involuntary job moves than men

and that the returns to job mobility are the same across gender, if one takes into account the type

of job mobility. Other studies show that the mobility wage gains for women are smaller than for

men. Loprest (1992) argues that job characteristics play an important role in mobility decisions

across women and men which partially explains di¤erences in the returns to job mobility. She

focuses on di¤erences between men’s and women’s patterns of job mobility and wage growth in

their …rst four years of working full time in the labour market. In her paper she tries to explore

the extent to which di¤erences in job mobility, returns to job mobility and the characteristics

of the jobs men and women hold can account for part of the gender wage gap. She …nds that

job mobility and wage growth rates in years of no job change are similar for men and women.

However wage growth for those women who change jobs is half of that of male job changers.

Khan and Griesinger (1989) show that women’s gains may be lower because women usually care

relatively more about job characteristics other than earnings. Light and Ulreta (1995) …nd that

although there are gender di¤erences in mobility patterns the returns to job mobility are very

similar for both genders. They …nd that women have longer and more frequent non working

5 M. Myck and G. Paull, 2001.

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spells than men in their early careers and thus women tend to require relatively more time to

accumulate a given amount of work experience. Royalton (1998) …nds that less educated women

have lower numbers of job to job (and higher numbers of job to unemployment) transitions

compared to their male counterparts and more educated women have more frequent job to job

(and job to unemployment) transitions.

Keith and McWilliams (1999) estimate the returns to job search, job mobility and the inter-

action between the two for a sample of young men and women, using the National Longitudinal

Survey of Youth. In their paper they suggest that men and women may have di¤erent returns

to job search activities. This can be because they may exert di¤erent search intensity, their

reservation wages may be di¤erent or their wage and o¤er functions may be di¤erent. Given

their household activities, the opportunity cost of search may be higher for women, which will

a¤ect both their reservation wage (lower reservation wage) and their search intensity (via lower

returns to search). They argue that young workers are more likely to search for a new job. This

is because workers in low wage jobs can gain more from search than highly paid workers. If

wages are positively correlated with experience, then younger workers should be more likely to

search.

The estimation of the returns to job mobility is not an easy task. In the existing empirical

literature, researchers have used three di¤erent methods to estimate the returns to job mobility.

The …rst involves the estimation of separate wage growth equations, one for job stayers and

one for job movers (Holmlund 1984). Then the two di¤erent group results are evaluated at

the mean observed characteristics of job movers and the di¤erence between these two mean

predictions is taken as the wage premium associated with a job change. However it is likely that

these estimates of job mobility wage gain underestimate the true value. This is because of the

heterogeneity between the two groups of workers. Wages may be correlated with unobserved

ability and thus if stayers have unobserved characteristics which lead to higher wages, using the

mean characteristics of movers would overstate the earning of movers had they not changed jobs.

It turns out that when this simulated change is compared with the actual change, the returns to

job mobility are underestimated. One way to solve this issue is to control for selection using the

standard Heckman procedure. Stayers are found to get higher wages than those which movers

would have got had they not changed jobs. The hypothetical wage growth derived with this

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method is then compare to the actual wage growth of movers in order to get an estimate of

mobility wage gains.

The second approach which has been employed in the literature involves the inclusion of

a dummy variable in the wage growth equation (Abbott and Beach 1994, sample of Canadian

women with data from the 1986-1987 Labour Market activity survey). The underlined assump-

tion necessary for this model is that the coe¢cients in the stayers and movers equations are

the same. In the same way as in the previous method, on-the-job wage growth of job stayers

is likely to be higher than that of job movers had they not moved (remember that workers

decide to change jobs if the expected payo¤ in a new job is higher than that in the current job).

Some of the papers in this strand of the literature attempt to …nd a third group which is more

comparable to current period job movers than job stayers and whose wage growth can better

approximate that of job movers if they had not moved. This is suggested to be current period

stayers who change jobs next period6.

As already discussed, estimating log wage change equations introduces two di¤erent sources

of bias. The …rst is the one due to unobserved individual characteristics which may be correlated

with mobility. The second source of bias is due to the endogeneity of job mobility in an earnings

equation arising because shocks to earnings may in‡uence mobility. A worker will decide to

change jobs if the expected value of his current job is lower than that o¤ered in a di¤erent job.

High wages in current job will make job change less likely and thus the error term in a wage

change equation will be correlated with job mobility. To correct for the …rst type of bias we use

individual …xed e¤ects (note that the problem will only be solved if the unobserved individual

component is constant over time). To reduce the possibility of the second type of bias we use

IV.

Finding an instrument for quits in the earnings equation is not an easy task. A possible

instrument is job satisfaction. As we will show in section 5.3, voluntary job change is decreasing

6 Campell (2001), distinguishes between short and long run wage gains from job mobility. In addition, in hispaper he stresses the need to include initial wages among the control variables (This set of explanatory variablesincludes: change in marital status, educational attainment and hours per week worked change). He argues thatthe initial wage in the wage change equation helps to identify any change in the coe¢cient of the explanatoryvariables between the two dates. However the inclusion of this variable in the regression is likely to introduce abias if the initial wage is correlated with the error term. To solve this correlation problem, Campell uses predictedwages instead of actual wages. He estimates a standard wage equation with controls for tenure, age, region andregional unemployment and uses the predicted wage as an instrument for initial wage in the wage growth equation.

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in the level of job satisfaction. In the mobility equations of section 5.3 we use satisfaction with

job security, with the hours of work and the work itself. In all cases the e¤ect of these variables

is negative and in most cases signi…cantly di¤erent from zero. In addition we do not expect

initial satisfaction to a¤ect wage change conditional on job change. This suggests that measures

of job satisfaction in the initial job could be used as instruments for job mobility in the wage

change equation.

3 The BHPS

3.1 Dataset Description

In this work we use the …rst eleven waves7 of the British Household Panel Survey8 (BHPS) in

combination with the job life history and occupation life history …les9. These have been merged

by the depositor to create a unique …le containing all the information about each respondent’s

work life. The second and last version of this …le was released in 2000 and it also contains

identi…ers which allow links with the household and individual record information provided in

each of the eleven waves. The sample will consist of an unbalanced panel of individuals with

labour market data for at least two years.

The BHPS is being carried out by the ESRC UK Longitudinal Studies Centre in the Institute

for Social and Economic Research, (ISER) at the University of Essex. The main objective of

the survey is to ”improve our understanding of social and economic change at the individual

and household level in Britain, to identify, model and forecast such changes, their causes and

consequences in relation to a range of socio-economic variables”.

The BHPS was designed as an annual survey of each adult (16+) member of a nationally

representative sample of more than 5,000 households, making a total of approximately 10,000

individual interviews. The same individuals are re-interviewed in successive waves and, if they

split-o¤ from original households, all adult members of their new households are also interviewed.

7 Information used from these …les inlude education, training (general and job speci…c), marital status, age,demographics, job satisfaction etc.

8 Reference: ”BHPS, User Manual, Volume A, Introduction, technical summary and appendices”, edited byMarcia Freed Taylor with John Brice, Nick Buck and Elaine Prentice-Lane.

9 From the life and occupation history …les we get the following variables: industry, occupation, sector, employersize, pay, hours of work, managerial duties, boss etc. in each job held in the respondent’s life. In addition reasonwhy left each job, whether this involved employer change. All these provide links to the respective wave.

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Children are interviewed once they reach the age of 16; there is also a special survey of 11-15

year old household members from Wave Four onwards. Thus the sample should remain broadly

representative of the population of Britain as it changes through the 1990s. Additional sub-

samples were added to the BHPS in 1997 and 199910.

3.2 The Sample

One decision to be taken was about the starting point for each individual. One could argue

that given that the …rst labour market experience is important in one’s life, this would be a

good starting point. It would be indeed interesting to look at the moment of entry in the labour

market. However it is rather di¢cult to identify the …rst move and thus we decided to start with

the annual information provided in the eleven waves. Moreover if we only looked at individuals

whose date of entry in the labour market takes place within the eleven years of the panel, then

we would be left with a very small number of observations. In other words job mobility is

examined between two interviews, held around September of each year. This method does not

account for multiple job changes within a calendar year and for this reason the sample has been

restricted to respondents who changed at most one time during the year.

An important issue is the distinction between voluntary and involuntary job changes. In

section 4, we report job changes for di¤erent reasons. We have conducted the econometric

analysis looking at quits versus non change and promotions (i.e. all stayers) as well as quits

versus non change (or separately quits, promotions and no changes, in the multinomial logit

section) .

10 All individuals enumerated in respondent households became part of the longitudinal sample. The samplefor the subsequent waves consists of all adults in all households containing at least one member who was residentin a household interviewed at Wave One, regardless of whether that individual had been interviewed in WaveOne. The following rules applied in subsequent waves di¤ered from the sampling rules in Wave One in only onerespect. In both sets of rules, eligibility depended on domestic residence in England, Wales, or Scotland south ofthe Caledonian Canal. In waves after Wave One, however, OSMs were followed into institutions (unless in prisonor in circumstances where the respondent was not available for interview e.g. too frail, mentally impaired etc.)or into Scotland north of the Caledonian Canal.

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3.3 The Dependent variable

In the wage equations, wages are gross weekly (and hourly) payments11 for full time, non

self employed workers.

The BHPS contains information on workers’ satisfaction. They are asked questions related

to their satisfaction levels from work. The respective question is ”how satis…ed are you with a

speci…c aspect of your job?”, there being seven di¤erent aspects12. The answers take the values

1-7 with 1 being ”not satis…ed at all” and 7 being ”completely satis…ed”. Using that information

we create a dummy variable which takes the value one if the worker reports that he is satis…ed

with his job and zero otherwise. We use the overall satisfaction index which does not focus on

speci…c aspects of the work, either as a dummy variable or as an ordered outcome variable.

Job change will be measured in many di¤erent ways to serve the purpose of each speci…c part

of the analysis. First we use a dummy variable to capture quits as opposed to no change and

within …rm promotions and second we de…ne job change as quits versus no change, i.e. omitting

promotions. We have also estimated multinomial logit models for the three di¤erent outcomes,

namely quits, promotions and no job change.

3.4 The Explanatory Variables

The …rst group of regressors contains the usual family and social background controls. We

use a dummy for gender (equal to one if female), for marital status (married), two dummies for

race (white and black) and region dummies. In addition controls for the number of children of

four age groups are used (age groups: 0-4, 5-11, 12-15 and above 16). Moreover we use controls

for formal education (…ve dummies for the di¤erent quali…cation levels13).

The special feature of the BHPS is that it allows us to merge the job history …les with the

11 This is constructed using the answers to the questions about the last payment and the period that it covers incombination with the question about whether that payment was the usual one. The questions are the following:”The last time you were paid, what was your gross pay, that us including any overtime, bonuses, commission,tips or refund but before any deductions for tax, national insurance or pension contributions, union dues and soon?” and ”how long a period did that cover?”

12 The detailed satisfaction variables in the BHPS are the following:JBSAT1: promotion prospects, JBSAT2: Job satisfaction: total pay, JBSAT3: Job satisfaction: relations with

boss, JBSAT4: Job satisfaction: security, JBSAT5: Job satisfaction: use of initiative, JBSAT6: Job satisfaction:work itself, JBSAT7: Job satisfaction: hours worked, and the overall measure of job satisfaction, JBSAT.

13 These quali…cation dummies are the following: qual1: higher degree, qual2: nursing, qual3: A-level, qual4:O-level, commercial, apprentiship, qual5: no quali…cations.

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eleven waves of the panel. In that way one can track back the working life of the individual

in order to construct labour market experience variables as well as experience in the speci…c

occupation and/or industry. However actual work experience and actual job tenure are endoge-

nous to the mobility decision (the time until leaving a particular job is a choice variable in the

model). For that reason we use predicted work experience and job tenure by assigning to each

individual the average value of work and unemployment experience for each individual’s region.

We have also done this by education-region cell and the choice between the two does not make

any signi…cant di¤erence14.

Other controls include a dummy for work in the public15 sector, three dummies for employer

size (the …rst for less than 50 employees, the second for between 50 and 200 and the third

for above 200 employees) and sectoral dummies. Dummy variables for other measures of job

satisfaction, related to speci…c aspects of the job16 are also used. One dummy for job satisfaction

with respect to security, one for job satisfaction related to the work itself and one for the hours

worked17. Other work related controls include times of day usually work (four dummies: work

during the day (reference category), work morning only or afternoon only or lunch time and

afternoon only, work evening and night, and shifts or varying times), travel time to work, dummy

for managerial duties, dummy for promotion opportunities. The BHPS asks the respondents to

evaluate their current …nancial situation relative to the past year. Using this information we

construct three dummies. The …rst takes the value one if the …nancial condition is better now,

the second is one if the …nancial situation is worse now and the third (the reference group) if it

is the same. The same questions were asked about the …nancial expectations of the respondents

for the next year. We construct the same dummies as for the …nancial situation questions.

14 So we decide to use the …rst version in this paper.15 i.e. civil servants, central government, local government, NHS, higher education, nationalised industries, non

pro…t organisations16 One may argue that the variables re‡ecting non pecuniary aspects of job satisfaction may be correlated with

the overall measure of job satisfaction. However this overall level of job satisfaction is not a composite measureconstructed from the di¤erent sources of job satisfaction.

Dummy =1 if satis…ed, =0 otherwise.17 Ideally we would like to test other apsects of job satisfaction as well but the questions on total ”relationship

with boss”, promotion prospects and use of initiative were not asked in the last four years of the panel (1998-2001).

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Table 1: Percentage of Job Change by Reason for ChangeWOMEN

no change Promotion Non Family Family Other InvoluntaryReasons Reasons Change

all 77.23 8.03 6.69 0.68 4.2 3.19

Marital Status 81.04 7.22 4.64 0.71 3.84 2.58married 73.12 8.91 8.91 0.65 4.59 3.84

not married

Marital status- Kidsmarried, kids 77.88 8.51 5.5 0.84 4.51 2.79

married, no kids 82.87 6.47 4.14 0.62 3.46 2.46not married, kids 71.58 7.55 9.72 1.24 5.08 4.86

not married, no kids 73.53 9.27 8.7 0.5 4.47 3.57Source: Waves 1-11 of the BHPS.Notes: Question Asked: "Why did you leave your previous job?" Each row should add up to 100, although thismay be slightly higher because of rounding.

4 Data Patterns

In Tables 1 and 2 we tabulate the reasons for voluntary job changes reported by employed

women and men, working full time. This is done for di¤erent sub groups de…ned by marital

status and the existence of children. We have grouped the answers into promotions, quits for non

family reasons, quits for family reasons and quits classi…ed in the ”other” category18 . Moreover

we report the percentage of women or men in each group who do not change jobs between the

two interviews and those who experience involuntary job loss19.

The main di¤erence between men and women is in the percentage of those who quit for

family related reasons. 0.68% of women per annum working full time versus 0.09% per annum

for men working full time.

In table 3 we tabulate the aspects of the job which make it attractive, for the sample of full

time workers who report to have their previous job for a better one.

Focusing on full time workers, the most important factor explaining the attraction of the

current job is pay. This is true for both men and women, although the percentage for women

(28%) is lower than that for men (37%). The second most important aspect (leaving out the

18 These groups are de…ned in the following way. The answers given which were considered as separations forfamily reasons: ”left to have baby”, ”children, home care”, ”care of other person” and ”moved away for familyreasons”. Those recorded as non family reasons separations are: ”left for better job”, ”started college, university”.Then we de…ne another category which covers the ”other reason” separations. Involuntary separations consist ofthe following answers: ”made redundant”, ”dismissed or sacked”, ”retirement”, and ”stopped for health reasons”.

19 Each row should add up to 100, although this may be slightly higher because of rounding.

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Table 2: Percentage of Job Change by Reason for ChangeMEN

no change Promotion Non Family Family Other InvoluntaryReasons Reasons Change

all 78.18 7.39 6.58 0.09 3.78 4.01

Marital Statusmarried 80.91 6.88 5.05 0.03 3.62 3.54

not married 73.67 8.23 9.11 0.18 4.06 4.79

Marital status- KidsMarried, kids 79.23 7.57 5.55 0.03 4.26 3.38

Married, no kids 82.98 6.04 4.42 0.02 2.83 3.74Not married, kids 69.72 7.74 10.76 0.37 5.31 6.12

not married, no kids 74.55 8.34 8.74 0.14 3.78 4.49Source: Waves 1-11 of the BHPS.Notes: Question Asked: "Why did you leave your previous job?" Each row should add up to 100, although thismay be slightly higher because of rounding.

”other reasons” response) is promotion prospects. 13% of men believe this is the main attraction

of their new job, whereas only 8% of women report the same. For women, the speci…c type of

their new job as well as its interest content seems to be very important, and actually more

important than for men. Moreover ”less commuting” does matter more for women than for

men,which can be well linked to family responsibilities and home production for women. The

evidence in this table can be seen as a …rst expression of di¤erences in tastes and preferences

between men and women. In the same table we report tabulations for married versus non

married men and women. Looking …rst at pay as the main attraction of the job, the di¤erence

between the percentage of married and unmarried who report it is much larger than that for

men. For both genders though it seems that pay is more important for the unmarried ones.

The same holds for promotion prospects and and the type of work (however this is no very clear

whether it relates to content of work). On the contrary, it appears that marriage makes security

a more important factor in the determination of an attractive job. For example 9% of full time

married women who changed job for a better one report job security to be the reason, whereas

this percentage falls to 6% for non married women. The respective numbers for men are 9 and

5.5%. We note that there is an important di¤erence between married and non married workers

suggesting ‡exible hours as the positive aspect of their new ”better” job. In particular 5.5%

(3.4%) of married women (men) report this as the main reason, versus 2% for unmarried (same

for men). Finally there is a di¤erence in the percentage of married versus non married men and

15

Table 3: Attraction of current job relative to previous job (percentage), 1991- 2001

Full Time WorkersWOMEN MEN

married non married married non married

more/better money 29.77 27.7 30.93 36.67 35.16 37.97promotion prospects 8.31 6.34 9.28 13.22 11.3 14.83more responsibility 2.45 1.9 2.75 1.53 1.86 1.24

job security 7.2 9.09 6.19 7.12 9.19 5.39more interesting job 7.8 9.51 6.87 5.37 5.71 4.98speci…c type work 8.91 6.77 10.08 4.58 3.48 5.5

be own boss 0.37 0.21 0.46 0.79 0.75 0.83greater initiative 2.15 1.9 2.29 1.58 1.86 1.35less commuting 4.31 5.5 3.67 3.22 4.22 2.39

less hours 1.11 1.27 1.03 1.3 1.61 1.04more ‡exible hrs 3.12 5.5 1.83 2.6 3.35 1.97health reasons 0.22 0.63 0.45 0.62 0.31to use skills 4.01 3.81 4.12 3.84 3.11 4.46

less demanding wrk 1.04 1.9 0.57 1.02 1.24 0.83prefer this job 6.01 5.07 6.53 5.08 4.84 5.29new job better 3.79 2.96 4.24 3.9 3.73 4.05

other 9.43 9.94 9.16 7.74 7.95 7.57Total 100 100 100 100 100 100

Number of Observations 1347 473 873 1770 805 964

Source: Waves 1-11 of the BHPS.Notes: Question Asked: "What is the attraction of your current job relative to your previous one?" Question onlyasked to workers who answered to have left their job for a better one. Only allowed to give one answer.

women who …nd less commuting as the good aspect of their new job.

Another way of looking at the di¤erences in male and female preferences is to estimate

satisfaction equations using as the dependent variable a measure of overall job satisfaction. On

the right hand side we include only the di¤erent components of job satisfaction, described in

section 3.3. Table 4 reports the estimated coe¢cients separately for full time men and women.

One can see that women are less concerned with pay as well as promotion prospects in their

current job. In contrast they place more weight than men on the relations with their boss issue as

well as the work itself. Moreover the component of job satisfaction attributed to working hours is

more important for women than for men. The di¤erences in the coe¢cients are signi…cant with

the exception of promotion prospects, use of initiative and hours worked. The results are very

similar when full time and part time workers are pooled, with a more pronounced di¤erential

on the impact of hours of work.

In the appendix, section 8.2, we present graphs (1- 9) of the proportion of men and women

16

Table 4: Aspects of Job Satisfaction, 1991-2001 (full time workers)

WOMEN MEN ALL interactionsfemale* satisf.

(1) (2) (3) (4)promotion prospects 0.103 0.114 0.115 -0.013

(0.007)** (0.006)** (0.006)** (0.010)total pay 0.088 0.119 0.120 -0.033

(0.008)** (0.007)** (0.007)** (0.010)**relations with boss 0.172 0.143 0.143 0.031

(0.008)** (0.007)** (0.007)** (0.010)**security 0.090 0.133 0.134 -0.045

(0.008)** (0.006)** (0.006)** (0.010)**use of initiative 0.106 0.115 0.115 -0.009

(0.010)** (0.008)** (0.008)** (0.013)work itself 0.369 0.318 0.317 0.054

(0.010)** (0.008)** (0.008)** (0.013)**hours worked 0.134 0.141 0.139 -0.002

(0.009)** (0.007)** (0.007)** (0.011)Constant -0.118 -0.323 -0.310

(0.075) (0.060)** (0.046)**Observations 9419 13657 23076R-squared 0.53 0.51 0.52

Chow Test F(7, 16121)= 8.40(0.000)

Notes: Fixed e¤ects job satisfaction regression. Overall measure of jobsatisfaction as the dependent variable. No additional controls. Standard errors in parentheses

17

(of certain groups) who remain in the same job, get promoted and those who quit their jobs, in

the 10 years covered by the BHPS.

5 Model and Empirical Procedure

5.1 Wage Equation

In the …rst stage we estimate a standard wage equation in order to derive the residuals that

will be used in the next section. The estimated equation has the following form:

wit = Xit³ + "it (1)

where wit is the log wage and vector X contains the usual individual characteristics (age, age

square, marital status, general health dummies, whether there are kids of speci…c age groups

(or number of children of this age) along with job related controls. This last set of variables

consists of occupation and industry dummies as well as times of the day the individual usually

worked and distance to work in minutes20 . "it = ®i + ®t + uit, a standard error term in panel

data models. ®i is the individual speci…c component of the error which remains constant across

time. uit is the time varying component. ®t is a set of time dummies. The …rst term can be

thought of as unobserved ability whereas the second can be pure luck. Dispersion between the

wage paid to a particular worker and that paid to individuals with similar characteristics can

be explained by di¤erences in unobserved ability or luck. The …rst is represented by ®i in the

error and the second by uit.

The results from the wage regression are presented in table 5, separately for men and women.

Interpreting the results from the wage equation is not our objective in this paper and thus we

proceed to the next section.

20 The BHPS contains information on the times of the day that the respondent ussually works. From this weconstruct the following four dummies: wktime1=1 if working during the day, wktime2=1 if working morning only,afternoon only, or lunch time and evenings, or other times during the day, wktime3=1 if working during the nightand evening, wktime4=1 if shifts and varying times.

18

Table 5: Wage Regression, Full time Workers, 1991-2001

Women Men

age 0.066 0.099(0.009)** (0.008)**

agesq -0.001 -0.001(0.000)** (0.000)**

higher degree 0.052 0.045(0.030) (0.024)

nursing 0.146 -0.078(0.039)** (0.116)

A-level + 0.005 0.044(0.033) (0.026)

O-level and -0.060 -0.006(0.032) (0.026)

married -0.048 0.007(0.009)** (0.009)

professional 0.130 0.057(0.022)** (0.014)**

managerial 0.142 0.081(0.013)** (0.010)**

non skil led 0.057 -0.016manual (0.013)** (0.011)skilled 0.034 0.023manual (0.013)* (0.008)**

manufacturing 0.028 -0.034(0.031) (0.017)*

services -0.035 -0.056(0.030) (0.017)**

other -0.056 -0.083sectors (0.030) (0.017)**public 0.010 -0.001

(0.012) (0.012)size [50.200] 0.039 0.054

(0.008)** (0.006)**size 200+ 0.071 0.074

(0.009)** (0.007)**good/fair -0.014 -0.003

health (0.006)* (0.005)bad, very bad -0.153 -0.080

health (0.029)** (0.027)**Constant 4.695 3.988

(0.359)** (0.245)**Observations 16665 23187R-squared 0.2107 0.2122

Note: These are the results from a wage regression with …xed e¤ects. Time dummies are also included in all regressions.Standard errors in parentheses.

19

5.2 Satisfaction Equation

The relationship between job changes and job satisfaction has been investigated in the

literature in the past. Mainly it has been analysed in terms of predicting job quits for dissatis…ed

workers. Freeman (1978) provides evidence that reported job satisfaction is a good predictor

for job mobility over and above the e¤ect of past wages. Akerlof, Rose and Yellen (1988) and

Clark, Georgellis and Sanfey (1997) con…rm Freeman’s …ndings using US and German data.

However job satisfaction can be very interesting in its own right. By examining the de-

terminants of job satisfaction one can shed some light on di¤erences in preferences and work

tastes between men and women when choosing a speci…c job and thus make inference about job

mobility decisions.

The relationship between wages and job satisfaction has also been extensively investigated

in the literature. Job satisfaction can be thought of as a function of both the actual wage (wage

in current period) and an indicator of the relative wage. Wages are likely to determine job

satisfaction, not only in absolute terms but also in relative terms. Individuals derive satisfaction

from their wage level and also from the way this compares to the wages of other workers21 .

In this section we estimate job satisfaction as a function of the worker’s characteristics

(vector X), the wage gap (measured by the residuals from the wage equation, of section 5.1),

c"it = (wit ¡ cwit) and/or actual wages22. In addition we attempt to control for non pecuniary

aspects of job satisfaction (vector Z). These we capture either by including non pecuniary aspects

of satisfaction directly or by including controls for work related aspects such as ”times of day

usually work”, hours of work, whether the worker would like to work more or fewer hours,

distance to work, public sector dummy, employer size, etc. This information is available in the

21 This in turn can be either a measure of wage relative to other individuals or one which compares the actualwage of the individual to that received by the same worker in the last period. The …rst measure has been usedby Philippe Moguerou (2002). He constructs a measure of relative earning and then using its residuals in the jobsatisfaction regression. An alternative solution suggested again by Moguerou is the creation of a dummy variablewhich takes the value one for the well paid individuals (that is those with positive residuals) and zero otherwise.

Job satisfaction and wages are likely to be simultaneously determined. As suggested by Chevalier and Lydon(2002) it would be more appropriate to estimate the two as a system of equations using identifying restrictions.Their results show they cannot reject the hypothesis that wages are endogenous in a job satisfaction regression.It is very di¢cult to …nd a valid instrument for wages in the job satisfaction equation (They use partner’s wagesas an instrument for satisfaction in the wage equation.). In their paper they add controls for occupation inthe satisfaction regression instead of accounting for job conditions and thus make an attempt to account forcompensating di¤erentials.

22 In some versions we also use predicted wages instead of actual wages. For more details see table 6.

20

BHPS and included in vector Z. So the latent job satisfaction has the form,

S¤it = ¯1 + Xit¯2 + Zit¯3 +¯4c"it + 'it (2)

The observed job satisfaction is Sit =

8<:

1 if S¤it > 0

0 if S¤it · 0

9=;.

The purpose of looking at job satisfaction is to get a better understanding of the di¤erent

aspects shaping worker’ preferences with respect to job choice. That is we examine the di¤erent

aspect of work which may a¤ect the way people perceive their occupation. This will be useful

later on, in the attempt to explain mobility decisions and career choices. In particular we want

to investigate whether there are di¤erences in the factors determining job satisfaction for men

and women. If there are di¤erences in preferences and tastes, then these should be captured in

sign and/ or magnitude of the coe¢cients.

We want to analyse the factors determining job satisfaction, with particular interest in

possible di¤erences between men and women. If the common belief that women have a weaker

attachment to the labour market is true, we would expect to …nd pecuniary aspects of the job to

be more important for males than females. On the contrary hours of work, especially ‡exibility

of working hours, should matter more for women.

We investigate the determinants of job satisfaction. Here the dependent variable is a simple

binary variable which takes the value one if the individual reports to be satis…ed with his job

and zero otherwise23. We estimate a standard probit model as described in equation 2. Table

6 presents the results in the form of marginal e¤ects separately for men and women. These are

de…ned as the partial change in the predicted probability of job satisfaction for a change in one

of the explanatory variables24 .

De…ne

Pr(Sit = 1) = F(X

Wikt¯k) (3)

where W is a vector containing X;Z; c"it: Then the marginal e¤ect with respect to say Wk is:

23 See sections 3.3 and 3.4 for a detailed description of the dependent variable and the controls used.24 One should be very cautious when interpreting marginal e¤ects in the job satisfaction equation. This is

because there no measurement units for job satisfaction and thus we can only comment on the direction of theimpact but not literally interpret it.

21

@F(X

Wikt¯k)

@Wk= f(

XWikt¯k)¯k; (4)

which for the probit model, can be written as:

@F(X

Wikt¯k)

@Wk= Á(

XWikt¯k)¯k; (5)

where Á is the standard normal density function.

In Table 6, in the …rst speci…cation we use the logarithm of current wage instead of the

residual whereas in the second speci…cation we use the residuals, c"it. With this last term

we attempt to capture the relative wage e¤ect, documented in the literature. In the third

speci…cation we use the residuals, c"it and the set of variables regarding current …nancial situation

or expectations about future …nancial situation.

Promotion prospects in current job have a positive impact on job satisfaction but this e¤ect

is smaller for women than for men. More importantly female workers become unsatis…ed the

bigger the distance to work and there is a large di¤erence between that coe¢cient for males and

females. As expected the number of working hours has a negative e¤ect on job satisfaction for

women. Moreover this coe¢cient becomes larger when interacted with the presence of children

aged 5-11 for women. We believe this as preliminary evidence of di¤erent preferences for men and

women as well as distinct constraints (family related obligations) for the two groups. Consistent

with the existing evidence we …nd that satisfaction is U-shaped in age and married workers

seem to be more satis…ed. The size of the workplace seems to a¤ect negatively the level of job

satisfaction, and we con…rm the standard e¤ect of the general state of health.

Considering the e¤ect of wages on job satisfaction, we …nd the well documented positive

e¤ect for men but not for women. The higher the wage in current job, the more satis…ed the

worker. For the relative wage measure, the e¤ect is larger than that of the actual wage for women

and it is also statistically signi…cant. It has the expected positive sign, meaning that workers

who are paid more than other individuals with similar characteristics, tend to be more satis…ed

with their job, on average. For men, the e¤ect of relative wages is also positive but smaller

than that of actual wages. However it remains larger for men than for women (5.5 versus 3.6),

implying that wages (either absolute or relative) are more important for men than for women.

22

Table 6: Determinants of Job Satisfaction

(Equation 3, Marg. e¤ects)

WOMEN MEN

(1) (2) (3) (4) (5) (6)age -0.007 -0.006 -0.006 -0.018 -0.013 -0.009

(0.003)** (0.003)* (0.003)* (0.003)** (0.002)** (0.002)**age squared 0.000 0.000 0.000 0.000 0.000 0.000

(0.000)** (0.000)** (0.000)** (0.000)** (0.000)** (0.000)**married 0.048 0.049 0.046 0.026 0.032 0.031

(0.009)** (0.009)** (0.009)** (0.010)* (0.010)** (0.010)**public 0.012 0.013 0.013 0.000 -0.000 0.006

(0.010) (0.010) (0.010) (0.013) (0.013) (0.013)size [50.200] -0.032 -0.030 -0.029 -0.037 -0.029 -0.028

(0.011)** (0.011)** (0.010)** (0.010)** (0.010)** (0.010)**size 200+ -0.043 -0.039 -0.039 -0.043 -0.031 -0.029

(0.010)** (0.010)** (0.010)** (0.010)** (0.010)** (0.010)**good/fair -0.070 -0.071 -0.066 -0.080 -0.084 -0.075

health (0.009)** (0.009)** (0.009)** (0.009)** (0.009)** (0.009)**bad, very bad -0.137 -0.137 -0.112 -0.194 -0.218 -0.186

health (0.049)** (0.049)** (0.046)* (0.049)** (0.049)** (0.049)**promotion opportunities 0.081 0.080 0.076 0.121 0.126 0.116

(0.008)** (0.008)** (0.008)** (0.008)** (0.008)** (0.008)**morning afternoon 0.010 0.013 0.009 -0.044 -0.039 -0.040

(0.024) (0.024) (0.024) (0.032) (0.032) (0.032)evening, night 0.014 0.014 0.008 -0.079 -0.081 -0.084

(0.027) (0.027) (0.027) (0.025)** (0.026)** (0.025)**shifts varying -0.004 -0.002 -0.002 -0.037 -0.032 -0.032

(0.012) (0.012) (0.012) (0.011)** (0.011)** (0.011)**travel to work time -0.027 -0.025 -0.025 -0.029 -0.021 -0.020

(0.012)* (0.012)* (0.012)* (0.009)** (0.009)* (0.009)*whether 0.033 0.034 0.048 0.007 0.007 0.025kids 0-2 (0.015)* (0.015)* (0.014)** (0.012) (0.012) (0.011)*whether 0.023 0.023 0.022 0.021 0.024 0.025kids 3-4 (0.017) (0.017) (0.017) (0.011) (0.011)* (0.011)*whether 0.021 0.020 0.017 0.015 0.016 0.014kids 5-11 (0.011)* (0.011) (0.011) (0.010) (0.010) (0.010)whether 0.033 0.030 0.031 0.023 0.021 0.023

kids 12-15 (0.010)** (0.010)** (0.010)** (0.010)* (0.010)* (0.010)*Number of -0.001 -0.001 -0.001 0.001 0.001 0.001hours worked (0.001) (0.001) (0.001) (0.001) (0.001) (0.001)

wants -0.101 -0.102 -0.100 -0.132 -0.130 -0.126fewer hours (0.008)** (0.008)** (0.008)** (0.008)** (0.008)** (0.008)**

wants -0.014 -0.016 -0.008 -0.079 -0.083 -0.067more hours (0.018) (0.018) (0.017) (0.015)** (0.015)** (0.015)**

wage 0.036 0.026 0.055 0.031residuals (0.014)** (0.014) (0.014)** (0.014)*log wage 0.009 0.068

(0.010) (0.009)**…nancial situation 0.016 0.063

better now (0.008)* (0.008)**…nancial situation -0.063 -0.080

worse now (0.010)** (0.010)**exp …n -0.010 0.013

better than now (0.007) (0.007)exp …n -0.060 -0.073

worse than now (0.013)** (0.013)**Observations 14511 14358 14350 19754 19518 19507

Notes. These are probit regressions. Robust standard errors reported, corrected for within group correlation(clustering). Many of the non signi…cant variables are not reported in the table. Additional controls for occupation,industry, region and time dummies included in all regressions.

23

In columns (3) and (6) we estimate the satisfaction with additional controls. In particular

we want to look at the responsiveness of women to di¤erent aspects of their current …nancial

situation (relative to that of last year) and their expectations for the following year. These as

further evidence of the pecuniary e¤ect on job satisfaction on top of the actual and/or relative

pay e¤ect. This table veri…es what we found earlier, that women are less motivated by pay and

their …nancial situation. Current …nancial conditions better than those of the previous year do

increase the satisfaction level of both men and women, but the e¤ect is much smaller for the

females. The same holds for the expectation variables.

Overall, in this section, we …nd that the factors shaping job satisfaction may di¤er between

men and women. The later are less interested in pay and other pecuniary aspects of the job. In

contrast, they are more concerned with non monetary aspects of their job.

5.3 Job Change Equation

An individual will change jobs if the expected value of his alternative job, EV mit ; exceeds

that of the expected value of his current job, EV sit; plus the cost of job change, Cit. Thus he

will move if

M¤it = EV mit ¡EV sit ¡Cit > 0 (6)

EV sit (EV mit ) is the expected value of the current job (new job/ outside opportunities) forecasted

in the future conditional on experience at time t. Note that the expected value of the current job

and outside option is not formed only on the basis of pecuniary payo¤s. Non pecuniary aspects

of the job, such as the content of the job, working hours and ‡exible arrangements, travel time

to workplace etc, are also expected to be important.

We have …ve di¤erent sets of variables: individual characteristics, pecuniary aspects of job,

non pecuniary aspects of job, cost parameters and family responsibilities (i.e. constraints).

We can write the latent model as:

M¤it = Xit°1+Kit°2 +Cit°3 +°4j

JX

j=1

satjit +°5

2X

j=1

HRjit + ³it (7)

where ³it = ®0i + ®0t + u0it, a standard error term in panel data models. ®0

i is the individual

24

…xed e¤ect and u0it a standard error term. ®0i is again a vector of time dummies. The estimated

job change equation includes controls for personal characteristics Xit: Vector Kit (vector K is a

subset of vector Z, of equation 2) contains controls for job attributes which can be seen as proxies

for the di¤erence in the expected value as presented in (6): In addition it contains variables which

re‡ect the expectations of the individuals about the future as well as their judgement about their

current …nancial situation relative to last year. In particular, we include two dummy variables,

the …rst equals one if the respondent believes he is better o¤ at present and the second takes

the value one if he is worse o¤ (the default is that the worker believes his …nancial situation

remained the same or he is unsure about it). These two variables are also constructed for the

expectations of the worker for the next year regarding his …nancial situation. However these

two variables are likely to be endogenous in the mobility decision, in the sense that they take

account of planned job change. Moreover we include controls for hours worked (vector HR).

Vector Cit is the cost of moving and will be proxied by family constraints i.e. marital status,

presence of children of di¤erent age groups. The age groups are the following: 0-4, 5-11, 12-15

and 16-18. Furthermore this vector includes regional dummies to control for local labour market

characteristics. We expect gender di¤erences to exist both in preferences and constraints.

The observed binary variable for job change is given by:

Mit =½

1 if M¤it > 0

0 if M¤it · 0

¾; (8)

or using equation 7, the decision to change jobs between t and t+1 will be taken according to:

Mit =

8<:

1 if Xit°1 +Kit°2 + Cit°3 +°4jPJj=1 satjit°5 +

P2j=1HRjit + ³ it > 0

0 if Xit°1 + Kit°2 +Cit°3 + °4jPJj=1 satjit°5 +

P2j=1 HRjit + ³it · 0

9=; (9)

Job change will also be estimated in a multinomial logit model to investigate the determinants

of job mobility distinguishing between quits and promotions relative to workers who remain in

the same job. In addition it would be interesting to distinguish between those who stay with

the same employer and those who change employers along with jobs. This will be left for future

research.

25

5.3.1 Binary Choice Model

This section presents the results from job mobility equations with the dependent variable

being quits versus non job change, without promotion. The results are presented in tables 7

and 8 separately for men and women. In tables 16 and 17 in the appendix we report additional

results where the dependent variable is a dummy which takes the value one if the worker quits

his job and zero if he remains in the same job or is promoted (thinking of promoted workers as

successful stayers). Booth and Francesconi (1999) …nd that the quit rates of men and women

are quite similar and di¤erences can only be found in the layo¤ probabilities. Moreover Booth,

Francesconi and Frank (2001) …nd that women are as likely as men to be promoted. However

there are underlined di¤erences in the factors determining quit decisions between men and

women. These are the ones we will investigate in this paper

Highly educated workers are more likely to change jobs voluntarily. This can be explained

in terms of larger variety of alternatives that better quali…ed individuals may have. This is

equally true for men and women. In addition we …nd a negative e¤ect of both job tenure and

general labour market experience on the probability of voluntary job change, con…rming previous

…ndings in the literature. Given that this is not the primary focus of the paper we do not present

these results in tables 7, 8, 17 and 16 but they are available upon request.

To start with, comparing the results from tables 7, 8, 16 and 17, we can see that it does

not make a great di¤erence whether we look at quits versus non change only or quits versus

promotions and non change25. Married women are less likely to quit (by 2%), the same holds

for men, but the e¤ect is half the size of that for women. Quits are less frequent in the public

sector and TU membership reduces the probability of quitting.

The …rst interesting e¤ect, related to the di¤erences in family constraints and cost functions

story, is that of the travel to work time variable. The marginal e¤ect of this variable appears

positive and statistically signi…cant in almost all speci…cations. It implies that the larger the

distance from the current workplace, the more likely to quit within the next period. This

marginal e¤ects is much larger for women, 2.5-3.5% than for men, 0.6-1.3%. This can be seen

as a …rst piece of evidence that household responsibilities may be more important for women

which makes their opportunity cost of non home production higher. Moreover the di¤erence in

25 More on this in the multinomial logit analysis, in the following section.

26

the coe¢cient is statistically di¤erent from zero.

If the hypothesis that women are less concerned with pay and career prospects is true, then

this should be re‡ected on the coe¢cients of the pay variables in the job change equation. Sur-

prisingly we …nd exactly the opposite. The marginal e¤ect of log wage is statistically signi…cant

in all regressions, for both men and women. It has the expected negative sign and it is larger

in magnitude for women. The di¤erence in the coe¢cient is not signi…cant, but it still appears

that women do make decisions regarding quitting, taking into account pecuniary aspects of the

job, unlikely what our initial suspicion was and what our job satisfaction results of the previous

section suggested.

The other wage variable, the residuals from the wage equation, is used as a relative wage

measure. The higher the residuals, the larger the wage gap between the actual wage paid

to worker i and all other workers with similar characteristics (note that this may be due to

unobserved ability or luck). The higher the wage residual, the less likely to leave one’s job. This

relative wage measure is negative and large in magnitude especially for men, the marginal e¤ect

being -6.2%. For women this is much smaller (less than half that for men, i.e. -3.1%) and the

di¤erence between the two is signi…cant.

One more variable which may re‡ect strategic career planning and might reveal something

about di¤erences in preferences between men and women is the promotion prospects variable.

This takes the value one if there are promotion prospects in current job, and zero otherwise. It

comes up with a negative sign, as expected. Individuals in jobs with promotion prospects are

less likely to quit their jobs. For women, this e¤ect is not statistically signi…cant in any of the

speci…cations. For men, it is much larger (of size -1%) and comes up signi…cant in almost all

speci…cations. The negative e¤ect holds for both genders but it is larger for men (however the

di¤erence is not signi…cant).

The job satisfaction measures seem to determine quitting. Job satisfaction with pay, hours

of work and the work itself have negative marginal e¤ects. The e¤ects of the …rst two appear to

be the same for men and women. Workers who are satis…ed with di¤erent aspects of their job

are less likely to quit. It is worth noting that satisfaction with the work itself is more important

for women, and actually its e¤ect is larger than that of satisfaction with pay for this group of

workers.

27

A last set of variables which are likely to shed some light on workers’ quitting behaviour are

those related to family commitments. Speci…cally we use two variables. The …rst is a dummy

variable which takes the value one if the worker had to work fewer hours because of family

commitments and zero otherwise. The second dummy is one if family constraints prevented job

search. We …nd that the …rst is positively correlated with quitting for women, the marginal e¤ect

(of the order of 6%) being signi…cant at the 10% level. However job search because of family

constraints does not seem to impede changing jobs. For men both variables have no statistically

signi…cant e¤ects on quits. This is direct evidence -although weak- that family responsibilities

do impact on the mobility decisions for women, whereas this does not seem to be the case for

men.

5.3.2 Multiple Choice Model: Promotions, Quits and no Job change

In this section we estimate a multinomial logit model by treating promotions, quits and no

job change as three distinct outcomes. We can then test whether two of these groups can be

pooled together. We de…ne:

M0it =

8>>><>>>:

0 if no change

1 if promotion

2 if quit

9>>>=>>>;

(10)

Pr(M0= m) =

e°0jN

P2j=0 e°0jN

; where j = 0;1;2

The marginal e¤ects in the multinomial logit are given by:

µj =@ Pr(M 0 = m j N )

@Nk= Pr(M

0= m j N)

"°km ¡

2X

k=0

°kj Pr(M0= j j N )

#

The results are given in tables 9 and 10. For purposes of presentation, we only report the

marginal e¤ects for the variables of interest. All regressions include controls for quali…cations,

tenure and experience, number of children in speci…c age groups, occupation, dummy for public

sector, establishment size and time dummies. We have performed Wald tests in order to see

whether two categories can be pooled together. In all speci…cations and for both men and

28

Table 7: Quits versus no change (Marg. e¤ects)

Binary Choice Model, Full Time Women

(1) (2) (3) (4) (5) (6) (7)Promotion -0.007 -0.004 -0.004 -0.004 0.001 0.000 0.002

Opportunities (0.006) (0.006) (0.006) (0.006) (0.006) (0.006) (0.008)married -0.022 -0.021 -0.022 -0.018 -0.016 -0.014 -0.010

(0.006)** (0.007)** (0.007)** (0.006)** (0.006)* (0.007)* (0.009)number of -0.001 -0.000 -0.000 -0.001 -0.000 -0.000 0.000overtime (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001)

number of 0.000 0.001 0.001 0.000 0.000 0.001 0.002working hours (0.001) (0.001) (0.001) (0.001) (0.001) (0.001)* (0.001)

TU -0.017 -0.012 -0.013 -0.015 -0.020 -0.016 -0.015(0.007)* (0.008) (0.008) (0.007)* (0.007)** (0.008)* (0.011)

Travel to 0.027 0.035 0.034 0.029 0.025 0.029 0.035work time (0.009)** (0.010)** (0.010)** (0.009)** (0.009)** (0.010)** (0.014)*log wage -0.036 -0.025 -0.034 -0.034

(0.008)** (0.009)** (0.008)** (0.010)**wage -0.031

residuals (0.016)*satisfaction -0.012 -0.008with money (0.002)** (0.002)**satisfaction -0.004 -0.005 -0.008with hours (0.002)* (0.002)* (0.003)**satisfaction -0.014 -0.015 -0.016

with initiative (0.002)** (0.002)** (0.003)**satisfaction 0.001 -0.003 0.001

with work itself (0.002) (0.002) (0.003)wants -0.012 -0.003

fewer hours (0.007) (0.009)wants 0.016 0.020

more hours (0.016) (0.021)…nancial situation 0.009

better now (0.007)…nancial situation 0.014

worse now (0.009)…nancial exp 0.029

better than now (0.007)**…nancial exp 0.031

worse than now (0.013)*fam. Commit. 0.059

work fewer hours (0.038)fam. Commit. 0.012

prevent job search (0.026)Observations 8659 7870 7869 8655 8621 7809 4053

Notes. Robust standard errors reported, corrected for within group correlation (clustering).Controls for occupation, industry, region and time dummies included in all regressions. Standard errors reportedbelow the marginal e¤ects.

29

Table 8: Quits versus no change (Marg. e¤ects)

Binary Choice Model, Full Time Men

(1) (2) (3) (4) (5) (6) (7)Promotion -0.010 -0.011 -0.010 -0.005 -0.002 -0.006 0.002

Opportunities (0.004)* (0.005)* (0.005)* (0.004) (0.004) (0.005) (0.006)married -0.012 -0.009 -0.011 -0.011 -0.010 -0.003 0.004

(0.005)** (0.005) (0.005)* (0.005)* (0.005)* (0.005) (0.006)number of 0.000 0.001 0.001 0.000 0.000 0.000 0.001overtime (0.000) (0.000)* (0.000) (0.000) (0.000) (0.000) (0.000)

number of 0.000 0.001 0.001 0.000 0.000 0.001 0.000working hours (0.000) (0.000)* (0.000)* (0.000) (0.000) (0.000) (0.000)

TU -0.018 -0.017 -0.016 -0.018 -0.019 -0.018 -0.027(0.005)** (0.005)** (0.005)** (0.005)** (0.005)** (0.005)** (0.007)**

Travel to 0.007 0.013 0.011 0.007 0.006 0.012 0.005work time (0.005) (0.005)* (0.005)* (0.005) (0.005) (0.005)* (0.006)log wage -0.032 -0.016 -0.026 -0.024

(0.006)** (0.006)** (0.005)** (0.007)**wage -0.062

residuals (0.012)**satisfaction -0.012 -0.008with money (0.001)** (0.001)**satisfaction -0.002 -0.004 -0.004with hours (0.001) (0.001)** (0.002)*satisfaction -0.008 -0.010 -0.007

with initiative (0.002)** (0.002)** (0.002)**satisfaction -0.002 -0.005 -0.005

with work itself (0.002) (0.002)** (0.002)**wants -0.008 -0.006

fewer hours (0.005) (0.006)wants 0.016 0.013

more hours (0.010) (0.012)…nancial situation 0.006


worse now (0.006)*…nancial exp 0.026

better than now (0.005)**…nancial exp -0.004

worse than now (0.008)fam. Commit. -0.023

work fewer hours (0.017)fam. Commit. -0.002



30

women, the null hypothesis (that two categories can be collapsed) is clearly rejected. This

con…rms our decision to investigate separately quits, promotions and no job change.

In table 9 we report the results for women. As expected, women who are in jobs with

high promotion opportunities are more likely (5%) to be promoted than those in jobs without

promotion prospects, versus no job change. On the contrary, high promotion opportunities

reduce the probability of quitting. Women who work longer hours are more likely to be promoted

(this without any signi…cant e¤ect of overtime).

The travel to work time variable con…rms the results previously found in the binary model.

That is, the longer the travel to work time, the higher the probability that a female worker

leaves her job. The marginal e¤ect is of the order of 3%.

Actual wage has a positive impact on the probability of promotion of the order of 1.4-3.2%

and a negative and signi…cant impact on the probability of quitting of -3.5%. The residuals

from the wage equation enter the promotion equation with a negative sign, suggesting that in

this case it may be the luck parameter which is captured by the residuals. That is it may be

that workers who are paid more than those with similar characteristics, are less likely to be

promoted. This can be because the higher pay suggests that they have been luckier than the

others and thus their probability of being promoted is lower. In other words this could be seen

against the existence of unobserved ability (captured by the residuals).

The satisfaction variables are mostly negative both when quits and promotions are concerned.

It is worth noting that work satisfaction with initiative seems to be the strongest aspect of job

satisfaction determining mobility decisions.

It is interesting to look at the variables re‡ecting the workers’ expectations about their

…nancial situation, one year later26. When individuals believe their …nancial situation will be

worse than at the date of the interview, they are more likely (by 4.5%) to quit their job. This

is as if they are trying to avoid the down turn of their …nancial situation by changing jobs. In

addition those who believe they’ll be better o¤ are more likely to be promoted (marginal e¤ect

of 2.5%) but also more likely to leave their job. In the …rst case, it seems that they actually

foresee their promotion in the following year. In the second case, they expect an improvement

in their …nancial situation possibly because they anticipate a quit and a better job in the near

26 The ones which compare their current …nancial situation to that of one year ago are not statisticaly di¤erentfrom zero, for women.

31

future. It could also be that they have already started searching for a new job, although still

employed in their current job. However these variables are likely to be endogenous in the sense

that they may take account of future quits, as already discussed in the previous section.

Finally the two variables re‡ecting further family commitment e¤ects suggest the following.

Individuals who report that family commitments oblige them to work fewer hours are signif-

icantly less likely to be promoted, the marginal e¤ect being -7%. What is surprising is the

positive and signi…cant e¤ect of the ”family commitments prevent job search” variable on the

probability of quitting.

Turning to men now, the results are reported in table 10. The e¤ect of promotion opportu-

nities is slightly smaller for men than for women, but it remains sizeable and signi…cant. Being

married has a negative impact on quitting for men too, but its size is again smaller than for

women. The travel to work time variable has a much smaller e¤ect for men. This is in line

with our initial expectations given that it may be seen as expressing the fact that women may

be more constrained by household responsibilities and thus distance to work can be by itself an

important job attribute for them and thus a primary determinant of quitting behaviour.

However we …nd that actual wages for men have a negative e¤ect on quits but this is smaller in

magnitude than that for women. But it is still sizeable of -2%. Wage residuals are only signi…cant

when concerning quits. For men we …nd a marginal e¤ect of -5% suggesting that those individuals

who perceive being better paid than individuals of the same observable characteristics, are

less likely to quit their job. Note that this ”over compensation” can be explained by either

unobserved ability di¤erences, or simply luck.

The satisfaction variables are even more signi…cant for men than for women. In particular,

satisfaction with initiative at work is the …rst aspect, with a marginal e¤ect of the order of

-6% to -8%. Then satisfaction with the work itself and hours worked seem to be of the same

importance, among the di¤erent aspects of job satisfaction.

Workers who expect to be in better …nancial situation next year, are again 3-3.6% more

likely to be promoted, and 1-2% more likely to quit their jobs. In addition men who believe

they are in a worse …nancial situation in period t relative to that in t-1, are 1% more likely to

leave their job.

It is very interesting that the family commitment variables are not signi…cant for men in

32

Table 9: Multinomial Logit: Promotions, Quits versus no change (Marg. e¤ects)

Ful l time women

(1) (2) (3) (4) (5) (6)promotion quit promotion quit promotion quit

Promotion 0.054 -0.014 0.055 -0.008 0.053 -0.011Opportunities (0.006)** (0.006)* (0.006)** (0.006) (0.008)** (0.008)

married -0.004 -0.015 0.001 -0.012 0.001 -0.006Number of kids: (0.005) (0.007)* (0.005) (0.006) (0.008) (0.009)

0-4 -0.006 0.026 -0.002 0.026 0.014 0.011(0.009) (0.009)** (0.009) (0.008)** (0.014) (0.014)

5-11 0.011 0.003 0.012 0.004 0.015 0.004(0.005)* (0.005) (0.005)** (0.005) (0.007)* (0.007)

12-15 0.000 -0.005 0.001 -0.004 0.011 0.000(0.006) (0.007) (0.006) (0.007) (0.008) (0.009)

16-18 -0.003 -0.014 -0.003 -0.011 -0.003 -0.017(0.012) (0.015) (0.012) (0.015) (0.016) (0.022)

number of 0.001 -0.000 0.001 -0.000 0.001 -0.000overtime (0.000) (0.001) (0.000)* (0.001) (0.001) (0.001)

number of 0.001 0.001 0.002 0.001 0.002 0.001working hours (0.000)** (0.001) (0.001)** (0.001) (0.001)* (0.001)

TU 0.002 -0.013 -0.000 -0.017 0.001 -0.017(0.007) (0.007) (0.007) (0.007)* (0.010) (0.010)

Travel to -0.014 0.033 -0.013 0.029 -0.009 0.033work time (0.009) (0.009)** (0.009) (0.009)** (0.012) (0.012)**log wage 0.026 -0.031 0.013 -0.037 0.032 -0.035

(0.008)** (0.009)** (0.007) (0.007)** (0.010)** (0.010)**wage -0.048 -0.015

residuals (0.013)** (0.015)satisfaction -0.003 -0.004 -0.001 -0.006with hours (0.002) (0.002) (0.003) (0.003)*satisfaction -0.004 -0.013 -0.006 -0.013

with initiative (0.002) (0.002)** (0.003)* (0.003)**satisfaction 0.001 -0.004 0.000 0.001

with work itself (0.002) (0.002) (0.003) (0.003)wants -0.014 -0.009 -0.015 -0.002

fewer hours (0.006)* (0.006) (0.009) (0.009)wants 0.022 0.005 0.027 0.007

more hours (0.012) (0.013) (0.016) (0.016)fam. Commit. -0.070 0.021

work fewer hours (0.033)* (0.022)fam. Commit. 0.009 0.045

prevent job search (0.027) (0.021)*Constant -0.324 0.084 -0.258 0.220 -0.412 0.173

(0.048)** (0.055) (0.052)** (0.054)** (0.084)** (0.077)*Observations 8761 8761 8700 8700 4545 4545

Notes. Robust standard errors reported, corrected for within group correlation (clustering).Controls for occupation, industry and time dummies included in all regressions. Standard errors reported belowthe marginal e¤ects.

33

contrast to what was true for women. As expected they are weaker determinants of both

promotions and quitting behaviour for men.

Finally, it is worth looking at the existence of children in the family and in particular the

number of children in speci…c age groups. These marginal e¤ects are also reported in tables 9 and

10. The existence of young children (aged 0 to 4) increases the probability of quitting, especially

for women but also for men. For women one extra kid aged 0-4 increases the probability of

quitting by 2.6%. The respective marginal e¤ect for men being 1%.

Overall as expected, workers in jobs with promotion opportunities are less likely to leave their

jobs and more likely to be promoted. Married people are also less likely to quit, the e¤ect being

stronger for women. Both working hours and overtime increase the probability of promotion.

Here we …nd again the positive impact of distance to work, on the probability of quit. It is

positive and signi…cant and it is a stronger predictor of quitting behaviour for women than for

men.

34

Table 10: Multinomial Logit: Promotions, Quits versus no change (Marg. e¤ects)

Ful l time men

(1) (2) (3) (4) (5) (6)promotion quit promotion quit promotion quit

Promotion 0.046 -0.012 0.045 -0.007 0.037 -0.002Opportunities (0.005)** (0.004)** (0.005)** (0.004) (0.007)** (0.005)

married -0.008 -0.008 -0.006 -0.003 -0.002 0.004Number of kids: (0.005) (0.004)* (0.005) (0.004) (0.007) (0.005)

0-4 0.003 0.010 0.003 0.010 -0.002 0.006(0.006) (0.005)* (0.006) (0.005)* (0.009) (0.006)

5-11 -0.001 -0.001 -0.000 0.000 0.002 0.003(0.004) (0.003) (0.004) (0.003) (0.006) (0.004)

12-15 -0.005 -0.009 -0.005 -0.008 -0.010 -0.013(0.005) (0.004)* (0.005) (0.004) (0.008) (0.006)*

16-18 -0.020 0.002 -0.020 -0.004 -0.028 0.000(0.013) (0.009) (0.013) (0.009) (0.019) (0.011)

number of 0.001 0.000 0.001 0.000 0.001 0.000overtime (0.000)* (0.000) (0.000)* (0.000) (0.000)* (0.000)

number of -0.000 0.001 0.000 0.001 0.000 0.000working hours (0.000) (0.000)* (0.000) (0.000) (0.001) (0.000)

TU 0.003 -0.014 0.005 -0.016 0.014 -0.023(0.005) (0.005)** (0.005) (0.004)** (0.008) (0.006)**

Travel to 0.007 0.007 0.006 0.007 0.011 0.002work time (0.006) (0.004) (0.006) (0.004) (0.007) (0.004)log wage 0.002 -0.013 -0.001 -0.024 -0.011 -0.021

(0.006) (0.005)** (0.006) (0.005)** (0.008) (0.006)**wage -0.016 -0.052

residuals (0.012) (0.010)**satisfaction 0.002 -0.003 0.001 -0.003with hours (0.002) (0.001)* (0.002) (0.001)*satisfaction -0.002 -0.008 -0.002 -0.006

with initiative (0.002) (0.001)** (0.002) (0.002)**satisfaction -0.001 -0.004 0.000 -0.005

with work itself (0.002) (0.001)** (0.003) (0.002)**wants -0.005 -0.004 -0.006 -0.002

fewer hours (0.005) (0.004) (0.008) (0.005)wants 0.007 0.013 0.006 0.008

more hours (0.009) (0.006)* (0.012) (0.008)fam. Commit. 0.015 -0.005

work fewer hours (0.022) (0.017)fam. Commit. 0.033 -0.026

prevent job search (0.024) (0.026)Constant -0.241 -0.002 -0.245 0.085 -0.163 0.061

(0.040)** (0.030) (0.047)** (0.036)* (0.065)* (0.049)Observations 11947 11947 11843 11843 6293 6293

Notes. Robust standard errors reported, corrected for within group correlation (clustering).Controls for occupation, industry and time dummies included in all regressions. Standard errors reported belowthe marginal e¤ects.

35

6 Wage growth

Having examined the determinants of job mobility separately for men and women, we now

turn to the estimation of the returns to job mobility. We want to see …rst if job movers experience

wage growth after the move and whether this is greater than the one they would have experienced

if they had not moved. Second we want to investigate if there are di¤erences in the returns to

job change between men and women. As already mentioned in the literature review in section

2, there are di¤erent ways to estimate the returns to job mobility. The …rst method consists

of estimating a wage growth equation for stayers and use this to get predicted wage growth for

movers if they had not moved. Then this predicted wage growth is compared to actual wage

growth of movers to derive the wage gains from job mobility.

We make use of the selection model proposed by Lee (1978, 1982) and applied to job mobility

in di¤erent papers, and more recently by Manning (2003). Assuming the wage growth equations

are di¤erent for movers and stayers, and dropping the time subscript, we write the one for

movers as:

¢wmi = µm1 + Xiµm2 + ²mi (11)

and for stayers:

¢wsi = µs1 + Xiµs2 + ²si (12)

The decision to move is taken according to the following rule:

M¤i = ±1 + Ki±2 + ±3(¢wmi ¡¢wsi) + vi (13)

where individual i decides to move to a new job if M¤i > 0: That is we observe ¢wmi only

if M¤i > 0 and we observe ¢wsi otherwise. Vector X contains the standard human capital

and personal characteristics of the worker and vector K contains other than wage gap factors

determining the probability of job change, described in section 5.3. This a standard model

with selection bias. OLS on equations 11 and 12 would give inconsistent estimates because

E(²mi j M¤i > 0) 6= 0 and E(²si j M¤

i · 0) 6= 0:

36

Table 11: Average Wage and Wage Growth

Average Wage Average Wage Growth(in period t) (in period t+1) (in period t+1)

Women Men Women Men Women Men

no Change 5.568 5.885 5.598 5.912 0.13 0.134promotion 5.652 5.934 5.756 6.027 0.109 0.113

quit. non family reasons 5.469 5.705 5.546 5.857 0.144 0.141quit. Family reasons 5.359 5.685 5.415 5.894 0.036 0.434

no Change 5.568 5.885 5.598 5.912 0.13 0.134promotion 5.652 5.934 5.756 6.027 0.109 0.113

quit 5.464 5.705 5.541 5.857 0.139 0.145

In table 11, we report average wages for di¤erent groups of people, by gender. In the

…rst row we report average wages for stayers, then for promoted workers and the for quitters

distinguishing between those who quit for family and non family reasons, as de…ned in section

4. We report wages both in period t (before the move, if any, is observed) and in period t+1.

In addition, in the last two columns of the table we report average wage growth for the di¤erent

groups. We notice that there is no di¤erence in average growth for stayers between men and

women. Yet wage growth for promoted individuals is higher for men than for women, although

the di¤erence is not very large. More importantly we …nd a small di¤erence in average wage

growth for those workers who quit for non family reasons, and a very large one for those who

quit for family reasons.

If we estimate log wage change equations, we are possibly introducing two types of bias. The

…rst comes from unobserved individual characteristics which may be correlated with mobility

and are included in the error terms. We use individual …xed e¤ects to correct for this …rst source

of bias. Yet this will only be solved if the unobserved individual component is constant over

time. The second bias will arise if job mobility is endogenous in an earnings equation. In other

words, expected wage growth is likely to determine job mobility. As already seen in section

5.3.1 a worker will decide to change jobs if the expected value of his current job is lower than

that o¤ered in a di¤erent job. High wages (as well as better wage growth prospects) in current

job will make job change less likely and thus the error term in a wage change equation will

be correlated with job mobility. To reduce the possibility of this source of bias we use IV and

37

Table 12: Wage gains from job mobility

(Quits versus non Change)

Women Menno Heckman Heckman selection no Heckman Heckman selection

actual ¢ws 0.124 0.133 0.128 20.132(d¢wmjM = 0) 0.056 0.134 0.021 0.135actual ¢wm 0.22 0.221 0.246 0.246average gain 0.17 0.087 0.226 0.11

Wage gains from job mobility(Quits versus Promotions and non Change)

Women Menno Heckman Heckman selection no Heckman Heckman selection

actual ¢ws 0.132 0.13 0.13 0.133(d¢wmjM = 0) 0.068 0.134 0.034 0.135actual ¢wm 0.22 0.22 0.243 0.246average gain 0.152 0.086 0.209 0.111

instrument quitting in the wage growth equation.

There are unobservable characteristics which make the choice of staying more attractive

for stayers. The same holds for unobservable characteristics which make moving worth more

for movers. Workers who decide to move are those for whom wage growth in the current job is

smaller than wage growth in the new job. This implies that using stayers to predict wage growth

for movers will underestimate the true returns to job mobility. In order to see the direction of

bias we write wage growth conditional on moving as: E(¢wsi j i moves) = µs1+Xicµs2+E(²si j i

moves), where E(²si j imoves) < 0: This implies that µs1 + Xicµs2 > E(¢wsi j i moves) and

thus we overestimate wage growth for movers if we use wage growth for stayers to estimate their

wage growth if they had stayed. This further implies that we underestimate the returns to job

change.

One can use the standard Heckman two step procedure to solve the problem of selection bias

that arises in this case. We can rewrite the wage growth equation for movers, conditional on

moving as:

¢wmi = µm1 +Xiµm2 + ½¾mÁ(g0mb±)©(g0mb±)

+ ´m (14)

38

and that of stayers conditional on staying:

¢wsi = µs1 + Xiµs2 ¡ ½¾mÁ(g0mb±)

1 ¡ ©(g0mb±)+ ´s (15)

where ¾m is the standard deviation of ²m and ½ is the correlation between ²m and the error and

g0

m contains all the variables of the …rst stage probit determining the job change decision.

The second method we can use to estimate the wage gains from job mobility is an instru-

mental variable technique. By imposing the restriction that the parameters are the same for

movers and stayers, then we can view the sample selection model as one with an endogenous

dummy variable. In that case:

¢wi = µ +Xiµ2 +¹Mi + "i (16)

with Mi = 1 if M¤i = ±1 + Ki±2 + v0i > 0 and Mi = 0 otherwise.

In this case, we need to get estimates for ±2 from a standard probit model (say eq. 13) and

compute the residuals from this model. In the second step this residual should be added in the

¢wi equation which can then be estimated for movers and stayers.

For both of these methods we require exclusion restrictions. As already mentioned we will

use job satisfaction to predict job change. More speci…cally we use job satisfaction with other

than pay factors, and particularly satisfaction with the work itself and the hours of work. We

chose these two aspects of job satisfaction which are likely to predict quits but are not correlated

with wage growth.

In table 12 we present a summary of the results from the Heckman selection model separately

for men and women. In the …rst part of the paper we report the results looking at job quitters

versus stayers (that is those who do not change jobs) whereas in the second part we include

promoted workers among the stayers. In the …rst row of each block we report the average actual

wage growth for stayers. This is very similar for men and women. In the second row we …nd

the average wage growth of movers, conditional on staying on the same job, that is using the

stayers equation with the movers characteristics to predict their wage growth had they staid

in the same job. In the second raw we report actual wage growth for movers. Wage growth

following a quit is much higher for men than for women. Men who quit their job, get on average

39

Table 13: Wage Growth: The Impact of Job Change

Full Time Workers (Quit versus non Change)Fixed E¤ects Estimates

All Women Men(1) (2) (3)

job Change OLS 0.077 0.053 0.092(0.011)** (0.016)** (0.014)**

IV 0.331 0.069 0.451(0.102)** (0.174) (0.124)**

(Note: in IV we use job satisfaction as the instrument)

the double wage, whereas the increase is smaller for women. In the …nal row of the table we

report the di¤erence between the two conditional means, which is expected to represent returns

to mobility. We …nd that the returns to quitting for men are on average twice as large than

those of women. Comparing the two blocks of table 12 we see that the average wage gain after

job quitting is slightly larger when promoted individuals are not considered among stayers.

In table 13 we report the results from the second method, using the IV technique. We only

report the …xed e¤ects estimates. The returns to quits were slightly higher in the random e¤ects

models. OLS reveals a 5-9% wage gain (in terms of log wage change) due to quits. These returns

appear to be smaller for women. In particular, job mobility increases women wage growth by 5%

versus 8% for men. When IV is used the returns to quitting go up. IV and …xed e¤ects reveal

statistically signi…cant and very large wage gains for men, of 42%. However the corresponding

number for women is only 7% and the e¤ect is not signi…cantly di¤erent from zero. We have

estimated the same IV and OLS wage growth equations for quitters versus workers who don’t

change jobs and those who get promoted. As expected the returns to job change are slightly

lower there given that we include promoted individuals in the non changers group. These results

are reported in table 15 in the appendix.

One might think that these sharp di¤erences in the returns to quits are mainly due to

di¤erent career choices of older women. For that reason we have done the same analysis for

di¤erent age groups and the results remain the same. Even for the group of young workers, who

have just entered the labour market, we …nd a positive and substantial wage gain for men but

this appears much smaller and insigni…cant for young women. We report a summary of these

results in table 14.

40


Full Time Workers (Quit versus non Change)IV, …xed e¤ects estimates

Age group 15-29 30-45 46+ 15-35 36+women 0.191 -0.543 1.548 0.279 -0.214

(0.207) (0.442) (1.100) (0.178) (0.557)men 0.303 0.534 1.206 0.434 0.447

(0.147)* (0.218)* (0.673) (0.128)** (0.302)

7 Conclusion

In this paper we are trying to shed more light on job preferences and the factors determining

job satisfaction separately for men and women. In addition we want to analyse the determinants

of job mobility and investigate the existence of di¤erences between male and female workers.

There is a vast literature on mobility patterns, focusing on gender di¤erences. Certain papers

in this literature focus on quits, others on promotions and others on layo¤ probabilities for men

and women. In this paper we …rst investigate the determinants of quitting versus job staying

(considering promoted individuals as stayers) and then we distinguish between promotions, quits

and stayers.

For this purpose we are using the British Household Panel Dataset which permits a distinc-

tion between voluntary and involuntary job separations. In addition it contains rich information

on job attributes and personal characteristics.

In the …rst part of the paper we look at job satisfaction. In particular we want to investigate

the existence of di¤erences between men and women in association with the job attributes which

determine their job satisfaction. In addition we can check what job satisfaction means for female

and male workers. We …nd that there are di¤erent parameters determining job satisfaction for

female and male workers. Pay is equally important for the two genders but other aspects of pay

(or monetary payo¤) are di¤erent. In addition no pecuniary aspects of the job, such as travel

to work time or working hours, appear to be more important for women.

Next we are trying to analyse the set of parameters which are likely to a¤ect men’s and

women’s mobility decisions in a distinct way. The decision to leave a job is determined by

a broad set of characteristics which include both personal characteristics of the worker and

features of the job. In particular we …rst look at family factors and household composition.

41

We expect to …nd that women are more restricted by family responsibilities and thus these

parameters should be of greater importance in determining their mobility decisions. Second we

are interested in the e¤ect of pecuniary variables (pay) on job mobility. Again, we would expect

that if women were more constrained by family commitments, they would then be less motivated

by pay. Our results suggest that non monetary aspects of the job seem to have a higher weight

in the quitting decision for women than for men. However we …nd that women are equally, if

not more, motivated by pay as men. Yet promotion prospects matter more for men, which can

be seen as evidence of more strategic career planning for them.

In the …nal part of the paper we are interested in the wage growth following a job change.

In other words we attempt to estimate the returns to job mobility. We want to investigate the

existence of di¤erent wage gains from job mobility for men and women. If women’s decisions

are motivated less by monetary payo¤s than men, then it may be that wage gains from mobility

may be lower for them. We estimate returns to job mobility …rst correcting for selection in the

two states and second using an IV technique. With both methods we …nd that wage growth

for women following a quit is much lower than that for men. This suggests that career choices

and job mobility could explain part of the gender wage gap. In the sense that women do

make di¤erent career choices from them, which is mainly due to di¤erent family and household

responsibilities and which lead to unequal gains from job moves for the two groups.

42

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46

8 Appendix

8.1 Additional Results


Full Time Workers (Quits versus Promotions and non Change)Fixed E¤ects Estimates

Al l Women Men(1) (2) (3)

job Change OLS 0.066 0.042 0.081(0.010)** (0.016)** (0.014)**

IV 0.342 0.174 0.419(0.104)** (0.180) (0.126)**

(Note: in IV for each group we use job satisfaction as the instrument)

47

Table 16: Quits versus no change or promotion (Marg. e¤ects)

Binary Choice Model, Full Time Women

(1) (2) (3) (4) (5) (6) (7)Promotion -0.013 -0.010 -0.010 -0.010 -0.005 -0.005 -0.002

Opportunities (0.005)* (0.006) (0.006) (0.005) (0.005) (0.006) (0.008)married -0.020 -0.019 -0.019 -0.016 -0.015 -0.014 -0.009

(0.006)** (0.006)** (0.006)** (0.006)** (0.006)* (0.006)* (0.009)number of -0.001 -0.000 -0.000 -0.001 -0.000 -0.000 0.000overtime (0.000) (0.001) (0.001) (0.000) (0.000) (0.001) (0.001)

number of 0.000 0.001 0.001 -0.000 0.000 0.001 0.001working hours (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001)

TU -0.015 -0.011 -0.011 -0.014 -0.017 -0.014 -0.012(0.007)* (0.007) (0.007) (0.007)* (0.007)** (0.007)* (0.010)

Travel to 0.025 0.033 0.032 0.027 0.023 0.027 0.032work time (0.009)** (0.009)** (0.009)** (0.008)** (0.009)** (0.009)** (0.012)*log wage -0.034 -0.027 -0.033 -0.038

(0.008)** (0.009)** (0.007)** (0.010)**wage -0.024

residuals (0.015)satisfaction -0.011 -0.007with money (0.002)** (0.002)**satisfaction -0.003 -0.004 -0.007with hours (0.002) (0.002)* (0.002)**satisfaction -0.012 -0.013 -0.013

with initiative (0.002)** (0.002)** (0.003)**satisfaction 0.001 -0.003 0.000

with work itself (0.002) (0.002) (0.003)…nancial situation 0.007


worse now (0.008)…nancial exp 0.023

better than now (0.006)**…nancial exp 0.029

worse than now (0.012)*fam. Commit. 0.062

work fewer hours (0.037)fam. Commit. 0.017



48

Table 17: Quits versus no change or promotion (Marg. e¤ects)

Binary Choice Model, Full Time Men

(1) (2) (3) (4) (5) (6) (7)Promotion -0.014 -0.015 -0.015 -0.009 -0.006 -0.010 -0.002

Opportunities (0.004)** (0.004)** (0.004)** (0.004)* (0.004) (0.004)* (0.006)married -0.011 -0.007 -0.009 -0.009 -0.008 -0.002 0.003

(0.004)* (0.005) (0.005)* (0.004)* (0.004)* (0.004) (0.006)number of -0.000 0.001 0.000 0.000 0.000 0.003 0.000overtime (0.000) (0.000) (0.000) (0.000) (0.000) (0.010) (0.000)

number of 0.000 0.001 0.001 0.000 0.000 -0.018 0.000working hours (0.000) (0.000)* (0.000)* (0.000) (0.000) (0.005)** (0.000)

TU -0.018 -0.016 -0.016 -0.018 -0.019 0.001 -0.027(0.005)** (0.005)** (0.005)** (0.005)** (0.005)** (0.007) (0.007)**

Travel to 0.006 0.011 0.009 0.006 0.005 0.004work time (0.004) (0.005)* (0.004)* (0.004) (0.004) (0.005)log wage -0.030 -0.015 -0.025 -0.024

(0.005)** (0.006)** (0.005)** (0.007)**wage -0.057

residuals (0.011)**satisfaction -0.011 -0.008with money (0.001)** (0.001)**satisfaction -0.002 -0.004 -0.005with hours (0.001) (0.001)** (0.002)**satisfaction -0.007 -0.009 -0.006

with initiative (0.001)** (0.002)** (0.002)**satisfaction -0.002 -0.004 -0.005

with work itself (0.001) (0.002)** (0.002)**more hours (0.009)

…nancial situation 0.005better now (0.005)

…nancial situation 0.012worse now (0.006)*

…nancial exp 0.021better than now (0.004)**…nancial exp -0.004

worse than now (0.008)fam. Commit. -0.025

work fewer hours (0.015)fam. Commit. -0.004



49

8.2 Graphs

Figure 1: Proportion of men, women who do not change jobs

year

men women

1990 1995 2000

80

85

90

Figure 2: Proportion of men, women with kids who do not change jobs

year

men women

1990 1995 2000

80

85

90

50

Figure 3: Proportion of men, women without kids who do not change jobs

year

men women

1990 1995 2000

75

80

85

90

Figure 4: Proportion of men, women who get promoted

year

men women

1990 1995 2000

6

8

10

12

Figure 5: Proportion of men, women with kids who get promoted

year

men women

1990 1995 2000

6

8

10

12

51

Figure 6: Proportion of men, women without kids who get promoted

year

men women

1990 1995 2000

6

8

10

12

Figure 7: Proportion of men, women who quit

year

men women

1990 1995 2000

0

5

10

Figure 8: Proportion of men, women with kids who quit

year

men women

1990 1995 2000

4

6

8

10

12

52

Figure 9: Proportion of men, women without kids who quit

year

men women

1990 1995 2000

0

5

10

15

53

Documents

Job Satisfaction, Mobility Decisions and Wage Gains by Gender