Female Wage Growth, Occupational Segregation, and Technological ...

Male - Female Wage Growth, Occupational Segregation, and Technological Change1 Christian Dustmann† and Astrid Kunze†† January 2001

Abstract: In this paper, we investigate differences in entry wages, and wage growth for young male and female workers during their first 2 decades in the labour market. Our analysis looks at Germany, and we consider skilled workers who all went through the German apprenticeship system. We distinguish between jobs which are predominantly male, jobs which are predominantly female, and jobs which have fairly equal percentages of the two gender. Individuals choose either of these occupational categories. Our estimation strategy takes account of selection and match specific effects, and we distinguish between general and occupation specific human capital. Our results show that wage growth is steeper in female jobs for both males and females. This is in contrast to what life cycle models explaining job selection would predict. The explanation we put forward is that male jobs are traditionally blue collar jobs, while female jobs are predominantly service jobs. The technological changes over the passed two decades may have enhanced productivity more in the latter category. We provide some evidence for this hypothesis.

† University College London, Institute of Fiscal Studies, CEPR †† IZA, Bonn. E-Mail address for correspondence: [email protected]; [email protected].

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Very Preliminary!

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1

Introduction

Over the last three decades many industrialised countries have witnessed an increase in labour force participation of females. Over the same period, average wage differentials between males and females have decreased. Early research in economics, using simple decomposition analyses on standard wage regressions, finds that only a part of the gender wage gap can be explained by differences in observable characteristics, like work experience, and education. The major part of the wage differential is explained by differences in the constant, and by differences in the returns to observed characteristics. To refer to this remaining differential as discrimination would be misleading, however. One explanation may be that females work in different occupations than males. If wage growth in predominantly female occupations is lower than in predominantly male occupations, then this manifests itself in different returns for equal observable characteristics in simple pooled regressions. There are various explanations for why males and females may segregate into different occupations. Becker (1971) and Bergmann (1974) argue that discriminatory tastes on the side of the firm are a major cause for segregation. If that is the case, differences in occupational choice between genders are determined by a process which is exogenous to the individual. Others argue that segregation of males and females into different occupations is the result of optimising behaviour. Mincer and Polachek (1974), Polachek (1981), and Weiss and Gronau (1981) consider models where human capital is non-homogenous, and individuals choose the amount and the type of human capital (which corresponds to the occupation). Different types of human capital are characterised by differences in human capital depreciation. If females have better opportunities outside the labour market than males, at least at certain 2

stages of their life cycle, then the higher reservation wage leads, on average, to more intermittent participation patterns. They therefore jobs which punish interruptions less. The empirical consequences of this are that females should be found in jobs which penalise time spent out of the labour force less. These jobs are, on the other side, characterised by moderate wage growth (and maybe high entry wages). Most of the empirical papers do not find evidence for this. England (1982), using a cross-section taken from the NLS 1967 for 30 to 44 year old females, rejects the hypothesis that women are penalized less for time spent out of work when they are in female occupations.2 Corcoran et al. (1983) use a longitudinal sample of women taken from the PSID 1967-1979, and use first difference estimation. They do not find that wage growth is lower in female occupations. Females do earn less in female occupations, however. Lazear and Rosen (1990) set up a model where different promotion rules for males and females generate segregation, and differential average wage growth. Again, their model is driven by the assumption that women have better outside options than men. Female workers are more likely to quit jobs due to a comparative advantage outside the labour market. Therefore, firms are less likely to promote women into jobs that demand training. As a consequence, females are more likely to be in jobs with relatively flat wage-profiles, and males in jobs with relatively steeper profiles. Furthermore, women have to be better than comparable men to obtain promotions.

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Other approaches argue that there are male-female differences in preferences (Sorenson, 1989), or that segregation is due to differences in subjects chosen at 2 3

See also England et al. (1988). See for a test of the prediction about the probability of promotion in Winter-Ebmer and

Zweim¨ uller (1997).

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college, test scores, and grades achieved at college (see e.g. Brown and Corcoran (1997) and Paglin and Rufolo (1990).) Another branch of the gender literature explores the process of occupational segregation and its consequences for the male-female wage gap. A number of empirical studies establish a strong link between occupational segregation and the average wage gap between genders. Brown et al. (1980) estimate an extended decomposition of the male-female differential, taking account of occupational choice. Occupational choices are determined by other characteristics than wages, such as childhood influences, personal characteristics or discriminatory constraints. They find that a substantial share of unexplained differences results from differences across occupation groups.4 Groshen (1991) finds that among industry, job-cell and occupation, the occupational component explains the largest share, approximately half, of the observed male-female wage differential. Carrington and Troske (1995) argue that females may also segregate into lower paying firms. However, they point out that inter-firm segregation partly captures differences in other factors, such as occupational segregation. In this paper, we re-consider occupational segregation, and its contribution to explaining male-female wage differentials. Our analysis is based on administrative data, which allows us to construct complete work histories for young individuals from labour market entry onwards, for up to 18 years in the labour market. All these individuals have received apprenticeship training. This allows us to identify the initial wage gap between individuals with almost identical educational background (except for their occupational specialisation), and its evolution over the first one and a half decades in the labour market. We illustrate mobility between occupational categories. We then estimate entry wages, as well as wage growth within 3 broadly classified occupational categories, which we label female 4

Similar results are found by Kidd and Shannon (1996).

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, integrated, and male occupations. Our estimation approach takes account of unobserved heterogeneity, and match specific effects. Our findings are quite surprising. We find that overall wage growth for females is lower than for males. The difference is at around 0.5 - 1.2 percentage points for the first 5 years of labour market experience. We establish a substantial overall wage differential immediately after labour market entry. This is most interesting, since all individuals in our sample have gone through the same educational system. When we distinguish between broad occupational categories, this wage differential decreases. This indicates that occupational segregation is in fact an important contributor to male-female wage differentials. We establish higher entry wages for both males and females in male occupations, and lower entry wages in female occupations. Wage growth, however, is higher in female occupations, which leads to closing the initial gap after between male and female occupations within the first decade of labour market experience. One explanation for this finding is that male professions are predominantly in sectors which are characterised by low growth (like craft), and which benefitted less from technological change, witnessed over the last decades. Female professions, on the other side, are predominantly located in sectors which benefitted more from technological innovations, and which may also be more human capital intensive. We provide some evidence for this hypothesis. We commence by describing our data, and some institutional details surrounding the German apprenticeship system (section (2)). We then illustrate wage patterns and mobility decisions of young workers over their early career histories, distinguishing between male occupations, female occupations, and integrated occupations, according to the percentage of males employed (section 3). In section (4), we describe the underlying model for our empirical analysis. We then estimate wage equations (section 5), where we distinguish between wage growth due 5

to accumulation of general human capital, and human capital which is occupationcategory specific. Furthermore, we allow for job category specific atrophy rates. Finally, in section 7 we conclude.

2

The Data and Institutional Details

Our analysis is carried out for Germany, where we draw on detailed data, based on social security records and available over a 21 years period. The data set allow us to construct complete work histories for young workers over a 21 years time period, with all wage information being clearly attributable to a particular employer. The data base we use is a 1 percent sample from the German Social Security records (IAB data), which has been supplemented by information from the official unemployment records. This data is available for the years 1975-1995. Over this period, it records for each worker the exact date of state changes, like change of employer, change into or out of unemployment, or nonemployment. It further provides information about whether a worker has been on an apprenticeship training scheme. Furthermore, all records are updated at the end of each calendar year. For each employment record, it records the average daily wage over the last employment spell (which is at most one year, if the worker has not changed state). In addition, and despite being drawn from administrative records, it includes an unusually large array of background information. Besides age and gender, we observe educational background, marital status, occupation and industry. The data base contains also information about the establishment in which each worker is employed. Furthermore, we have information about the year in which the size of the establishment of any worker in our sample drops to zero (which corresponds to a closure). Accordingly, our data allows us to recover the year of 6

closure (and foundation) for establishments over a 17 years period. In addition, we have regional information. This allows us to obtain a picture of the regional occupational composition. Compared to data sets used in other studies, our data has several advantages. The administrative nature of the data ensures that the information on wages and employment spells is very accurate. Thus we largely avoid measurement problems. Furthermore, we can exactly match wages to employment spells with a particular employer – there is no overlapping wage information across firms as in many data sets such as the PSID. Also, the data set is large and covers a long period, which allows us to construct the sample explained below. Finally, the additional information on firm closure and regional information may help us to identify a more general model structure. The data is not without shortcomings, however. It does not cover the entire German labour force, as the self employed and civil servants are not paying social security contributions, and are therefore excluded. However, when estimating the effects of tenure and experience, civil servants (who do in practice not change employer) are not an interesting group to consider anyway. Moreover, as with many administrative data sets, the data is top coded. In our analysis, we consider only young individuals who went through apprenticeship training. Top coding hardly affects the wages of workers in these groups.5

The Sample ¿From this data base, we construct a sample of young male and female workers whom we observe from the entry to the labour force onwards. To ensure that we 5

Less than 1 percent of our sample population experience a right censoring later in their

career.

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do not miss out early employment spells, we restrict our sample to workers who have been not older than 15 years in 1975 (which is minimum compulsory full time schooling age). We consider only workers who go through an apprenticeship training scheme early on in their careers. The longest labour market history in our data set is 19 years, and the shortest 2 years. Our final sample consists of xxxx male workers, with xxxx employment spells, and xxxx female workers, with xxxxx employment spells. In Germany, about 65 percent of each birth cohort goes through the apprenticeship system. As we explain below, this system provides young workers with a well - defined vocational training in a given profession. For our present analysis, this puts us in the unique position to observe a large sample of male and female workers who all obtained an apprenticeship degree, and whom we observe from labour market entry onwards. This allows us to observe in detail their wage profiles over up to 20 years in the labour market. We now give some more details about the apprenticeship system. The Education and Apprenticeship System The German Apprenticeship System is a vocational training programme which combines on the job training, provided by the firm, with school education, provided by the state. The roots of the apprenticeship system can be traced back to the middle ages, when the craft guilds regulated journeymanship type training schemes, and issued training certificates. Entry into training schemes was highly regulated, and there were strict requirements on entry age, training duration, and family background (for instance, children had to be legitimate). These early training schemes shared already many characteristics with today’s apprenticeship system. For instance, 8

only in recognised occupations was training provided; a master had to be present in the company; there were regular exit examinations. During the industrial revolution in the 19th century, shortage of qualified workers led the industry to adopt vocational training schemes, which had been so successful in the crafts sector. Compulsory school laws, introduced at the turn of the last century, added to the pure on the job training scheme state-provided school education, which resulted in the modern apprenticeship system. Today, more than 60 percent of each cohort in Germany goes through the apprenticeship system (see Soskice, 1994). In 1990, there were about 370 recognised apprenticeship occupations. These occupations include both blue and white collar professions, and cover many professions which require college attendance in the US (as, for instance, nurse, medical assistant, accountant etc.) Apprenticeship training schemes last between 2 and 3.5 years. During this time, apprentices attend for 1 - 2 days a week a vocational state school, where they acquire general knowledge, as well as knowledge which is specific to their occupation. For the remaining days, they are on specific on-the-job training schemes within the firm. Qualified personnel is responsible for the apprentice, and allocates apprentices to particular tasks. Larger firms run also specific classes or seminars for apprentices.

3

Wage Profiles and Mobility

We first describe the definition of the three groups we use. We assume that if the distribution of men and women across occupations was unaffected by discriminatory processes and men and women had equal preferences and outside opportunities, they would be equally distributed across occupations. In our sample of young skilled workers, 58.8 percent are male and 41.2 percent female. Thus, 9

Table 1: Occupation types: female, male, integrated Years of Work experience Female Integrated Male Total

1 year 39 ( 14.71) 27 ( 10.18) 199 ( 75.09) 265 ( 100.00)

3 years

5 years

7 years

38 28 201 267

38 27 198 263

37 28 195 269

12 years 32 (14.95) 22 (10.28) 160 (74.76) 214 (100.00)

we assume that the expected proportion of jobs within occupation j held by men is equal to their proportion in our sample (allowing for a 10 percent deviation from the expected proportion). We define male, female, and integrated occupations as follows: Male occupations are occupations in which men’s share of employment is greater than 68.8 percent on average over the period 1975 to 1995. Female occupations are occupations in which men’s share of employment is less than 48.8 percent on average. Integrated occupations are the remaining ones in which men’s share of employment is between 48.8 and 68.8 percent on average. Table (1) shows the allocation of occupations to the three categories. Individuals in the sample start working in 265 different occupations. 39 of those are female occupations, 27 are integrated, and 199 are male occupations. Even though during the course of the early career the number of observations used in our analysis diminishes, as shown in table (2), this leaves the distribution of occupations in the sample across these three groups unaffected. For instance, individuals with 12 years of actual work experience are observed working in 214 occupations. These are grouped into 32 female occupations, 22 integrated occupations and 160 male occupations. In table (3), the six most frequently observed occupations for males and females are displayed. The figures indicate that men are predominantly observed in blue collar and craft occupations, while women hold more often service jobs. One exception, though, is the profession professional clerical worker which is ranked 10

Table 2: Number of individuals observed within occupation types Sex label

1.spell

Female Integrated Male Total

female 12591 2454 3233 18278

male 2939 1965 19981 24885

3 years of work exper. female male 2149 13130 1572 1228 7976 1963 11697 16321




Table 3: Most frequent occupations Panel A: Most frequent occupations for men Occupation in first job # of men % motor vehicle mechanic 1427 6.8 electrician 1347 6.4 professional clerical workers 1107 5.3 machinist/locksmith 896 4.3 joiner 871 4.2 pipe fitter 844 4.1 total 6492 31.2 ....... total # of individuals 20794 100.00 Panel B: Most frequent occupations for women Occupation in first job # of women % professional clerical worker 3508 23.3 sales person 2349 15.6 receptionist 1285 8.5 889 5.9 hygienist banking professional 787 5.2 nurse 634 4.2 total 9452 62.7 ........ total # of individuals 15055 100.00

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# of women 8 18 3508 7 20 2 3563

% ∼0 ∼0 23.3 ∼0 ∼0 ∼0 23.6

15055

100.00

# of men 1107 507 3 50 546 55 2268

% 5.3 2.4 ∼0 ∼0 2.6 0.26 10.9

20794

100.00

amongst the six occupations for men as well as for women. Furthermore, while five out of the six most frequently observed male occupations are completely segregated, this is only the case for 2 female occupations. Finally, for both sexes a substantial percentage of the overall sample is concentrated in these six occupations. Females are more concentrated than males: 63% of females are can be found in the most frequent 6 occupations, but only 31 % of males.6

Wages and Occupational Segregation We are in the privileged position to observe wages of individuals from the first year of labour market entry onwards. This allows us to construct complete wage histories for individuals, including entry wages. In figure (1), we display the conditional sample mean function of wages on labour market experience for both males and females, and the wage differential between the two groups. The figures are surprising. First, they suggest an immediate wage differential for young workers in the German labour market after apprenticeship education, and at labour market entry, of about 21 percent. This substantial differential occurs despite the fact that all workers are almost identical in the amount of schooling and training they received. Table 4 reports secondary education before individuals enrolled on the apprenticeship scheme. The numbers show that more than 90 percent of males and females in the sample received between 9 and 10 years of schooling. The percentage of those who received a high school degree is even higher among females: 8.35 percent, as compared to 4.5 percent amon the male sample population. Second, the raw wage-experience profiles are nearly parallel. There is an initial 6

There is evidence in the literature that women are observed in a more narrow range of

occupations than men (see e.g. Bergmann (1974)).

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Table 4: Secondary education, first wage spell lower or intermediate secondary degree (9-10 years) higher secondary degree (13 years)

Log Wage-Experience

()

Females 91.65 %

Males 95.50 %

8.35 %

4.50 %

Wage Differential

Figure 1: Log Wages, Males and Females

slight increase in the wage differential, and a slight decrease after about eight years of labour market experience. Over the entire period, the differential is in the range between 19 and 23 percent. The raw conditional means can clearly not be seen as an indication about how wages develop with labour market experience for the two genders. The figures do not distinguish between at least three major components of wage growth: First, aggregate wage growth. In Germany, an important component of wage growth is due to aggregate growth. This is a result of the specific bargaining structure between unions and employers, where wage negotiations are industry specific, and results apply to everybody in the labour force, regardless of their union status. The nature of longitudinal data of the type considered in our anal13

Log Wage-Experience

()

Wage Differential

Figure 2: Log Wages, Males and Females, grouped by occupation

ysis leads to an over sampling of younger cohorts at lower levels of labour market experience, as opposed to older cohorts. Accordingly, if aggregate wage growth was larger for females than for males towards the end of the observation window (because, for instance, of anti-discrimination legislation), the simple figures above may underestimate the differential at lower levels of experience. Second, aggregate wage growth may be due to mobility and search across occupational groups. If entry wages, as well as wage growth differ substantially between occupational categories, then differences in occupational mobility across genders may lead to differential wage growth for males and females. Third, unobserved ability and selection. If a larger fraction of female workers leave the labour force, positive/adverse selection may lead to a downward/upward bias in the average wage gap in mean wages conditional on experience. In figures (2), we distinguish between male, female and integrated jobs, and plot the conditional mean functions of wages within categories, as well as the wage differentials. The left figure indicate that there are indeed substantial differences in mean wages between occupational categories, particularly so for females.

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Males

Females

() Figure 3: Within and Between Log Wage Growth, Males and Females Females in female jobs have considerably lower wages than in integrated jobs. Within occupational categories, the wage differential is smaller than the overall wage differential, at various levels of experience, as indicated by the right panel of figure (2). Interestingly, the differential is most pronounced, and slightly increasing for female jobs, while it decreases after about 8 years in integrated jobs. The figures do indeed suggest that segregation, as well as occupational mobility, are important for understanding male-female wage differentials. In figures (3) we display wage growth within and between occupational categories for both males and females. The figures show that, on average, a change of occupational category is beneficial, and leads to higher wage growth than remaining within the same category.

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4

Estimating returns to Experience and Occupational Experience

We distinguish between 3 types of human capital: human capital in male professions (j = M ), human capital in female professions (j = F ), and human capital in integrated professions (j = I). Individuals accumulate human capital which is general, and transferable across types, and human capital which is type specific. Each type of human capital may be accumulated at a different rate. In our model, the quantity of human capital (in terms of years of education and training) before labour market entry is the same across individuals - we only consider apprentices. Individuals choose however the type of human capital, by choosing an apprenticeship education, which falls into one of the three categories. Changes between categories are possible, but individuals loose their type specific human capital. Consider the following simple wage regression (where for simplicity we suppress an index for males and females):

lnwitj = aj0 + a1 EXit + aj2 OCCitj + aj3 Uitj−1,j + uijt ,

(1)

uijt = eit + ηi + mij + pt ,

(2)

where

where j is an index for the type (or occupational category) in which we observe the individual; j ∈ {M, F, I}. Labour market experience is denoted by EXit , and experience in the occupation in which the individual works by OCCitj . The time the individual has spent outside the labour market before entering occupation j is 16

denoted by Uit (where the previous occupation is denoted by j−1). For simplicity, we only consider linear terms here. In the application below, we fit a spline function, which allows for differential returns at different levels of (occupational) experience. The error structure consists of four components: An idiosyncratic error term eit , the individual effect ηi , the match specific effect mijt , and the aggregate price of human capital pt (which we model as a flexible function of time). In our empirical specifications, we make two additional assumptions. First we assume that occupational capital is lost after the individual has changed to another occupation. Second, we assume that the loss of human capital due to spending some time outside the labour market is only felt immediately in the next job after return. We allow however for different losses according to the current and previous occupation. The parameters of interest are the differentials in entry wages, aj0 , the returns to labour market experience (a1 ), the returns to occupational specific human capital aj2 , and the penalisation for intermittancy (aj3 ). OLS estimation of equation (1) (separately for males and females) results in consistent estimates of the parameters of interest if

E(eit + ηi + mij |EXit , OCCitj , Uitj−1,j ) = 0 .

(3)

This is unlikely to be a credible assumption. Labour market experience, as well as occupational experience and intermittancy, are correlated with the individual specific effect. Individuals with higher abilities are likely to have a stronger labour market attachment. The match specific component is likewise correlated with the experience and intermittancy variables. If good matches survive longer, than individuals who had a good match specific draw are less likely to change 17

the type of occupation. Also, those who are well matched are likely to have a stronger labour market attachment, leading to a positive correlation with general experience. One strategy to proceed is to estimate equation (1) in differences, and within types:

∆lnwitj = a1 ∆EXit + aj2 ∆OCCitj + aj3 ∆Uitj−1,j + ∆uijt .

(4)

Estimation of equation (4) gives consistent estimates of the parameters a1 + aj2 and a3 as long as

E(∆uijt |∆EXit , ∆OCCitj ) = 0 .

(5)

We will not be able to identify the level effects in the returns due to experience and the returns due to occupational experience, since the difference in the acquisition of both equals one within occupations. What is identified though is the difference in returns in a given occupation category between the baseline (which later on we will choose to be male jobs), and female and integrated jobs. Assumption (5) is violated if innovations to the match quality affect mobility decisions. In this case, we are likely to oversample individuals who stay in the job because of good match innovations, leading to upward biased estimates of wage growth. Our difference estimator proceeds in two stages. We first estimate the returns to aggregate wage growth, where we distinguish between males and females. Estimation of this parameter is not unproblematic, since the wage trend is correlated with variables like experience and tenure, in particular in cohort samples which age (like for instance the NLSY, GSOEP, PSID). The structure of our sample 18

implies that new cohorts enter the data every period. Entrants have zero years of tenure and experience; therefore, conditional on other individual characteristics, like age and education, their entry identify aggregate wage growth pt . We estimate

lnwitj = aj0 + pt + uijt .

(6)

which gives us estimates of pt . Wages net of aggregate effects are then given by:

]j = lnwj − p . lnw t it it

(7)

and within-type wage growth by

]j = a ∆EX + aj ∆OCC j + aj ∆U j−1,j + ∆u . ∆lnw 1 it ijt 2 3 it it it

(8)

where only the sum of the linear terms of EX and OCC is identified.

5 5.1

Results Wages and Occupational Mobility

In Table (5), we display results from simple wage regressions, where we regress wages on year dummies, potential experience, and dummy variables on the number of occupational changes. The first two columns report results for males, and the last two columns for females. Columns 1 and 3 are OLS estimates, and columns 2 and 4 are fixed effects estimates. The results from OLS estimation indicate that changes across occupations tend to be negatively associated with wages for both males and females. For males, every additional occupational change is 19

Table 5: Occupational Differentials No. of occupational Changes 1 2 3 4 5 6 potential experience (pot. experience)2 (pot. experience)3 Constant

OLS Males

FE Males

OLS Females

FE Females

-0.040 (20.61) -0.057 (21.77) -0.097 (18.99) -0.129 (15.96) -0.132 (8.72) -0.255 (10.37) 0.083 (49.32) -0.005 (22.39) 0.000 (13.52) 4.435 (537.23) 191575 0.21

0.010 (4.00) 0.022 (7.01) 0.020 (3.84) 0.006 (0.84) 0.053 (4.13) -0.041 (2.02) 0.092 (57.33) -0.003 (17.40) 0.000 (10.73) 4.624 (623.39) 191575

0.001 (0.28) -0.062 (12.35) -0.005 (0.43) -0.038 (1.96) 0.002 (0.06) -0.240 (4.04) 0.107 (42.04) -0.008 (22.86) 0.000 (13.77) 4.113 (371.74) 115889 0.18

0.051 (14.25) 0.029 (5.68) 0.079 (8.67) 0.086 (5.64) 0.149 (5.97) -0.079 (1.81) 0.123 (56.57) -0.005 (20.44) 0.000 (9.26) 4.446 (417.20) 115889

Observations R-squared Number of Individuals All Regressions include year dummies.

20

23667

17584

associated with a larger loss in wages. For females, a pattern is less clearly visible. This seems to be in contrast to across occupational mobility leading to positive wage growth, as indicated in figure (3) above: The raw average wage growth during the first 10 years in the labour market is 3.9 percent within jobs for both males and females, but 6.4 and 8.6 percent between occupational categories for females and males, respectively. The reason could be selection. If we condition on individual heterogeneity by estimating some simple fixed effects models, we see in fact that mobility across occupational categories leads to positive wage growth; this is clearly pronounced for females, and less so for males. These results do indicate that wages grow as a result of occupational changes within individuals; however, those individuals who are mobile across occupational categories hold, on average, inferior matches, or are lower quality workers. This supports an estimation strategy which takes account of fixed effects.

5.2

Entry Wages

As a first step, we estimate the entry wage differentials, as well as aggregate wage growth for males and females. We obtain estimates of both by using spells of individuals in their first year in the labour market. All these individuals have zero years of occupation specific experience, and zero years of general experience. Table (6) presents the results for the estimates of wage differentials across occupational categories. We use male jobs as the base category. For both genders, wages are significantly lower in female jobs, with the differential being more pronounced for males. Males in integrated jobs have only slightly lower wages than males in male jobs, while females gain from being in integrated jobs. In figure (4), we display aggregate wage growth over the period between 1980 and 21

Table 6: Occupational Wage Differentials Males

Females

Variable

Coeff

t-stat

Coeff

t-stat

Differential Female-Male Job

-0.114

-23.70

-0.089

-10.62

Differential Integrated-Male Job

-0.018

-2.86

0.032

2.99

Entry Wage Male Jobs

4.569

406.39

4.354

279.76

All Regressions include year dummies.

Figure 4: Aggregate Wage Growth, Males and Females 1995. We have normalised wages to be equal to zero for both gender in 1980. The plot shows that over the 14 years period, wage growth due to aggregate growth has been substantive. Furthermore, it indicates that wages of males and females have moved parallel until the mid 1980’s. From then onwards, aggregate wage growth of females has been much more pronounced than aggregate wage growth of males. The economic crisis which set in during the early 1990’s has lead to a slight drop in real wages for males, but not so for females. There are a number of factors which may explain these patterns. First, enforcement of anti-discriminatory legislation may have lead to these aggregate wage growth differentials. Secondly, if initially females segregated into occupations which are characterised by low wages, but changed occupational choices over the 22

Table 7: OLS Regressions, Males and Females Males Variable Coeff t-stat Coeff Occ. Fem. -0.103 -38.17 -0.103 Occ. Int. -0.006 -1.88 -0.007 0.038 51.46 0.038 Exp5 0.019 27.14 0.018 Exp OF 5 Exp OM 5 -0.005 -6.33 -0.005 Exp OI 5 -0.004 -2.72 -0.004 Out OM -0.012 -0.022 Out OF Out OI -0.008 const 4.572 618.84 4.572 All Regressions include year dummies.

t-stat -37.72 -2.22 50.93 27.02 31.11 6.93 15.09 -6.38 17.38 -2.72 -7.91 -5.60 -1.05 619.01

Coeff -0.074 0.051 0.026 0.016 0.022 -0.002 0.024 -0.008 0.026 -0.000

4.349

Females t-stat Coeff -13.97 -0.072 7.81 0.051 20.86 0.026 13.09 0.016 17.42 0.022 -1.77 -0.002 10.67 0.024 -2.08 -0.007 13.26 0.026 -0.21 -0.000 -0.080 -0.094 -0.080 392.68 4.347

t-stat -13.54 7.77 21.24 12.53 17.52 -1.72 10.75 -2.08 13.37 -0.12 -6.94 -41.62 -8.65 395.12

last decade, then this may have let to the observed patterns. Third, female jobs may be jobs which are located in sectors which are faster growing, due to benefiting more from the rapid technological change, starting in the mid 1980’s. The timing of the widening of the wage gap is compatible with this hypothesis.

5.3

Male and Female Wage Growth

We commence by estimating equation (1) by simple OLS, where results are displayed in table (7). The first two columns refer to males, and the last two columns to females. Columns 1 and 3 exclude the time the individual has spent outside the labour market, before entering the respective job. The results indicate that returns to general labour market experience are lower for males than for females over the first 5 years, and returns decrease sharply for both groups after 5 years. Similar to the results obtained above, for males, entry wages are 10 percentage points lower in female occupations as compared to male 23

occupations, while the difference is only 7 percentage points for females. Compared to male occupations, females have an initial wage advantage in integrated occupations, while for males this difference is negligible. As a compensation for low entry wages, males seem to obtain higher returns in female occupations than in male occupations. For females, returns are similar in all three occupational categories. Finally both males and females seem to loose when returning to work after an intermittancy, with losses for females being far more pronounced than losses for males. The simple OLS estimates do not take account of individual and match specific effects which may be correlated with experience, or intermittancy, and which may lead to biased parameter estimates. In table (8), we display results of estimating difference equations, as in equation (8). As we explained above, this specification does not allow to identify the level components of the returns to experience, and occupational experience in the three categories. For instance, the entry in the first row is the return to experience and occupational experience for individuals who have been in a male occupation in both periods. For both males and females, the combined returns to experience and occupational specific human capital are lower than in the OLS estimation, indicating that there is positive correlation between labour market and job attachment, and the match- and individual specific effects ηi and mij . Most interesting, for both males and females, returns are lowest in male occupations, and highest in female occupations; integrated occupations are intermediate. The difference between the returns to male occupations and female occupations in the first 5 years is of very similar magnitude for both males and females (between 1.3 and 1. 5 percentage points, depending on the specification). However, the returns in male occupations (the base category) are considerably lower for females. Accordingly, 24

Table 8: FD Regressions, Males and Females Variable Exp OM < 6 Exp OI < 6 Exp OF < 6 Exp OM >5 Exp OI >5 Exp OF >5 Exp >5 Out OM Out OF Out OI

Coeff 0.035 0.041 0.050 -0.005 -0.008 -0.005 -0.012

Males t-stat Coeff 50.138 0.032 20.505 0.039 33.931 0.047 -2.385 -0.004 -1.951 -0.008 -1.642 -0.004 -6.645 -0.011 0.019 0.029 0.026

t-stat 44.766 19.259 31.377 -1.984 -1.961 -1.359 -5.738 16.439 10.028 4.204

Coeff 0.017 0.027 0.030 -0.004 -0.011 -0.010 -0.018

Females t-stat Coeff 6.537 0.021 11.966 0.030 38.858 0.035 -0.672 -0.006 -1.942 -0.011 -3.530 -0.012 -6.734 -0.019 -0.047 -0.059 -0.053

t-stat 7.629 13.177 44.991 -0.866 -2.004 -4.179 -7.061 -6.550 -42.197 -9.181

All Regressions include year dummies.

females and males seem to obtain higher returns in female occupations; however, overall returns are substantially higher for males. Compare this to the OLS results. Overall returns are lower in the difference equations, suggesting upward biased estimates in simple linear models, as suggested by our discussion above. Furthermore, there is a pronounced difference in returns to occupational specific human capital for females, which was not evident in the OLS regressions. The second order terms indicate that after 5 years, returns to occupational specific human capital decrease significantly. For females, the largest decrease is in female and integrated occupations, while for males, decreases are more moderate, and similar across occupations. Thus, females seem to exhibit slower wage growth in all occupations for the first 5 years; after 5 years, profiles become even flatter, as compared to males. Interesting are the effects of time out of the labour force for the two groups. For both males and females, the coefficients become larger, relative to OLS results, indicating that intermittancy is negatively correlated with individual ability, and/or the match component in the new job. While for females, the coefficients are still 25

Table 9: Distribution of age at entry into transition, percentages age 15 16 17 18 19 20 21 22 23 24 > 25 Total

Females 7.08 22.21 26.86 17.79 12.13 7.92 3.42 1.30 0.57 0.24 0.48 100.00

Males 11.64 28.09 29.75 15.44 6.20 3.10 2.50 1.59 0.66 0.43 0.60 100.00

all 9.71 25.60 28.53 16.44 8.71 5.14 2.89 1.47 0.62 0.35 0.55 100.00

negative (and are similar for the three occupations), they turn positive for males. Males may spend non-participation periods differently than females - by enrolling for instance in educational programmes. For females, one year of intermittancy decreases wages by around 5 percentage points, and this number is similar for the three occupations.

6

Wages, Segregation, and the Use of Technology

One explanation why wages in female occupations grow faster than in male occupations is that female occupations are predominantly located in sectors which are characterised by use of new technologies. These technologies may necessitate additional training initially, but are complementary to the individual’s skills. This could lead to steeper wage growth in these jobs. The tables above were suggestive for this hypothesis, since female jobs are mostly located in the service sector, while male jobs are mostly in crafts and blue collar professions. Compatible with this is the increase of gap of aggregate wage growth between females and males, 26

as shown in figure. Also, if this hypothesis is true, then we should observe a higher use of new technologies in female jobs for both males and females; furthermore, the difference in the usage of new technologies should have widened over the last two decades. To investigate this, we construct for each occupation a technology variable. This variable is generated from the BIBB/IAB-surveys which are available for the years 1979, 1985/86 and 1991/92. One question in the survey asks for the equipment or tool which is used most frequently at work. Possible answers include 60 different items, among them transport vehicles, computers, technical machinery, office equipment (such as writing tools, or type writers) and computerised office equipment. Some items included vary across years. From this data, we construct a binary variable (which we label tech) that equals one if computers are been used at work (in production or in the office). The data reports also the occupation on three digit level, and the gender of the individual. Distinguishing between gender and occupation, we have generated mean values for the variable tech within three digit occupations, for males and females for each survey year. We merge this information to the sample sample of young workers from the IABS. Table (6) reports the mean of this technology index for the three occupational categories for the various years, where we distinguish between males and females in the lower two panels. We report standard errors in parentheses. The numbers are quite astonishing. Overall, the use of computer technology has been similar in male and female occupations in 1980. However, in 1985, there is a significantly higher use of these technologies in female occupations, for both males and females. This gap is even more pronounced in 1990.

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Table 10: Computer Use, fraction of employees Year Total

1980 1985 1991 .0418 .0648 .1745 (.0010) (.0005) (.0008) All Workers .0519 .0524 .1397 Male Occupations (.0019) (.0007) (.0012) Female Occupations .0275M .0776M,I .2193M,I (.0010) (.0008) (.0012) Integrated Occupations .0592M .0689M .1638M (.0025) (.0012) (.0022) Female Workers Male Occupations .0243 .0583 .1945 (.0052) (.0042) (.0059) Female Occupations .0256I .0713M,I .2208M,I (.0256) (.0008) (.0014) M Integrated Occupations .0446 .0533 .1794M (.0035) (.0023) (.0043) Male Workers Male Occupations .0542 .0519 .1350 (.0020) (.0007) (.0012) Female Occupations .0342M,I .0960M,I .2150M,I (.0023) (.0020) (.0026) M M Integrated Occupations .0678 .0782 .1538M (.0033) (.0013) (.0023) BIBB/IAB Survey 1979, 1985/86, 1991/92. Computer use is measured by main equipment at work. Standard errors are in parentheses. Superscript index for significantly different from group (M, I, F).

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7

Conclusion

Our main conclusions are as follows. There is a considerable wage gap between males and females right at labour market entry (of about 21 percent). This is despite the fact that males and females in our sample have identical educational backgrounds, at least in terms of years of education. Simple descriptive analyses suggest that this is partly due to occupational segregation. The raw mean wage differential suggests that there is no dramatic change in the mean wage gap over the experience cycle. We also find that for both males and females, there are considerably lower entry wages in female occupations, as compared to male or integrated occupations. Furthermore, while occupational mobility leads to wage increases within individuals, it is negatively related to wages when using across individual variation as well. This suggests that, although individuals improve on their wage situation by occupational changes, those who do in fact move are inferiorly matched, or of lower quality, conditional on observables. Our regression analysis suggests that OLS overestimates returns to occupational capital. Estimating difference equations, we find that males have about 1.5 percentage points higher wage growth than females, on average, and for the first 5 years of labour market experience. Furthermore, for both genders, wage growth is highest in female occupations, and lowest in male occupations. This is compatible with lowest entry wages being found in female occupations, and highest entry wages in male occupations. The initial wage differential between male and female jobs is almost completely closed after about 5 years of labour market experience. We do not find stark differences in loss of human capital due to labour market interruptions. 29

The results deviate from the predictions of the literature, where we should expect wages in male occupations to grow faster than wages in female occupations. This is in line with other empirical studies. One explanation for the results is that male occupations, primarily located in traditional industries and craft, are located in low technology industries, or slowly growing industries. Female occupations, on the other side, are traditionally located in service sectors. Technological developments may have been advantageous for these sectors. We construct a technology index and merge it with our data. The mean of this index turns out to be higher in female occupations, thus providing support for our hypothesis. In the future, we will test this hypothesis by investigating wage growth differentials.

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