Transcript
Page 1: Promotions in the Spanish Labour Market: Differences by Gender

Promotions in the Spanish labour market:differences by gender

DOLORES GARCIA-CRESPO�

Department of Statistics and Econometrics, Universidad de MaÂlaga, Spain

I. Introduction

In all labour markets, women earn a lower wage than men, on average.1 To

explain this gap a large amount of empirical research has been carried out, in

which special emphasis has been placed on individuals' productive character-

istics (Mincer and Polachek, 1974; Landes, 1977). This approach has

emphasized the human capital theory point of view, in the sense that workers

in¯uence their professional career through their educational and training

decisions. According to this theory, employers play a passive role in workers'

professional careers. In fact, the typical method used in these investigations

has mainly consisted in the estimation of wage equations by gender, where

the occupation and activity of workers are only included as control variables.

On the other hand, some authors have insisted on the relevance of

professional careers to explain the salary distribution according to sex

(Duncan and Hoffman, 1979; Malkiel and Malkiel, 1973). More recently,

there has been an increasing interest in discrimination regarding opportu-

nities for professional advancement in many countries (Cabral, Ferber and

Green, 1981; Groot and Maassen van den Brink, 1996; Jones and Makepeace,

1996; Lewis, 1986; Olson and Becker, 1983; Winter-Ebmer and ZweimuÈller,

1997). Most of these papers conclude that women are required to have higher

quali®cations than men at the time of promotion, or that they have fewer

opportunities for obtaining jobs with higher probabilities of promotion. In

Spain, Garcia and Malo (1996) analyze educational mismatch and its relation

OXFORD BULLETIN OF ECONOMICS AND STATISTICS, 63, 5 (2001) 0305-9049

# Blackwell Publishers Ltd, 2001. Published by Blackwell Publishers, 108 Cowley Road, Oxford OX4 1JF, UK and 350

Main Street, Malden, MA 02148, USA.

599

�I am grateful for comments from the Schooling, Training and Transition seminar participants inAmsterdam in September 1998 and to Lucia Navarro, Andrew Clark and an anonymous referee forhelpful comments and suggestions. I also wish to acknowledge ®nancial support from EuropeanCommision, proyect PL 95-2124. Any remaining errors are my own responsibility.

1See Blau and Kahn (1996) for international comparisons of the wage gap by gender.

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to internal mobility in the Spanish labour market, but they do not consider

differences by gender.

Following this second view, we consider that the internal mobility of

workers in the ®rm is a fundamental part of their professional career, and

therefore their wages. In this paper we use the variable number of promotions

received by workers in their ®rm, taken from a Spanish survey, as a proxy

variable of internal mobility. By using this data set, ®rst, we estimate count

data models to identify the main factors that determine mobility by gender;

second, regarding fewer promotions for women, we try to evaluate which part

of this can be attributed to lower productivity, and which part can be

associated with discriminatory behaviour in the market.

The structure of the paper is as follows. In section 2 we present the data

source used in our analysis and a description of the sample. Section 3

contains a short discussion about the econometric speci®cation of the model.

The results of estimated count data models to explain the number of

promotions obtained by workers within ®rms are presented in Section 4. In

Section 5 we apply a variant of the Oaxaca decomposition method to measure

discrimination against women regarding mobility opportunities. The last

section contains the main conclusions.

II. The Data

The data source used for the empirical analysis is the Encuesta de Estructura,

Conciencia y Biogra®a de Clase (1991).2 It is a rich cross-sectional data set

which contains detailed information on 6632 individuals concerning perso-

nal, educational, and training characteristics and work histories. Moreover,

this survey contains information about the internal mobility of the workers.

They answered the following question: `How many promotions have you

received since you started to work with your current ®rm?', where to have

received a promotion means to work at a superior level with more responsi-

bility or authority. This information is available for those who are currently

employed as well as for those who were previously employed. We restricted

the sample to currently employed men and women aged 19±65.

We should mention some limitations concerning the data. First, the fact

that career advancement is an essentially dynamic phenomenon means that

we would need longitudinal data for an analysis of internal mobility. However,

in Spain, logitudinal information is very scarce and there is no survey with

these characteristics yet, so we use cross-sectional data. This implies that we

cannot explain the probability of receiving a promotion, nor can we identify

2Financed jointly by the Spanish Statistical Institute, the Community of Madrid, and the Instituteof Women.

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jobs that offer promotion opportunities. We have only the accumulated num-

ber of changes in the current ®rm. A second limitation of our data set is that

we cannot distinguish between different kinds of promotion. This means that

we consider all promotions as the same sort of promotion; thus, we analyze

internal mobility in the ®rm only from a quantitative but not a qualitative

standpoint. On the other hand, a possible positive aspect of the data is that

individuals report the number of promotions received, so we do not have to

construct a measure of mobility, but can use it directly from the survey.3

Table 1 shows summary statistics by promotion status for men and

3For example, Winter-Ebmer and ZweimuÈller (1997) construct a proxy for internal mobility fromthe data.

TABLE 1

Summary Statistics: Means

Males Females

One One

No promotion No promotion

promotions minimum promotions minimum

Individual characteristics

Age (years) 36.88 41.82 33.46 36.95

University (d) 0.10 0.15 0.14 0.16

Secondary (d) 0.17 0.26 0.21 0.33

Previous experience (years) 11.14 7.63 6.79 4.28

Married (d) 0.60 0.80 0.44 0.51

Job characteristics

Firm tenure (years) 8.22 16.80 6.48 13.15

On-the-job training (d) 0.30 0.51 0.28 0.53

Permanent contract (d) 0.67 0.90 0.57 0.91

Part-time (d) 0.06 0.02 0.24 0.07

Professional occup. (d) 0.16 0.21 0.30 0.31

Hourly wage (pesetas) 613.10 808.93 562.24 723.85

Hourly wage female/male ± ± 0.92 0.89

Firm characteristics

Administration (d) 0.20 0.18 0.25 0.29

State ®rm (d) 0.06 0.13 0.10 0.06

Private (large) (d) 0.06 0.18 0.03 0.14

Private (medium) (d) 0.09 0.19 0.07 0.16

Private (small) (d) 0.48 0.23 0.38 0.27

No classi®cation (d) 0.13 0.10 0.18 0.07

Sample proportions

by gender (%) 61.24 38.76 77.00 23.01

N 872 552 659 197

N for wage 687 437 563 154

Note: d indicated dummy variable.

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women. We distinguish those who declared that they had never received a

promotion from those who had received at least one promotion in their

current ®rm. The main features in this table are the following. First, there are

more promoted men than women. In fact, 77% of women have had no

promotion in their current ®rm. However, in the case of men, this ®gure is

61%. Second, for both genders, we observe that those who have received at

least one promotion are older, have longer tenure in their current job, have

less experience with their previous employer, and have received more on-the-

job training. Finally, in relation to wages, we ®nd two important differences.

One is that the salaries of promoted workers are higher, independent of their

gender; the other is that men earn more than women in any situation.

These data seem to show that women, on average, have a professional

career with less internal mobility than men, and this could partially explain

the wage gap by gender.4 Nevertheless, we cannot directly deduce from this

information whether this unfavorable situation for women is due to their

productivity characteristics or to different treatment by employers. To answer

this question an analysis has to be carried out with the variables which

determine the promotion opportunities for individuals in the ®rm.

III. Modelling Internal Mobility

One of the aims of the paper is to explain our proxy of internal mobility: the

number of promotions received by the workers in their current ®rm (Yi).

Since our dependent variable takes only non-negative integer values, we need

to estimate a count data model. The basic speci®cation for this kind of data is

the Poisson regression model5 in which the observed values, yi, are i.i.d.

drawings from a Poisson distribution with parameter ëi, the probability

function of the model being the following:

P(Yi � yi) � eÿëië yi

i

yi!, ëi . 0, yi � 0, 1, 2, . . . , i � 1, 2, . . . , n

(1)

In order to incorporate individual characteristics, X i, including a constant,

the parameter ëi is speci®ed as an exponential function to ensure the non-

negativity of Yi:

ëi � exp(X iâ) (2)

Note that in this model, ëi is a deterministic function of X i and the

4The consequences of the lower internal mobility of women on the Spanish wage gap areanalyzed in Garcia-Crespo (1997).

5Basic references on count data are Maddala (1983) and Greene (1993). Cameron and Trivedi(1998) and Winkelmann (2000) are specialized texts on count data regressions.

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randomness of the model comes from the speci®cation of Poisson for

Yi � yi. This model implies that conditional variance and mean are equal:

E(Yi=X i) � Var(Yi=X i) � ëi � exp(X iâ) (3)

This characteristic of equality of mean and variance is called equidispersion

and is a strong assumption for real data. In words, the Poisson regression

model states that individuals with identical covariates have the same expected

count ëi. That is, the individuals are heterogenous only with respect to

observed characteristics. Nevertheless, data frequently reject this restriction

and present overdispersion, that is, the variance exceeds the mean.6 Cameron

and Trivedi (1998) point out that failure of the Poisson assumption of

equidispersion may produce spuriously small estimated standard errors of â̂.

One way of relaxing this restriction is by saying that ëi, the mean of the

variable, incorporates a random component to capture additional unobserved

heterogeneity between individuals not contained in X i. With this assumption,

expression (3) is replaced by the following stochastic equation:

ëi � exp(X iâ� Ei) (4)

where the random disturbance term Ei can re¯ect speci®cation errors as well

as the existence of omitted unobserved exogenous variables. Depending on

the assumption about the probability model for ëi, it is possible to obtain a

family of models for count data called negative binomial models. Cameron

and Trivedi (1990) distinguish two kinds of negative binomial model depend-

ing on the assumptions about the characteristics of ëi, the Poisson model

being a particular case of them:

Poisson: Var(Yi=X i) � E(Yi=X i)

Negative Binomial I (NegBin I): Var(Yi=X i) � E(Yi=X i)(1� ä)

Negative Binomial II (NegBin II): Var(Yi=X i) � E(Yi=X i)[1� äE(Yi=X i)]

where ä is called the dispersion parameter of the model. Note that the

conditional variance function of the NegBin I model is a multiple of the

mean, whereas this relationship is quadratic in the NegBin II model. In both

cases the dispersion parameter ä is to be estimated.

To choose between Poisson and negative binomial models Cameron and

Trivedi propose testing ä � 0 in the Poisson model in the following way.

First, to choose the Poisson model instead of NegBin I, they propose

performing the following auxiliary regression by least squares:

6An indication of the magnitude of overdispersion can be obtained simply by comparing thesample mean and the variance of the dependent count variable. If the sample variance is more thantwice the sample mean, then data likely retain overdispersion after the inclusion of regressors(Cameron and Trivedi, 1998).

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(yi ÿ ë̂i)2 ÿ yi

ë̂i

� ä� ui (5)

where ë̂i � exp(X i â̂), with â̂ being the estimated value of â from the Poisson

regression, and ui being an error term. The t-statistic for ä is asymptotically

normal under the null hypothesis of equidispersion (ä � 0) against the

alternative of overdispersion of the NegBin I form. Second, to test the Poisson

model against the NegBin II model, expression (5) is replaced by:

(yi ÿ ë̂i)2 ÿ yi

ë̂i

� äë̂i � ui (6)

In sum, in a ®rst stage we estimate the Poisson model and perform the

tests presented above. In a second stage, depending on the results of these

tests the appropriate model to describe the data is chosen. The models are

estimated by maximum likelihood.

IV. Results

The dependent variable in the estimated models is the number of promotions

received in the current ®rm. The independent variables are divided into two

groups. In the ®rst group, there are two dummies for formal education, years

of previous experience with other employers and a dummy for whether the

individual is married; in the second group there are job-related characteristics

such as years of tenure in current ®rm, tenure squared, a dummy for whether

the worker has participated in job-related training in the current ®rm, two

dummy variables for part-time and permanent employment contract, a

dummy for whether the worker is working in a professional occupation, four

dummy variables for size of the ®rm, and two dummy variables for whether

the worker is working in the Administration sector or in a public ®rm. First,

we estimate the Poisson model for men and women. Table 2 shows the results

of auxiliary regressions of the Cameron and Trivedi tests from these models.

According to the t-statistics, we reject the Poisson model against the NegBin

II model whose results we present in Table 3. In this table we observe that the

overdispersion parameter (ä) is signi®cant in the male and female samples.

The likelihood ratio statistics for the null hypothesis that the coef®cients for

men and women are equal is 43.29 and the critical value, ÷20,05 � 26:3 with

16 degrees of freedom. Thus, we can conclude that the parameter values are

different by gender.

Almost all the human capital variables show a signi®cant in¯uence on the

probability of receiving an additional promotion. First, formal education has

a positive in¯uence on the number of promotions received, in line with

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TABLE 2

Results of Overdispersion Tests in the Poisson Model

Alternative Models

NegBin I NegBin II

ä̂ t-values ä̂ t-values

Males 0.31 1.03 0.63� 3.06

Females 0.43 0.83 0.67� 1.91

Note: These tests are based in equation (5) and (6).�Signi®cant at the 5% level.

TABLE 3

Determinants of the Number of Promotions Received. Negative Binomial Model (II) (Absolute

t-values are in parentheses)

Males Females

Intercept ÿ2.231� (15.98) ÿ2.927� (11.70)

Individual characteristics

University 0.325� (2.62) 0.471�� (1.77)

Secondary 0.473� (4.66) 0.777� (4.15)

Previous work experience ÿ0.023� (4.50) ÿ0.028� (2.11)

Married 0.402� (3.68) ÿ0.111 (0.66)

Job characteristics

Firm tenure 0.083� (5.64) 0.092� (3.11)

Tenure squared/100 ÿ0.103� (2.89) ÿ0.091 (1.12)

On-the-job training 0.425� (4.97) 0.839� (3.98)

Permanent contract 0.310� (2.56) 1.020� (3.80)

Part-time ÿ0.595� (3.02) ÿ0.611� (2.44)

Professional occupation ÿ0.118 (1.55) ÿ0.397 (1.64)

Firm characteristics

Administration 0.011 (0.09) ÿ0.395 (1.59)

State ®rm 0.681� (4.61) ÿ0.548�� (1.76)

Private (large) 0.892� (6.15) 0.225 (0.49)

Private (medium) 0.872� (6.89) 0.786� (3.67)

No classi®cation 0.332� (2.36) ÿ0.564�� (1.86)

ä̂ 0.560� (6.82) 0.789� (3.84)

Mcfadden's R2 14.1 17.9

Log-likelihood (L) ÿ1467.5 ÿ532.7

Restricted log-L (L0) ÿ1708.9 ÿ648.7

Likelihood ratio test (LR) 482.8 232.0

Number of observations 1424 856

�, ��Signi®cant at the 5%, 10% level.

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Sicherman and Galor (1990). In fact, these authors deduce from their model

that an important part of the return to investment in education is obtained in

the form of greater promotion opportunities within the company. We ®nd that

the comparative advantage for university graduates and secondary academic

levels is greater for women than for men but this advantage is not statistically

signi®cant.7 Nevertheless, notice that people with secondary education enjoy

higher promotion returns than those with university education, for both the

male and female samples. We could infer that after primary education it

becomes more dif®cult to be promoted the higher the initial education

position. In this way, workers with secondary-level education could have

more upward professional changes over their careers than university gradu-

ates, and ®nally can reach a high position in the ®rm. In sum, we observe that

there are differences by education but not by gender.

Accumulated experience in previous jobs has a negative in¯uence on

professional careers in the curent job.8 We interpret this seemingly strange

result in two ways. First, it is possible that when an employer gives

promotions to employees general experience in the labour market is consid-

ered as a sign of high external labour mobility, and because of this is viewed

as a handicap for worker promotion in the ®rm in comparison to other

workers. Thus, when the current employer makes decisions regarding the

promotion process, he values the length of time spent and expected by

individuals in his ®rm but not the time spent with other employers. This is

like a reward for loyalty to the ®rm and a penalty for inter-form mobility.

Second, it is possible that previous experience partially picks up the effect of

the worker's age on the probability of promotion, and so the negative sign of

the coef®cient may indicate that older workers have less probability of

promotion. If an employer views a promotion as an investment in training

and loyalty, a negative coef®cient of the variable previous experience can be

due to the employer preferring to invest in young people, because by doing

this he expects to obtain a higher return on such an investment.

A married man has a greater opportunity for additional promotion than a

single, widowed, or separated man, possibly due to the fact that the employers

detect lower mobility and greater loyalty to the company because of his

family responsibilities. In contrast, employers do not distinguish between

married and unmarried women. This diverging in¯uence of marriage on

promotion opportunities is consistent with Lazear and Rosen's (1990) argu-

ment that employers expect higher exit rates from women.

7In order to verify whether the gender differences in coef®cients are signi®cant, in the Appendixwe report estimates for the whole sample. Differences in coef®cients for males and females aretested by means of interaction terms between each explanatory variable and a dummy for males.

8This negative relationship between previous work experience and promotion opportunities hasalso been found in the empirical literature in Groot and Maassen van den Brink (1996).

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Tenure increases the probability of receiving an additional promotion for

both genders. This is an intuitive result because we expect that the greater the

years spent in the ®rm, the greater the probability of an additional promotion

for the worker, ceteris paribus. Moreover, there is no signi®cant difference

between them. On the other hand, the negative sign of tenure squared in

males shows that internal mobility increases with tenure in the ®rm, but not

in a linear way; however, this difference is not statistically signi®cant by

gender.

The job-related training received in the ®rm increases the upward mobility

of both men and women. Moreover, the advantage for women is statistically

signi®cant. We interpret this diverging in¯uence of training in the framework

of Lazear and Rosen's model according to which females have to meet higher

standards of ability in order to receive training and thus to be promoted.

However, having received training, they have a higher probability of being

promoted. This suggests that an employer invests in professional training for

a woman when he is sure about her permanence at the ®rm and when her

productivity is higher than that of a man for the same job. For this reason,

when a women receives training she has more probability of promotion than

a man with the same investment. It seems that women obtain higher returns

from their speci®c training but face more dif®culty in receiving training.

Women with a permanent contract have a higher probability of being

promoted than men with the same kind of contract. This result is also in line

with :azear and Rosen's (1990) model because after the employer is sure

about the permanence of the woman in the ®rm, she has a higher probability

of being promoted than a man with the same kind of contract. Moreover,

working part-time makes it harder to receive promotion for both sexes. The

dummy variable for highly quali®ed workers9 is not statistically signi®cant in

either sample. Nevertheless, the negative sign of the coef®cient would

indicate that the higher the initial job position of the worker the lower the

probability of promotion, and so, the lower the number of upward changes in

their professional careers.

Finally, there are large differences in the coef®cients obtained by gender

in the promotions equations depending on whether people are working in a

public or private ®rm and, at the same time, on the size of the private ®rm.

For males, except for those working in the Administration, the probability of

promotion is higher than in the small ®rm, and the return increases according

to the ®rm's size. However, in the female sample, there is only a positive

advantage if the woman works in medium size ®rms, because otherwise the

coef®cient is not statistically signi®cant or the opportunities of promotion are

smaller. However, note that there are only signi®cant differences by gender in

9We also experimented with seven groups of occupation but these were found to be insigni®cant.

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large, state, and `without classi®cation' ®rms, with a positive advantage for

men. Thus, actual differences in gender distribution according to kind and

size of ®rm could explain the observed unequal promotion frequency.

In common with the literature, we assume that the estimated coef®cients,

â̂m and â̂ f , are the return that males and females obtain, in terms of the

probability of upward mobility in the ®rm. We use these as values to obtain a

different promotion distribution by gender depending on the assumption

made about the productive characteristics of the individuals and the way in

which the market rewards such characteristics in order to receive an

additional promotion in the ®rm. The results of these simulations and the

comparison with the actual promotion distribution for each gender are shown

in Table 4.

First, columns (1) and (2) of this table show the observed promotion

distributions, in percentages, in the sample for men and women, respectively.

The predicted distributions according to the estimated models, that is, using

â̂m and â̂ f , are presented in columns (3) and (4) for each gender. We can

observe that the ®t of the models is quite good. After this, we conduct a

simulation exercise: the characteristics of females are evaluated according to

the way that the market rewards males in terms of promotion opportunities.

Formally, we use â̂m to compute the ®tted values10 for the female sample.

The hypothetical female distribution of the number of promotions received

TABLE 4

Actual and predicted promotion distributions by gender�

Predicted (%)

Actual (%) Males Females

Number of Males Females with â̂m with â̂ f with â̂m

promotions (1) (2) (3) (4) (5)

0 61.2 77.0 61.7 79.0 69.7

1 14.6 13.4 20.9 13.7 19.1

2 10.5 5.0 8.3 4.0 6.2

3 7.4 2.6 4.0 1.6 2.5

4 3.3 0.7 2.1 0.7 1.1

>5 3.0 1.3 3.0 1.0 1.4

�â̂m and â̂ f are the estimated coef®cients for the male and female samples from the negativebinomial models of Table 3.

10Following Belman and Heywood (1990) and Jones and Makepeace (1996) we compute thepredicted probability of having each number of promotions for each individual and take the mean ofthese probabilities across all individuals rather than the probability for an individual with averageendowments.

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according to that is shown in column (5). We observe that 77% of females

declare having no promotions, the estimated model allocated 79%, and

®nally, if female endowments were rewarded in the same way as male ones,

the percentage of women without promotions would decrease to 70%, that is,

seven percentage points under the actual values. At the same time, also

according to column (5), we observe how the percentage of women with 1, 2,

and more promotions would increase if they were rewarded in the same way

as men.

We conclude that when female endowments are evaluated in the same way

as males ones, the resulting promotion distribution is more favourable for

women, that is, 30% of the female population would have received some

promotion in their career, against the actual 23%.

V. Discrimination in Promotion Opportunities

In the previous section we observed that workers are treated in the ®rm in a

different way depending on their gender. In this section we divide the

promotion differential by gender into two parts. One of them can be

attributed to the differences in human capital between the male and female

samples (endowments) and the other is associated with the differential

opportunities for upward mobility (unexplained residual). To this end, we

apply a variant of Oaxaca's decomposition method (Oaxaca, 1973), as

follows.

Let the sample relative frequencies of promotions by FRm and FRf for

males and females, respectively. The mean number of promotions can be

obtained in the following way:

Pm �X

k

kFRm and Pf �X

k

kFRf k � 0, . . . , k m (7)

where k is the number of promotions received, and k m the maximum number

of promotions in the sample. Using the maximum likelihood estimates, â̂m

and â̂ f , the mean number of promotions ®tted by the model can be obtained:

P̂m �X

k

k[Prob(k, X m, â̂m)] (8)

P̂f �X

k

k[Prob(k, X f , â̂ f )] (9)

where Prob(k, X i, â̂ j), j � m, f, is the expected probability of having k

promotions in the sample, given the sample characteristics of individuals, X i,

and the set of estimated parameters â̂ j. This is equal to the average value of

the probabilities of each individual having k promotions.

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The promotion differential between groups can thus be derived:

P̂m ÿ P̂f �X

k

k[Prob(k, X m, â̂m)]ÿX

k

k[Prob(k, X f , â̂ f )] (10)

The gap in gender-estimated promotion probabilities can be broken down

into two parts:11

P̂m ÿ P̂f �X

k

k[Prob(k, X m, â̂m)]ÿ Prob(k, X f , â̂m)] endowments

�X

k

k[Prob(k, X f , â̂m)ÿ Prob(k, X f , â̂ f )] residual (11)

In equation (11) the ®rst summation on the right holds the male coef®-

cients constant but allows individual characteristics to vary,12 that is, it

evaluates the difference in observed productivity characteristics of males

(X m) and females (X f ) according to the return obtained in the estimated

promotion model for males ( â̂m). Implicitly, we assume that in a world

without discrimination regarding internal mobility opportunities, women

would be treated in the same way as men and so we use the male coef®cients

( â̂m) to evaluate the endowment differential by gender. The term in the

second summation of the equation (11) holds female characteristics constant

but allows the coef®cients to vary. Thus, this term is the gender difference in

predicted promotions that results from the gender differences in the coef®-

cients on the variables explaining promotions, having eleminated that part

which is due to gender differences in the values of the explanatory variables

when their effects are evaluated using the male coef®cients. This term is a

residual term. It holds when the observed productivity effect is eliminated.

In summary, expression (11) shows that apart from endowment differ-

ences, a gender gap remains in the estimated probabilities of promotion due

to different coef®cients between males and females.

Table 5 presents the results of the above decompositon for promotion

differential by gender.13 From equation (10) we calculate that the estimated

differential in the average number of promotions between men and women is

0.410. By using equation (11) we ®nd that gender differences in human

capital explain 66% of this gap, and 34% cannot be explained by endowment

differences. Moreover, in relation to the ®rst part of the decomposition, we

11A similar decomposition in a probability model appears in, among others, Brown, Moon andZoloth (1980), Jones and Makepeace (1996), Waddoups, Daneshvary and Assane (1995) andWinter-Ebmer and ZweimuÈller (1997).

12Equation (11) is obtained by adding and subtracting Prob(k, X f , â̂m) to equation (10).13As before, I calculated the average probabilities rather than the probability for an individual

with average endowments.

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observe that the higher proportion of married males, with higher tenure and

related on-the-job training, a permanent contract, full-time, and working in a

large ®rm14 are the main factors behind the explained differential by

endowments.15 On the other hand, we ®nd that the unexplained residual

component is mainly due to differences in the probability of promotion for

males and females according to the ®rm in which they work, and because

marriage offers higher opportunities for promotion for men than for women,

whereas to have received on-the-job training in the ®rm or a permanent

contract leads to the expectation that women are more likely to move into

better grades than men.16

It is worth noting that we are unable to distinguish what part of this

unexplained residual could represent average differences in unobserved

productive characteristics, such as a higher quitting rate among women

looking after children, and what part to market discrimination.17 In this sense,

Aigner and Cain (1977) and Lazear and Rosen (1990) point out that no

assumption of between-group discrimination is warranted if mean separation

rates are correctly estimated by the ®rm. However, as Winter-Ebmer and

ZweimuÈller (1997) point out, if feedback effects exist, the denial of promo-

tion may indeed increase female quitting behaviour. According to this

argument, the quit decision must be made endogenous but this cannot be

done with the current data.

In sum, the analysis shows that the difference in productive characteristics

by gender does not totally explain the promotion differential. On the contrary,

a part of the favourable advantage which exists for men cannot be attributed

to observed factors.

TABLE 5

Decomposition of the estimated average difference in promotions

Total %

Observed differential 0.419

Predicted differential� 0.410 100%

Explained part (Ä productivity)�� 0.271 66%

Unexplained part (Ä coef®cients)�� 0.139 34%

�This number is based on binomial estimates from Table 3.��Based in equation (11).

14Note that only 5% of women are working in a large ®rm, whereas this ®gure is 10% for men.15This ®nding is obtained by comparing the average of the explanatory variables by gender.16This ®nding is obtained by comparing the estimated coef®cients by gender.17This helpful comment is due to an anonymous referee.

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VI. Conclusions

In this paper we analyze internal mobility by gender in the Spanish labour

market. To this end we use a sample from the Encuesta de Estructura,

Conciencia y Biogra®a de Clase (1991) and consider the number of promo-

tions received by workers in their current ®rm as our proxy for worker

mobility. We try to explain why women, on average, have a professional

career with fewer promotions. Count data models are estimated in order to

®nd the main determinants of receiving an additional promotion in the ®rm

by men and women separately. The more interesting empirical results are,

®rst, that human capital variables are very signi®cant for both sexes. Formal

education, job-related training, tenure in the company, and a permanent

contract increase the probability of receiving an additional promotion.

Second, returns to training and a permanent contract are signi®cantly larger

for women than for men.

By using the estimated coef®cients for the male and female samples we

®nd that only a part of the unequal promotion distribution by gender is

explained by differences in endowments and the rest can be attributed to

differences in market returns to such endowments. With a variant of Oaxaca's

decomposition approach we ®nd that around 34% of the observed gap in

average promotions is asociated with market discrimination and differences

in unobservable characteristics. In particular, there are better opportunities

for promotion for men when they are married and working in a state ®rm or a

large private ®rm, whereas women have a better position when they receive

on-the-job training and have a permanent contract. Thus, it would be of great

interest to know more about the distribution of workers according to the kind

and size of ®rm, with the aim of better understanding internal mobility in

Spain.

References

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Appendix

TABLE A.1

Determinants of the Number of Promotions Received. Negative Binomial Model (II). Whole Sample (Absolute t-values are in parentheses)

Intercept ÿ2.914 (11.95)�Individual characteristics

G 0.680� (2.45)

University 0.456�� (1.76) University 3 G ÿ0.128 (0.45)

Secondary 0.770� (4.23) Secondary 3 G ÿ0.293 (1.41)

Previous work exper. ÿ0.027� (2.07) Previous work exper. 3 G 0.004 (0.31)

Married ÿ0.110 (0.65) Married 3 G 0.511� (2.57)

Job characteristics

Firm tenure 0.091� (3.18) Firm tenure 3 G ÿ0.008 (0.25)

Tenure squared/100 ÿ0.089 (1.14) Tenure squared/100 3 G ÿ0.015 (0.17)

Training 0.821� (4.11) Training 3 G ÿ0.396�� (1.83)

Permanent contract 1.008� (3.76) Permanent contract 3 G ÿ0.700� (2.39)

Part-time ÿ0.595� (2.51) Part-time 3 G 0.002 (0.007)

Professional occup. ÿ0.393�� (1.66) Professional occup. 3 G 0.217 (0.83)

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Firm characteristics

Administration ÿ0.377 (1.55) Administration 3 G 0.387 (1.43)

State ®rm ÿ0.525�� (1.73) State ®rm 3 G 1.206� (3.57)

Private (large) 0.240 (0.54) Private (large) 3 G 0.652�� (1.64)

Private (medium) 0.751� (3.65) Private (medium) 3 G 0.122 (0.50)

No classi®cation ÿ0.563�� (1.85) No classi®cation 3 G 0.896� (2.67)

ä̂ 0.599� (7.88)

Log-likelihood (L) ÿ2035

Restricted log-L (L0) ÿ2105

Likelihood ratio test (LR) 139

Number of observations 2280

�, ��Signi®cant at the 5% and 10% level; G takes value 1 for males.

Pro

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