Promotions in the Spanish Labour Market: Differences by Gender

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<ul><li><p>Promotions in the Spanish labour market:differences by gender</p><p>DOLORES GARCIA-CRESPO</p><p>Department of Statistics and Econometrics, Universidad de Malaga, Spain</p><p>I. Introduction</p><p>In all labour markets, women earn a lower wage than men, on average.1 To</p><p>explain this gap a large amount of empirical research has been carried out, in</p><p>which special emphasis has been placed on individuals' productive character-</p><p>istics (Mincer and Polachek, 1974; Landes, 1977). This approach has</p><p>emphasized the human capital theory point of view, in the sense that workers</p><p>inuence their professional career through their educational and training</p><p>decisions. According to this theory, employers play a passive role in workers'</p><p>professional careers. In fact, the typical method used in these investigations</p><p>has mainly consisted in the estimation of wage equations by gender, where</p><p>the occupation and activity of workers are only included as control variables.</p><p>On the other hand, some authors have insisted on the relevance of</p><p>professional careers to explain the salary distribution according to sex</p><p>(Duncan and Hoffman, 1979; Malkiel and Malkiel, 1973). More recently,</p><p>there has been an increasing interest in discrimination regarding opportu-</p><p>nities for professional advancement in many countries (Cabral, Ferber and</p><p>Green, 1981; Groot and Maassen van den Brink, 1996; Jones and Makepeace,</p><p>1996; Lewis, 1986; Olson and Becker, 1983; Winter-Ebmer and Zweimuller,</p><p>1997). Most of these papers conclude that women are required to have higher</p><p>qualications than men at the time of promotion, or that they have fewer</p><p>opportunities for obtaining jobs with higher probabilities of promotion. In</p><p>Spain, Garcia and Malo (1996) analyze educational mismatch and its relation</p><p>OXFORD BULLETIN OF ECONOMICS AND STATISTICS, 63, 5 (2001) 0305-9049</p><p># Blackwell Publishers Ltd, 2001. Published by Blackwell Publishers, 108 Cowley Road, Oxford OX4 1JF, UK and 350Main Street, Malden, MA 02148, USA.</p><p>599</p><p>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.</p><p>1See Blau and Kahn (1996) for international comparisons of the wage gap by gender.</p></li><li><p>to internal mobility in the Spanish labour market, but they do not consider</p><p>differences by gender.</p><p>Following this second view, we consider that the internal mobility of</p><p>workers in the rm is a fundamental part of their professional career, and</p><p>therefore their wages. In this paper we use the variable number of promotions</p><p>received by workers in their rm, taken from a Spanish survey, as a proxy</p><p>variable of internal mobility. By using this data set, rst, we estimate count</p><p>data models to identify the main factors that determine mobility by gender;</p><p>second, regarding fewer promotions for women, we try to evaluate which part</p><p>of this can be attributed to lower productivity, and which part can be</p><p>associated with discriminatory behaviour in the market.</p><p>The structure of the paper is as follows. In section 2 we present the data</p><p>source used in our analysis and a description of the sample. Section 3</p><p>contains a short discussion about the econometric specication of the model.</p><p>The results of estimated count data models to explain the number of</p><p>promotions obtained by workers within rms are presented in Section 4. In</p><p>Section 5 we apply a variant of the Oaxaca decomposition method to measure</p><p>discrimination against women regarding mobility opportunities. The last</p><p>section contains the main conclusions.</p><p>II. The Data</p><p>The data source used for the empirical analysis is the Encuesta de Estructura,</p><p>Conciencia y Biograa de Clase (1991).2 It is a rich cross-sectional data set</p><p>which contains detailed information on 6632 individuals concerning perso-</p><p>nal, educational, and training characteristics and work histories. Moreover,</p><p>this survey contains information about the internal mobility of the workers.</p><p>They answered the following question: `How many promotions have you</p><p>received since you started to work with your current rm?', where to have</p><p>received a promotion means to work at a superior level with more responsi-</p><p>bility or authority. This information is available for those who are currently</p><p>employed as well as for those who were previously employed. We restricted</p><p>the sample to currently employed men and women aged 1965.</p><p>We should mention some limitations concerning the data. First, the fact</p><p>that career advancement is an essentially dynamic phenomenon means that</p><p>we would need longitudinal data for an analysis of internal mobility. However,</p><p>in Spain, logitudinal information is very scarce and there is no survey with</p><p>these characteristics yet, so we use cross-sectional data. This implies that we</p><p>cannot explain the probability of receiving a promotion, nor can we identify</p><p>2Financed jointly by the Spanish Statistical Institute, the Community of Madrid, and the Instituteof Women.</p><p>600 Bulletin</p><p># Blackwell Publishers 2001</p></li><li><p>jobs that offer promotion opportunities. We have only the accumulated num-</p><p>ber of changes in the current rm. A second limitation of our data set is that</p><p>we cannot distinguish between different kinds of promotion. This means that</p><p>we consider all promotions as the same sort of promotion; thus, we analyze</p><p>internal mobility in the rm only from a quantitative but not a qualitative</p><p>standpoint. On the other hand, a possible positive aspect of the data is that</p><p>individuals report the number of promotions received, so we do not have to</p><p>construct a measure of mobility, but can use it directly from the survey.3</p><p>Table 1 shows summary statistics by promotion status for men and</p><p>3For example, Winter-Ebmer and Zweimuller (1997) construct a proxy for internal mobility fromthe data.</p><p>TABLE 1</p><p>Summary Statistics: Means</p><p>Males Females</p><p>One One</p><p>No promotion No promotion</p><p>promotions minimum promotions minimum</p><p>Individual characteristics</p><p>Age (years) 36.88 41.82 33.46 36.95</p><p>University (d) 0.10 0.15 0.14 0.16</p><p>Secondary (d) 0.17 0.26 0.21 0.33</p><p>Previous experience (years) 11.14 7.63 6.79 4.28</p><p>Married (d) 0.60 0.80 0.44 0.51</p><p>Job characteristics</p><p>Firm tenure (years) 8.22 16.80 6.48 13.15</p><p>On-the-job training (d) 0.30 0.51 0.28 0.53</p><p>Permanent contract (d) 0.67 0.90 0.57 0.91</p><p>Part-time (d) 0.06 0.02 0.24 0.07</p><p>Professional occup. (d) 0.16 0.21 0.30 0.31</p><p>Hourly wage (pesetas) 613.10 808.93 562.24 723.85</p><p>Hourly wage female/male 0.92 0.89</p><p>Firm characteristics</p><p>Administration (d) 0.20 0.18 0.25 0.29</p><p>State rm (d) 0.06 0.13 0.10 0.06</p><p>Private (large) (d) 0.06 0.18 0.03 0.14</p><p>Private (medium) (d) 0.09 0.19 0.07 0.16</p><p>Private (small) (d) 0.48 0.23 0.38 0.27</p><p>No classication (d) 0.13 0.10 0.18 0.07</p><p>Sample proportions</p><p>by gender (%) 61.24 38.76 77.00 23.01</p><p>N 872 552 659 197</p><p>N for wage 687 437 563 154</p><p>Note: d indicated dummy variable.</p><p>Promotions in the Spanish labour market 601</p><p># Blackwell Publishers 2001</p></li><li><p>women. We distinguish those who declared that they had never received a</p><p>promotion from those who had received at least one promotion in their</p><p>current rm. The main features in this table are the following. First, there are</p><p>more promoted men than women. In fact, 77% of women have had no</p><p>promotion in their current rm. However, in the case of men, this gure is</p><p>61%. Second, for both genders, we observe that those who have received at</p><p>least one promotion are older, have longer tenure in their current job, have</p><p>less experience with their previous employer, and have received more on-the-</p><p>job training. Finally, in relation to wages, we nd two important differences.</p><p>One is that the salaries of promoted workers are higher, independent of their</p><p>gender; the other is that men earn more than women in any situation.</p><p>These data seem to show that women, on average, have a professional</p><p>career with less internal mobility than men, and this could partially explain</p><p>the wage gap by gender.4 Nevertheless, we cannot directly deduce from this</p><p>information whether this unfavorable situation for women is due to their</p><p>productivity characteristics or to different treatment by employers. To answer</p><p>this question an analysis has to be carried out with the variables which</p><p>determine the promotion opportunities for individuals in the rm.</p><p>III. Modelling Internal Mobility</p><p>One of the aims of the paper is to explain our proxy of internal mobility: the</p><p>number of promotions received by the workers in their current rm (Yi).</p><p>Since our dependent variable takes only non-negative integer values, we need</p><p>to estimate a count data model. The basic specication for this kind of data is</p><p>the Poisson regression model5 in which the observed values, yi, are i.i.d.</p><p>drawings from a Poisson distribution with parameter i, the probabilityfunction of the model being the following:</p><p>P(Yi yi) ei yiiyi!</p><p>, i . 0, yi 0, 1, 2, . . . , i 1, 2, . . . , n(1)</p><p>In order to incorporate individual characteristics, X i, including a constant,</p><p>the parameter i is specied as an exponential function to ensure the non-negativity of Yi:</p><p>i exp(X i) (2)Note that in this model, i is a deterministic function of X i and the</p><p>4The consequences of the lower internal mobility of women on the Spanish wage gap areanalyzed in Garcia-Crespo (1997).</p><p>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.</p><p>602 Bulletin</p><p># Blackwell Publishers 2001</p></li><li><p>randomness of the model comes from the specication of Poisson for</p><p>Yi yi. This model implies that conditional variance and mean are equal:E(Yi=X i) Var(Yi=X i) i exp(X i) (3)</p><p>This characteristic of equality of mean and variance is called equidispersion</p><p>and is a strong assumption for real data. In words, the Poisson regression</p><p>model states that individuals with identical covariates have the same expected</p><p>count i. That is, the individuals are heterogenous only with respect toobserved characteristics. Nevertheless, data frequently reject this restriction</p><p>and present overdispersion, that is, the variance exceeds the mean.6 Cameron</p><p>and Trivedi (1998) point out that failure of the Poisson assumption of</p><p>equidispersion may produce spuriously small estimated standard errors of ^.One way of relaxing this restriction is by saying that i, the mean of the</p><p>variable, incorporates a random component to capture additional unobserved</p><p>heterogeneity between individuals not contained in X i. With this assumption,</p><p>expression (3) is replaced by the following stochastic equation:</p><p>i exp(X i Ei) (4)where the random disturbance term Ei can reect specication errors as wellas the existence of omitted unobserved exogenous variables. Depending on</p><p>the assumption about the probability model for i, it is possible to obtain afamily of models for count data called negative binomial models. Cameron</p><p>and Trivedi (1990) distinguish two kinds of negative binomial model depend-</p><p>ing on the assumptions about the characteristics of i, the Poisson modelbeing a particular case of them:</p><p>Poisson: Var(Yi=X i) E(Yi=X i)Negative Binomial I (NegBin I): Var(Yi=X i) E(Yi=X i)(1 )Negative Binomial II (NegBin II): Var(Yi=X i) E(Yi=X i)[1 E(Yi=X i)]where is called the dispersion parameter of the model. Note that theconditional variance function of the NegBin I model is a multiple of the</p><p>mean, whereas this relationship is quadratic in the NegBin II model. In both</p><p>cases the dispersion parameter is to be estimated.To choose between Poisson and negative binomial models Cameron and</p><p>Trivedi propose testing 0 in the Poisson model in the following way.First, to choose the Poisson model instead of NegBin I, they propose</p><p>performing the following auxiliary regression by least squares:</p><p>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).</p><p>Promotions in the Spanish labour market 603</p><p># Blackwell Publishers 2001</p></li><li><p>(yi ^i)2 yi^i</p><p> ui (5)</p><p>where ^i exp(X i ^), with ^ being the estimated value of from the Poissonregression, and ui being an error term. The t-statistic for is asymptoticallynormal under the null hypothesis of equidispersion ( 0) against thealternative of overdispersion of the NegBin I form. Second, to test the Poisson</p><p>model against the NegBin II model, expression (5) is replaced by:</p><p>(yi ^i)2 yi^i</p><p> ^i ui (6)</p><p>In sum, in a rst stage we estimate the Poisson model and perform the</p><p>tests presented above. In a second stage, depending on the results of these</p><p>tests the appropriate model to describe the data is chosen. The models are</p><p>estimated by maximum likelihood.</p><p>IV. Results</p><p>The dependent variable in the estimated models is the number of promotions</p><p>received in the current rm. The independent variables are divided into two</p><p>groups. In the rst group, there are two dummies for formal education, years</p><p>of previous experience with other employers and a dummy for whether the</p><p>individual is married; in the second group there are job-related characteristics</p><p>such as years of tenure in current rm, tenure squared, a dummy for whether</p><p>the worker has participated in job-related training in the current rm, two</p><p>dummy variables for part-time and permanent employment contract, a</p><p>dummy for whether the worker is working in a professional occupation, four</p><p>dummy variables for size of the rm, and two dummy variables for whether</p><p>the worker is working in the Administration sector or in a public rm. First,</p><p>we estimate the Poisson model for men and women. Table 2 shows the results</p><p>of auxiliary regressions of the Cameron and Trivedi tests from these models.</p><p>According to the t-statistics, we reject the Poisson model against the NegBin</p><p>II model whose results we present in Table 3. In this table we observe that the</p><p>overdispersion parameter () is signicant in the male and female samples.The likelihood ratio statistics for the null hypothesis that the coefcients for</p><p>men and women are equal is 43.29 and the critical value, 20,05 26:3 with16 degrees of freedom. Thus, we can conclude that the parameter values are</p><p>different by gender.</p><p>Almost all the human capital variables show a signicant inuence on the</p><p>probability of receiving an additional promotion. First, formal education has</p><p>a positive inuence on the number of promotions received, in line with</p><p>604 Bulletin</p><p># Blackwell Publishers 2001</p></li><li><p>TABLE 2</p><p>Results of Overdispersion Tests in the Poisson Model</p><p>Alternative Models</p><p>NegBin I NegBin II</p><p>^ t-values ^ t-values</p><p>Males 0.31 1.03 0.63 3.06Females 0.43 0.83 0.67 1.91</p><p>Note: These tests are based in equation (5) and (6).Signicant at the 5% level.</p><p>TABLE 3</p><p>Determinants of the Number of Promotions Received. Negative Binomial Model (II) (Absolute</p><p>t-values are in parentheses)</p><p>Males Females</p><p>Intercept 2.231 (15.98) 2.927 (11.70)Individual chara...</p></li></ul>


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