Resultado de encuestas aplicadas

The theoretical background to occupational attachment is briefly sketched and follows in spirit the methodology outlined in both Hill (1983) and Trost and Lee (1983). Occupational attachm ent may be viewed in a utility based framework. Each individual is assum ed to select from M m utually exclusive categories. The individual is further assumed to com pute the utilities attainable from each category and choose that one which provides the maximum utility level.

M ore conveniently it is possible to express the maximum attainable utility fo r each of the M

alternatives in terms o f indirect utility functions. For the j * occupational category, for instance, this m ay be expressed as

Vy -V (W ;.Y , T .K y.R ) (

6

6

wy is the w age offer associated with occupation j

Y is non-labour income, T is the endow nment o f time,

Ky is a vecto r o f job characteristics associated with occupation j ,

and R is a vector o f exogenous variables. 33

The utility based framework need not be interpreted as being inconsistent with labour m arket discrimination. For example, wj and/or the vector Kj m ay differ across gender due to, for exam ple, an em ployer’s taste for discrimination. Lower wage offers and/or unfavourable job characteristics may reduce a female’s indirect utility and hence her willingness to select given occupations.

The indirect utility function may be decomposed into stochastic and non-stochastic parts. If

\ j i is the maximum utility attainable for individual < if occupation j is chosen then the indirect utility function may b e expressed as

T h e probability that th e i * individual chooses the j * occupational category is given by

Assum ing the stochastic components have independent and identical Weibull distributions then th e difference between the error terms (■ * -■ > ) has a logistic distribution and the resultant m odel is the m ultinom ial logit model due to M 'Fadden (1973). As is obvious from the above only binary com parisons are involved and this follows from the strong behavioural assumption o f the independence o f irrelevant alternatives which gives the logit m odel its form.

For estimation purposes if S> is replaced by X,y, then the m ultinom ial logit model m ay be expressed as

w here X, is assumed to capture all the relevant demand and supply effects contained in the indirect utility function and y is vector o f unknown occupational coefficients. Schmidt and Strauss (1976) and B row n et ai. (1980) employed this particular model in estimating occupa­ tional attachment equations. Occupation is treated as a categorical, unordered, discrete polycho- tom ous variable and th e logistic approach is used to estimate the im pact o f a vector o f explana­ V* - Sx + «X (6.7)

** -P r[ v j» > V * . tor* «•>. J - I ... J#] (6.8)

o r alternatively.

(6.9)

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tory variables on th e probability o f being in a particular occupation relative to another. The esti­ mation o f a m ultinom ial logit model o f occupational attainment allows prediction o f an individual’s occupational level on the basis o f that individual's set o f personal characteristics.

Miller and V o lk er (1983) suggest advantages for the use o f an ordered probit approach and Miller (1987) uses th is approach in an occupational application. However, for the purposes o f this chapter the use o f such an ordered approach is avoided for two reasons. Firstly, the sequen­ tial ranking o f occupations should be on the basis o f life-cycle earnings. In terms o f the young workers used in this sam ple estimation o f life-cycle earnings is not possible and to the author’s knowledge no additional evidence on this particular subject is available for Ireland. Secondly, use o f the ordered p ro bit approach may possess greater advantages if the focus o f attention (as in the M iller and V o lk er (1983) case) is vertical occupational mobility. In the context o f this chapter the occupational mobility o f interest is o f the horizontal kind and this allows the unor­ dered framework pro vid ed by the multinomial logit to be exploited.

A reduced form equation is assumed w hich describes the interaction o f the relevant demand and supply conditions in the labour market and determines an individual’s attachment to a certain occupation. Because o f the reduced form nature o f the estimating equations it is not possible to provide unambiguous interpretations for the coefficient estimates in term s o f explicit demand or supply side effects. A n eclectic theoretical view should be adopted in the interpretation o f the coefficient estimates. In terms o f (6.10) above only the parameters o f h i- 1 o f the M occupational categories can be identified. The following normalisation £ ym * 0 is used in estimation and (6.10) becomes

Alternatively the above expression may be expressed in term s o f the log odds o f being in a cer­ tain occupational category and this is a function that is linear in its param eters and is given by

(6.12) .

- * 1 > (6.12)

A dummy variable is used to define the event o f an individual being in a certain occupation.

ytJ - 1 if the i* individual falls into the J* category and - 0, otherw ise. In this case the log likelihood function is given by

where N equals the num ber o f observations in the sample. M axim um likelihood m ethods are used to estimate (6.13). As pointed out above in estim ation th e param eters of the AT* occupa­ tional category are normalised to zero. The interpretation o f the estimated m ultinomial coefficients are therefore in relation to this omitted category. Furtherm ore, the inclusion o f inter­ cept terms in the multinomial logit model ensures that the m ean o f the predicted probabilities equals the means o f the actual probabilities. This is important in term s of the "index num ber” decomposition.

The next step is to use the information concerning occupational attachment in the estim a­ tion o f the occupational wage equation. If one starts by assum ing th at the market wage in the j *

occupation is given by where

W j is the logged m arket wage for the j * occupation,

Zj is a vector o f exogenous variables assumed to determine the w age in the j * occupation, (J, is a vector of unknown parameters,

and t j is an error term for which the usual properties are assumed satisfied.

I f a systematic process governs the observation o f the j * sam ple o f wages and if the error term in that process is correlated with t j then the application o f O L S to the above equation leads to biased and inconsistent coefficient estimates. Following Lee (1 9 8 3 ) the wage equation to be estimated may be modified to take into consideration the effects o f th is occupational selectivity bias. As Lee (1983) shows the wage equation conditional on category j being chosen is

(6.13)

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W ; « Z jfij - OjPj (6.15) where

+ is the standard normal density function,

J is a strictly increasing transformation that transforms the random variable associated with the occupational attachm ent equation into a standard normal variate where J » 0 " ‘F where O is the standard normal distribution function and F is the probability distribution function. Oj is the standard error o f the disturbance term e>, and p, are the correlations between e; and the error term from the occupational attachment equation for the i* individual.

Estimation is carried out in a two step framework analogous to the Heckman procedure em ployed in chapter five. Firstly, maximum likelihood estimation is used to obtain estimates for X from (6.11). Then, these estimates are inserted into the wage equadon o f (6.15) which may be re-written as

where F(XiY>) are the predicted probabilities from the multinomial logit model o f (6.11). More conveniently this equation may be expressed as

cation o f OLS to the above equation (6.17). The disturbance terms are obviously heterosce- dastic and Lee el al. (1980) outline an appropriate variance/covariance matrix in this regard36. However, this proved computationally difficult to calculate in the context o f this chapter and so the W hite (1980) heteroscedastic consistent variance/covariance matrix is reported below for the occupational wage equations. Though the White variance/covariance matrix corrects for heteros- cedasticity in the regression model it doesn’t take into consideration the fact that a predicted 56

56 See Chapter Five. *ectma 5.3.

(6.16)

where everything is as above with the exception o f

selectivity bias term is used in estimation. Nevertheless, though the variance/covariance matrix reported may be inappropriate differences are not anticipated to be large and as M addala (1983) points out little is even lost in the use the OLS variance/covariance matrix. However, for the pur­ poses o f this chapter the White (1980) consistent variance/covariance matrix estim ates are reported for both the OLS and the selectivity bias corrected occupational wage equations o f (6.14) and (6.17) respectively.

The inclusion o f the selectivity bias term has clear implications for the "index num ber decomposition". The modification suggested by Reimers (1983) and used in chapter five will also be used in this chapter. Thus, in summary, occupational attachment equations and wage equations are estimated for each gender. On the basis o f the male occupational equation estim ates female occupational distributions are simulated in order to obtain a handle on the occupational segregation effects. In addition female wages will be simulated on the basis o f male wage struc­ tures for each occupational category to ascertain explained and unexplained wage effects within occupations. The analysis is presented for both observed wages and the wage offers associated with the consistent estim ator.

In terms o f the dependent polychotomous variable five relatively broad occupational categories are assumed. These are

(a) Professional and M anagers, (b) Clerical and Interm ediate Non-Manual. (c) Other Non-Manual,

(d) Skilled. (e) Semi and Unskilled.

The five-way categorisation is dictated by the need to have a sufficient number o f observa­ tions in all the relevant groups. A finer classification would lead to a reduction in the num ber of observations in particular estimating cells and w ould place the results in a somewhat dubious light. Too few females in the semi-skilled occupational category prevented a wider categorisa­ tion. However, it is felt that the classification used is broad enough to allow for some confidence

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in the estimation results and fine enough to exam ine the issue of within occupation wage discrim­ ination.

Finally, the omitted occupational category in terms o f estimation is the Semi and Unskilled category. Thus all the occupational equation coefficients should be interpreted in relation to this particular category.

6.4 Data

The number o f individuals used in the analysis in this chapter is the same as chapter five, however, some o f the variables used are slightly different. The sub-sample was allocated across the five occupational categories outlined in section 6.3 on the basis o f the Census o f Population Classification of Occupations (1981). Higher Professional, Lower Professional, Self-Employed, M anagers, Salaried Employees were allocated to (a). Intermediate Non-Manual Workers to (b), and O ther Non-Manual Workers were allocated to (c). Skilled Manual W orkers were allocated to (d) and Semi-Skilled Manual Workers and the Unskilled Workers were allocated to (e).

The full set o f variables used in the estim ation o f the reduced form occupational equations for both gender groups are

(i) An education variable defined in terms o f the number o f years spent in post-compulsory edu­ cation.

(ii) A previous experience variable defined as the time spent working in jo bs prior to the current one. The unit o f measurement is years.

(ill) A set o f residence o f schooling dum m ies controlling for the area o f an individual's last school prior to leaving compulsory education. The three estimated dum m ies are Dublin city and county, the east and midlands and the southern region with the om itted category schooling in the north-w est

(iv) A duration o f unemployment variable defined in terms o f the aggregate num ber o f months o f unemployment experienced by an individual since leaving school.

thcir current job.

(vi) A set o f Father’s occupational dummies designed to capture parental background influences on occupational uptake. Tw o such dummies are defined, one for non-manual and another for the manual category. The agricultural category is treated as the reference category in estimation.

The occupational wage equations are estim ated using variables (i), (iv) and (v) above in addition to

(vii) A full experience variable defined in terms o f two linear splines with a four year s p lit (viii) A set o f two firm size dum m ies (see chapter five).

(ix) A set o f four current region o f residence dummies. The four estimated dummies are D ublin city and county, the east, midlands and the southern region with the omitted category current residence in the north/west57.

A num ber o f other variables were also used in estimation but to little statistical effect Industry dummies were used in the wage equations but proved sensitive to alterations in the specification and thus are not included. F o r the occupational equation Father’s occupation was broken down into a finer classification but some o f the estimated coefficients possessed high standard errors. The num ber o f jobs held by the individual since leaving school was also used in both o ccupa­ tional and wage equations but again to little effect.

The total number o f observations for which no missing values are recorded is 2827, o f which 1505 are male and 1332 female. Appendix A1 contains a set o f summary statistics fo r the full set o f variables used in the estimation.