December 2006 ON THE MEASUREMENT OF ILLEGAL WAGE DISCRIMINATION Juan Prieto, Juan G. Rodríguez and Rafael Salas

Overview lMotivation lDiscrimination: classical view lA new appoach: endogenous allocation to groups lApplication to Germany and the UK lDiscussion

Discrimination: classical view Oaxaca-Blinder (1973) gender discrimination: Two wage equations for men (m), women (w): The women wage discriminatory gap (w.r.t men):

Discrimination: classical view (2) Oaxaca-Blinder (1973) gender discrimination: The average women wage discriminatory gap (w. r. to men): Quantiles analysis can improve estimates locally: Newell and Reilly, 2001 Albrecht, Björklund and Vroman, 2003 Gardeazábal and Ugidos, 2005.

Discrimination: latent class view Latent class models for gender discrimination: Two wage equations for two different structures type/class 1 and type/class 2: Plus a vector of probabilities of individual i belonging to groups 1,2. This is estimated simultaneous and endogenously by maximum likelihood estimators that allocates individuals to groups according to their human capital characteristics, observed wages and sex, and trying to reduce internal errors of the two wage equations (by maximizing the log likelihood function)…

Discrimination: latent class view (2) The log likelihood function: Where f(·) is the standard normal density function

Discrimination: latent class view (3) The vector of probabilities of individual i belonging to groups 1,2 are estimated as follows: First, we estimate a priori probabilities of i belonging to j: P ij By maximaizing the log likelihood function. Then we update ex post probabilities by using the Bayes rule and we obtain:

Discrimination: latent class view (4) The women wage discriminatory gap (w.r.t men) for i=women : which is more general than Oaxaca-Blinder, for i=women (and 1 is the high wage class)

Example: let i = Hillary Clinton [HC] Pick X HC the human capital characteristics of HC:

Example: i= Hillary Clinton [HC] (2) The HC gap is: which is “normaly” positive since: Oaxaca-Blinder assume

Applications European households panel data: Germany : Model 1 and 2 (extended) UK : Model 1 and 2 (extended) Tables

Discrimination orderings Distributional appoach: Jenkins 1994: discrimination curves from discrimination gaps in a decreasing order del Río et al. 2006: discrimination curves from discrimination gaps in an increasing order, eliminating negative gaps

Table 1: definitions NameDefinition Ln(W/H)natural logarithm of the hourly real wage EDUC1=1 if the individual has university studies; =0 otherwise EDUC2=1 if the individual has secondary school studies; =0 otherwise POTEXPpotential experience (present age-age when started work) POTEXP2square of potential experience TENUREyears of experience at the current firm TENURE2square of tenure

Table 2: summary statistics GERMANYUNITED KINGDOM MeanStd. Dev.MeanStd. Dev. Male’s Ln(W/H) EDUC EDUC TENURE POTEXP N observations N individuals Women’s Ln(W/H) EDUC EDUC TENURE POTEXP N observations N individuals

Table 3: Two models: class 1 GERMANYUNITED KINGDOM Model 1Model 2Model 1Model 2 Estimated coefficient Standard error Estimated coefficient Standard error Estimated coefficient Standard error Estimated coefficient Standard error Wage equation for latent class 1 CONSTANT EDUC1 EDUC2 TENURE TENURE2 POTEXP POTEXP2 NTUNEMP OCCUP1 OCCUP2 OCCUP3 OCCUP4 OCCUP5 OCCUP6 OCCUP7 INDUST1 INDUST2 SIZE1 SIZE2 PUBSEC 

Table 4: Two models: class 2 GERMANYUNITED KINGDOM Model 1Model 2Model 1Model 2 Estimated coefficient Standard error Estimated coefficient Standard error Estimated coefficient Standard error Estimated coefficient Standard error Wage equation for latent class 2 CONSTANT EDUC1 EDUC2 TENURE TENURE2 POTEXP POTEXP2 NTUNEMP OCCUP1 OCCUP2 OCCUP3 OCCUP4 OCCUP5 OCCUP6 OCCUP7 INDUST1 INDUST2 SIZE1 SIZE2 PUBSEC 

Table 5: prior probabilities GERMANYUNITED KINGDOM Model 1Model 2Model 1Model 2 Estimated coefficient Standard error Estimated coefficient Standard error Estimated coefficient Standard error Estimated coefficient Standard error Estimated prior probabilities CONSTANT WOMAN N observations N individuals Log-likelihood

Results Germany unambiguously more discrimination than in the UK GERMANYUNITED KINGDOM Model 1Model 2Model 1Model 2 LC Wage Gap OB Wage Gap Total Wage Gap

Results

Conclusions lA new appoach: endogenous allocation to groups lApplication to Germany and the UK shows positive discrimination bias of Oaxaca-Blinder model lPositive gender discrimination in both countries

July 2006 ON THE MEASUREMENT OF ILLEGAL WAGE DISCRIMINATION: THE MICHAEL JORDAN PARADOX Juan Prieto, Juan G. Rodríguez and Rafael Salas