5.1 The Original Decompositions

Oaxaca (1973) used a 12-variable model to investigate the gender wage gap in the natural-log of hourly wages (ln-W). The model for females only contained the number of children, and both male and female models contained another eleven sets of variables, of which nine sets were comprised of indicator variables. These variable categories were: experience 7 (linear and quadratic terms); education (linear and quadratic); worker class; industry (2-digit); occupation (2-digit); presence of health conditions (indicator variable); part-time status (indicator variable); migration; marital status; urban area type; and region. The wage differentials were calculated separately for whites and blacks, with a log differential of 43% for whites (non-log differential of 54%) and a log differential of 40% (49%) for blacks. Industry, in particular, and also occupation and class of worker (union, government, or self-employed), had the largest effects on the gender pay gap.

Blinder (1973) reported the results from two decompositions: a white/black wage differential and a male/female wage differential for whites. The second decomposition will be examined here. The structural wage regression used 12 variables, in which only the two age variables were continuous: age (linear and quadratic); region; local labour market; migration; health conditions; education (indicator variables); occupation; union membership (indicator variable); veteran status (indicator variable); seasonal employment (indicator variable); vocational training (indicator variable); and tenure (indicator variables). The reduced form regression used ten variables and, again, only the age variables were continuous: age (linear and quadratic); region; local labour market; migration; health conditions; seasonal employment (indicator variable); siblings (two indicator variables); father's education; parent's economic status; and childhood residence.

The structural differential found large influences of age, education, and local labour market conditions (Blinder, 1973). Age explained most of the difference in the gender pay gap, as women's wages did not tend to rise over the life span whereas the wages for men did tend to rise. The other two largest contributors were education and local labour market conditions. While men and women had the same average endowments of these factors, men received greater returns for education and were less affected by the local labour market conditions. In the reduced form differential, age accounts for the whole gender pay gap. Again, men were less affected by the local labour market conditions compared to women.

7 Defined as the Mincer proxy for experience.

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