Hello,
I'm using the Blinder-Oaxaca decomposition to study the wage differentials between males and females. I used logarithm or hourly wages to do that and reported the pooled method (is pooled a good idea or I was better reporting the two-fold or three-fold?). I read a lot of research papers including Jann's paper. I'm a little confused on how to pick a reference category (because I thought we are always comparing same category between males and females so for example,
- Looking at the married variable, I'm comparing married females to married males.. so won't married males be automatically considered the reference here for me..
- Same for educational attainment, when reporting a female with a bachelor degree, isn't a male with a bachelor degree considered automatically to be the reference group?
- Looking at working full time, is this coefficients comparing females working full time versus males working full time? or females working full time versus females working part time (which I think would be wrong)?. how would this be interpreted? any help will be really appreciated
Thanks
1325/5000
Good evening, I hope this answer is useful to you, although I am not very sure that it is a very good one. The analysis introduced by Chinhui, Murphy, and Brooks (1991) is based on the hypothesis that the two groups, in your case women and men, are perfect substitutes on the labor market in terms of the characteristics that the two groups possess. Therefore, changes in the supply and demand of certain sectors or changes in the relative supply of women do not generate changes in relative wages. The emphasis of this type of analysis falls rather on changes in the observable and unobservable characteristics that both women and men possess and on the change in the remuneration of these characteristics. However, if women and men are not perfect substitutes, ie the two groups have different product characteristics, changes in the price of these characteristics (such as, for example, changes in the remuneration of predominantly female occupations and sectors) or changes in the relative supply for these characteristics (such as would be an increase or decrease in women’s labor supply) become important factors in determining salary gap. The innovation of the three authors consists in the decomposition of the residue from the Oaxaca-Blinder decompositions based on its statistical distribution.
Goodbye!
Thank you so much for your reply. I was hoping to know more about the interpretation of the coefficients. I understand the idea of the Oaxaca-Blinder decomposition but once I ran the Oaxaca command on stata and I got my results, I was confused on how to interpret the. For example, if I have a total wage gap of 0.275 percentage points and from the explained part, I have the "married variable" contributing to this gap by 0.007 percentage point so I can conclude, that being marriage contribution to the total wage gap will be 0.007/0.275 = 2.55%. What is confusing me the most is how to pick (or what is the reference category). Being married contribute 2.55% to the total gap compared to what?
With great pleasure, I am sorry that the answer offered did not meet your expectations.
From the question you asked me, regarding the interpretation of the coefficients, what I can tell you is that a coefficient "ai" has the interpretation: the change by 1 of the value of the variable "xi" produces a change of the value "y" by "ai" units. Because the measurement scales are generally different, the interpretation of the coefficients in this sense can distort the image of the importance of independent variables in the model.
As a reference category, you can refer to the professional category compared to the field of education, where there is a tendency for women to be overrepresented, while in others, men are overrepresented. In some countries, predominantly female occupations, such as education and sales, offer lower wages than predominantly male occupations, even when the same level of experience and education is required. I hope this suggestion helps you.
All the best!
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