Gender Differences in Wages and Human Capital: Case Study of Female and Male Urban Workers in Mexico from 1984 to 1992
Article Sidebar
Main Article Content
Abstract
Article Details
Authors publishing work in this journal agree to the following conditions:
Authors retain copyright and assign first publication rights to the journal Frontera Norte (RFN), with the texts registered under an Attribution-NonCommercial-NoDerivatives 4.0 International Creative Commons license (CC BY-NC-ND 4.0), which allows third parties to use published material provided they give credit to the authors and acknowledge this journal as the first publisher.
They authorize the reproduction, publication, translation, communication, and transmission of their paper and all accompanying material, publicly and in any form and by any means; its public distribution in as many copies as required; and public communication thereof in any form, including making it available to the public through electronic means or any other technology, and solely for dissemination and scientific, cultural, and non-commercial purposes.
Authors may enter into further independent contractual agreements for the non-exclusive distribution of the version of the paper published in this journal (for instance, to include it in an institutional repository or personal webpage, or publish it in a book), provided it is not for commercial purposes and they clearly state that the work was first published in Frontera Norte (RFN) [and add the corresponding bibliographical record: Author/s (Year). Title of paper. Frontera Norte, volume (number), pp. doi: xxxx].
To that end, authors must submit the form assigning ownership of first publication rights, duly completed and signed. This document is to be uploaded in PDF format as a complementary file on the OJS platform.
This work is released under an Attribution-NonCommercial-NoDerivatives 4.0 International Creative Commons license (CC BY-NC-ND 4.0)..
References
This is potential experience since it is calculated as age - years of formal schooling - 6. The advantage that male workers enjoy may be underestimated since women workers tend to have less continuous on-the-job experience than male workers.
For a more general discussion of changes in wage differentials in Mexico during this period, see Diana Alarcón and Terry McKinley, "Wage Differentials in Mexico from 1984 to 1992: A Profile of Human Capital and Earnings," paper presented for the Conference on The Impact of Structural Adjustment on Labour Market and Income Distribution in Latin America, San José, Costa Rica, September 7-9, 1994.
These percentages for 1992 are calculated based on a model with monthly wages, rather than the logarithm of monthly wages, as the dependent variable. We used this new model because for all urban workers the semilogarithmic model explained only 8.4 percent of the total variance in wages. For 1992 the standard Mincerian human capital model of wage determination became markedly less useful, in part because the relationship between years of education and the rate of increase in earnings became decidedly more convex and in part because a great deal of the increased variance in wages was not explained at all by differences in human capital among workers.
For a more general discussion of the unexplained nature of wage dispersion under structural adjustment in Mexico, see Terry McKinley and Diana Alarcón, "Widening Wage Dispersion under Structural Adjustment in Mexico," paper prepared for the Conference on The Impact of Structural Adjustment on Labour Markets and Income Distribution in Latin America, San José, Costa Rica, September 7-9, 1994.
For a discussion of issues of comparability between the 1984 and 1989 data, see Diana Alarcón, Changes in the Distribution of Income in Mexico and Trade Liberalization, El Colegio de la Frontera Norte, Mexico, forthcoming.
For the classic statement of a human capital model, see Jacob Mincer, Schooling, Experience and Earnings, Columbia University Press, New York, 1974. An alternative model which is often used to identify changing rates of return to schooling includes dummy variables for each level of education. For a recent example and statistical test, see Christopher R.S. Dougherty and Emmanuel Jiménez, "The Specification of Earnings Functions; Tests and Implications," Economics of Education Review, vol. 10, #2, 1991, pp. 85-98.
A more elaborate model of an earnings function is utilized for 1989 data in Diana Alarcon with Terry McKinley, "The Persistance of Labor Market Segmentation in Mexico in the Aftermath of Economic Restructuring," mimeo, 1994. Because of the inclusion of dummy variables for border states, poor states, urban areas, and gender, as well as other variables, the private rates of return to education are generally lower for this more comprehensive model than those reported in the current paper. A recent illustration of the reduction in estimated rates of return when dummy variables are included for geographical area and economic sector is found in Peter Griffin and Alejandra Cox Edwards, "Rates of Return to Education in Brazil: Do Labor Market Conditions Matter?" Economics of Education Review, vol. 12, #3, 1993, pp. 245-255.
The full expression for the rate of return to education is as follows: a1 + 2a2(mean S) + b1(l) + b2(mean X)(1). Generally this rate of return will be lower than that found for the first two terms since the third term tends to be a large negative relative to the positive fourth term.
See George Psacharopoulos, "Returns to Education: A Further International Update and Implications," Journal of Human Resources, vol. XX, #4, 1985, pp. 583-597; and George Psacharopoulos and Ying Chu Ng, "Earnings and Education in Latin America: Assessing Priorities for Schooling Investments," World Bank Working Papers WPS #1056, Technical Department, Latin America and the Caribbean Region, December 1992