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June 1984, Vol. 107, No. 6
Investigating the differences
in weekly earnings of women and men
Studies seeking to identify and rank the most important reasons for the earnings disparity between men and women have proliferated in recent years. Although the many compendiums of such studies frequently emphasize different viewpointsof business, government, or academiathey have one aspect in common: each reports an astonishingly wide variance in the explanatory power of the factors used in the studies. For example, in a summary of 16 studies published by various analysts between 1964 and 1979, Cynthia Lloyd and Beth Niemi show that the variables in these studies explained from little or none of the sex-earnings gap to as much as 71 percent.1 Such large differences arise mostly from the variables selected for analysis, the measure of earnings used (for example, hourly, annual), and the source of the data. In general, models employing only a small number of variablesfor example, age, race, and educational attainmentexplain far less of the earnings gap than those with many more variables, including occupational detail, hours worked, and several work experience items.
This article looks at sex-earnings differences using a relatively newer data series published by the Bureau of Labor Statistics. The information comes from the Current Population survey, conducted by the Bureau of the Census for the Bureau of Labor Statistics. The monthly survey includes data on how much full-time wage and salary workers usually earn per week, by race, age, education, occupation, hours worked, and several other characteristics. Most of the analysis is based on a statistical technique called standardization. This technique permits us to examine each characteristic at the macroeconomic level, and then to estimate what the earnings of women would be if, for each characteristic, the distribution of women had been the same as that for men, and all other characteristics remained unchanged.
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1 Cynthia B. Lloyd and Beth T. Niemi, The Economics of Sex Differentials (New York, Columbia University Press, 1979). The table beginning on page 232 summarizes the results of these studies, most of which use multiple regression analysis.
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