The Low-Wage Labor Market: Challenges and Opportunities for Economic Self-Sufficiency. Defining and Characterizing the Low-Wage Labor Market . Data and Methods

12/01/1999

Wage Data:  The wage data for tables 2, 3, and A1 come from the Outgoing Rotation Group (ORG) files of the Current Population Survey (CPS) for 1979-97. The sample includes all wage and salary workers, ages 18 to 64, with positive hourly wages between $0.50 and $100 in 1989 dollars. For hourly paid workers, the reported hourly wage is used; for weekly workers, the hourly wage is constructed by dividing usual weekly earnings by usual weekly hours. Top-coded weekly earnings were replaced with the estimated value of the mean weekly salary above the top code, using the assumption that the upper "tail" of the distribution follows a Pareto format. Quantile estimates, such as those shown in figure 6, use a smoothing procedure to accommodate "clumps" in the reported distribution of earnings. The construction of this wage series is discussed in greater detail in Webster (1999).

Table A1, Oaxaca decomposition:  The wage data for this table also come from the CPS ORG, as described above. We use the following equation to decompose the changes in characteristics (Xs) and returns (Bs):

Equation to decompose the changes in characteristics and returns. Text discusses this equation.

where p is the change in the probability of low-wage work (in our case, the change in the likelihood of earning less than $7.90 in 1997 dollars), B bar is the average of the returns between the two time periods, X bar the average of characteristics between the two time periods, a the intercept term, and I indexes the independent variables, 1 through k. Variables in the regression include education, potential experience (age-education-6), industry, occupation, race, region, and marital status. The regressions use the CPS ORG population weights, and separate equations were estimated for men and women.

Thus, the first term represents that part of the change attributable to changing characteristics, the second term represents that part of the change attributable to shifts in returns, and the third term captures the change in the intercept.27

Table A1. Decomposition of Changes in the Probability of Low-Wage Work
Men 1979-89 1989-97 1979-97
Total Change 0.078 0.013 0.091
  Characteristics Returns Characteristic Returns Characteristics Returns
Education -0.010 -0.008 -0.007 0.014 -0.017 0.006
Industry 0.008 -0.043 0.004 0.027 0.011 -0.014
Occupation 0.007 0.021 0.000 -0.019 0.007 0.001
Experience -0.009 -0.059 -0.012 -0.021 -0.020 -0.080
Race 0.003 -0.017 0.004 -0.016 0.006 -0.033
Marital 0.007 -0.022 0.001 0.020 0.008 -0.002
 
Intercept na 0.200 na 0.018 na 0.218
Sum 0.007 0.071 -0.010 0.022 -0.005 0.095

Women 1979-89 1989-97 1979-97
Total Change -0.002 -0.017 -0.018
 
Education -0.019 -0.015 -0.016 0.011 -0.033 -0.006
Industry 0.003 0.001 0.003 0.027 0.006 0.027
Occupation -0.013 0.044 -0.008 0.005 -0.022 0.050
Experience -0.012 -0.016 -0.012 -0.022 -0.024 -0.038
Race 0.001 -0.024 0.002 -0.002 0.003 -0.025
Marital 0.001 -0.046 0.001 0.011 0.002 -0.035
 
Intercept na 0.092 na -0.015 na 0.076
Sum -0.038 0.036 -0.031 0.014 -0.068 0.049
Source:  CPS ORG data. Dependent variable is the probability of low-wage work, measured using wage categories from table 2 (multiple of poverty line, or less than $7.90 in 1997). Oaxaca decomposition uses linear probability model, with CPS population weights. See data appendix for details.