1 Decomposition Methods - Illustrative Example • Follow up to “Decomposition Methods in Economics by Nicole Fortin, Thomas Lemieux, and Sergio Firpo in the recent Handbook of Labor Economics (Volume 4A, 2011) 1.1 Oaxaca-Blinder and the Gender Pay Gap • Case featured in O’Neill and O’Neill (2006)“What Do Wage Differen- tials Tell Us about Labor Market Discrimination?’ NBER WP11240, published in The Economics of Immigration and Social Policy, edited by S. Polachek, C.Chiswich, and H. Rapoport. Research in Labor Economics 24:293-357.
Transcript
1 Decomposition Methods - Illustrative Example
• Follow up to “Decomposition Methods in Economics by Nicole Fortin,
Thomas Lemieux, and Sergio Firpo in the recent Handbook of Labor
Economics (Volume 4A, 2011)
1.1 Oaxaca-Blinder and the Gender Pay Gap
• Case featured in O’Neill and O’Neill (2006)“What Do Wage Differen-
tials Tell Us about Labor Market Discrimination?’ NBER WP11240,
published in The Economics of Immigration and Social Policy, edited
by S. Polachek, C.Chiswich, and H. Rapoport. Research in Labor
Economics 24:293-357.
• Use 2000 wage data from the NLSY79 when the cohort was 35-43
years of age.
• The NLSY being a longitudinal survey has actual labor market expe-
rience and a AFQT score.
• The sample is restricted to civilian wage and salary workers, thereby
omitting self-employed workers.
• The wage rates are the hourly wage as reported directly by those paid
by the hour. For those who are paid on another basis –day, week,
month, usual weekly earnings are divided by usual weekly hours.
1.2 Know the distribution of interest
• Plotting the density of wage by gender or the gender differential byquantile illustrates potential issues of economic interest
. *** use vkdensity to check bandwidth ;
. foreach aut in silver scott hardle {;2. vkdensity lropc00 if female==0, epan ‘aut’;3. vkdensity lropc00 if female==1, epan ‘aut’;4. };
Table 2. Means and OLS Regression Coefficients of Selected Variables from NLSY Log Wage Regressions for Workers Ages 35-43 in 2000
Pooled Coef
Note: The data is an extract from the NLSY79 used in O'Neill and O'Neill (2006). Industrial sectors were added (at a lost of 89 observations) to their analysis to illustrate issues linked to categorical variables. The other explanatory variables are age, dummies for black, hispanic, region, msa, central city. Standard errors are in parentheses.
(1) (2) (3) (4) (5)
1.3 Gender Wage Gap and Female Dummy
• What’s wrong with the sequential introduction of explanatory vari-
ables?
• It depends on the order of the “decomposition”
• Let’s see “part-time” work example
Explanatory VariablesFemale -0.233 ( 0.015) -0.209 ( 0.015) -0.113 ( 0.014) -0.092 ( 0.014)"Explained by % worked part-time" -0.025 -0.021Education and skill level <10 yrs. --- --- --- --- -0.092 ( 0.014) -0.045 ( 0.033) 10-12 yrs (no diploma or GED) --- --- --- --- --- --- --- --- HS grad (diploma) --- --- --- --- -0.092 ( 0.014) -0.003 ( 0.02) HS grad (GED) --- --- --- --- -0.012 ( 0.044) 0.006 ( 0.03) Some college --- --- --- --- 0.101 ( 0.03) 0.131 ( 0.022) BA or equiv. degree --- --- --- --- 0.282 ( 0.036) 0.330 ( 0.026) MA or equiv. degree --- --- --- --- 0.399 ( 0.046) 0.468 ( 0.034) Ph.D or prof. Degree --- --- --- --- 0.763 ( 0.1) 0.807 ( 0.06) AFQT percentile score (x.10) --- --- --- --- 0.041 ( 0.004) 0.042 ( 0.003)L.F. withdrawal due to family resp. --- --- --- --- -0.083 ( 0.019) -0.067 ( 0.015)Lifetime Work Experience Years worked civilian --- --- --- --- 0.033 ( 0.002) 0.033 ( 0.002) Years worked military --- --- --- --- 0.021 ( 0.004) 0.021 ( 0.004) % worked part-time --- --- -0.288 ( 0.055) --- --- -0.346 ( 0.044)Industrial Sectors Primary, Constr. & Utilities --- --- --- --- --- --- --- --- Manufacturing --- --- --- --- 0.084 ( 0.021) 0.072 ( 0.021) Education, Health, & Public Adm. --- --- --- --- 0.008 ( 0.021) -0.001 ( 0.02) Other Services --- --- --- --- 0.038 ( 0.018) 0.036 ( 0.018)Constant 2.763 ( 0.01) 2.777 ( 0.011) 2.955 ( 0.106) 2.949 ( 0.105)
Note: The data is an extract from the NLSY79 used in O'Neill and O'Neill (2006). Industrial sectors were added (at a lost of 89 observations) to their analysis to illustrate issues linked to categorical variables. The other explanatory variables are age, dummies for black, hispanic, region, msa, central city. Standard errors are in parentheses.
Table 2b. OLS Regression Coefficients of Selected Variables from NLSY Log Wage Regressions for Workers Ages 35-43
Note: The data is an extract from the NLSY79 used in O'Neill and O'Neill (2006). The other explanatory variables are age, dummies for black, hispanic, region, msa, central city. In column (1), the omitted industrial sector is "Primary, Construction, and Utilities". In column (2), the omitted industrial sector is "Education, Health and Public Admin". Standard errors are in parentheses. The means of the variables are reported in Table 2.
Using Female Coef. Using Weighted Sum Using Male Coef. from col. 4, Table 2
Using Pooled from col. 5, Table 2
1.4 Oaxaca-Blinder in Stata
• Choose package by Ben Jann for ETH Zurich
st0151 from http://www.stata-journal.com/software/sj8-4SJ8-4 st0151. The Blinder-Oaxaca decomposition for linear... / TheBlinder-Oaxaca decomposition for linear regression / models / by Ben Jann,ETH Zurich / Support: [email protected] / After installation, type helpoaxaca
