1. Adult Education and the Structure of Earnings in the United States
Dave E. Marcotte
Department of Public Policy
UMBC

2. Introduction
Growth in U.S. earnings inequality persistent, but uneven.
Traditional explanation points to skill-biased demand shifts, and supply shifts rooted in Baby Boom.
“Revisionist” explanation: Growth is not persistent, and not due to demand shifts, rather to declining value of minimum wage.

3. Introduction
Implications of Traditional and Revisionist explanations very different.
Here, I consider the role of a factor not directly addressed by either: adult education or job training.
Why?
Burgeoning training industry.
Returns to training are sizeable.
Stylized facts about training patterns suggestive.

4. Background Trends in earnings inequality Overall: Within Groups:
Growing since the 1960s
Rapid acceleration in the 1980s
Slowed in the 1990s
Within Groups:
Persistent increase during period
May have slowed in the 1990s
Between Groups:
Unchanged in the 1970s
Growth since then(?)
Where does growth bite:
Mostly in the tails
Esp. in the upper tail in the 1990s

5. Background source: Autor et al (2005)

6. Background Traditional explanations: Revisionist explanations:
Skill-biased technical change
Supply shifts
Revisionist explanations:
Growth has abated – and now mainly in upper tail.
Consistent with decreasing value of minimum wage in 1980s, and increasing value in 1990s.
Residual increase due to composition effects.

7. Why would training matter?
Training increases earnings: ~6-10%
Returns are substantial (Mincer estimates returns on order of 40% -- Loewenstein, much higher)
Industry is growing rapidly.

8. Objectives: Consider role training may have played
Did participation increase?
Did trends vary between groups?
How has training propensity changed across the earnings distribution?
Within comparable groups, could training be associated with earnings dispersion?

9. Insights?
Assess role training may have played in b/w group inequality growth
Assess whether training exacerbated conditional or unconditional spread
Suggestive/Descriptive not Causal
Data limitations
Inherent empirical problems

10. Data: National Household Education Survey (NHES)
Large cross-sectional sample of American adults in 1995, 1999, 2001, and 2003.
Detailed questions on participation in training of various types during previous 12 months, along with demographic/economic information.
Inconsistencies.

11. Characteristics of NHES respondents
Table 1: Descriptive Statistics of Pooled NHES Sample
Variable
Mean
Std. Error
Salary (2005 $)
$52,157.76
28547
Age
42.17
10.45
Female (0/1)
0.528
0.499
White (0/1)
0.775
0.417
Black (0/1)
0.113
0.317
Hispanic (0/1)
0.098
0.298
Dropout (0/1)
0.078
0.269
High School Graduate (0/1)
0.239
0.426
Some College (0/1)
0.287
0.452
College Graduate (0/1)
0.394
0.488
Work Related Training (0/1)
0.462
0.498 n
29,973

12. Trends in Work-Related Training

13. Trends in Training by Education

14. Trends in Training by Experience

15. LPM estimates of training propensity: Main Effects

16. LPM estimates of training propensity: Interaction Effects

17. Trends in Training by Earnings

18. Conditional Salary Quartile Effects

19. What Role for Training?
Overall trends consistent with supply response to a demand shift.
Training may have exacerbated between group inequality.
Training may be a cause of within group earnings differences. But, this is not changing over time.
Not clear how training could be associated with different trends above/below median.

20. What Role for Training?
Training received by those with more formal schooling.
Training received by those faring better than average.
Can training serve as a mechanism for redressing equality?
Not as it stands
Depends on the roots of current maldistribution.

21. Learning More
Training is dynamic – and capturing its effects is tough in the cross-section
NLSY estimate: 40% of increase in college/high school wage gap due to training.
Need for panel estimates