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Life

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Life's Ups and Downs: Understanding the Earnings Profile. Alan Manning. Outline of Talk. Advertisement for book, Monopsony in Motion' ... – PowerPoint PPT presentation

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Title: Life


1
Lifes Ups and DownsUnderstanding the Earnings
Profile
  • Alan Manning

2
Outline of Talk
  • Advertisement for book, Monopsony in Motion
  • Overview of implications for modelling labour
    markets
  • Specific application to evolution of earnings
    over life-cycle
  • Very specific application why do women do worse
    than men in the early years?

3
Monopsony in Motion Imperfect Competition in
Labour Markets
  • employers have monopsony power over workers
  • need to recognize this explicitly
  • what happens if an employer cuts wage by 1p do
    all workers immediately leave?
  • how is market perceived by employers and workers?

4
Employers Perspective
  • Higher wages deter quits and make recruitment
    easier
  • Upward-sloping labour supply curve as a result
  • Textbook model of monopsony is right model to use
    for single employer

5
Workers Perspective
  • Getting, changing and losing jobs are big deals
  • Market does not satisfy law of one wage
  • Search model best way to think about workers
    behaviour
  • Not new, but more commonly applied in
    macroeconomics

6
Self-reported Important Life Events in Past Year
7
Specific Application
  • Evolution of Wages Over the Life-Cycle
  • Evolution of the Gender Pay Gap Over the
    Life-Cycle

8
Basic Facts to be Explained
9
Importance of Cohort Effects
10
Stylized Facts
  • Gender pay gap zero on labour market entry
  • Rises rapidly to 20-30 log points by late 30s
  • Most recent cohorts of women are not doing better
    than slightly earlier cohorts
  • Continued fall in aggregate gender pay gap hides
    this stalling in progress for women

11
Competitive Approach (Human Capital)
  • Wages understood by earnings functions
  • ln(w)ßxe
  • For understanding profile most important x
    variables are experience and job tenure
  • experience general human capital
  • tenure specific human capital
  • Women accumulate less of both than men

12
The Mincerian Approach
wages
W(0,0)
experience
0
13
The Mincerian Approach
wages
W(1, 1)
W(0,0)
experience
1
0
14
The Mincerian Approach
wages
W(1, 1)
W(1, 0)
W(0,0)
experience
1
0
15
The Mincerian Approach
wages
W(1, 1)
Returns to tenure
W(1, 0)
Returns to experience
W(0,0)
experience
1
0
16
Bias in Cross-Sectional Earnings Functions
  • Suppose individuals differ in W(0,0)
  • Estimated cross-sectional return to experience
  • eE(W(0,0)S0)- E(W(0,0))
  • Estimated cross-sectional return to tenure
  • tE(W(0,0)S1)- E(W(0,0)S0)
  • These are biased if S correlated with W(0,0)
  • This stayer bias can be large
  • Cross-sectional return to one year of tenure
    10.3
  • Stayer bias 7.9
  • Problem is caused by monopsony aspects

17
Evolution of Mincerian Model
Observation Life-Cycle Profile
18
Evolution of Mincerian Model
Observation Life-Cycle Profile
Theory human capital accumulation links to
returns to experience/tenure
19
Evolution of Mincerian Model
Observation Life-Cycle Profile
Theory human capital accumulation links to
returns to experience/tenure
Evidence Cross-sectional earnings functions
20
Evolution of Mincerian Model
Observation Life-Cycle Profile
Theory human capital accumulation links to
returns to experience/tenure
Evidence Cross-sectional earnings functions
Criticism Bias due to job match heterogeneity
21
Evolution of Mincerian Model
Observation Life-Cycle Profile
Theory human capital accumulation links to
returns to experience/tenure
Evidence Cross-sectional earnings functions
Criticism Bias due to job match heterogeneity
22
Evolution of Mincerian Model
Observation Life-Cycle Profile
Theory human capital accumulation links to
returns to experience/tenure
Evidence Cross-sectional earnings functions
Criticism Bias due to job match heterogeneity
23
An Alternative Approach
wages
W(1, 1)
W(0,0)
experience
1
0
24
An Alternative Approach
wages
Wq(1, 0)
W(1, 1)
W(0,0)
experience
1
0
25
An Alternative Approach
wages
Wq(1, 0)
W(1, 1)
Wl(1, 0)
W(0,0)
experience
1
0
26
An Alternative Approach
wages
Wq(1, 0)
Return to job mobility
W(1, 1)
Cost of job loss
On-the-job wage growth
Wl(1, 0)
W(0,0)
experience
1
0
27
Points to Note
  • Mincerian approach implicitly assumes quits and
    lay-offs are identical
  • Separate literatures on
  • Returns to experience and job tenure
  • Costs of job loss
  • Returns to job mobility
  • Evolution of wages within jobs
  • Cant all exist independent of each other

28
Empirical Analysis
  • Data from BHPS, 1991-2000
  • Wages de-trended to extract part of wage growth
    that is due to aggregate wage growth
  • Wage growth measured from now to next interview
    observed in employment
  • Vast majority have gap1
  • Some have gapgt1

29
Reduced Form Estimates of Annual Wage Growth
Men Women
0 years of experience 0.131 0.019 0.115 0.012
10 years of experience 0.028 0.005 0.011 0.004
20 years of experience 0.006 0.004 0.007 0.004
30 years of experience -0.003 0.004 0.006 0.004
40 years of experience -0.013 0.009 0.011 0.007
30
Introduce Job Tenure and Gaps
Men Women
0 years of experience 0.141 0.019 0.115 0.012
10 years of experience 0.043 0.005 0.035 0.004
20 years of experience 0.024 0.005 0.029 0.004
30 years of experience 0.019 0.006 0.027 0.005
40 years of experience 0.011 0.011 0.037 0.010
Gap Penalty (per yr) 0.074 0.023 0.065 0.012
Decade of Job Tenure -0.016 0.003 -0.021 0.004
31
Summary so far
  • Emergence of gender pay gap is result of lower
    wage growth for young women
  • Can explain hardly anything by women having more
    intermittent employment
  • But this excludes job mobility

32
Introduce Job Mobility
33
Reasons for Jobs Ending
Reason given Men () Women ()
Left for better job 36.3 33.7
Lost Job 34.3 24.3
Retirement /Health 6.3 8.0
Baby/Kids/Care 0.7 10.3
Other 22.4 23.6
34
Job Mobility Rates for better job
35
Job Mobility Rates not for better job
36
Impact of Job Mobility on Wage GrowthExogenous
and Homogeneous
Men Women
Move for better job 0.099 0.020 0.082 0.018
Lost Job -0.142 0.028 -0.054 0.027
Family-Related Move -0.046 0.087 -0.019 0.053
Other Mover -0.005 0.042 -0.010 0.032
0 years of experience 0.129 0.016 0.106 0.015
10 years of experience 0.025 0.007 0.025 0.005
Gap Penalty (per yr) 0.009 0.024 0.059 0.021
37
Impact of Job Mobility on Wage GrowthExogenous
and Heterogeneous
Men Men Women Women
Good Move Bad Move Good Move Bad Move
Level 0.276 0.058 0.115 0.101 0.266 0.065 0.215 0.075
Interaction with wage -0.236 0.108 -0.339 0.133 -0.281 0.107 -0.746 0.172
Interaction with exp -0.488 0.280 -0.047 0.217 -0.692 0.292 0.236 0.173
Interaction with ten 0.008 0.068 -0.183 0.051 0.025 0.070 -0.032 0.081
38
  • If significant observable heterogeneity in
    effects of job changes then likely to be
    significant unobserved heterogeneity
  • This is likely to be correlated with job mobility
  • Need to worry about endogeneity
  • Programme evaluation literature considers this
    situation
  • Might think multiple treatments better job,
    lost job etc
  • But really only changing job

39
A Simple Model
  • Wage Growth on this job
  • ?W0ß0XU0
  • Wage Growth on alternative job
  • ?W1ß1XU1
  • Model of Quits
  • QI(?ZVgt0)
  • (U0,U1,V) likely to be correlated

40
Treatment Effects
  • Average Return to Job Mobility
  • E(?W1X,Z,Q1)- E(?W0X,Z,Q1)
  • treatment effect on the treated
  • Average Cost of Job Loss
  • E(?W1X,Z)- E(?W0X,Z)
  • average treatment effect
  • Average On-the-Job Wage Growth
  • E(?W0X,Z)

41
Empirical Approach
  • Control Function Approach
  • Use as Z variables
  • Lagged wage
  • Job satisfaction with non-wage aspects
  • Domestic constraints
  • Focus on returns to job mobility and on-the-job
    wage growth as these are the most important for
    younger workers

42
Results
  • Returns to Job Mobility
  • Higher estimates than previously but much higher
    for women than men
  • Men 22.8
  • Women 40.0
  • Still lower wage growth for young women

43
Summary
  • Return to job mobility does differ for women and
    men
  • Contributes relatively small amount to emergence
    of gender pay gap
  • Differences in on-the-job wage growth is the most
    important factor

44
Hypotheses
  • Anticipation of future employment gaps leads to
    less training
  • Training rates Men 35.3 Women 34.4
  • Lower Promotion Rates
  • Promotion rates
  • Men 11.2 Women 10.2
  • Satisfaction with promotion
  • Men 4.25 Women 4.54
  • Wage Discrimination

45
Conclusions
  • Gender pay gap zero on labour market entry but
    then starts rising
  • Latest cohorts of women doing no better than
    slightly older cohorts
  • Main explanation is gender gap on-the-job wage
    growth
  • Need to understand this and focus policy on it
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