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Intergenerational earnings mobility: Changes across cohorts in Britain

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Intergenerational earnings mobility: Changes across cohorts in Britain Cheti Nicoletti and John Ermisch ISER, University of Essex ESRC Research Methods Festival – PowerPoint PPT presentation

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Title: Intergenerational earnings mobility: Changes across cohorts in Britain


1
Intergenerational earnings mobility Changes
across cohorts in Britain
  • Cheti Nicoletti and John Ermisch
  • ISER, University of Essex
  • ESRC Research Methods Festival
  • Oxford, 2nd July 2008

2
Motivation
  • Studies on intergenerational (im)mobility examine
    the association between childrens and parents
    socio-economic outcome (usually income, earnings,
    occupational prestige or class).
  • It is believed that low intergenerational
    mobility is indicative of unequal opportunities
    between people born in advantaged and
    disadvantaged families and that policy should
    improve opportunities for those from
    disadvantaged backgrounds.

3
Motivation
  • Notice that two societies could have the same
    level of inequality in earnings within a
    generation but a completely different level of
    intergenerational transmission of earnings.
  • A society where the relative position of a
    person in the earnings distribution is exactly
    inherited from the parents one is considered
    unfair.

4
Intergenerational mobility equation
5
Comparing Intergenerational across cohorts in
Britain
  • Comparing measure of intergenerational mobility
    across children (sons) born in different cohorts
    is very difficult because of data comparability
    and data availability issues.
  • It is for example difficult to observe earnings
    for both children and their parents for very long
    cohort period
  • Blanden, Gregg, MacMillan (2007, The Economic
    Journal) compare NCDS1958 and BC1970 (but the
    two datasets have a lot comparability issues) and
    find a negative trend in mobility
  • Ermisch and Francesconi (2002) use the BHPS to
    estimate intergenerational mobility in
    occupational prestige and find a positive trend
  • Breene and Goldthorpe (2001, European
    Sociological Review) and Goldthrope and Jackson
    (2007, British Journal of Sociology) study class
    mobility and find little change across the two
    generations when considering measures of exchange
    mobility, in contrast to the negative trend in
    mobility found in Blanden et al. (2004, 2007))
    using the same two cohorts.

6
  • Estimating intergenerational earnings mobility in
    Britain
  • for the period 1950-1972
  • We would like to estimate ? and ? in Britain and
    check whether there is a trend
  • PROBLEM Absence of British surveys with
    information on both sons and fathers earnings
    covering a long period.

7
We use the BHPS
  • PROBLEM We can observe both sons and their
    fathers earnings only if they have been living
    together in at least 1 wave during the panel.
    This is possible in 12 of the cases in our
    sample.
  • SOLUTION we use a TS2SLS estimator which
    combines two separate samples from the BHPS

8
The two-sample two-stage least squares (plim
TS2SLS plim TS2SIV)
  • The TS2SLS estimator combines two separate
    samples from the BHPS
  • 1st dataset (Full sample) containing information
    on sons earnings, and fathers education and
    occupational characteristics when sons were 14
    years (collected through retrospective questions
    to sons)
  • 2nd dataset (Supplemental sample) containing
    information on earnings and occupational
    characteristics of potential fathers.
  • References 2SIV Angrist and Krueger (1992),
    Arellano and Meghir (1992), Ridder and Moffitt
    (2005), Inoue and Solon (2005)

9
Two-sample two-stage least squares estimator
(TS2SLS)
  • Combining the two samples
  • Estimation of the log earnings equation for
    fathers using the supplemental sample (imputation
    regression)
  • xZ?v
  • Estimation of the main equation using the full
    sample and replacing (imputing) x

10
Previous studies on intergenerational mobility
using TS2SLS estimator
  • The choice of IV in previous studies is quite
    often was
  • dictated by the few variables available
  • Bjorklund and Jantti (1997) use education level
    and occupation in Sweden,
  • Fortin and Lefebvre (1998) use 16 occupational
    groups in Canada
  • Grawe (2004) uses education levels for
    Ecuador, Nepal, Pakistan and Peru.
  • Our potential IV are instead given by
  • Hope-Goldthorpe index the Cambridge scale
    dummies to distinguish occupations in
    professional, managerial and technical, skilled
    non-manual, skilled manual and unskilled 19
    dummies for socioeconomic groups, education
    level and age. (similarly to Lefranc A, Trannoy
    A. 2005)

11
How to choose the instruments for the imputation
in the first step
  • The well-known rule for the choice of the
    instruments in the instrumental variable
    estimation based on a single sample applies to
    the TS2SLS estimation too.
  • Instruments should be chosen among the ones with
    the least correlation with the error in the main
    equation and with maximum multiple correlation
    with the variable to be instrumented, the
    fathers earnings.

12
Data requirement
  • We need to observe a long run permanent measure
    of earnings for both fathers and their children
  • Earnings observed at too young or too old ages
    are not a good proxy of permanent earnings

13
Life cycle bias
  • Controlling for sons and fathers age in the
    intergenerational mobility equation can help in
    reducing the measurement error bias. But this
    correction is not enough if the earnings growth
    is heterogeneous across individuals.
  • Imposing and upper and a lower bound for sons and
    fathers age can be a solution (ex. Blanden et al
    2004 and 2007, 30-33 Gershuny 2002, 34-36
    Ermsich and Nicoletti 2007, 31-45).
  • Lee and Solon (2005) suggest to estimate an
    intergenerational mobility equation using sons
    observed at any age but allowing the elasticity
    to vary across cohort and sons age. (See for
    example Ermsich and Nicoletti 2007)

14
BHPS 1991-2003
  • The full sample is given by all men, sons, born
    between 1950 and 1972 self-employed or in paid
    employment, responding and with a labour income
    in last month greater than zero in at least one
    wave of the panel and aged 30-45.
  • The supplemental sample is then given by all men
    born between 1930 and 1946.

15
First step
Variable Coeff S.E.   Variable Coeff S.E.
Log (Hope-Goldthorpe score) cohort 1930-38 0.510 0.123 Manager, cohort 1930-38 0.737 0.106
Log (Hope-Goldthorpe score) cohort 1939-46 0.452 0.112 Manager, cohort 1930-46 0.313 0.078
Education1, cohort 1930-38 0.126 0.073 Foreman/supervisor cohort 1930-38 0.437 0.11
Education1, cohort 1939-46 0.079 0.058 Foreman/supervisor cohort 1939-46 0.078 0.081
Education2, cohort 1930-38 0.395 0.145 No managerial duties cohort 1930-38 0.459 0.089
Education2, cohort 1939-46 0.256 0.096 No managerial duties cohort 1939-46 0.108 0.071
Cohort 1939-46 0.565 0.611 Age 0.259 0.097
Constant -1.599 2.537 Age2 -0.003 0.001
Number of observations 896
R2 0.259     Adjusted R-squared 0.246  
16
Intergenerational earnings mobility(sons 31-45
and fathers 31-55)
  • Second step
  • y ? ? x u
  • y sons log earnings
  • x fathers log earnings
  • ?, ? and ? are coefficients
  • u is i.i.d (0, s2)
  • We estimate ? separately for rolling cohort
    groups
  • 1950-55, 1951-56, 1952-57, , 1967-1972

17
Elasticities and correlations for single year
earnings
18
Elasticities and correlations for average
earnings
19
Testing for the presence of a linear trend
  • Without any control for sons age (neither
    considering sons age and age square, nor
    bounding the sons age range) the trend is
    negative, significant and weak
  • y ? ? x xcoh d u for sons 18-53
  • This result is in line with Ermisch and
    Francesconi (2002) and Prandy et al (2002) who do
    not limit the sons age range

20
Variables 1 1 3 3
  Coeff  SE Coeff SE   
x 0.277 0.034 0.323 0.063
x cohort/10 -0.019 0.004 0.020 0.014 
ages     0.117 0.052
ages2     -0.001 0.001
agef     -0.110 0.055
agef2     0.001 0.001
coh 50-57     0.135 0.142 
coh 58-65     0.136 0.086 
cohf 18-30 cohf 18-30   -0.068 0.125 
cohf 31-38 cohf 31-38   0.018 0.077 
_cons 5.387 0.243 4.754 1.372
R2 0.025   0.031
N. Obs. Sons age 9673 19-53   6413 31-45  
21
Comparing results with those inBlanden et al
(2004)
Variable Coeff S.E. Variable Coeff S.E.
x 0.282 0.085 x 0.331 0.070
x cohort/10 0.067 0.024 xcohort/10 0.019 0.012
Ages 0.031 0.124 Ages 0.018 0.008
Ages2 0.000 0.001 Ages2 -0.001 0.001
Agef -0.004 0.013 Agef -0.009 0.010
Agef2 0.002 0.001 Agef2 0.002 0.001
_cons 4.978 0.607 _cons 4.971 0.493
Cohort period 1960 1972   Cohort period  1956  1972
R2 0.042     0.038  
N. obs. 3509     5292  
22
Conclusions
  • The intergenerational mobility does not seem to
    have changed much over the cohort period
    1950-1972.
  • The trend does not seem to be linear.
  • But when imposing a linear trend between the
    1958-1970 we find that it is significant and
    negative as in Blanden et al (2004, 2006)

23
Extensions for future research
  • If the intergenerational transmission differs at
    different points of the earnings distribution, it
    could be interesting to estimate different
    quantile regressions instead of the mean
    regression.
  • The relationship between trend and changes in the
    education system across cohorts requires
    investigation.
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