Intergenerational earnings mobility: Changes across cohorts in Britain Cheti Nicoletti and John Ermisch ISER, University of Essex ESRC Research Methods.

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Presentation transcript:

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

Motivation Studies on intergenerational (im)mobility examine the association between children’s and parents’ socioeconomic 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.

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.

Intergenerational mobility equation

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.

Estimating intergenerational earnings mobility in Britain for the period We would like to estimate  and ρ 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.

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

The two-sample two-stage least squares (plim TS2SLS = plim TS2SIV) The TS2SLS estimator combines two separate samples from the BHPS: 1 st 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); 2 nd 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)

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) x=Z  +v Estimation of the main equation using the full sample and replacing (imputing) x

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 socio–economic groups, education level and age. (similarly to Lefranc A, Trannoy A. 2005)

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.

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

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 son’s age. (See for example Ermsich and Nicoletti 2007)

BHPS 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 The supplemental sample is then given by all men born between 1930 and 1946.

VariableCoeffS.E. VariableCoeffS.E. Log (Hope-Goldthorpe score) cohort Manager, cohort Log (Hope-Goldthorpe score) cohort Manager, cohort Education1, cohort Foreman/supervisor cohort Education1, cohort Foreman/supervisor cohort Education2, cohort No managerial duties cohort Education2, cohort No managerial duties cohort Cohort Age Constant Age Number of observations896 R2R Adjusted R-squared0.246 First step

Intergenerational earnings mobility (sons and fathers 31-55) Second step: y =  +  x + u y = son’s log earnings; x = father’s log earnings; ,  and  are coefficients; u is i.i.d (0, σ 2 ) We estimate  separately for rolling cohort groups , , , …,

Elasticities and correlations for single year earnings

Elasticities and correlations for average earnings

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 + x*coh δ +u for sons This result is in line with Ermisch and Francesconi (2002) and Prandy et al (2002) who do not limit the sons’ age range

20 Variables13 Coeff SECoeffSE x x* cohort/ ** ages ages agef agef coh coh cohf cohf _cons R N. Obs. Son’s age

21 Comparing results with those in Blanden et al (2004) VariableCoeffS.E.VariableCoeffS.E. x x x cohort/ x*cohort/ Ages Ages Ages Ages Agef Agef Agef Agef _cons _cons Cohort period Cohort period R N. obs

Conclusions The intergenerational mobility does not seem to have changed much over the cohort period The trend does not seem to be linear. But when imposing a linear trend between the we find that it is significant and negative as in Blanden et al (2004, 2006)

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.