Accounting For Intergenerational Income Persistence: Non .
Accounting for Intergenerational IncomePersistence: Non-Cognitive Skills,Ability and EducationJo BlandenPaul GreggLindsey MacmillanSeptember 2006
Published byCentre for the Economics of EducationLondon School of EconomicsHoughton StreetLondon WC2A 2AE Jo Blanden, Paul Gregg and Lindsey Macmillan, submitted August 2006September 2006ISBN 07530 2084 XIndividual copy price: 5The Centre for the Economics of Education is an independent research centre funded by theDepartment for Education and Skills. The views expressed in this work are those of the authorand do not reflect the views of the DfES. All errors and omissions remain the authors.
Executive SummaryIntergenerational persistence is the association between the socio-economic outcomes ofparents and their children as adults. Recent evidence suggests that mobility in the UK is lowby international standards (Jantti et al, 2006) and that mobility fell when the 1958 and 1970cohorts are compared (Blanden et al, 2004).This paper seeks to understand the level and change in the intergenerational persistence ofsons by exploring the contribution made by noncognitive skills, cognitive ability andeducation as transmission mechanisms.In order to explain intergenerational persistencethese factors must be correlated with family income and have an influence on labour marketearnings in the early 30s (our measure of adult outcomes).There has been considerable research considering the relationship between educationaloutcomes and family income (e.g. Blanden and Machin, 2004), and numerous studiesdocument the positive returns to education in the labour market. Educational attainment istherefore an obvious transmission mechanism. Similarly we would expect children of betteroff parents to have higher cognitive skills that improve their chances in the labour market, inpart by helping them to achieve more in the education system. Labour market experience isalso explored as early unemployment has been shown to have a negative effect on laterearnings (Gregg and Tominey, 2005).The consideration of non-cognitive skills as an intergenerational transmission mechanism is anew contribution made in this paper. Bowles et al (2001) provide an interesting review of howpersonality influences wages. James Heckman and co-authors have produced a number ofpapers which emphasise the importance of noncognitive skills in determining educationaloutcomes and later earnings. Heckman and Rubinstein (2001) first identified the importanceof noncognitive skill with their observation that high school equivalency recipients earn lessthan high school graduate despite being smarter.noncognitive attributes of those who drop out.They attribute this to the negativeIn the most recent paper in this seriesHeckman, Stixrud and Urzua (2006) model the influence of young people’s cognitive andnon-cognitive skills on schooling and earnings. They find that better noncognitive skills leadto more schooling, but also have an earnings return over and above this. Carneiro et al (2006)find noncognitive skills measured in childhood to have similar effects in the British 1958
National Child Development Study1. If parental income is correlated with noncognitive skillsthen these could be another important factor driving intergenerational persistence.In the first part of this paper we assess the ability of our chosen transmission mechanisms toaccount for the elasticity between earnings at age 30 and parental income averaged at age 10and 16 for the cohort of sons born in 1970. We find that our most detailed model is able toaccount for 0.17 of the 0.32 elasticity we observe (54%). Of this, the greater part (0.10) iscontributed by education, although early labour market experience also has a role (0.03). Thecontribution of cognitive and noncognitive variables is also sizeable but largely occursthrough their role in improving education outcomes. The most important of the noncognitivevariables are the child’s (self-reported) personal efficacy and his level of application (reportedby his teacher at age 10).The latter half of the paper is concerned with understanding the role these mediating variablesplay in the fall in intergenerational mobility between the 1958 and 1970 cohorts. One strikingchange is that the noncognitive variables are strongly associated with parental variables in thesecond cohort, but not in the first. There is also greater inequality in educational outcomes byparental income in the second cohort. Overall intergenerational mobility increases from anelasticity of 0.205 to 0.291, an increase of 0.086, of this over 80% can be explained by ourmodel (the part that is accounted for has increased by 0.07). The largest contributors to thischange are increasingly unequal educational attainment at age 16 and access to highereducation. Noncognitive traits also have a role, but affect intergenerational persistencethrough their impact on educational attainments; this is in contrast to the results found byHeckman, Stixrud and Urzua (2006) reported above. Cognitive ability makes no substantivecontribution to the change in mobility.Our findings highlight, once again, the importance of improving the educational attainmentand opportunities of children from poorer backgrounds for increasing social mobility.Moreover, they provide suggestive evidence that that policies focusing on noncognitive skillssuch as self-esteem and application may be effective in achieving these goals.1Note these studies have concerned non-cognitive characteristics as a dimension of skill; this is separate fromexploring the impact of social capital.
Accounting for Intergenerational IncomePersistence: Non-Cognitive Skills,Ability and EducationJo BlandenPaul GreggLindsey Macmillan1.Introduction12.Modelling Approach23.Data5British Cohort Study5Comparative data on the two cohorts6Accounting for Intergenerational Persistence7Estimates of intergenerational persistence7Decomposing intergenerational persistence8Accounting for the Decline in Intergenerational Mobility10Estimates of the change in intergenerational mobility10Accounting for the change in ces23Appendices26
AcknowledgmentsJo Blanden is a Lecturer at the Department of Economics, University of Surrey and ResearchAssociate at the Centre for Economic Performance and the Centre for the Economics ofEducation, London School of Economics.Paul Gregg is a Professor of Economics at the Department of Economics, University ofBristol, and a Senior Research Fellow at the Centre for Economic Performance, LondonSchool of Economics.Lindsey Macmillan is a Research Assistant at the Centre for Market and Public Organisation,University of Bristol.
1IntroductionIntergenerational mobility is the degree of fluidity between the socio-economic status ofparents (usually measured by income or social class) and the socio-economic outcomes oftheir children as adults. A strong association between incomes across generations indicatesweak intergenerational income mobility, and may mean that those born to poorer parentshave restricted life chances and do not achieve their economic potential.Recent innovations in research on intergenerational mobility have been concentratedon improving the measurement of the extent of intergenerational mobility, and makingcomparisons across time and between nations. The evidence suggests that the level ofmobility in the UK is low by international standards (Jantti et al., 2006, Corak, 2006 andSolon, 2002). Comparing the 1958 and 1970 cohorts indicates that mobility has declined inthe UK (see Blanden et al. 2004).This paper takes this research a stage further by focusing on transmissionmechanisms; those variables that are related to family incomes and that have a return in thelabour market. First we evaluate the relative importance of education, ability, noncognitive(or ‘soft’) skills and labour market experience in generating the extent of intergenerationalpersistence in the UK among the 1970 cohort. In the second part of the paper we seek toappreciate how these factors have contributed to the observed decline in mobility in the UK.We focus here on men for reasons of brevity.Education is the most obvious of these transmission mechanisms. It is wellestablished that richer children obtain better educational outcomes, and that those with highereducational levels earn more. Education is therefore a prime candidate to explain mobilityand changes in it. Indeed, Blanden et al. (2004) find that a strengthening relationship betweenfamily income and participation in post compulsory schooling across cohorts can help toexplain part of the fall in intergenerational mobility they observe.Cognitive ability determines both educational attainment and later earnings, making itanother likely contributor to intergenerational persistence. We might expect a strong linkbetween parental income and measured ability, both because of biologically inheritedintelligence and due to the investments that better educated parents can make in theirchildren. We seek to understand the extent to which differing achievements on childhoodtests across income groups can explain differences in earnings, both directly, and through1
their relationship with final educational attainment. Galindo-Rueda and Vignoles (2005)demonstrate that the role of cognitive test scores in determining educational attainment hasdeclined between these two cohorts.A growing literature highlights that noncognitive personality traits and personalcharacteristics earn rewards in the labour market and influence educational attainment andchoices (see Feinstein, 2000, Heckman et al., 2006, Bowles et al., 2001 and Carneiro et al.,2006).If these traits are related to family background then this provides yet anothermechanism driving intergenerational persistence. Osborne-Groves (2005) considers thispossibility explicitly and finds that 11% of the father-son correlation in earnings can beexplained by the link between personalities alone; where personality is measured only bypersonal efficacy.Finally, labour market experience and employment interruptions have long beenfound to influence earnings (see Stevens 1997). Gregg and Tominey (2005) highlight, inparticular, the negative impacts of spells of unemployment as young adults; we thereforeanalyse labour market attachment as another way in which family background mightinfluence earnings.In the next section we lay out our modelling approach in more detail. Section 3discusses our data. Section 4 presents our results on accounting for the level ofintergenerational mobility while Section 5 describes our attempt to understand the change.Section 6 offers conclusions.2Modelling ApproachIn economics, the empirical work on intergenerational mobility is generally concerned withthe estimation of β in the following regression;ln Yi children α β ln Yi parents ε i(1)where ln Yi children is the log of some measure of earnings or income for adult children, andln Yi parents is the log of income for parents, i identifies the family to which parents andchildren belong and ε i is an error term. β is therefore the elasticity of children’s income with2
respect to their parents’ income and (1- β ) can be thought of as measuring intergenerationalmobility.Conceptually, we are interested in the link between the permanent incomes of parentsand children across generations. However, the measures of income available in longitudinaldatasets are likely to refer to current income in a period. In some datasets multiple measuresof current income can be averaged for parents and children, moving the measure somewhatcloser to permanent income. Additionally it is usual to control for the ages of bothgenerations.1 In the cohort datasets we use, substantial measurement error is likely to remain,meaning that our estimates will be biased downwards as measures of intergenerationalpersistence. The issue of measurement error becomes particularly important whenconsidering the changes in mobility across cohorts and this will be returned to whendiscussing our findings.We report the intergenerational partial correlation r, alongside β because differencesin the variance of ln Y between generations will distort the β coefficient. This is obtainedsimply by scaling β by the ratio of the standard deviation of parents’ income to the standarddeviation of sons’ income, as shown below.r CorrlnYparents , lnYson β (SD ln YparentsSD ln Yson)(2)The main objective in this paper is to move beyond the measurement of β and r, andto understand the pathways through which parental income affects children’s earnings. Therole of noncognitive skills can be used as an example, assuming for the moment that these aremeasured as a single index. We can measure the extent to which these skills are related toparental income Noncog i α 1 λ ln Yi parents ε 1i , and estimate their pay-offs in the labourmarket InYi child ϖ 1 ρNoncog i u1iThis means that the overall intergenerational elasticity can be decomposed into thereturn to noncognitive skills multiplied by the relationship between parental income and theseskills, plus the unexplained persistence in income that is not transmitted through noncognitivetraits.Cov(u1i , ln Yi parents )β ρλ Var (ln Yi parents )(3)3
In our analysis we consider noncognitive skills among several other mediating factors:cognitive test scores, educational performance and early labour market attachment.Our decomposition approach requires the estimation of the univariate relationshipsbetween the transmission variables and parental income. These are then combined with thereturns found for those variables in an earnings equation. We build up the specifications ofour earnings equations gradually, as we believe that many of the associations operate in asequential way. For example, Heckman et al. (2006) show that part of the advantage ofhigher noncognitive skills works through enabling children to reach a higher education level.In the previous example we have shown the unconditional influence of noncognitive skills onintergenerational persistence. To how noncognitive skill works through education levels, wecan add education to the earnings equation.InYi child ϖ 2 δ Noncog i π Ed i u2i(4)Then estimate the relationship between educational attainment and parental income.Ed i α 2 γ ln Yi parents ε 2i(5)The conditional decomposition is then:β δλ πγ (6)Cov (u 2i , ln Yi parents )Var (ln Yi parents )Where δλ is the conditional contribution of noncognitive skill and πγ is the contribution ofage 16 exam results. Therefore the difference between ρλ and δλ shows the extent to whichthe noncognitive skills contribute to intergenerational persistence by enabling more affluentchildren to achieve better qualifications at 16.In the second part of this study we use the same approach to account for the change inintergenerational persistence. If we continue with the simple example shown above, we canwriteβ 70 β 58 δ 70 λ70 δ 58 λ58 π 70γ 70 π 58γ 58 (7)Cov(u 2i 70 , ln Yi 70parents ) Cov(u 2i 58 , ln Yi 58parents ) Var (ln Yi 70parents )Var (ln Yi 58parents )Or in words, the difference in persistence is formed of two parts; the difference between theexplained persistence across the cohorts plus the difference between the unexplained4
persistence. If the explained part of β is larger in the second cohort than in the first then thisindicates that the factors we explore are responsible for part of the increase inintergenerational persistence.3DataWe use information from the two mature publicly accessible British cohort studies, theBritish Cohort Study of those born in 1970 and the National Child Development Study ofthose born in 1958. Both cohorts began with around 9000 baby boys, although as we shall seeour final samples are considerably smaller than this. We shall first provide a discussion ofhow we use the 1970 cohort, before considering how the data are used in the comparativesection of the paper.British Cohort StudyThe BCS originally included all those born in Great Britain between 4th and 11th April 1970.Information was obtained about the sample members and their families at birth and at ages 5,10, 16 and 30. We use the earnings information obtained at age 30 as the dependent variablein our intergenerational models. Employees are asked to provide information on their usualpay and pay period. Data quality issues mean we must drop the self-employed. Parentalincome is derived from information obtained at age 10 and 16; where parents are asked toplace their usual total income into the appropriate band (there were seven options at age 10and eleven at age 16). We generate continuous income variables at each age by fitting aSingh-Maddala distribution to the data using maximum likelihood estimation. This isparticularly helpful in allocating an expected value for those in the open top category.2 Weadjust the variables to net measures and impute child benefit for all families.3 Theexplanatory variable used in the first part of the paper is the average of income over ages 10and 16.In the childhood surveys parents, teachers and the children themselves are asked toreport on the child’s behaviour and attitudes. These responses are combined to form thenoncognitive measures as described in Box 1. Information on cognitive skills is obtained at5
age 5 from the English Picture Vocabulary test (EPVT) and a copying test. At age 10 thechild took part in a reading test, maths test and British Ability Scale test (close to an IQ test).Exam results at age 16 were obtained from information given in the age 30 sample. Thisincludes detailed information on the number of exams passed (both GCE O level and CSE).Information on educational achievements beyond age 16 is also available from the age 30sample, as is information on all periods of labour market and educational activity from age 16to 30. This information is used to generate the measure of labour market attachment which isthe proportion of months from age 16 to 30 when the individual is out of education and not inemployment.Comparative data on the two cohortsSome modifications must be made to the variables used when comparing the BCS with theearlier National Child Development Study (NCDS). The NCDS obtains data at birth and ages7, 11, 16, 23, 33 and 42 for children born in a week in March 1958. Parental income data isavailable only at age 16, meaning that the comparative analysis of this data is based only onincome at this age. The questions that ask about parental income in the two cohorts are notidentical and adjustments must be made to account for differences in the way income ismeasured (see Blanden, Chapter 4 for full details). Intergenerational parameters for theNCDS are obtained by regressing earnings at age 33 on this parental income measure.Comparative results for the BCS are generated by regressing earnings at 30 on parentalincome at age 16.Careful consideration is needed when using the noncognitive variables to makecomparisons across the cohorts. In both cohorts, mothers are asked a number of items fromthe Rutter A scale (this is the version of the Rutter behaviour scale which is asked of parents,see Rutter et al. 1970). Indicators of internalising behaviour from the Ruttter scale includedin both cohorts are headaches, stomach aches, sleeping difficulties, worried and fearful, atages 11/10. Externalising behaviours are fidget, destructive, fights, irritable and disobedientat the same age. Principal components analysis is used to form these variables into twoscales, we refer to these as the Rutter externalising and Rutter internalising scales.5The teacher-reported variables in the NCDS are from the Bristol Social AdjustmentGuide (Stott, 1966, 1971). The teacher was given a series of phrases and asked to underlinethose that he/she thought applied to the child. The phrases were grouped into 11 differentbehavioural “syndromes”. We have inv
Accounting for the change in mobility 12 6. Conclusion 13 Notes 15 Tables 16 References 23 Appendices 26. Acknowledgments Jo Blanden is a Lecturer at the Department of Economics, University of Surrey and Research Associate at the Centre for Economic Performance and the Centre for the Economics of Education, London School of Economics. Paul Gregg is a Professor of Economics at the Department of .
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