Presentation to IFS Poverty Review Workshop 7

th Sept 2010 ‘Intergenerational Mobility in UK, life chances and the Role of Inequality and Education

Paul Gregg

1

Introduction & Background

“Most people are willing to accept wide inequalities if they are coupled with equality of opportunities” –

The Economist

(Oct 2006) High profile policy area – previous gmt addressed both poverty and life chances and saw them as inherently linked but is new gmt decoupling them?

2

Introduction & Background

Intergenerational mobility has been widely studied using Income, Education and Social Class But in a wider sense covers Social Gradients in children‘s life chances – how life chances differ by a measure of (permanent) social background e.g. Marmot commission highlighting extent of social gradients in physical and mental health and how these emerge in childhood 3

Concepts

Original concept of Intergenerational Earnings mobility is ideally comparison of life time (permanent) earnings of father and sons This is very data intensive so shorter tem earnings measures used More recently the question has shifted towards childhood experience and later life chances which has shifted emphasis toward family income in childhood This also allows for absentee fathers which varies across cohorts in a non-random way

Methodology – Income based

Intergenerational Income Mobility ln

Y i son

ln

Y i parents

 

i r

= Corr

lnY parents , lnY son  

parents

(

SD

ln

Y SD

ln

Y son

)

Income/earnings  Partial correlation (

r

)

NCDS 0.205

(.026)

0.166

(.021)

BCS 0.291

(.025)

0.286

(.025)

Measurement I

Early earnings based research had highlight high levels of mobility but concerns raised over biases generated by measurement error and life cycle stage So using American data NLSY single period income = 0.32, average over 3 periods = 0.45 implies true estimate = 0.54 Life cycle bias comes from the age(s) at which earnings are measured and how good a proxy they are for lifetime earnings When earnings is measured early or late in life course it is a less good proxy for lifetime earnings (optimal is at about 40)

Life Cycle bias in UK

Age-beta profiles across cohorts 0,4 0,35 0,3 0,25 0,2 0,15 0,1 0,05 0 23 24 25 26 27 28 29 30 31 32 33 NCDS 34 35 BCS 36 37 38 39 40 41 42 43 44 45 46

Poverty

Income mobility and intergenerational poverty are not the same thing Blanden and Gibbons suggest the odds risk for poor children being poor adults rose from twice that of non-poor children to 3.5+ times between the NCDS and BCS cohorts All this work asks what happens to poor children not who become the poor parents in the next generation – including who has children from the earnings/employment distribution

Education/Inequality/Genes

The recent literature has been looking at the key patterns of mobility and beginning to look at the drivers Whether mobility is high or low needs a benchmark so international comparisons and changes across time in countries have been widely investigated A natural next step was to explore how these patterns match on to inequality and education (for example Blanden, 2009)

Estimated Intergenerational Income Persistence and Income Inequality Across Countries

France USA Italy GBrit .2

Germany Sweden Finland Norway Denmark .22

.24

Income beta inc_gini .26

.28

Fitted values Australia Canada .3

10

Estimated Intergenerational Income Persistence and Education Expenditure Countries

Brazil .03

Italy France Germany GBrit Australia Finland USA Sweden Canada Norway Denmark .04

.05

.06

% GDP on education 1970-1974 Income beta Fitted values .07

.08

11

Educational Transmission

Blanden et al. (2007), following Solon, explore the drivers of intergenerational mobility that are measured at earlier ages. The process of obtaining β can be thought of in two stages.

Educ i

  1   ln

Y i parents

  1

i InY i child

  1  

Educ i



u

1

i

   

Cov

(

u

1

i

, ln

Y i parents

)

Var

(ln

Y i parents

) 12

Cross-cohort decomposition

1958 - NCDS

0.32

0.27

0.22

0.17

0.12

0.07

0.02

-0.03

1 2 3 4 1

Specification

2

1970 - BCS

3 4

Educational Transmission

In data with moderately detailed education records, around 55% of intergenerational mobility in UK comes through education Further, around 80% of the rise in intergenerational income persistence comes from increased strength of the relationship between family background and education (Blanden et al. 2007) Following Heckman big interest in non-cog Mood et al. (2010) explore personality traits as well as education for Sweden and suggest that that about 45% of IGE is explained with 2/3 by cognitive/ed and 1/3 by personality measures

Education and Family Background – recent picture

GCSE year Birth year PARENTAL OCCUPATION (SEG)

Managerial/Professional Other non-manual Skilled manual Semi-skilled manual Unskilled manual

Top - Bottom Ratio of top / bottom PARENTAL OCCUPATION (NS SEC)

Higher professional Lower professional Intermediate Lower supervisory Routine Top - Bottom Ratio of top / bottom

‘88 ‘90 ‘91 ‘93 ‘95 ‘97 ‘99 ‘01 ‘03 ‘06 ‘72 ‘74 ‘75 ‘77 ‘79 ‘81 ‘83 ‘85 ‘87 ‘90

52 42 21 16 12

40 4.3

58 49 27 20 15

43 3.9

60 51 29 23 16

44 3.8

66 58 36 26 16

50 4.1

68 58 36 29 24

44 2.8

69 60 40 32 20

49 3.5

70 59 45 35 30

40 2.3

75 62 49 34 26

49 2.9

77 64 51 34 31

46 2.5

76 65 53 41 33

43 2.3

81 73 59 46 42

39 1.9

Parental Educational and Genetics

Estimates of the impact these drivers is moving into causal analysis For instance, looking at increased parental education on child education/earnings Results suggest raising a parents education by 1 year results in increase in child's education by 0.1-0.25

Parental Educational and Genetics

Studies of genetics using partialling out variances between identical twins, siblings etc suggest about 40-50% of IQ is heritable. For personality it is lower (20%) but measurement less well developed Similar approaches being used in IGE estimation in Sweden (Bjorklund et al. 2006) Years of schooling Income

Non adoptees Biological father

.24

.241

Adoptees Adoptee Father

.114

.173

Adoptees Biological father

.113

.059

BCS intergenerational test scores

from Claire Crawford, Alissa Goodman and Robert Joyce (IFS) There is a strong link between the cognitive skills of parents and their children  This remains even after controlling for many detailed environmental factors   Forms an important reason why children from poor families do less well at school than richer ones (which the other studies could not capture) Direct effect alone explains 17% of gap in rich-poor decomposition – this is an upward biased estimate of genetic influence

SES gradients are apparent across a range of outcomes at age 7 to 9

Income gradients in child outcomes in middle childhood

108 106 104 102 100 98 96 94 92 90 88 30 80 130 180 230 280 330 380 430 480 530 580 630

Equivalised disposable weekly household income age 3/4

IQ (5.85) Key Stage 1 (5.46) Locus of control (3.30) Self esteem (1.71) Behaviour (2.01) Fat mass (1.34)

Source: Gregg, Propper and Washbrook (2008) (ALSPAC)

Attainment through childhood

from JRF funded research (CMPO and IFS) 80 70 60 50 40 30 20

Age 3 (M CS) Highest Age 5 (M CS) Age 7 (ALSPAC) Age 11 (ALSPAC/ LSYPE) Age 14 (LSYPE) Age 16 (LSYPE) Quintile 4 Quintile 3 Quintile 2 Lowest

How much of the socio-economic gap in cognitive outcomes at age 3 is explained by these factors?

0% 1% Residual Gap Parental Education 16% Family Background/Demographics 1% 3% 4% 34% Family Interactions Health and Well-Being 25% 16% Childcare Home-Learning Environment Parenting Style/Rules Missing Data

Total gap to be explained: 23 percentile points

© Institute for Fiscal Studies

Evolution of the socio-economic gap in cognitive outcomes at ages 7 to 11 Residual Gap 7% 13% 4% 6% Parental Education and Family Background Child's attitudes and behaviours Parent's attitudes and behaviours Pre-school environments 63% 6% Schools -1% Missing Data

Total gap to be explained: 31 percentile points

© Institute for Fiscal Studies

Evolution of the socio-economic gap in cognitive outcomes at ages 11 to 16 Residual Gap 7% 6% 59% 15% Parental Education and Family Background Child's attitudes and behaviours Parent's attitudes and behaviours Schools 1% 4% 8% Missing Data Prior Ability

Total gap to be explained: 33 percentile points

© Institute for Fiscal Studies

Key messages

1.

2.

3.

Educational achievement persists strongly over the course of childhood. Those who start a developmental stage ahead generally finish it ahead. This implies earlier interventions are likely to be more effective than later ones.

Low SES children exhibit poorer social and emotional development. These skills matter and lead low SES children to fall further behind at school, even conditional on prior academic ability. Parental behaviours and values play an important role in the transmission of family background to educational achievement and other outcomes.

Conclusions

Rather speculatively Societies with higher inequality have lower mobility for two reasons – 1) higher inequality gives parents greater incentives/different resource to invest in children. 2) the educational inequalities get higher pay offs in high inequality countries School environment is more equal than home environment and tends to generate mobility but this will depend on the extent of resources in the schooling system and the degree of inequality in schooling experience as the conflict with inequalities in the broad home learning environment incl. parenting

Additional slides

Permanent Income Decomposition

Components of Permanent Childhood and Current Income in the BHPS Percentage share of variance Correlation with permanent childhood income

Permanent childhood income, components associated with:



p SC p

) 

p X p

Residual permanent income (  ˆ

p

) )

Current income, components associated with:

Fathers’ social class (

p SC p

Other income predictors (  ˆ

p

)

X p

) Transitory and measurement error (

u p



e p

) Residual permanent income (  ˆ

p

  ˆ

p

  ˆ

p

) Error and residual unmeasured income, (  ˆ

p

  ˆ

p

  ˆ

p



p



p

) Current income ( ( 

p SC p

 

p

)

y p

 (  ˆ

p X

)

p

 

p

)  

p



u p



e p

Current income without error = permanent childhood income ( 

p SC p

 

p

)  (  ˆ

p X p

 

p

)  

p

15.67 22.26 62.07 7.54 17.41 40.55 34.52 75.06 0.431 0.615 0.716 0.398 0.514 -0.041 0.706 0.487 0.735 1.000

Ed-Income recent evidence

Variable Number of O levels (A*-C)

N

Stay on post – 16

N

Number of A levels (any)

N

Stay on post – 18

N

Degree

N

Proportion time NEET

N

NCDS 1958

0.7165 [0.036]***

7841

0.0963 [0.006]***

7196

0.1618 [0.010]***

7841

0.0621 [0.004]***

7196

0.0553 [0.004]***

7949

-0.0049 [0.002]***

5907

BCS 1970

1.1315 [0.046]***

5428

0.1360 [0.006]***

6420

0.4164 [0.023]***

3769

0.1047 [0.006]***

5529

0.1158 [0.006]***

5520

-0.0197 [0.003]***

5546

BHPS 1 1975-1980

1.0647 [0.155]***

815

0.1110 [0.019]***

964

0.4703 [0.075]***

638

0.0697 [0.021]***

946

0.0916 [0.017]***

932

-0.0676 [0.009]***

949

BHPS 2 1981-1986

0.7958 [0.258]***

515

0.0846 [0.031]***

583

0.4512 [0.128]***

373

0.0730 [0.033]**

568

0.0884 [0.033]***

484

BHPS 3 1987-1990

0.9880 [0.249]***

345

0.0885 [0.029]***

386

LSYPE 1989/1990

0.9336 [0.035]***

10935

0.0463 [0.005]***

8205