The Wounds That Do Not Heal: The Life-time Scar of Youth Unemployment

Transcription

1 The Wounds That Do Not Heal: The Life-time Scar of Youth Unemployment Gianni De Fraja Sara Lemos James Rockey Nottingham Leicester Leicester CASE, LSE 28 th June

2 Introduction

3 A Spectre is Haunting Europe... Unprecedented levels of youth unemployment in many developed economies over last decade. Currently 51.4% percent in Greece, 45.6% in Spain, and 39.2% in Italy. 2

4 A Spectre is Haunting Europe... Unprecedented levels of youth unemployment in many developed economies over last decade. Currently 51.4% percent in Greece, 45.6% in Spain, and 39.2% in Italy. 7.2% in Germany, 13.5% in UK, 10.3% in the US. 2

5 A Spectre is Haunting Europe... Unprecedented levels of youth unemployment in many developed economies over last decade. Currently 51.4% percent in Greece, 45.6% in Spain, and 39.2% in Italy. 7.2% in Germany, 13.5% in UK, 10.3% in the US. What can/should be done? This depends on two questions: Better Evidence: Will these workers suffer long term consequences? New Question: Will these consequences be different because they are young? 3

6 A Spectre is Haunting Europe... Unprecedented levels of youth unemployment in many developed economies over last decade. Currently 51.4% percent in Greece, 45.6% in Spain, and 39.2% in Italy. 7.2% in Germany, 13.5% in UK, 10.3% in the US. What can/should be done? This depends on two questions: Better Evidence: Will these workers suffer long term consequences? YES New Question: Will these consequences be different because they are young? 3

7 A Spectre is Haunting Europe... Unprecedented levels of youth unemployment in many developed economies over last decade. Currently 51.4% percent in Greece, 45.6% in Spain, and 39.2% in Italy. 7.2% in Germany, 13.5% in UK, 10.3% in the US. What can/should be done? This depends on two questions: Better Evidence: Will these workers suffer long term consequences? YES New Question: Will these consequences be different because they are young? YES 3

8 Is all unemployment equal? Matters for (at least) 3 reasons: Policy: If losses are large and unevenly distributed this has substantial implications for optimal labour market policy. It might suggest that labour market institutions which protect older, incumbent, workers at the cost of younger workers incarnate substantial efficiency losses. 4

9 Is all unemployment equal? Matters for (at least) 3 reasons: Policy: If losses are large and unevenly distributed this has substantial implications for optimal labour market policy. It might suggest that labour market institutions which protect older, incumbent, workers at the cost of younger workers incarnate substantial efficiency losses. Human Capital Accumulation: Our results suggest that early labour market experiences matter more à la Cunha, Heckman, Sennenach (2011) for children s non-cognitive skills. 4

10 Is all unemployment equal? Matters for (at least) 3 reasons: Policy: If losses are large and unevenly distributed this has substantial implications for optimal labour market policy. It might suggest that labour market institutions which protect older, incumbent, workers at the cost of younger workers incarnate substantial efficiency losses. Human Capital Accumulation: Our results suggest that early labour market experiences matter more à la Cunha, Heckman, Sennenach (2011) for children s non-cognitive skills. Labour/Macro models: Recent evidence suggests importance of early shocks for labour market shocks/experimentation. (c.f. ) 4

11 Summary We find that Unemployment when aged has permanent consequences. 5

12 Summary We find that Unemployment when aged has permanent consequences. Quantitatively large 6 months unemployment causes 5% permanent reduction in wages 5

13 Summary We find that Unemployment when aged has permanent consequences. Quantitatively large 6 months unemployment causes 5% permanent reduction in wages Later periods of unemployment have no permanent consequences. 5

14 Summary We find that Unemployment when aged has permanent consequences. Quantitatively large 6 months unemployment causes 5% permanent reduction in wages Later periods of unemployment have no permanent consequences. Use DWP Admin data for the UK (LLMDB) 1% Sample of all NI records. 5

15 Summary We find that Unemployment when aged has permanent consequences. Quantitatively large 6 months unemployment causes 5% permanent reduction in wages Later periods of unemployment have no permanent consequences. Use DWP Admin data for the UK (LLMDB) 1% Sample of all NI records. Effects concentrated on low-ability workers. 5

16 Literature Ruhm (1991) Seminal analysis of long-term effects of past displacement 6

17 Literature Ruhm (1991) Seminal analysis of long-term effects of past displacement Large Subsequent Literature, see Couch and Placzek (2010). 6

18 Literature Ruhm (1991) Seminal analysis of long-term effects of past displacement Large Subsequent Literature, see Couch and Placzek (2010). Many other studies, analyse long-term effects of youth unemployment, 6

19 Literature Ruhm (1991) Seminal analysis of long-term effects of past displacement Large Subsequent Literature, see Couch and Placzek (2010). Many other studies, analyse long-term effects of youth unemployment, Lynch (1985), Nickell et al (2002), Gregg and Tominey (2005) (for the UK) 6

20 Literature Ruhm (1991) Seminal analysis of long-term effects of past displacement Large Subsequent Literature, see Couch and Placzek (2010). Many other studies, analyse long-term effects of youth unemployment, Lynch (1985), Nickell et al (2002), Gregg and Tominey (2005) (for the UK) Lynch (1989), Mroz and Savage (2006), (for the US) 6

21 Literature Ruhm (1991) Seminal analysis of long-term effects of past displacement Large Subsequent Literature, see Couch and Placzek (2010). Many other studies, analyse long-term effects of youth unemployment, Lynch (1985), Nickell et al (2002), Gregg and Tominey (2005) (for the UK) Lynch (1989), Mroz and Savage (2006), (for the US) Oreopoulos et al (2012) Canadian Graduates. 6

22 Model

23 Framework Simple Mincer Equation: w t i = f ( Z i, λ t, e t i, ) εt i 7

24 Framework Simple Mincer Equation: w t i = f ( Z i, λ t, e t i, ) εt i Where: Z i are individual time-invariant characteristics 7

25 Framework Simple Mincer Equation: w t i = f ( Z i, λ t, e t i, ) εt i Where: Z i are individual time-invariant characteristics λ t are local labour market conditions, 7

26 Framework Simple Mincer Equation: w t i = f ( Z i, λ t, e t i, ) εt i Where: Z i are individual time-invariant characteristics λ t are local labour market conditions, e t i is i s experience at time t, 7

27 Framework Simple Mincer Equation: w t i = f ( Z i, λ t, e t i, ) εt i Where: Z i are individual time-invariant characteristics λ t are local labour market conditions, e t i ε t i is i s experience at time t, is a random shock affecting earnings. 7

28 Importance of past experience depends on how far in the past it was. Thus rewrite e t i as the vector ( e t i, et 1 i,..., e 2 i, e1 i ). 8

29 Importance of past experience depends on how far in the past it was. Thus rewrite e t i as the vector ( e t i, et 1 i,..., e 2 i, e1 i ). Then denote each element as a function of previous experience and current shock: e 2 i = e 2 ( e 1 i, ε2 i ), e 3 i = e 3 ( e 2 i, e1 i, ε3 i ), etc. 8

30 Importance of past experience depends on how far in the past it was. Thus rewrite e t i as the vector ( e t i, et 1,..., e i 2 i, ) e1 i. Then denote each element as a function of previous experience and current shock: e 2 i = e 2 ( e 1 i, ) ε2 i, e 3 i = e 3 ( e 2 i, e1 i, ) ε3 i, etc. Then: w t i = f (Z t i, λ t, e t( e t 1 (...),..., e 2 ( e 1 i, ) ) ε2 i, e 1 i, ε t i,..., e 2 ( ) e 1 i, ) ε2 i, e 1 i, ε t i 8

31 Importance of past experience depends on how far in the past it was. Thus rewrite e t i as the vector ( e t i, et 1,..., e i 2 i, ) e1 i. Then denote each element as a function of previous experience and current shock: e 2 i = e 2 ( e 1 i, ) ε2 i, e 3 i = e 3 ( e 2 i, e1 i, ) ε3 i, etc. Then: w t i = f (Z t i, λ t, e t( e t 1 (...),..., e 2 ( e 1 i, ) ) ε2 i, e 1 i, ε t i,..., e 2 ( ) e 1 i, ) ε2 i, e 1 i, ε t i And the total effect of a period of unemployment when, e.g. t = 1 in period t = 2 is given by: dw 2 i de 1 i = w2 i e 2 e 2 e 1 + w2 i e i 1 i 8

32 Framework Assumption There exist τ Y, τ A, τ S, and τ R, with τ Y τ S τ A τ R, and positive constants M and δ, such that for t τ R Scarring effect: Healing effect: 0 w t i e τ i w t i e τ i > M, τ = 1,..., τ Y, < δ, τ = τ S,..., τ A. 9

33 Framework w e M Print as figure0.pdf t i i \label{bar} Y... S... A... R t 1 t The partial derivatives of the earnings at time t implied by Assumption 1. 10

34 Empirical Strategy

35 Empirical Model Empirical Analogue of Mincerian Equation: Assume that f( ) is log-linear, and consider earnings until age 40: log w t i = Zt i αt + γ t e t i }{{} + βt 1e t t 1 + β t i t 2e t β t i s+1e s+1 + β t i se s i, }{{} Current Unemployment Past Unemployment Scarring Effects t = 19,..., 40, s = 18,..., t 1. 11

36 Empirical Model log w 40 i = X 40 i α 40 +γ 40 e 40 i }{{} + β40 39 e 39 i + β38 40 e 38 i β19 40 e 19 i + β18 40 e 18 i }{{} +V4 i log w 39 i = X 39 i α 39 +γ 39 e 39 i }{{} + β39 38 e38 i β19 39 e19 i + β18 39 e18 i }{{} +V39. log w 20 i = X 20 i α 20 +γ 20 e 20 i }{{} + β20 19 e 19 i + β18 20 e 18 i }{{} +V20 log w 19 i = X 19 i α 19 +γ 19 e 19 i }{{} + β19 18 e18 i }{{} +V19 i i, i, 12

37 Empirical Model Or: log w i = αx i + γe i + βe L i + V i, where w i = ( w 40 i,..., w 19 ) i, Xi = ( X 40 i,..., X 19 ) i, E i = ( e 40 i,..., e 19 ) i, E L i = ( e 39 i,..., e 18 ) i and Vi = ( V 40 i,..., Vi 19 ) are β is the following upper triangular matrix: β39 40 β38 40 β37 40 β19 40 β18 40 β38 39 β37 39 β19 39 β18 39 β37 38 β19 38 β β19 20 β18 20 β

38 Restricting β Building on Oreopoulous et al (2012): Entrant Unemployment βs t = βe t, if s = 18, 19, 20 and t > 22; β t s = 0, otherwise. Youth Unemployment βs t = βy t, if s = 21, 22, 23 and t > 24; β t s = 0, otherwise. Adult Unemployment βs t = βa t, if s = 24, 25, 26 and t > 28; β t s = 0, otherwise. And: β t s = β t+1 s, t = 23, 25, 27,...,

39 Identification Decompose Z t i = Xt i + Vt i 15

40 Identification Decompose Z t i = Xt i + Vt i V t i = θ iµ a + η t ac + ε t i 15

41 Identification Decompose Z t i = Xt i + Vt i V t i = θ iµ a + η t ac + ε t i θ i µ a : Moretti(2004) term endogenous labour mobility. 15

42 Identification Decompose Z t i = Xt i + Vt i V t i = θ iµ a + η t ac + ε t i θ i µ a : Moretti(2004) term endogenous labour mobility. η t ac: Oreopoulous et al (2012) cohort effects. 15

43 Identification Decompose Z t i = Xt i + Vt i V t i = θ iµ a + η t ac + ε t i θ i µ a : Moretti(2004) term endogenous labour mobility. η t ac: Oreopoulous et al (2012) cohort effects. µ a {1,..., 409} defined by local area districts. 15

44 Benchmark Specification ln w t iac = Xt i α + γet i + 20 βt E e s i s=18 }{{} Scar effects of unemployment for Entrants + µ a θ i + η t ac + ε t i, + β t Y 23 e s i s=21 }{{} Scar effects of unemployment for Youths + β t A 26 e s i s=24 }{{} Scar effects of unemployment for early Adults (1) t = 23-24, 25-26,..., (2) 16

45 Data

46 Data Lifetime Labour Market Database (LLMDB), 17

47 Data Lifetime Labour Market Database (LLMDB), DWP Admin Dataset for the universe of those eligible to work in UK. 17

48 Data Lifetime Labour Market Database (LLMDB), DWP Admin Dataset for the universe of those eligible to work in UK. Derived directly from underlying National Insurance and Benefits records. 17

49 Data Lifetime Labour Market Database (LLMDB), DWP Admin Dataset for the universe of those eligible to work in UK. Derived directly from underlying National Insurance and Benefits records. National Insurance number provides unique ID to track individuals. 17

50 Data 1% random sample of UK residents who are eligible to work, 647, 068 individuals, between 1978 and

51 Data 1% random sample of UK residents who are eligible to work, 647, 068 individuals, between 1978 and We restrict our sample to UK nationals, with no missing observations, between age 18 and 40: those born between 1960 and 1966, who enter the labour market between 1978 and

52 Data 1% random sample of UK residents who are eligible to work, 647, 068 individuals, between 1978 and We restrict our sample to UK nationals, with no missing observations, between age 18 and 40: those born between 1960 and 1966, who enter the labour market between 1978 and Drop those ever self-employed. Results stronger if included. 18

53 Data 1% random sample of UK residents who are eligible to work, 647, 068 individuals, between 1978 and We restrict our sample to UK nationals, with no missing observations, between age 18 and 40: those born between 1960 and 1966, who enter the labour market between 1978 and Drop those ever self-employed. Results stronger if included. Focus on Men. But, results similar for Women. 18

54 Income Data Yearly Earnings

55 Unemployment Data 8 Weeks of Unemployment Cohort Cohort Calendar Year Cohort Cohort Age 40 Cohort 1979 Cohort 1981 Cohort 1983 Age 24 20

56 Data Experience is measured as 1 number of weeks unemployed. 21

57 Data Experience is measured as 1 number of weeks unemployed. Unemployment is based on DWP benefits data, and where missing we use data from HMRC. 21

58 Data Experience is measured as 1 number of weeks unemployed. Unemployment is based on DWP benefits data, and where missing we use data from HMRC. Address measured extremely precisely: µ a {1,..., 409} local area districts. 21

59 Data Experience is measured as 1 number of weeks unemployed. Unemployment is based on DWP benefits data, and where missing we use data from HMRC. Address measured extremely precisely: µ a {1,..., 409} local area districts. Address data is missing, not at random, pre Use meta fixed-effects strategy defined on first observed addressed for these observations. Results robust to other assumptions. Define µ 0 a {1,..., 409} 21

60 Results

61 Early Unemployment Scars, Later Unemployment Heals.6 Scar Effect (%) Entrant Youth Early Adult Age

62 Early Unemployment Scars, Later Unemployment Heals.6.4 Scar Effect (%) Entrant Youth Early Adult Aggregate Age

63 Early Unemployment Scars, Later Unemployment Heals Entrant: Youth: Early Adult: Aggregate: Way Split: Way Split: Scar Effect (%) Age

64 Different Cut-Off Ages.6.4 Scar Effect (%) Age 25

65 Largely homogenous across cohorts..8.6 Scar Effect (%) Age 26

66 Cohorts (Youths).4.2 Scar Effect (%) Age 27

67 Cohorts (Adults).4.2 Scar Effect (%) Age 28

68 Effects are concentrated on low-ability workers 1.8 Top Quintile Quintile 4 Quintile 3 Quintile 2 Bottom Quintile Scar Effect (%) Age 29

69 Quintiles (Youths).2.1 Top Quintile Quintile 4 Quintile 3 Quintile 2 Bottom Quintile Scar Effect (%) Age 30

70 Quintiles (Adults).2 Top Quintile Quintile 4 Quintile 3 Quintile 2 Bottom Quintile.1 Scar Effect (%) Age 31

71 Effects by Region.8.6 Scar Effect (%) Age East Midlands Eastern London North East North West Northern Irl Scotland South East South West Wales West Midlands Yorkshire 32

72 Effects by Region (Adults).4.2 Scar Effect (%) Age East Midlands Eastern London North East North West Northern Irl Scotland South East South West Wales West Midlands Yorkshire 33

73 Effects by Region (Youths).4 Scar Effect (%) Age 34

74 Decomposing the Scar Effect dw t i duw 1820 = (1) {}}{ β t E + }{{} (3) (2) {}}{ βy t uw 2123 uw 1820 } {{ } (4) + }{{} (6) } {{ } (7) (5) { ( }}{ βa t uw2426 uw 2123 uw 2123 uw 1820 Decomposition of the Scar Effect } {{ } (4) + uw 2426 uw 1820 }{{} (8) (1) (2) (3) (4) (5) (6) (7) (8) On Earnings Aged On Earnings Aged On Earnings Aged On Earnings Aged On Earnings Aged On Earnings Aged ). 35

75 Initial Inequality associated with lower wages Area Fixed Effect Variance (log) Income 36