For One More Year with You: Changes in Compulsory Schooling, - - PowerPoint PPT Presentation

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For One More Year with You: Changes in Compulsory Schooling, - - PowerPoint PPT Presentation

For One More Year with You: Changes in Compulsory Schooling, Education and the Distribution of Wages in Europe Margherita Fort Giorgio Brunello and Guglielmo Weber PRELIMINARY WORK European University Institute, Max Weber Post-Doctoral


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“For One More Year with You”: Changes in Compulsory Schooling, Education and the Distribution of Wages in Europe

Margherita Fort Giorgio Brunello and Guglielmo Weber

PRELIMINARY WORK

European University Institute, Max Weber Post-Doctoral Programme

Florence, January 18, 2007 – p. 1/31

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Motivation

Large literature on average returns to schooling Less is known on the heterogeneity of the returns Are returns lower or higher for individuals at the lowest quartile of the distribution of earnings?

Florence, January 18, 2007 – p. 2/31

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Are returns higher in the lowest quartile?

Y = F(A, E) where: Y earnings; A ability; E education If ability and education are complements for individual productivity (and wages), the returns are lower for the less able ∂ ∂A ∂Y ∂E > 0

Florence, January 18, 2007 – p. 3/31

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Motivation II

Implications for education and inequality Do education reforms which affect compulsory years

  • f education reduce or increase earnings inequality?

Difficult question but relevant for policy

Florence, January 18, 2007 – p. 4/31

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Our Paper

We study the effects of compulsory schooling reforms introduced in Europe in the 1960s and 1970s on the distribution of earnings.

By exploiting the exogenous changes in education induced by the reforms, we assess the causal effect of education on the distribution of earnings for individuals whose schooling level changed due to the reforms (compliers)

Florence, January 18, 2007 – p. 5/31

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Outline of the Talk

Education & Income: Association & Causality Moving On From Average Returns The Identification Strategy in a Nutshell Data Issues Preliminary Results (Intention-to-Treat Parameters)

Florence, January 18, 2007 – p. 6/31

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What the other have done

Association U.S. : returns to education increase dramatically over the quantiles of the conditional distribution of wages [Buchinsky (2004)]; (ii) Europe: stronger education-related earnings increment for individuals who receive higher hourly wages conditional on their

  • bserved characteristics [Pereira & Martins (2004)]

Florence, January 18, 2007 – p. 7/31

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What the other have done

Causality U.S. : ability & education act as complements in determining wages, after controlling for endogeneity [Arias et al. (2001), data on twins] UK: substitutability between education and cognitive & non-cognitive ability [Denny et al. (2004), controls for cognitive ability]

Florence, January 18, 2007 – p. 8/31

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Identification of Endogenous Quantile Treatment Effects (QTEs)

Abadie et al. (2002): move from the approach of Angrist & et

  • al. (1996); propose an IV estimation method for QTEs;

application to JPTA Chesher (2003): discuss identification in recursive non linear structural model (based on exclusion restriction); estimation developed by Ma & Koenker (2004) (weighted average derivative estimators) and Arias et al. (2001) (control variate approach) Chernozhucov & Hansen (2005, 2006): propose an IV QTE model and estimation (analogue to two stage least squares estimator)

Florence, January 18, 2007 – p. 9/31

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Identification of Endogenous Quantile Treatment Effects (QTEs) (continued)

Abadie et al. (2002): binary treatment, binary instrument setting; identification and estimation of QTEs for compliers; authors do not consider estimation of the potential outcomes distributions for compliers Chesher (2003): continuous treatment variable & continuous instrument; identification and estimation of QTEs; ! Chesher (2005) extension to the case of a discrete treatment variable (interval identification) Chernozhucov & Hansen (2005, 2006): use of marginal independence conditions

Florence, January 18, 2007 – p. 10/31

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Main Features of This Project

Identification: (Fuzzy) Regression Discontinuity Design (RDD) exploiting exogenous variation in schooling induced by school reforms rolled out in Europe in the 20th century. Data: European Community Household Panel (ECHP, 2001), International Social Survey Programme (ISSP, 1993-2002), Survey on Health Ageing and Retirement in Europe (SHARE, 2004) and OECD, ILO (participation and unemployment rates, time series by gender 1947-2005).

Florence, January 18, 2007 – p. 11/31

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The Identification Strategy in a Nutshell

Hahn et. al.(2001), Card & Lee (2006), Imbens & Rubin (1997)

Many European countries increased the minimum school leaving age (MSLA) in 1960s-1970s. Date of birth (randomly assigned) determines whether an individual had to stay longer in school. The causal effect of education of wages is identified for those individuals whose education changed due to the effect of changes in MSLA (compliers). The changes in wages experienced by these individuals are proportional to the causal effect of education on wages provided that (i) they cannot be explained by any other “event”, except the reform in MSLA; (ii) there are no defiers.

Florence, January 18, 2007 – p. 12/31

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Reforms in Minimum School Leaving Age (MSLA), Europe, 20th century

1st cohort Change in Country Reform affected MSLA yrs school. Denmark 1971 1958 13 → 15 7 → 9 France 1959 1950 14 → 16 8 → 10 Italy 1963 1949 11 → 14 5 → 8 Also in Austria (1962), Belgium (1971), Finland (1970s), Germany (1960s), Greece (1975), Ireland (1972), the Netherlands (1975), Portugal (1960s), Spain (1970), Sweden (1962), UK (1946-1957). Positive effect on average years of schooling (+0.3) and imperfect compliance documented for several countries.

Florence, January 18, 2007 – p. 13/31

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Wish List (and “Bones of Contention”)

A (sizeable) micro-data set on Pooling (surveys, countries) several European countries Comparable individuals Country specific institutional & around the cutoff point labour market features, age, period (businness cycle at entry and at the time wages are observed) Data quality Coding issues (wage, education); missing data

Florence, January 18, 2007 – p. 14/31

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Empirical Analysis

Binary treatment: D ≡ 1( years of schooling ≥ compulsory years of schooling) Binary instrument: Z ≡ 1(T ≥ 0), T = cohort-¯ ck, ¯ ck year in which the reform was introduced in coutry k

  • Countries with 1-2 years increase in MSLA: Austria,

Denmark, France, Germany, Ireland, Netherlands, Portugal, Spain, Sweden

  • Countries with 3-4 years increase in MSLA: Belgium,

Finland, Greece, Italy

  • Countries where the change in MSLA was introduced in

the late’50- early ’60s: Austria, France, Germany, Italy, Portugal, Sweden

Florence, January 18, 2007 – p. 15/31

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Empirical Analysis

Analysis limited to individuals aged more than 25 who are employed at the time of the interview, T ∈ [−9, +9], i.e. born between 1932 and 1975, aged between 25 and 67 at the time of the interview Dependent variable: logarithm of deflated (2000 prices) gross monthly earnings in purchaising power standards (PPSs) Controls (X): country & gender specific quadratic trend in T; survey, country dummies; GDP per capita; employment protection legislation; unemployment rate at the (estimated) time of entry into the labour market and at the time the wage is

  • bserved; net wage dummy.

Florence, January 18, 2007 – p. 16/31

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Data: Summary Statistics on Key Variables

N nt ∀t E[Y] E[D] N nt ∀t E[Y] E[D] A∗ 2, 153 150 1, 736 0.8 IR∗ 2, 436 130 1, 679 0.9 B∗ 1, 505 70 2, 174 0.8 IT∗ 3, 528 200 1, 709 0.8 DK 3, 006 170 2, 630 0.9 N∗ 3, 857 200 2, 019 0.9 FI 2, 190 140 1, 796 0.9 PO∗ 1, 977 100 1, 084 0.7 FR∗ 2, 162 120 2, 158 0.9 SP∗ 3, 775 200 1, 615 0.8 GE∗ 3, 879 180 1, 934 0.7 SW 3, 366 200 1, 784 0.8 GR 1, 399 70 1, 223 0.8

Legend N sample size (employed individuals, T ∈ [−9, +9]); nt sample size by T; Y deflated (2000 prices) gross monthly earnings in PPSs, D ≡ 1( years of schooling ≥ compulsory years of schooling)

Florence, January 18, 2007 – p. 17/31

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The Effect of MSLA Changes on Education

Country group Features E[D|Z = 1, Xd] − E[D|Z = 0, Xd](s.e.) Males Females 1-2 years increase in MSLA & reform 0.03 (0.01) 0.01 (0.01) (A, Dk, Fr, Ge, Ir, Ne, Po, Sp, Swe) 3-4 years increase in MSLA & reform 0.06 (0.01) 0.05 (0.02) (Be, Fi, Gr, It) Employed 1-2 years increase in MSLA & reform 0.01 (0.01) 0.01 (0.01) (A, Dk, Fr, Ge, Ir, Ne, Po, Sp, Swe) 3-4 years increase in MSLA & reform 0.04 (0.02) 0.04 (0.02) (Be, Fi, Gr, It)

Florence, January 18, 2007 – p. 18/31

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The Effect of MSLA Changes on Education

Country group Features E[D|Z = 1, Xd] − E[D|Z = 0, Xd] 1-2 years increase in MSLA & reform 0.02 (0.007) (A, Dk, Fr, Ge, Ir, Ne, Po, Sp, Swe) 3-4 years increase in MSLA & reform 0.05 (0.012) (Be, Fi, Gr, It) Employed 1-2 years increase in MSLA & reform 0.01 (0.009) (A, Dk, Fr, Ge, Ir, Ne, Po, Sp, Swe) 3-4 years increase in MSLA & reform 0.04 (0.015) (Be, Fi, Gr, It)

Florence, January 18, 2007 – p. 19/31

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The Effect of MSLA Changes on Education

Treatment: D ≡ 1( years of schooling ≥ compulsory years of schooling) Instrument: Z ≡ 1(T ≥ 0), T = cohort-¯ ck, ¯ ck year in which the reform was introduced in coutry k Effect (s.e.) 0.04 (0.01)

.7 .8 .9 −9 −8 −7 −6 −5 −4 −3 −2 −1 1 2 3 4 5 6 7 8 9 Year of birth−Year of birth of first cohort affected by the reform Observed Predicted (smpl<0) Predicted (smpl>0)

All countries

Proportion of Individuals with High Qualification (Observed and Predicted )

Florence, January 18, 2007 – p. 20/31

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The Effect of MSLA Changes on Labour Force Participation

.2 .4 .6 .8 1 −10 −5 5 10 Year of birth−Year of birth of first cohort affected by the reform Austria,observed Austria,fitted Belgium,observed Belgium,fitted Denmark,observed Denmark,fitted Finland,observed Finland,fitted France,observed France,fitted Germany,observed Germany,fitted Greece,observed Greece,fitted

Proportion of Employed Women

No clear evidence of effects on the proportion

  • f employed individuals.

Florence, January 18, 2007 – p. 21/31

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The Effect of MSLA Changes on Labour Force Participation

.2 .4 .6 .8 −10 −5 5 10 Year of birth−Year of birth of first cohort affected by the reform Ireland,observed Ireland,fitted Italy,observed Italy,fitted Netherlands,observed Netherlands,fitted Portugal,observed Portugal,fitted Spain,observed Spain,fitted Sweden,observed Sweden,fitted UK,observed UK,fitted

Proportion of Employed Women

No clear evidence of effects on the proportion

  • f employed individuals (exception: UK).

Florence, January 18, 2007 – p. 22/31

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The Effect of MSLA Changes on Labour Force Participation

.2 .4 .6 .8 1 −10 −5 5 10 Year of birth−Year of birth of first cohort affected by the reform Austria,observed Austria,fitted Belgium,observed Belgium,fitted Denmark,observed Denmark,fitted Finland,observed Finland,fitted France,observed France,fitted Germany,observed Germany,fitted Greece,observed Greece,fitted

Proportion of Employed Men

No clear evidence of effects on the proportion

  • f employed individuals .

Florence, January 18, 2007 – p. 23/31

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The Effect of MSLA Changes on Labour Force Participation

.2 .4 .6 .8 1 −10 −5 5 10 Year of birth−Year of birth of first cohort affected by the reform Ireland,obs. Ireland,fit. Italy,obs. Italy,fit. Netherlands,obs. Netherlands,fit. Portugal,obs. Portugal,fit. Spain,obs. Spain,fit. Sweden,obs. Sweden,fit. UK,obs. UK,fit.

Proportion of Employed Men

No clear evidence of effects on the proportion of employed individuals.

Florence, January 18, 2007 – p. 24/31

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Controlling for Differences in (Pre-Reform∗) Observ- ables over the Distribution of Earnings

  • yi = yi −

β(τ2)Xi if QY (τ1|Xi, Zi) ≤ yi ≤ QY (τ2|Xi, Zi), where

  • QY (·|Xi, Zi) estimated conditional quantile τ1 ≤ τ2

10 20 30 40 Percent 5 10 15 Log(wage in PPS) Percent Percent

Blue: Denmark; Red: Spain

Pre−reform Log(earnings) Uomini

20 40 60 80 Percent −6 −4 −2 Generalized Residual Percent Percent

Blue: Denmark; Red: Spain

Residuals Uomini

Florence, January 18, 2007 – p. 25/31

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The Effect of MSLA Changes on Distribution of Earnings

Belgium

.1 .2 .3 .4 −10 −5 5 10 Year of birth−Year of birth of first cohort affected by the reform Observed Predicted (mean) avg_F_20 Fitted values

Belgium

Cumulative distribution function at decile 0.20

.85 .9 .95 1 −10 −5 5 10 Year of birth−Year of birth of first cohort affected by the reform Observed Predicted (mean) avg_F_90 Fitted values

Belgium

Cumulative distribution function at decile 0.90

Florence, January 18, 2007 – p. 26/31

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The Effect of MSLA Changes on Distribution of Earnings

Finland

.1 .2 .3 −10 −5 5 10 Year of birth−Year of birth of first cohort affected by the reform Observed Predicted (mean) avg_F_20 Fitted values

Finland

Cumulative distribution function at decile 0.20

.85 .9 .95 1 −10 −5 5 10 Year of birth−Year of birth of first cohort affected by the reform Observed Predicted (mean) avg_F_90 Fitted values

Finland

Cumulative distribution function at decile 0.90

Florence, January 18, 2007 – p. 27/31

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In Short . . .

Empirical evidence on the role of education in reducing income inequality is ambiguous This study seems to be the first trying to tackle the issue in the case of Europe, exploiting changes in MSLA introduced in the 20th century Preliminary results suggest that changes in MSLA :

  • increase the education level of individuals

in countries where the change in MSLA was 3/4 years

  • reduce the proportion of individuals

at the lower deciles in some countries has generally negligible effects on the proportion

  • f individuals at the higher deciles

Florence, January 18, 2007 – p. 28/31

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What next?

Robustness checks (measure of earnings; set of countries considered; reforms considered) Discussion of the internal validity of the identification strategy Assessment of the causal effects of education on the distribution of earnings for compliers

Florence, January 18, 2007 – p. 29/31

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Data: Summary Statistics on Key Variables

All Individuals E[Z = 0] E[Z = 1] E[Z = 0] E[Z = 1] Austria 22.5 77.5 Ireland 47.7 52.3 Belgium 41.1 58.9 Italy 28.8 71.2 Denmark 49.8 50.1 Netherlands 52.4 47.5 Finland 52.8 47.2 Portugal 18.1 81.9 France 32.0 68.0 Spain 46.4 53.6 Germany 35.8 64.2 Sweden 28.6 71.4 Greece 50.9 49.1

Florence, January 18, 2007 – p. 30/31

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Data: Summary Statistics on Key Variables

Employed Individuals E[Z = 0] E[Z = 1] E[Z = 0] E[Z = 1] Austria 20.6 79.4 Ireland 45.0 55.0 Belgium 39.0 61.0 Italy 24.8 71.2 Denmark 49.5 50.5 Netherlands 51.7 48.3 Finland 54.4 46.6 Portugal 12.6 87.4 France 30.8 69.2 Spain 43.8 56.2 Germany 35.1 64.9 Sweden 28.8 71.2 Greece 49.0 51.0

Florence, January 18, 2007 – p. 31/31