Career Progression and Formal versus on the Job Training Public Policy and life-cycle decisions C....

Career Progression and Formal versus on the Job Training

Public Policy and life-cycle decisions

C. Meghir

UCL and IFS

with J. Adda, C. Dustmann, J.-M. RobinPresentation prepared for WISE conference in XIAMEN

Go to data

IntroductionEmpirical Public Finance and Dynamic Models

• Designing a tax and welfare benefit system in a 2nd best context requires inputs from empirically estimated parameters

• The literature tends to focus on estimation of commodity and labour supply elasticities, which are the basic variables that respond to taxes and benefits.

• However, individuals are likely to change behaviour in a number of different ways faced with a new regime of taxes and benefits.


• In particular we have in mind the implications for Human capital accumulation of programmes such as

– Unemployment Insurance

– Earned Income Tax Credit

• Such programmes will have long run dynamic effects on– education choice

– job mobility and job choice.


• The specific context we will consider is the choice by young Germans to follow apprenticeship training and the subsequent path of wages and employment.

• Our model will explain how individuals choose between the two alternative career paths and will quantify the returns to the career

• We will then use this framework to simulate the impact of programmes such as the EITC, which are not currently operating.

• Our emphasis will be on the effects of these programmes on human capital accumulation.

IntroductionThe Policy issue

• Welfare to work policies such as EITC may have deeper implications than just affecting employment

• They change educational incentives

• They may affect job search behaviour

The Approach

To achieve this we need:

1. A model of the education choice

• This allows us to associate incentives from welfare programmes with earlier choices

2. This model must be linked to career choices and job mobility

• This allows us to study how wage growth may be affected

Overall aims of the paper

1. To examine the relative benefits of formal versus informal on-the-job training.

• Returns and their source

• Labour market Flexibility

2. To model wage formation and identify the sources of wage growth.

3. To create a framework where the implications of welfare policies can be analysed in a life-cycle context with all their dynamic implications.

More specific questions relating to apprenticeship

• About 80% of Germans who stop school at 16 follow an apprenticeship training programme. The remaining 20% remain formally unskilled.

• How valuable or otherwise is a formal vocational system? Does it reduce flexibility?

• How does it compare with less structured on-the-job training that typically occurs in the labour market?

• How important are economic incentives for the choice of education?

Why a model and not just a field experiment or a natural experiment?

• The policy issue we are concerned with is longer term. It relates to the behaviour of future cohorts.

• Even with a longer term perspective in mind we cannot envisage the existence of a control group

• We need to infer the impact of the policy from analogous effects implied by variations in the current environment: How do changing incentives affect education choice?

• To achieve our aim we need to specify structure and use observational data.

IntroductionData

• We use administrative data from German Social Security records

• These contain a calendar of all jobs with start and end date excluding civil service job

• They report the average daily wage either over the year or part thereof when a job change occurs. No averaging across jobs.

• They also report periods in apprenticeship because these are jobs – albeit very low paid

IntroductionSome relevant literature

• Becker (Human Capital), Willis and Rosen JPE 1979

• Keane and Wolpin JPE `97 (Education, work and occupational choices)

• Eckstein and Wolpin Econometrica `99 (education, work and attainment)

• Chris Taber Restud

• Altonji and Shakotko `90

• Topel JPE `91 and Topel and Ward QJE

• Dustmann and Meghir REStud 2005

• Heckman, Lochner and Cossa (1999)

0.0

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diff

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4.5

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0 20 40 60 80time since entry on labor market (quarter) ...

Wage In apprenticeship Unskilled wageSkilled wage Log wage difference

The ModelOverview

• Describes choices from the point where statutory education ends up until mid career.

• We take those who end formal education at 16.

• Utility is linear in earnings – so effectively individuals would maximise lifetime income except for the fact that they like leisure

• In our context liquidity constraints and uncertainty are not a factor because of linearity.

• Wages are match specific – So part of wage growth comes from looking for better jobs.

Choices

• Individuals decide whether they will attend apprenticeship or not.

• They trade off future returns with current cost – Lost earnings and utility costs

• Individuals know the distribution of future shocks.

Choices

• At the start of their career individuals are assumed to receive with certainty a job offer and an offer for apprenticeship.

• The former involves a wage and a non-pecuniary benefit. The latter a cost shock as well as a wage and benefit.

• We do not model the wait from school to any of the two state above.

Choices

• Once in a firm individuals make a choice to change jobs or not when they receive an alternative offer. [Job arrival Rate for the employed]

• Given the shock to the match specific effect individuals may choose to move to unemployment.

• They can only choose to go back to work if they gent an offer [Job arrival Rate for the unemployed]

• Transitions to unemployment can also take place because of job destruction. [Job Destruction Rate]

The Source of Dynamics

• Education choices now affect job opportunities in the future

• The returns to experience and tenure

• The potential difference in the job arrival rates for the employed and the unemployed.

• The stochastic structure.

The environment and sources of uncertainty• We consider a stationary economy

• There are aggregate shocks to productivity.

• A random cost of education (how hard you find it to learn)

• Random arrival of job offers.

• The match specific effect on wages

• Match specific effects evolve as a random walk

• In every period a job may be destroyed exogenously

The wage equation

• Wages

• All parameters depend on apprenticeship status.

• Ability is denoted by i. This affects levels of wages, returns to apprenticeship status (Edi), experience and tenure.

• The match specific heterogeneity is denoted by ift

lnwift 0 i Ed iEdi XXi,Edi, i TTi,Edi, i GEdiGt ift #

A description of the statistical approach• The model consists of a set of value functions – Bellman equations.

• There is a value for:

– going into education, – working in the current job– Switching to a new job if an offer is available– Not working

• These value functions define the probabilities of observed events

• The probabilities of all transitions we observe and the density for observed wages constitute the likelihood function SKIP

• Skip

Formal Description of the model

• The term represents the price of human capital which depends on the business cycle

• The Business cycle evolves according to an AR(1) which we discretise

• represents the aggregate shock

G G v, v IID,N0, v2

GEdiGt


• The distribution of match specific effects is defined by

• The initial condition characterises the actual offer and its distribution depends on education

• The term 0 represents non-wage benefits of the job and enters utility

t t1 ut, ut N0, u2

0 N0, 02 and N0,

2


• The instantaneous flow utility from working is given by

• When unemployed German workers receive UI as a proportion of the earnings in their last job. The replacement rate is 55%.

• We also allow for utility of leisure. The flow utility from unemployment is given by

with U=0.55 and is an iid shock with a normal distribution.

RW wE,Gt,Xt,Tt, t,

RUE,X,w1,, Uw1 0E,X,

• The value of unemployment can now be defined by

Value functions and transitions

UE,G,X,w1, RUE,X,w1,,

UE,XEG, , 0, max

UE,G,X,w1,

WE,G,X,0, 0 ,

1 UE,XEG, UE,G,X,w1,

If you get an offer

If you do not get offer

Offer arrival rate when unemployed

• The value of employment can now be defined by


WE,G,X,T, , wE,Gt,Xt,Tt, t, E,XEG, UE,G,X,wE,G,X,T,,

1 E,XWE,XEG, ,u, 0, max

UE,G,X,wE,G,X,T,,

WE,G,X,T, u,WE,G,X,0, 0

,

1 E,X1 WE,XEG, ,umaxUE,G,X,wE,G,X,T,,

WE,G,X,T, u,





UE,G,X,wE,G,X,T,,


,


WE,G,X,T, u,

Job destruction rate





UE,G,X,wE,G,X,T,,


,


WE,G,X,T, u,

If job is destroyed

Job destruction rate





UE,G,X,wE,G,X,T,,


,


WE,G,X,T, u,

Job is not destroyed and alternative offer made





UE,G,X,wE,G,X,T,,


,


WE,G,X,T, u,

Offer arrival rate for the employed





UE,G,X,wE,G,X,T,,


,


WE,G,X,T, u,

Job is not destroyed but no alternative offer madeOffer arrival rate for the employed


• The value of work while being an apprentice is given by

WA G,X,T, , Aw0,G,X,T,

AEG,u, 0, max

WA G,X,T, u,WA

G,X,0, 0 ,

1 AEG,uWA G,X,T, u,

Fraction of unskilled wage earned during apprenticeship

Alternative job arrival rate while training

Constructing the Likelihood function• Each individual has a history of transitions and wage

observations:

– Work in the same form as before (w)

– move from employment to unemployment

– Move from unemployment to employment (w)

– move to a new firm (w)

• Each transition has a probability conditional on the state variables, which include unobserved heterogeneity


• Apprenticeship is chosen after integrating over all future possibilities based on the decision rule

One off utility cost of training

Value of apprenticeship Value of working as an unskilled worker

iid cost shock

WA G,0,0, 0, 0

0Z, 01Z,G WE 0,G,0,0,0,

Exogenous variation and identification

• 20 cohorts in 11 regions

• Industries not uniformly distributed across country – this makes different parts of the country vulnerable to different shocks.

• Factor price equalisation ensures wages are independent of local shocks

• Demand for labor and hence apprentices varies as a result of these shocks. Thus these vary differentially by region and cohort

Exogenous variation and identification

• Key identifying assumptions:

– Distribution of unobservables is the same across cohorts

– Mobility for apprenticeship is costly (but not necessarily prohibitively so)

– Factor price equalisation across regions

The Data

• German Administrative data

• 1% of all Social Security Records

• Men who do not work in the civil service

• All jobs since entry in the labour market are recorded with exact transition dates

• Average daily wages within firm for year.

• Bottom and top coded – Not relevant in practice for this educational group

The Data

• Period Covered 1975-1995.

• Only entry cohorts whose career start is observed are used.

• we have 27525 individuals. We use 1400.

• The average age at first observation is 16.7.

• The oldest individual in our data is 35 years old.

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Wage

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0 20 40 60 80time since entry on labor market (quarter)

Movers Stayers

Wage Growth - Skilled Workers

-.05

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.2W

age

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wth

0 20 40 60 80time since entry on labor market (quarter)

Movers Stayers

Wage Growth - Unskilled Workers

Estimation• Maximum likelihood.

• Assume problem stationary and solve using value function iterations

• Use Gaussian quadrature for integrals

• Use only 1400 individuals picked randomly, for feasibility purposes

• Skip to returns

• Skip to Simulations

Unobserved Heterogeneity

• We use a discrete bivariate distribution.

• Overall four types of individuals

• There are two types as far as the utility costs of education are concerned

• There are two types for wage levels / productivity

• We allow these to be correlated, which gives rise to the endogeneity of education

Unobserved Heterogeneity

• There is no initial conditions problem since we observe all individuals as they enter the labour market

• We assume that unobservables are independent of region of birth and of time/cohort.

The fit of the Model

0 5 10 150

5

10

15Mean Experience Apprentices

Time (Years)

Exp

erie

nce

ObservedPredicted

0 5 10 150

5

10

15Mean Experience Non Apprentices

Time (Years)

Exp

erie

nce

ObservedPredicted

0 5 10 150

2

4

6

8

10

12Mean Tenure Apprentices

Time (Years)

Ten

ure

ObservedPredicted

0 5 10 150

2

4

6

8

10

12Mean Tenure Non Apprentices

Time (Years)

Ten

ure

ObservedPredicted

Goodnessof Fit, AverageExperienceandTenureover TimebyEducation

0 5 10 150

0.2

0.4

0.6

0.8

1Employment Apprentices

% E

mpl

oyed

Years

ObservedPredicted

0 5 10 150

0.2

0.4

0.6

0.8

1Employment Non Apprentices

Years

ObservedPredicted

0 5 10 15 203

3.5

4

4.5

5

Time (Years)

Apprentices

0 5 10 15 203

3.5

4

4.5

5Non Apprentices

Time (Years)

GoodnessofFit, ObservedandPredictedLogWage, byEducation

Results

Low wage types High wage types

ResultsParameter Qualified Apprentices Non-Apprentices

Standard dev. of innovation to match specific effect ( u) 0.086 (6e-5)

Standard dev. of initialmatch specific effect ( 0) 0.285 (0.003) 0.34 (0.005)

Quarterly job destruction rate ()

0.019 (0.002) 0.029 (0.002)

Quarterly offer arrival rate when employed (W)

0.106 (0.004) 0.094 (0.006)

Quarterly offer arrival rate when unemployed ( U)

0.234 (0.009) 0.225 (0.006)

Note: asymptotic standard errors in parenthesis. When only one parameter estimate and its

standard error are presented in a row this parameter is restricted to be the same across

the two groups

Results

5 10 15 200

5

10

15

20

25

30

Years

% W

age R

etu

rn to A

ppre

ntic

esh

ipAverage Treatment on TreatedAverage Treatment EffectOLS Return

ResultsReturns

r EG, ,WA

G,X0,T0, ,EG, ,W

Eu,G,X0,T0, , 1

Average Type 1 Type 2 Type 3 Type 4

Low Wage High Wage

Low Cost High Cost Low Cost High Cost

Return to Apprenticeship at age 15

Average Treatment Effect (ATE) -1.7 % 5.9 % 2.2 % -1.2 % -5.5%

Average Treatment on the Treated (ATTE) 8.4 % 6.7 % 5.4 % 8.8 % 7.1%

ATE, net of utility of education 2.8 % 9.5 % 8.8 % 2.3% 2.3 %

ATE, net of opportunity cost of education 8.8 % 13.1 % 9.4% 9.6 % 5.3 %

Policy Simulations

• Consider introducing an EITC programme

• Employment effects for low paid workers

• Change of Incentives for education

• Change of incentives for Job Mobility

• Parameters

Policy Parameters

0 100 200 300Daily Wage

Density of Wages Benefits

DensityofWagesandIn-WorkBenefit Scheme

Effect of Policies on education

Flat UB EITC

-0.1

-0.05

0

0.05

% D

evia

tion fro

m B

aseline % Individuals trained as Apprentices, Type 1

Flat UB EITC

-0.1

-0.05

0

0.05

% D

evia

tion fro

m B


Flat UB EITC

-0.1

-0.05

0

0.05

% D

evia

tion fro

m B


Flat UB EITC

-0.1

-0.05

0

0.05

% D

evia

tion fro

m B


TheImpact ofPolicyonthetake-upof

Results

4 6 8 10 12 14 16 18 200

0.01

0.02

0.03

0.04

0.05

0.06

Time on Labor Market (Years)

% Individual in Employment

Flat Unemployment BenefitEITC

ProportionIndividualsWorking, Compared

Results

4 6 8 10 12 14 16 18 20-0.045

-0.04

-0.035

-0.03

-0.025

-0.02

-0.015

-0.01

-0.005

0

0.005

Time on Labor Market (Years)

Firm-Worker Match Specific Effect

Flat Unemployment BenefitEITC

PolicyEffect onFirm-WorkerMatchSpecific

Conclusions

• The overall returns to apprenticeship, taking into account all costs are very low.

• Apprentices earn more

• Apprentices keep more stable jobs.

• However, experienced non-apprentices are more effective at finding new jobs after a layoff or a quit – more flexible?

Conclusions

• The decision to join an apprenticeship is quite sensitive to costs of education and to future returns

• Introducing an EITC type programme overall reduces the incentive to obtain education

• However it can increase the incentive for lower ability types because it makes it easier to obtain a job and benefit from the subsidy

• EITC type programmes compress wages by reducing the accumulation of search capital.

Career Progression and Formal versus on the Job Training Public Policy and life-cycle decisions C....

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