the determinant of youth disadvantage. a panel data analysis. francesco pastore seconda università...

THE DETERMINANT OF YOUTH DISADVANTAGE. A PANEL DATA ANALYSIS. Francesco Pastore Seconda Universit di Napoli, IZA of Bonn and Italian Association of Labor Economists Email: [email protected]@unina2.it Luca Giuliani Seconda Universit di Napoli Email: [email protected]

Aims of the presentation To give you some information on key stylised facts To provide a frame of mind to understand the youth unemployment problem, its causes and, in part, its consequences To provide criteria to Define the objective And assess the ex ante effectiveness of youth employment policy To understand the evolution of the debate on this issue: From educational system to determinant of youth disadvantage Keywords: youth experience gap; SWT; flexicurity;

OUTLINE Youth experience gap Two policy approaches: The liberalist view The interventionist view Aim of presentation Methodology and data Descriptive analysis Static and dinamic panel data estimations Conclusion

Understanding the nature of youth unemployment from stylised facts The flow in and out of unemployment is higher for young people (Clark and Summers, 1982) because: Young people are in search for their best job-worker match And sometimes go back to education and training Especially low-skill young people Employers are also in search for their best job match Consequences of job shopping: Shorter average unemployment duration, but For the low-skill: High risk of falling into a chain of low pay temporary or part-time work Two paths for high-skill and low-skill young people)

The youth experience gap Young people have low human capital: Despite increasing educational attainment, they lack the other two components of human capital: generic and job-specific work experience; To fill in the youth experience gap, young people move in and out of employment in search for the best job-worker match; This search requires the least skilled to go back to education and/or training schemes; Youth unemployment is temporary;

The liberalist (optimistic) approach Why to bother? High YU is the consequence of a search for the best match by young people and employers The only thing to do is: make the market more flexible to increase the chances for young people to find a good job The market will provide training via temporary work, in the case of countries that implemented two-tier reforms Lower entry wages would be the solution to the lower degree of work experience of young people

Three main objections to the liberalist approach and to temporary work A. Beckers (1962) hypothesis of market failure for job specific training B. Heckman and Borjas (1980) hypothesis of omitted heterogeneity C. Empirically, it amounts to ask: Temporary work arrangements: Are they stepping stones or dead end jobs?

Arguments against labour market flexibility and temporary works PROS It is a stepping-stone for young people to find their best job-woker match Employers have a buffer stock of workers in expansionary times to be possibly fired in downturns Employers pay low wages for low productivity; Employers try young people; No compansation mechanisms but special intervention is needed only for particularly weak young people; Cons Sometimes they become low-pay traps Low pay jobs become permanent And cause precariuosness of labour market experiences (so-called training trap); Increasing power of insiders Therefore, the need for constraints to the use of temporary work; High cost for households;

Evidence on temporary work as stepping stone In almost all countries the net impact on employability is positive Spain and some USA programmes are exceptions Ichino et al. (2005, 2008) find a net positive effect of 19 in Tuscany and 11% in Sicily (here only weakly significant) a gross effect of 31 and 23%, respectively Berton, Devicienti and Pacelli (2008) find also evidence of a trap: increased probability of temporary work to remain such

Arguments against labour market flexibility: why temporary work may be a trap? Becker (1962) Temporary contracts reduce the gap in generic, not in job specific work experience; Short-time horizons work as a disincentive for employers and employees; Bentolilla and Dolado (1994) find for Spain an increase in the youth experience gap as a consequence of temporary work (see also Acemoglu 2002) Heckman and Borjas (1980) duration dependence in an individuals unemployment experience often is an artefact of statistical data; Once controlling for unobserved heterogeneity, duration dependence disappears It is not a consequence of low labour turnover, but of low skills and motivation; Education and ALMP are the solutions.

Fields of intervention: different policy mix The educational system: Rigid versus flexible systems Sequential versus dual systems The welfare system Pro-active schemes versus passive income support Targeting and scale of expenditure State- versus family-based welfare systems Fiscal incentives

The interventionist view: Flexicurity Flexicurity, meaning Passive income support during unemployment spells and social security provisions to increase the cost of temporary work for firms and reduce the Pro-active schemes to increase employability Employment rather than job stability

Objection: The training trap Also ALMP may become a trap: young people move from a programme to the next to get the linked benefits Van ours (2004) find a looking-in effect for training programmes participants in Slovakia Wunsch and Lechner (2008) find a similar effect in Germany Caroleo and Pastore (2005) find evidence of a training trap for some young people involved in training programmes in ALMP in Italy

Interventionist view: Reforms of the education and training systems Increase the quality of education, trhough: Evaluation mechanisms Incentives for quality increasing intevention Introduce a duality principle: Training should be provided together with general education Favour smooth STW transitions through job placement: Dual system in Germany Jisseki Kankei in Japan Job placement services in Anglo-Saxon countries

EU SWT models la Esping-Andersen? The German system: The dual educational system The Scandinavian system: ALMP on a large scale The Anglosaxon system: High quality of education and labour market flexibility The South Mediteranean System: The family and temporary work The new member states: Building a modern welfare system (????)

The Aim The red line of this paper is using econometric analysis to empirically test whether after controlling for some variables, such as: GDP, youth population, secondary & tertiary education attainment, ALMP & PLMP, employment protection index There remains part of the YUR and RD differential across-countries that is still unexplained and that can be caught by School-to-Work transition regimes ==

DATA: SWT regimes and countries SWTR is a set of 5 dummy variables that represent school-to-work transition regimes: North-European System: 1 if Estonia and Sweden, 0 otherwise; Dual-Educational System: 1 if Belgium, Germany, Austria, Netherlands, Denmark, France, Slovenia, Luxemburg, 0 otherwise; Anglo-Saxon system: 1 if United Kingdome and Ireland; 0 otherwise; South European System: 1 if Greece, Italy, Portugal, Spain; 0 otherwise; New Member State System: 1 if Poland, Slovakia, Hungary, Estonia and Czech Republic; 0 otherwise;

ModelVariableDescriptionUnit of Measurement Yl_yur1524 Youth unemployment rate (15- 24) Percentage, log X dl_gdpGrowth of GDP US$ current prices, difference of log l_gdpGDPUS$ current price, log l_yupopYouth population (ylf/tlf)Thousand of persons, log l_edu2Secondary educationPercentage, log l_edu3Tertiary educationPercentage, log l_epiEmployment protection indexIndex of costs, logs l_almpActive labour market policies Public expenditure as a percentage of GDP, log l_plmpPassive labour market policies Public expenditure as a percentage of GDP, log D D_NECountry dummy1 or 0, binary D_CECountry dummy1 or 0, binary D_ASCountry dummy1 or 0, binary D_SECountry dummy1 or 0, binary D_NMSCountry dummy1 or 0, binary CONTROL VARIABLE

Statistic Panel Data Fixed Effect Model (FE) - Is the dependent (endogenous) variable, - is a time invariant individual effect - it measures the effect of all the factors that are specific to individual i but constant over time, - is a row vector of observations on K explanatory STRONGLY EXOGENOUS factors for each i at time t, not including the constant term. It means that is not correlated with present or past. If it does not hold you will use dynamic panel.

Fixed Effect Model (FE) The FE model can be divided in to two parts: take in mind that the Within Estimator for fixed effect model could be written as: Where is the mean of the observations on the outcome for individual I, and, is the mean row of the observations on the explanatory variable x for individual i. The Between Estimator could be written as:

Random Effect Model (RE) The random effects model is an alternative to the Fixed effects model. The estimation equation is the same: contrary to the Fixed effects: the random effects are assumed not to be estimable-in contrast with Fixed Effect that can be estimated-; they measure our individual specific ignorance which should be treated similarly to our general ignorance.

It come out that the transformation for each individual-time observation is: Where is : =

RANDOM EFFECT= OLS RANDOM EFFECT = FIXED EFFECT

Hauseman Test The natural question that arises after introduction of RE and FE models is: Which one should we use?. True Consistent More Efficient Inconsistent [1] [1] Remember that the null hypothesis is that RE Model is the correct one (p-value ha sto be smaller than 0.05)

Dynamic Panel Data In order to measure the persistence of the results in long-run and short-run a lagged variable should be introduced in the previous model.

GMM We can apply directly to this model IV. Assume the existence of a T*r matrix for instrument, where r K is the number of instruments, that satisfy the r moment conditions: The GMM estimator based on these moment conditions minimizes the associated quadratic form:

GMM estimator Where denotes an r x r weighting matrix. Given some algebra gives the Panel GMM estimator: The essential condition for the existence of this estimator is, once again, :

One-Step Panel GMM The one-step GMM or two-stage leaste-square estimator uses weighting matrix : leading to: This estimation is called one-step GMM because given the data it can be directly computed using the equation above.

Two-Step Panel GMM The two-step GMM is based on the unconditional moment of using weighting matrix, where, where is consistent S defined as: Using you have the two-step GMM estimator :.

The Arellano-Bond estimator The microeconomics literature refers to the resulting GMM estimator as the Arellano-Bond estimator. The estimator is: Lags of or can additionally be used as instruments, and fore moderate or large T there may be a maximum lag of that is used as an instrument, such as not more than..

Constructing the Panel Data The data bank includes 21 countries observed over a period of 10 years, by 2000 till 2011. The number of variable used was around 97. Hence, the Panel was composed by 231 observation. VariableUnitNameSOURCE EPI_CIndices of costs Employment Protection Index_Collective Labour>Employment Protection> Strictness of employment protection collective dismissals (additional restrictions) EPI_I Indices of costs Employment Protection Index_Individuals Labour>Employment Protection>Strictness of employment protection individual dismissals (regular contracts) LTIR Long Term Interest rate General Statistics > key short-term Economic indicator > Long Term Interest Rate

AI Annual Inflation Prices and Purchasing power>prices and prices indices > consumer price (MEI)>consumer prices- Annual inflation RIR index (where the year 2005 is the base year) Real Interest Rate Finance>Monthly financial statistics>monthly monetary and financial statistics(MEI)> interest rates GDP US $, current prices, current PPPs, millions real GDP (98- 2012) National Account> Annual national account>Main aggregate> gdp> Gross domestic product (GDP) MetaData : GDP, US $, current prices, current PPPs, millions EMPL Thousands of persons Empoyed (98- 2012) Labour>LFS>Short-Term labour market statistics>Employed population YUR1519 percentages. Youth Unemployment 15-19 Labour>LFS>LFS by sex and age- indicator>unemployment rate YUR2024 percentages. Youth Unemployment 20-24 Labour>LFS>LFS by sex and age- indicator>unemployment rate YUR1524 percentages. Youth Unemployment 15-24 Labour>LFS>LFS by sex and age- indicator>unemployment rate UR1564 percentages. Unemployment rate 15-64 Labour>LFS>LFS by sex and age- indicator>unemployment rate ALMP public expenditure as percentage of GDP Active labour market policies Labour>LAbour Market programmes>public expenditure as percentage of GDP> Active PLMP public expenditure as percentage of GDP Passive labor market policies Labour>LAbour Market programmes>public expenditure as percentage of GDP> Passive UR2564 percentagesunemployment rate 25-64 Labour>LFS>LFS by sex and age- indicator>unemployment rate

RD=(YUR1524/UR2564) Relative Deasdvantag Computated APOP Thousands of persons Active Population aged 15 and over Labour>LFS>Short-term statistics>short term labour market statistics>Active population YUPOP Thousand of persons Youth population (lfs1524/tf) EDU3 percantage Tertiary education Education & training> Education at Glance> Appendix A>Atteined tertiary education degree, 25-34 years old(%) EDU2 percantageSecondary education Education & training> Education at Glance> Appendix A>attained below upper secondary education, 25-34 years old(%)

Expectation on Betas VariableExpectation on Betas sign Employment Protection Index Positive GDP & GDP growthNegative ALMP and PLMPPositive Secondary and Tertiary Education Negative Youth populationPositive Active Youth PopulationPositive

Descriptive Analysis

GDP growth

Youth Population

Secondary High School attainment

Tertiary Education attainment

Ratio of Expenditure for policies

Active youth population

Expectation on Beta Throughout the descriptive analyses it is clear that the expectation made theoretically are all fulfilled. The only variable that could create problems is going to be tertiary education attainment, found with positive beta.

RE estimation for YUR Variable modRE 1 modRE 2 modRE 3 modRE 4 modRE 5 modRE 6 GDP growth-0.329***-0.227**-0.208**-0.331**-0.329**-0.310** Youth population0.6261.8383.736**1.041.2472.156 Southern Europe0.062-0.402**-0.464*-0.309-0.197 Anglo- Saxon-0.464*-0.881***-0.928***-0.908***-0.795*** Central Europe-0.553***-1.011***-1.005***-0.879***-0.765*** Northen Europe-0.115-0.734***-0.777**-0.37 Secondary Education-0.232***-0.250***-0.162*-0.137-0.267** Teritary Education0.203**0.201**0.353***0.354***0.186 PLMP0.341***0.372*** EPI0.0750.501***0.587*** ALMP0.0850.0290.107 constant2.782***2.547***1.879**1.584*1.1821.993*

FE estimation for YUR VariablemodFE1modFE2modFE3modFE4modFE5modFE6 GDP growth-0.337***-0.074-0.083-0.312** -0.318** Youth population-1.19731.182***30.614***14.507** 14.579** Southern Europe(omitted) Anglo-Saxon(omitted) Central Europe(omitted) Northen Europe(omitted) Secondary Education-0.330***-0.342***-0.314** -0.298** Tertiary Education0.335***0.354***0.353** 0.325** PLMP0.528***0.502*** EPI-0.299*0.324 ALMP0.242*** 0.269*** _cons3.334-10.740***-10.865***-4.171 -3.774 N229223 203 ll-4.59281.96980.30817.234 16.034 AIC15.184-149.937-148.615-20.468 -20.068

Hauseman Test on model 2

Hauseman test on model 3

LSDV estimation for YUR VariableLSDV1LSDV2LSDV3LSDV4LSDV5LSDV6 GDP growth-0.382***-0.390***-0.384***-0.295**-0.288**-0.257*** Youth pop0.909*0.8450.991*1.900**1.609**2.769*** Southern Europe0.057-0.107*-0.058-0.079-0.151 Anglo-Saxon-0.466***-0.737***-0.712***-0.666***-0.752*** Central Europe-0.551***-0.835***-0.701***-0.692***-0.777*** Northern Europe-0.108*-0.02-0.351***0.215* Secondary Education-0.003-0.071*0.0220.001-0.176*** Tertiary Education0.298***0.248***0.378***0.399***0.049 PLMP-0.0280.052 EPI0.821***0.919***0.840*** ALMP-0.208**-0.145**-0.223** _cons2.657***0.909*2.116***-0.2080.1061.771** N229223 203 ll-73.846-31.306-62.637-22.076-23.926-94.75 aic159.69382.613143.27564.15265.851199.5

YUR: to sum up VariablemodFE2modRE2LSDV2 GDP growth-0.074-0.227**-0.390*** Young pop31.182***1.8380.845 Southern Europe(omitted)-0.402**-0.107* Anglo-Saxon(omitted)-0.881***-0.737*** Central Europe(omitted)-1.011***-0.835*** Northern Europe(omitted)-0.734***-0.02 Secondary education-0.330***-0.232***-0.003 lTertiary education0.335***0.203**0.298*** PLMP0.528***0.341***-0.028 EPI-0.299*0.0750.821*** _cons-10.740***2.547***0.909* N223 ll81.969-31.306 AIC-149.937.82.613

GMM Estimator for YUR VariableGMM1GMM2 l1l_yur15240.981***0.954*** l2l_yur1524-0.260*** l_gdp-2.019***-3.798*** l1l_gdp2.069***3.231*** l_edu30.413**0.739 l1l_edu3-0.375**-0.656 l_edu2-0.101* l_epic0.127-0.099 l1l_epic-0.280***-0.47 l2l_epic0.232* l_almp-0.008 l_plmp0.174*** l1l_plmp-0.104** D_SE-0.175** D_AS-0.234** D_CE-0.329*** D_NE-0.122 _cons0.5586.363 N172209

RE estimation for RD Variable modRE a modRE b modRE c modRE d modRE emodREf GDP growth0.577***0.664***0.667***-0.0090-0.002 Youth pop-2.479**-2.534**-2.499**-1.073-0.843-0.625 Southern Europe0.1210.2410.2510.209**0.116 Anglo-Saxon0.3050.510**0.519***0.459***0.359** Central Europe0.1490.450***0.471***0.295***0.217** Northen Europe0.160.520**0.486**0.371*** Secondary education-0.113-0.117-0.022-0.031-0.018 Tertiary education-0.351***-0.363***-0.029-0.0320.009 PLMP-0.151***-0.142*** EPI0.103-0.031-0.142 ALMP-0.117***-0.099**-0.107*** _cons2.559***3.735***3.880***1.951***2.115***1.821*** N229223 203

FE estimation for RD VariablemodFEamodFEbmodFEcmodFEdmodFEemodFEf GDP growth0.605***0.683*** 0.013 0.016 Youth pop5.519-9.009-9.0374.293 4.253 Southern Europe(omitted) Anglo-Saxon(omitted) Central Europe(omitted) Northern Europe(omitted) Secondary education-0.327** -0.013 -0.022 Tertiary education-0.639***-0.638***-0.003 0.012 PLMP-0.179***-0.181*** EPI-0.015-0.179 ALMP-0.121*** -0.136*** _cons-0.8428.598***8.592***-0.132 -0.352 N229223 203 ll-22.284-5.769-5.77144.765 143.473 aic50.56825.53723.541-275.53 -274.945

LSDV estimation for RD Variabl e LSDVaLSDVbLSDVcLSDVdLSDVeLSDVf GDP growth 0.765***0.756***0.758***-0.146***-0.139**-0.112** Youth pop -2.799**-2.403**-2.361*-2.152***-2.481***-2.294*** Southern Europe 0.124***0.209***0.223***0.099***0.017 Anglo-Saxon 0.299***0.338***0.345***0.340***0.243*** Central Europe 0.148***0.301***0.340***0.134***0.038 Northern Europe 0.1430.414***0.317***0.242*** Secondary education -0.028-0.047-0.033-0.058**-0.053** Tertiary education -0.039-0.054-0.02400.076** PLMP -0.158***-0.135*** EPI 0.240***0.082-0.007 ALMP -0.010.061***0.050*** _cons 2.703***2.335**2.687**2.474***2.828***2.500*** N 229223 203 ll -55.162-42.703-45.45650.65145.89537.348 aic 122.324105.407108.913-81.301-73.789-64.696

GMM for RD VariableGMMaGMMb RD(-1)0.693***0.102 RD(-2)-0.049 GDP-0.0720.556* GDP(-1)0.951**0.049 GDP(-2)-0.732*** Tertiary ed.0.2520.316 Tertiary ed. (-1)-0.196-0.511 Secondary ed.0.181* Secodnary ed. (-1)-0.136 ALMP0.023 PLMP-0.201*** PLMP(-1)0.116** Southern Europe0.102** Anglo-Saxon0.007 Central Europe0.035 Nourthen Europe0.085* EPI-0.471 EPI(-1)0.214 _cons-1.306***-3.849 N172209

Dual educational system result to be the best Reforms are needed in order to improve educational system Youth Guaratee: -apprenticeship -2015-2020, funds for ALMP CONCLUSIONs

Future research The next goal is to do the same work also taking into account the Relative Disadvantage

the determinant of youth disadvantage. a panel data analysis. francesco pastore seconda università...

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