1. ESF feasibility and impact study Elena Claudia Meroni CRIE ESF partnership meeting
Brussels, 7 December, 2017

2. Feasibility of CIE of overall ESF impact
Programming Periods:
; ;
Outcomes of interest:
NEET
Youth unemployment
Long term unemployment
ELET( Early leavers from education and
training)
Data sources:
Outcome variables: LFS data at NUTS 2 level.
ESF variables: Consolidated financial data provided by DG-REGIO, containing information on regional expenditures (actual payments made from the EC) for all ESI funds.
Control variables: Eurostat and Cambridge Econometrics:

3. ESF Impact study (work in progress…)
Data Issue
Region type mismatch > between DG-REGIO and LFS data
Time mismatch> missing infomation at different point in time
NUTS2 administrative changes> splits and merge of NUTS over time
Missing indicators> LFS data
Insufficient sample size> LFS data
Confounders and control variables> not always avaiable at NUTS2 level
Statistical Imputation> DG-REGIO data
Time lag issues > DG-REGIO data

4. This introduces bias in the analysis and a time-lag problem
Statistical Imputation> DG-REGIO data
Part of the observations were statistically imputed:
Whenever the expenditure of a particular NUTS2-region in a particular year could not be allocated -for instance, because data was available only at national level- the observation was unit-imputed by dividing the national expenditure by available regional indicators, such as the “weighted shares of unemployment and employment rates” (p.15).
This introduces endogeneity in the analysis and a reverse causality problem
Time lag issues> DG-REGIO data
The yearly breakdown of expenditure included in the dataset follows the cycle of the EC payments to the MS and not the dates in which expenditures took place on the ground.
This introduces bias in the analysis and a time-lag problem

5. ESF Impact study (work in progress…)
Econometric Specification
We analyze the impact of time lags in detail by considering a dynamic model/approach, estimated via a GMM estimator (Arellano and Bond, 1991; Blundell and Bond, 1998).
The use of a dynamic model should partially mitigate the impact of the time-lag problem, accounting also for the sluggish adjustment in the regional indicators considered.
It could also mitigate the likely reverse causality bias induced by imputation, by controlling (through the lagged dependent variable) for all the "history" of the model until the previous period.
By the end of the year a report will be produced for DG-EMPL presenting the results of the ESF impact study

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