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Forecasting of long-term unemployment at the individual level Tomáš Soukup RILSA Czech Republic

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Presentation on theme: "Forecasting of long-term unemployment at the individual level Tomáš Soukup RILSA Czech Republic"— Presentation transcript:

1 Forecasting of long-term unemployment at the individual level Tomáš Soukup RILSA Czech Republic

2 2 Content Introduction Theoretical approach (Job Search Theory) Data Results Future developement

3 Introduction Macro versus micro approach Use of forecasting at the individual level –defining vulnerable groups at the LM –forecasting the length of unemployment segmentation of claimants areas of services at labour offices (zones) precautions in social policy Predicting the effects of ALMP Foreign experience

4 Theoretical approach 1 Job Search Theory H = f (V, c U) H – final result of the job search process, matching demand and supply in the labour market V – number of Vacancies U – number of Unemployed c – job search efficiency on the part of the claimant

5 Theoretical approach 2 The success of the job search is principally affected by: The situation in the labour market The availability of vacancies and employer discrimination (Queuing Theory, Concept of Human Capital, Discrimination, Segmentation Theories) Job seeker motivation (Nominal Flexibility, Basic incentives for work, Concept of Feeling Efficacy)

6 Data and method Need for continuous data Panel survey , 2 waves N=759 Binary logistic regression

7 Output variable “Found a job in 6 months” 1 found a job 0 didn’t find a job. The probability of finding a job within the next 6 months was predicted.

8 Main input variables Subjective assessment of own chances Total duration of past unemployment Plans concerning exit from the labour market Number of claimants per one vacancy (in region and education) Nominal flexibility Woman with children up to 7 years Willingness to change area of work Promise of a new job School leaver

9 Results 1 Model Nagelkeke R-square Correctly predicted cases Model with constant only-54% General model % Model with variables in the PES database % Model with variables in the PES database + 5 interview questions %

10 Results 2 Prediction versus reality

11 Results 3 Predicted probability of finding a job Job YES Job NO TotalN 0-30%13%87%100%266 31%-70%52%48%100%303 71%-100%83%17%100%189 Total46%54%100%758

12 Future development Analysis of register data Prediction of the effects of ALMP Scheme for practical use at labour offices

13 Thank you very much for your attention Tomas Soukup RILSA, CZ


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