evaluation of the effects of the active labour measures on...
TRANSCRIPT
Evaluation of the effects of the active
labour measures on reducing
unemployment in Romania
National Scientific Research Institute for Labor and Social Protection
Speranta PIRCIOG, PhDSenior Researcher 1st degree, Scientific Director, NSRILSP
Madalina Ecaterina POPESCU, PhDSenior Researcher 3rd degree, NSRILSP
EC-RWC 2015
Eastern-Central Regional Workshop on Counterfactual Impact Evaluation Methods 2015
Budapest, 22-24 September 2015
Project:
ASSESSMENT OF THE ACTIVE MEASURES EFFECTS ON REDUCING
UNEMPLOYMENT AND INCREASING EMPLOYMENT
Stage 1:
Elaborating the assessment methodology of the active measures impact on reducing unemployment and increasing employment
Stage 2:
Ex-post impact assessment of active measures on employment in Romania
Stage 3:
Factor analysis on sustainable employment. Recommendations for increasing the efficiency and effectiveness of active employment measures.
Elaborating an ex-ante methodology to assess the active measures impact on reducing unemployment and increasing employment
Stage 4:
Main results
Methodology for ex-post impact assessment of active measures upon employment
The ex-post impact assessment report of
active measures upon employment in Romania
Factor analysis on the active successful
measures.
Recommendations for increasing the efficiency
and effectiveness of active employment
measures
Ex-ante methodology to assess the active measures impact
upon employment
1
2
34
5
The approaches
Data collection
According to the beneficiary’s request, different survey subsamples were
required, in order to be able to form distinct groups for both the participants
and the non-participants of each of the 7 labour measures considered in the
analysis.
Based on the particularities of each measure, 7 different questionnaires
were elaborated addressing the participants of each measure, while other 3
questionnaires were required to address the non-participants of the
measures at different moments in time: in 2010, 2011 or 2013.
The respondents were randomly selected from the database of the National
Romanian Agency for Employment.
Main problems occurring:
some cases of incorrect or inexistent addresses in the database
some cases of people unwilling to participate the survey
some cases of unreachable persons
The micro model
The propensity score matching method (PSM) was applied distinctly for each
labour measure using STATA software, by:
first estimating Probit models and generating the propensity coefficients
checking the balancing property of the models
applying a non-parametric method for matching the treated with the untreated
units based on the propensity scores and determining the net impact of the
intervention.
In order to select the control and the treatment group using PSM, the following
variables were used:
the output variables: employment (in 2013 and at the time of the survey);
duration of unemployment and net wage
the covariates grouped into:
socio-demographic variables (sex, age, marital status, education, residence
and region);
economic variables (sector of activity, years of work experience);
The micro model
The general form of the Probit model was:
Treatment = f (AGE, GENDER, MARITAL STATUS, REGION, EDUCATION LEVEL,
RESIDENCE, SECTOR OF ACTIVITY, WORK EXPERIENCE)
Once the dichotomous model was estimated and the propensity scores were
determined, 4 matching algorithms were applied:
The Nearest Neighbour Matching Method
The Radius Matching Method
The Kernel Matching Method
The Stratification Method
The model allowed us to assess the ex-post impact of the active measures upon
employment in Romania, by quantifying the odds of a participant to the measure to
find a job as compared to a non-participant.
Labour active measures
Professional training measure
“Labour mobility stimulation” measure
"Salary income supplementation" active measure
• Structure of the sample: 295 treated and 346 non-participants
• Beneficiary’s profile: young urban male, with medium level of education
Measure “Subsidies for employers who employ individuals aged over 45”
Measure ‘‘Subsidies at employers who employ graduates of education institutions’’
Measure ‘‘Employment premiums for graduates’’
Measure ‘‘Subsidies for the employers who employ individuals who have 3 years more to meet the requirements for early retirement or for age limit retirement, unless they meet the conditions to request partial early retirement’’
• Structure of the sample: 155 treated and 346 non-participants
• Beneficiary’s profile: urban male aging 25-45, with high education level
• Structure of the sample: 165 treated and 317 non-participants
• Beneficiary’s profile: urban male over 45, with medium education level
• Structure of the sample: 154 treated and 251 non-participants
• Beneficiary’s profile: urban male over 45, with medium education level
• Structure of the sample: 125 treated and 251 non-participants
• Beneficiary’s profile: urban male over 45, with low education level
• Structure of the sample: 183 treated and 247 non-participants
• Beneficiary’s profile: young urban female, with high education level
• Structure of the sample: 84 treated and 81 non-participants
• Beneficiary’s profile: young urban female, with medium education level
Net impactSubjective
impact Net impact at the time of the survey
Net impact at the end of the application
Professional training measure
“Labour mobility stimulation” measure
"Salary income supplementation" active measure
Measure “Subsidies for employers who employ individuals aged over 45”
Measure ‘‘Subsidies at employers who employ graduates of some education institutions’’
Measure ‘‘Employment premiums for graduates’’
Measure ‘‘Subsidies for the employers who employ individuals who have 3 years more to
meet the requirements for early retirement or for age limit retirement, unless they meet the
conditions to request partial early retirement’’
55.5% -7% -8%
22.6% 35% 28%
27.5% 36.6% 27.1%
42.9% 44.7% 11.6%
65.6% 56.2% -18.1%
24.6% 33.5% 15.7%
26.2% 35.4% -24.5%
Factor analysis
Active policies are only some mechanisms among many others (unemployment
insurance policies (passive policies), wage policies, policies concerning the
safety of employment, policies on wage-setting mechanisms, policies concerning
social dialogue, etc). The lack of a correlation of all the objectives of these
policies may cancel or mitigate the partial positive effects obtained.
Given the multitude of possible factors that may influence the results of
implementation of some active measures, it is absolutely mandatory that any
impact assessment try to contextualizeze the best possible results.
Therefore we decided to customize the impact analysis at the level of socio-
demographic characteristics (gender, residence area, level of education and age
groups) in order to emphasize the effectiveness of each measure at the level of
some specific subgroups.
Factor analysis
Several Logit models were then estimated on each subgroup of individuals:
Male versus female
Urban versus rural
Low, medium and high level of education
Age groups: under 25; 25-45, over 45 years old
The output variable was the occupational status of the respondents, while the
explanatory variables were the covariates: sex, age groups, marital status, levels
of education, residence area and the "treatment" binary variable.
Valid econometric models were estimated in relation to LR test and pseudo R².
and also with statistically significant coefficients.
A particular attention was paid to the significance of the "treatment" variable,
which indicates the chances of a beneficiary of a certain measure to find a
remunerated job at the time of the survey, compared to those of a non-
beneficiary of the measure, with the same characteristics.
Factor analysis
Professional training measure
“Labour mobility stimulation” measure
"Salary income supplementation" active measure
• Has stronger effect on: young people under 25 years
Measure “Subsidies for employers who employ individuals aged over 45”
Measure ‘‘Subsidies at employers who employ graduates of education institutions’’
Measure ‘‘Employment premiums for graduates’’
Measure ‘‘Subsidies for the employers who employ individuals who have 3 years more to meet the requirements for early retirement or for age limit retirement, unless they meet the conditions to request partial early retirement’’
• Has stronger effect on: young rural male with low education level
• Has stronger effect on: young rural female with medium education level
• Has stronger effect on: rural female over 45 with medium education level
• Has stronger effect on male over 45, in general.
• Has stronger effect on: rural male with high education level
• Has stronger effect on : young rural male
Conclusions
Young people under 25 years of age proved to be more receptive to the active
employment measures regarding Labour mobility stimulation, Wage income
supplementation and Employment premium granted to graduates of
educational institutions, showing a higher level of availability for changing jobs
and home address for an additional financial gain.
Young people aged up to 24 years were the only category that recorded a positive
impact of the Training courses measure, showing the occupational inadequacy of
them related to labour market requirements, which result in reporting the need for
better information on the dynamics of the labour market.
In terms of gender differences, we notice that the active employment measures
relating to Labour mobility stimulation, Subsidizing of jobs for graduates,
Employment premium granted to graduates of educational institutions, as
well as Subsidizing of jobs for people who still have 3/5 years up to
retirement proved to be more effective among men, while the effects of measures
regarding the Wage income supplementation and Subsidizing of jobs for
people over 45 years were felt more strongly among women.
Conclusions
Effects with higher intensity amoung people from the urban area were
recorded only for the measure regarding the Employment premium granted
to graduates of educational institutions, while the remaining measures
exercised a stronger impact upon individuals from the rural area.
Considering the level of education as an influence factor of the impact of active
employment measures, we emphasize the following: graduates with general
education resonate more powerfully to the implementation of measures
relating to Labour mobility stimulation, Wage income supplementation
and Subsidizing of jobs for persons over 45 years of age, while the most
pronounced effects of the measure on the Employment premium granted to
graduates of educational institutions are felt especially among people with
higher education.
Conclusions
Policy recommendations aimed at increasing the efficiency and effectiveness of
active employment measures are considering three levels:
1. At a conceptual, strategic level:
Periodic assessment of the impact of active measures
Designing a new and larger database of unemployed persons looking for a job
Reinforcing the role of activation of labour market policies
Periodic studies and forecasts on short and medium term of the labour demand
Strengthening the principle for equal opportunities and access to labour market;
Stimulating labour demand through balancing the ratio between the level of
unemployment allowance and the minimum wage
Reinforcing the role of informing for the diminishing of activities in the informal
economy.
Conclusions
Policy recommendations aimed at increasing the efficiency and effectiveness of active
employment measures are considering three levels:
2. At measure level: A better profiling of the persons looking for a job before being recommended for a
certain measure;
Offering training vouchers within vocational training active measure
Offering vouchers for apprenticeship to support employment in jobs that require
long-term specialization (e.g. industry);
Supporting the development of start-ups
Development of customized mediation or based on a standardized mechanism of
profiling the unemployed and persons looking for a job
3. At the level of packages of measures: Setting up packages of measures for groups with difficulties in accessing jobs (e.g.
youth) and finding a better way to define the education graduate;
Questions
?