Evaluation of the effects of the active labour measures on ...pubdocs.worldbank.org/.../2d-Matching-Romania-LaborMarket-Popes… · Factor analysis on sustainable employment. Recommendations

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’’


• Beneficiary’s profile: urban male aging 25-45, with high education level


• Beneficiary’s profile: urban male over 45, with medium education level


• Beneficiary’s profile: urban male over 45, with medium education level


• Beneficiary’s profile: urban male over 45, with low education level


• Beneficiary’s profile: young urban female, with high education level


• 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





Measure ‘‘Subsidies at employers who employ graduates of some education institutions’’


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




• Has stronger effect on: young people under 25 years


Measure ‘‘Subsidies at employers who employ graduates of education institutions’’


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

?

Documents

Evaluation of the effects of the active labour measures on ...pubdocs.worldbank.org/.../2d-Matching-Romania-LaborMarket-Popes… · Factor analysis on sustainable employment. Recommendations