The chances of the unemployed to find jobs are a consequence of how employment enhancing characteristics develop over longer time periods. Therefore, our analysis of factors affecting flows from unemployment to employment started off with assessing the im- pact of the most basic characteristics, or attributes, such as gender, age, size of place of residence or education. In the next step we add- ed factors accounting for unemployment duration and job experience prior to April 2006. Last but not least, we took into consideration the impact of ALMP participation, job counselling and job broking as well as obtaining additional qualifications by respondents. We have constructed three binominal logit models with dependent variable defined as the fact of performing unsubsidised work47 in January 2008. Although the actual dependent variable was discrete – it was transitions from unemployment in 2nd quarter of 2006 to unsubsidised work in January 2008, mathematical transformations generated a model which explains the probability of such transi- tion.
Since a vast majority of variables considered as potential determinants of transitions from unemployment to employment were quali- tative, binary variables were used to estimate them. For this reason, a reference group was selected for every determinant, usually the most numerous group in the sample.
The following explanatory variables have been used in the initial model specifications (reference groups in the brackets): enia):
Model 1:
•
gender (woman);•
age, age^2;•
people with a child aged 3 or younger (childless people or people with children aged 3+);•
set of binary variables for the size of place of residence (village);•
set of binary variables for education (people with upper secondary vocational education);•
set of binary variables for the type of respondents’ poviat of origin. Poviat types have been determined on the basis of such local labour market characteristics as share of registered unemployed in the population of over-15-year-olds, share of people working in agriculture in the population of over-15-year-olds, average wage, number of job offers per unemployed person, population size of a given poviat and of neighbouring territorial units, share of tertiary education graduates in the population of over-15-year-olds, districts’ own income per capita. In this analysis, we have used the classification used in the pervious issue of Employment in Poland (see Bukowski et al 2006). These poviat types have been characterised as development centres, suburbs, towns, former state farms, low-productivity agriculture, agricultural and industrial.Model 2, additionally:
•
duration of unemployment in months – as in 2nd q. 2006;•
work experience possessed – as in 2nd q. 2006 (no work experience). Model 3, additionally:•
set of binary variables for participation in ALMP programmes (non-participation in ALMP);•
assistance from a job counsellor or broker (people getting no assistance from a job counsellor or broker);•
qualification development at own initiative (people undertaking no qualification development at own initiative).Since the logit model is non-linear, interpreting parameters estimations is not straightforward. For this reason, we have transformed the parameters thus obtaining the so-called odds ratio. In the case of binary variables, odds ratio can be interpreted as a change in the probability of transition from unemployment to employment, if an individual has a characteristic identified by a given binary vari- 46 For more information about differences between logit regression and PSM see works by Smith (2000) and Bryson et al. (2002).
able, and odds ratio equal to 1 means no such probability change, odds ratio greater than 1 means that it is growing, and smaller than 1 – that it is falling. For instance, if the odds ratio for a binary variable referring to training participation (where 1 is for people partici- pating in trainings and 0 is for those not participating) equals 1.8 and is statistically significant, it means that people participating in trainings have the probability of transition from unemployment to employment greater by 80 percent than for non-participants, with all other characteristics being exactly the same for both groups.
Primary factors, which determine the characteristics attributed to respondents, are altogether “responsible” for slightly more than 6 percent of the variation of the dependent variable (see Table IV.7.). This value is small though significant. The probability of men finding jobs is higher than that of women by approx. 90 percent with all other social and demographic characteristics being held con- stant. Respondents living in rural areas have less chance of transition from unemployment than respondents from all other residence categories. The only fact which turned out to be statistically insignificant was that of people living in cities of more than one million in- habitants, which was probably due to a small share of respondents in the sample. Categories referring to the size of place of residence are statistically insignificant in all regression models. Moreover, most variables referring to poviat types turned out to be statistically insignificant, except one category labelled “development centres” which is significant and which increases the chance of engaging in unsubsidised work by almost 40 percent with all other characteristics held constant. When the impact of unemployment duration, work experience and ALMP participation are taken into account in models two and three, the predicting power of this variable goes down to 26 and 24 percent respectively.
One factor that favours transition from unemployment is the fact of possessing higher education; it increases the chance of finding a job by approx. 50 percent in relation to the reference category. The impact of this variable remained considerable and statistically significant (38 and 34 percent) also when respondents’ participation in ALMP and their position in the labour market were taken into account. Persons with primary education have least chance of finding a job, even if participation in ALMP, job counselling and broking as well as extra qualifications on own initiative are taken into consideration.
As it turned out unemployment duration hardly reduces the chance of finding a job with all other primary social and demographic characteristics held constant, whereas the fact of possessing work experience increased the likelihood of engaging in unsubsidised employment by 23.8 percent. The model which takes these factors into account allows us to “explain” 8.67 percent of the variation of the dependent variable. Taking ALMP participation into consideration increased this effect to 25 percent.
The obtained model results suggest (see Table IV.7.) that beneficiaries of particular ALMP forms, namely business incentives, trainings, apprenticeship or on-the-job training, had considerably greater chances of transition from unemployment to unsubsidised employ- ment than all other unemployed groups.
Beneficiaries of start-up incentives were characterised by an approximately four times greater probability of being in employment than persons who had not benefited from any assistance under ALMP. Businesses started by unemployed people turned out to last, prob- ably also because ALMP procedures require that the unemployed who apply for start-up funding undergo preparation in the scope of business running (assistance in business law, making business plans, accounting) and prove that their prospective businesses can be successful.
The fact of participating in trainings increased the probability of moving from unemployment to employment by almost 80 per- cent compared with non-participation in any ALMP programme, whereas participation in apprenticeship or on-the-job training – by 43 percent. Also, additional qualifications obtained by the unemployed on their own initiative increased their chances of finding jobs in the future (by 26 percent). When it comes to participation in intervention works and public works, it did not have any considerable impact on the likelihood of engaging in work.
This analysis has also made it possible to assess job counsellors’ or job brokers’ impact on the probability of transition from unemploy- ment to unsubsidised work.48 The results of the model suggest that people benefiting from job counselling or job broking stood approx. 33 percent less chance of engaging in employment than people not participating in this form of assistance with all other characteristics held constant. In the light of international experience (see Chapter 1), these results should not, however, be interpreted as evidence for the harmfulness of this assistance form as such but rather as evidence for the imperfection of the Polish job counselling and job broking system. This negative impact of job counselling and job broking may suggest that this form of assistance “supplants” individual job-seeking attempts as well as that job broking offered by PUPs is – on average – less effective than individual job searches in the labour market. Consequently, the fact of seeking assistance from a job broker may lower the chance of finding a job.49 However, the problem of ineffectiveness of job counselling and job broking requires further in-depth analysis because data collected for the purposes of this study do not permit us to draw firm and far-reaching conclusions.
48 The PULS database did not permit us to single out a group of unemployed benefiting from assistance from a job counsellor or broker. These people have been singled out on the basis
of answers given by respondents in the survey. The analysis of answers to questions about receiving assistance from job counsellors or brokers indicates that respondents did not evidently identify these two forms of assistance. This is why it is unjustified to make a distinction between them in this analysis and they are therefore considered as a one category.
49 This negative impact of job counselling on the chances of finding a job may imply some auto-selection of beneficiaries of this form of assistance. Maybe it was mainly “high risk” respon-
Table IV.7.
Logit model results for transitions from unemployment to employment
Explanatory variable Model 1 Model 2 Model 3
man 1.93*** 1.696*** 1.645***
age 1.117*** 1.133*** 1.141***
age^2 0.998*** 0.998*** 0.998***
having a child aged 3 or younger 0.594*** 0.591*** 0.614***
town of up to 24,00 people 1.290*** 1.256** 1.231**
town of 24,000-100,000 people 1.429*** 1.377*** 1.320***
city of 100,000-500,000 people 1.071 1.037 0.986
city of 500,000 – 1 million people 1.789*** 1.746*** 1.650***
city of more than one million people 0.878 0.728 0.649
development centres 1.392** 1.260* 1.244*
suburbs 1.228 1.221 1.144
former state farms 1.013 0.996 0.972
low-productivity agriculture 0.859 0.858 0.835
agricultural and industrial 0.928 0.892 0.859
no formal education or incomplete primary education 0.917 0.983 1.051
lower secondary education 0.774 0.791 0.915
primary education 0.557*** 0.560*** 0.623***
basic vocational education 0.902 0.925 0.988
general secondary education 1.003 0.983 0.991
post-baccalaureate / post-secondary education 0.99 0.93 0.95
tertiary education (incl. a bachelor degree) 1.506** 1.384** 1.341**
duration of unemployment 0.981*** 0.982***
work experience before April 2006 1.238*** 1.253**
trainings 1.792***
public works 0.722
apprenticeship or on-the-job training 1.433**
intervention works 1.044
business incentives 4.040***
additional qualifications on own initiative 1.263**
job counselling or broking 0.671***
Pseudo R-square 0.0605 0.0867 0.1015
Standard errors provided in the brackets, number of observations: 3830, 3761, 3741, *** p<0.01, ** p<0.05, * p<0.1
To sum up, the first part of the microeconometric analysis indicates very high effectiveness of business incentives, trainings and ap- prenticeship in increasing the chance of finding a job, ineffectiveness of intervention works, public works, and – what is slightly surpris- ing – counter-effectiveness of job counselling and broking. In the subsequent part of this Sub-chapter we further analyse these results using the PSM method.
3.4. ALMP effectiveness – propensity score matching (PSM) results
Which ALMP forms enhance employment and which of them do not
In this part of the Chapter we present the results of ALMP effectiveness analysis carried out using the PSM method. The core element of this method is matching each of ALMP beneficiaries with their “closest neighbours”, or respondents with identical or similar character- istics from the group of ALMP non-participants. For a more detailed description of the algorithm of control groups see Appendix IV. The PSM procedure provides an answer to the question to what extent the gross effect of ALMP was a result of the effectiveness of pro- grammes (net effect of ALMP) and to what extent it was due to the fact that programme participants had different social and demo- graphic characteristics than other unemployed people or came from different areas with different local labour market conditions (selec- tion effect). As shown in Chart IV.19, the following ALMP proved effective when it comes to impact on employment (net effect): business incentives, trainings, apprenticeship and on-the-job training. On the other hand, intervention works and public works did not have any significant effect on the chances of taking up employment. Assistance from job counsellors or job brokers at PUPs also turned out to be insignificant in this context. Therefore, the results obtained using the PSM method essentially confirm the conclusions drawn from the logit models presented in the preceding Sub-chapter. Nevertheless, it should be emphasised that – contrary to the logit model – PSM does not indicate that job counselling and job broking are counter-effective. This may suggest that assistance from job counsellors and job brokers was sought by those who most needed it, namely unemployed people with low job searching skills. Probably, their attempts to find jobs without any assistance, would not have been more effective than those assisted by PUPs – even considering the current job counselling and job broking system. Consequently, the effect of supplanting individual job-seeking attempts by those effected through public employment services (which effect has most probably been taking place) did not affect the chances of moving to employment.
Chart IV.19.
Gross and net effects of ALMP (in percentage points)
Gross effect of ALMP – difference between the share of employed persons in the group of ALMP beneficiaries and their share in the group of non-participants. Net effect of ALMP – difference between the group of ALMP beneficiaries and the control group selected using the kernel method. Positive values mean that the share of employed persons was higher than in the groups of ALMP beneficiaries.
Source: Own calculations based on data derived from the PLUS database and CATI research
The results of the analysis indicate (see Table IV.8) that business incentives was effective in assisting the unemployed. It can be assumed, however, that their net effect was overestimated because beneficiaries of this assistance form most probably differed from the rest of the unemployed in having such characteristics as positive attitude to risk taking and entrepreneurship, which could not be taken into consideration when selecting control groups. Hence, it seems plausible that the best way of assisting the unemployed was by devel- oping their skills – both via traditional methods (trainings) as well as workplace training (apprenticeship and on-the-job training). Had beneficiaries of training programmes not obtained assistance under ALMP, the share of people engaging in unsubsidised work would have been lower among them by approx. 12.8 percentage points, whereas among apprentices and on-the-job trainees – by approx. 10.4 percentage points.
At the same time, ALMP programmes – excluding public works, intervention works, job counselling and job broking, were accom- panied by a dead weight loss – the gross effect outweighed the net effect. In the case of trainings, selection was responsible for
-10 0 10 20 30 40 50
trainings public works intervention works apprenticeship / on-the-job training business incentives job broking or counselling
40 percent of the gross effect (9.6 percentage points), while in the case of apprenticeship and on-the-job training – for 30 percent (5 percentage points). This means that beneficiaries of these ALMP included above all those unemployed people whose prospects in the labour market were relatively better (for instance, those with better qualifications, those living in higher developed poviats) and of whom many would also have found jobs without ALMP assistance. Directing active labour market policies at unemployed people with relatively high chances of finding jobs without any assistance is not in line with the practice adopted in Western European countries, where such programmes are addressed to people at high risk of withdrawal from the labour force or long-term unemployment (see Chapter 1).
Table IV.8.
Gross and net effects of ALMP and selection scale (in percentage points)
Type of ALMP gross effect of ALMP net effect of ALMP selection
(gross effect – net effect)
trainings 22.4 12.8 9.6
public works -4.8 -3.7 -1.1
intervention works 5.1 0.4 4.7
apprenticeship / on-the-job training 15.4 10.4 5.0
business incetives 40.1 31.9 8.2
job counselling and job broking - 0.8 -5 4.2
Italics have been used for irrelevant values. Gross effect of ALMP – difference between the share of employed persons in the group of ALMP beneficiar- ies and their share in the group of non-participants. Net effect of ALMP – difference between the group of ALMP beneficiaries and the control group selected using the kernel method. Positive values mean that the share of employed persons was higher than in the groups of ALMP beneficiaries. It ought to be borne in mind that the sample selection procedure did not account for stratification for the type of poviat of residence of the unemployed (defined, for example, as poviats belonging to particular clusters – see Section 3.3.2 or Bukowski et al. 2006, Part II). Therefore, the actual distribution of the unemployed by poviat type is slightly different to that in the sample (see Appendix V). For instance, in the group of participants of training and apprenticeship or on-the-job training programmes, such clusters as agricultural and industrial areas, former state farms and low-productivity agriculture are underrepresented in the sample. At the same time, the share of unemployed people who took up employment – among ALMP participants and others alike – clearly varies between clusters. Moreover, as suggested by Chart IV.20, the effectiveness of ALMP may depend on local labour market conditions.50 Trainings proved effective above all in poviats classified as agricultural and industrial areas (net effect at 29 percentage points), whereas apprenticeship and on-the-job training – in former state farms. This means that on the national level the net effect of trainings was probably slightly greater than shown in Table IV.8, whereas the selection effect was actually slightly smaller. A similar – although probably weaker, rela- tionship emerged in the case of apprenticeship and on-the-job training.
Chart IV.20.
Effectiveness of trainings (left graph) and apprenticeship or on-the-job training (right graph) by poviat type (in percentage points)
Gross effect of ALMP – difference between the share of employed persons in the group of ALMP beneficiaries and their share in the group of non-participants. Net effect of ALMP – difference between the group of ALMP beneficiaries and the control group selected using the kernel method. Positive values mean that the share of employed persons was higher than in the groups of ALMP beneficiaries.
Source: Own calculations based on the PULS system and on CATI research
50 This analysis has been carried out for training and apprenticeship or on-the-job training programmes. Other ALMP categories have been left out due to insufficient sample size.
-10 0 10 20 30 40 50 Development centres / Suburbs
Towns Former state farms Low-productivity agriculture Agricultural and industrial Development centres / Suburbs
Towns Former state farms Low-productivity agriculture Agricultural and industrial - gross effect -10 -5 0 5 10 15 20 25 30
gross effect net effect (+/- standard error) net effect (+/- standard error)