threat of long-term unemployment of the elderly in the context of … · 2017-02-01 · 1 threat of...

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Threat of long-term unemployment of the elderly

in the context of raising the retirement ages

Monika Wojdylo-Preisner, Kamil Zawadzki

Abstract

Ageing of societies was a trigger for raising the retirement ages in many European countries. This

instrument, however, can be responsible for larger exposure of older individuals to social exclusion

risk connected with the long-term unemployment. To assess this risk we explore the determinants of

elderly workers’ long-term unemployment in the context of the pension system reform introduced in

Poland in 2013. We examined over 8800 unemployed aged 50 to 64 registered in public employment

agencies to analyse the relationship between the probability of long-term unemployment and basic

socio-demographic variables, human capital characteristics and specificity of the local labour market.

The outcomes base on three models: for all elderly unemployed, for unemployed men aged 50-64 and

for unemployed women aged 50-59. Results indicate that the characteristics as: male gender, younger

age, married, first unemployed registration within the last three years, living in rural area (significant

only for men), long work experience, knowledge of a foreign language (significant only for men) and

multi-professionalism each considerably decrease the likelihood of being unemployed for more than

365 days among the elderly registered unemployed. The outcome shows a need for actions supporting

the increase in labour market activity of women, life-long learning and deregulation of the labour

market policy for the elderly.

Keywords: raising the retirement age, pension system reform, older workers, long-term

unemployment

JEL Classification: J64.

M. Wojdylo-Preisner Faculty of Economic Sciences and Management, Nicolaus Copernicus University in Torun, Poland e-mail: [email protected] K. Zawadzki Faculty of Economic Sciences and Management, Nicolaus Copernicus University in Torun, Poland e-mail: [email protected]

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1. Introduction

Disadvantageous demographic changes, present in Western Europe, affect also Poland. The Polish

society, as well as the labour force, is getting older. Pension system reform, which was introduced in

2013 with a strong community opposition, raised gradually the retirement ages of women from 60 to

67 and men from 65 to 67. Arguments of the change regarded mainly the prospect negative budget

consequences if would be ceased. Though the results of the reform will be fully visible after many years

(men are to reach the target retirement age in 2020 and women in 2040), today it is possible to foresee

some labour market risks connected with this move. Despite positive intentions of the legislators (e.g.

higher pensions thanks to prolonging the contributory period and shortening the time of receiving the

benefit) some negative outputs are also possible, because of very low employment ratio and high

unemployment rate in this age group in Poland (40.6% and 7.7% respectively for people aged 55 to 64

in 20131). Long-term unemployment seems to be the most serious problem affecting this fraction of

labour market: 53.6% of all unemployed registered in public employment offices aged 55 to 64 were

jobless for more than 365 days (q4 2013). This is why we analyse here the risks of social exclusion

coming from the pension reform in the context of the long-term unemployment threat.

The topic of unemployment of the 50+ group is present in the economic literature on active labour

market policy, especially in the context of ageing (OECD 2005, 2006; ILO 2013). At the same time, a

matter of consequences of the global economic crisis on the long-term unemployment (Junankar

2011), as well as a question of implementation measures that can reduce the long-term

unemployment risk, especially regarding the unemployment profiling procedures, is discussed (Bronk,

Wisniewski & Wojdylo-Preisner 2014). The literature draws attention to the problem of a particularly

high share of long-term unemployed in the oldest group of unemployed aged 50 to 64. These are,

however, macroeconomic analyses, based on large aggregates and indexes (Pavelka, p. 301-304), or

research on efficiency of ALMP measures for the elderly and long-term unemployed (Bennmarker,

Skans & Vikman 2013).

In our study we intended to combine the topics of risk of labour market exclusion by the elderly

unemployed and the long-term unemployment problem at the microeconomic level, based on the

original individual data. We examine the factors affecting the risk that the 50+ unemployed become

the long-term unemployed and, in consequence, that they permanently exclude the labour market or

stay without a job until the pension entitlements.

Following research questions were formulated:

What are the determinants of long-term unemployment among the elderly unemployed?

1 Eurostat database; yearly data.

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Is the risk of long-term unemployment significantly different for unemployed women and

unemployed men?

What elements of human capital are the core defenders against labour market exclusion of the

elderly?

To what extent is the risk of long-term unemployment determined by the specificity of the local

labour market?

What actions should be taken to avoid the increase of social exclusion risk among the elderly, as a

consequence of raising the retirement ages?

Besides, the following hypotheses were formulated:

1. Unemployed women 50+ are more likely in danger of the long-term unemployment than men.

2. Length of professional experience and the flexibility of an individual in the labour market are

significant determinants of the long-term unemployment risk.

3. The elderly unemployed are especially affected by the structural unemployment; this is why the

living place of the unemployed as well as specificity of the local labour market influence the

likelihood of long-term unemployment within this age group.

2. Data

The employment promotion as well as alleviating negative consequences of the unemployment belong

to the statutory tasks of Public Employment Services (PES) in Poland. The PES system is decentralised

and based on local self-government structure. At the central level it is represented by the ministry of

labour, at the medium level – by 16 regional employment offices and at the lowest level – by 343 local

employment offices. Both, the local and regional offices are considerably autonomic in adjusting the

central government’s targets to the needs of their region or district.

Everyone who is looking for a job and fulfil the requirements can register as the unemployed in the

relevant local employment office. These criteria to be fulfilled embrace first of all: age 18 to 60 for

women and 18 to 64 for men, ability to start full-time working immediately, will of looking for a job

actively and not being a full-time student. Moreover, there are additional restrictions referring

permitted source and level of income in the relevant legal acts.

A computer application called Sirius (SyriuszStd) is nowadays a basic tool used by local employment

offices. As there is no central database of all registered unemployed in Poland, the individual data

collected in Sirius is the only available source of individual data on the registered unemployment. Our

research bases on records taken from the Sirius system of six local employment offices from different

districts (called poviat – NUTS-4) in six different regions (voivodship – NUTS-2): three township districts

(Bialystok, Przemysl, Wloclawek) and three country districts (Dzialdowo, Sierpc, Krasnystaw). Each of

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these areas represents a different type of local economy: modern, industrialized or with traditional

small-scale farming (Dolny & Wojdylo-Preisner 2014).

Data for our research was taken out of PES databases in November and December 2012. We collected

information on almost 44,000 individuals, who were in offices’ registers on 31 December 2010. Next,

the unemployment duration was settled, as a difference between the date of the last registration

before 31.12.2010 and the end of 2010. Then, a sample of over 8800 unemployed aged 50–64 was

created. This bunch of individual records is the general sample for the analysis in this paper. Variants

of the characteristics of the unemployed were established on the basis of the state in the end of 2010.

3. Description of the samples

For the empirical analysis we used a set of data regarding the unemployed registered in relevant local

employment offices on 31.12.2010. This set, here called the General Sample (GS), consists of 8831

individuals aged between 50 and 64 years. The GS embraces two subsamples specified by sex – the

Female Sample (FS), N=3737 (42.3% of the GS) containing individual data of unemployed women aged

50–59, and the Male Sample (MS), N=5094 (57.7% of the GS) with unemployed men aged 50–642.

Detailed structure of the samples mentioned above is presented in Appendix, Table A.

A feature of all three groups of the unemployed we are especially interested in is the length of the

unemployment period before the end of 2010. In the GS 52.6% of the individuals had been

unemployed for more than 365 days. Almost every second investigated men and over 57% of women

appeared to be a long-term unemployed.

In the GS persons aged 50–54 dominate (ca 58%), over one third is 55 to 59 years old, and less than

7% GS consists of men aged 60 to 64. Almost 70% of the FS is younger than 54, the others are 55 to 59

years old. In the MS every second individual is 50 to 54 years old, and every ninth – 60 to 64. Men aged

55 to 59 are less than 40% of the MS.

Family status is different in both subsamples: the share of married women is one tenth higher than

married men. By contrast, unemployed men are much often childless that unemployed women are.

Tertiary education level appears rarely in both samples (3.4% of women and 4.1% of men), but

unemployed women have upper secondary education level much more often than men have (36.5%

and 20.5%, respectively). In spite of the fact that the examined women are better educated than men

are, the knowledge of foreign languages is very low both in the MS as well as in the FS (ca 12%).

2 The retirement age in 2010 for men was 65, for women – 60.

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Moreover, some other differences in determinants of human capital of the unemployed can be

observed between two subsamples. As the individuals in the study are a quite mature part of the

labour force, the share of unemployed having over 20-year work experience is surprisingly low - 38.7%

of the GS, 42.2% of the MS and only one third of the FS. The work experience of every sixth of the

individuals in examination is shorter than one year or none.

On the other hand, many of the unemployed women (40%) and men (48%) have more than two

occupations or professions. Fifth major occupation group according to ISCO-08, that is Service and sales

workers, and the ninth one – Elementary occupations, are the most popular professions in the FS (22%

and 11%, respectively). Among men, on the contrary, the representatives of the seventh major group

(Craft and related trades workers) dominates (38%). Every tenth of the GS has no formally studied

profession. Among men also the seventh major group dominates as regards the longest occupation,

but among women – the third one (Technicians and associate professionals).

The elderly unemployed women are only a bit less picky than men are. 86.7% of the FS and 84.2% of

the MS is prone to take any job, even not exactly coherent with their professions or experience.

It is worth mentioning, that the structures of the subsamples by the type of living place are similar –

ca 84% of the FS and MS live in urban areas. On the other hand, the structures of both subsamples

were different by the type of the district. More unemployed men come from a modern, post-industrial

district (Bialystok), from a traditional, agricultural one without well-developed service sector

(Krasnystaw), as well as from an agricultural and industrial district with old structure of the economy

(Sierpc). On the contrary, there are more unemployed elderly women than men in more industrialized

districts.

4. Econometric models

In our models we intended to find significant determinants of probability of being in employment

offices’ registers for longer than 365 days, separately for the GS, the FS and MS. For such estimations

econometric models of binary logit regression are useful. In our logit models the probability of the

occurrence of the long-term unemployment among the elderly unemployed is determined by the

function (Dougherty, p. 294):

𝑝𝑖 =1

1+𝑒−𝑍𝑖,

where 𝑍𝑖 is a linear function of the explanatory variables.

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As logit regression models cannot be estimated using OLS, we used maximum likelihood technique,

which chooses coefficients’ estimates that maximize the log of likelihood function (Greene, p. 475).

In all three estimated models the dependent variable was the probability of being unemployed for

over one year (365 days) since the date of the last registration in the employment office. This binary

variable equals:

y=1, when unemployment period is longer than one year and

y=0 otherwise.

Explanatory variables

A list of potentially useful independent variables consists of 15 categories for GS and 14 for MS and FS.

All of these qualitative variables have been recoded into dummies. There are same groups of

explanatory variables in both subsamples: social-demographic characteristics of the unemployed (age

– as a bivariant variable for the FS and a three variant one for the MS, marital status, dependent child)

and factors determining the quality of human capital (education, knowledge of foreign languages, work

experience, numbers of professions and practised occupations, class of the highest studied profession

and the longest practiced occupation – according to major groups of ISCO-08, and health). We included

also the information on an individual’s occupational flexibility (willingness to take any job, not

necessarily in accordance with one’s formal profession). Besides, we prepared a bivariant variable

showing the moment of the first registration of the unemployed in the employment office. Moreover,

two variables in our models explain a type of living place of the unemployed3. Apart from the set of

explanatory variables mentioned above, gender was an additional variable in the GS model. Detailed

set of all variables and their descriptions is presented in Appendix, Table B.

3 We used the k-means method to divide all the districts (poviats) in Poland into possibly homogeneous groups. We start with nine, potentially important variables available in public statistics at this level of data deaggregaion, which characterize the economic situation of the districts in 2010. Then, the variables have been standardized and correlation coefficients calculated. Consequently, we omitted the variables that appeared to be strongly correlated and use only four variables to group the districts: unemployment rate at the end of 2010, entrepreneurship, share of employment in agriculture, and share of employment in finance, insurance and real estate. Finally, we separated six groups of districts, reflecting different types of economy. Out of each group we chose a representative district, in which the long-term unemployment rate, a number of long term unemployed, and a number of the registered unemployed were high. Additionally, we assumed that each representative should come from different region of the country (Dolny, Wojdylo-Preisner 2014, p. 84–91).

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5. Results of estimation

The logit model estimation of the probability of the long-term unemployment in the general sample

showed that three entire categories of variables do not have a significant influence on the risk of long-

term staying without work, that is: education (EDU), professional flexibility of the unemployed (FLEXIB)

and one's acquired professions (PROFESS). Besides, lack of significant impact was noticed with

reference to two of three variants of a variable describing the character of area the unemployed lives

in (LIVING_PLACE) and two out of six variants of the district type (REGION). Moreover, lack of any job

experience (JOB) or a situation when the longest occupation of the unemployed belonged to one of

four major occupation groups (ISCO-08) appeared to be non-significant.

The same results as in the GS, namely: lack of significant impact of education level (EDU), professional

flexibility (FLEXIB) and studied profession (PROFESS) on the long-term unemployment, proved to be

valid also with reference to the FS. Among unemployed women in the research, knowledge of foreign

languages (NO_LANGUAGE), health (HEALTH), character of the area the unemployed lives

(LIVING_PLACE) are non-significant. Moreover, the long-term unemployment risk in the FS was not

determined by type of district (in case of four out of six types), job experience (JOB) or type of the

occupation practised for the longest time (in case of seven out of nine major occupation groups).

Next, the long-term unemployment probability of unemployed men – in the general sample and in

female sample alike - appeared to be not determined by willingness to take any kind of job (FLEXIB).

On the other hand, only in the MS a fact of having dependent children (NO_CHILD) does not impact

the long-term unemployment risk. Besides, in the MS living in rural or mixed (rural-urban) areas

(LIVING_PLACE) as well as in three out of six types of districts showed to be non-significant. Moreover,

in a sample of elderly unemployed men seven out of nine major groups of the longest occupation (JOB)

and almost all variants of the studied profession (PROFESS) proved to be insignificant (with an

exception of category ‘Professionals’, which significantly lowers the risk of long-term unemployment

in this group of unemployed.

Gender (GENDER) is an important factor impacting the long-term unemployment risk. Elderly women

are, ceteris paribus, by 26% more likely to be long-term unemployed than men are.

In all three samples age (AGE) appeared to be a significant factor influencing the long-term

unemployment risk: in the FS the risk is the highest for women aged 55+, in the MS and GS – for

individuals aged 60 to 64.

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Similarly, influence of the marital status (MARIT) is significant for each of the samples: the married

unemployed are, ceteris paribus, less likely to become the long-term unemployed by 24% in the GS, by

13% in the FS and by 31% in the MS.

In all the samples the long-term unemployment risk was related to the moment of the first registration

of the unemployed in the employment office. Those, who were registered for the first time three years

before the checking moment or earlier, proved to be several times more likely to enter the long-term

unemployment than the other.

A number of studied or ever practiced professions and occupations (PROF_NUMB) appeared to be the

next significant factor. The individuals in the GS, FS as well as MS without any and with not more than

two professions or occupations are in the worst situation. Relatively less threatened by long-term

unemployment are the individuals with three or four professions and occupations. The lowest long-

term unemployment risk was found, when the unemployed has at least five occupations and

professions.

Work experience of the individuals – represented here by the total number of years in work

(YEARS_EXP) – is another important factor that has impact on the long-term unemployment risk in all

three samples. In the GS and MS the unemployed who have no work experience are the most

threatened with the long-term unemployment; in the FS – those, who before the checking moment

had worked for maximum one year. In all samples the unemployed with longest work experience (20

years or more) have the highest chance to be deleted from the unemployed register.

The likelihood of long-term unemployment in each of the tested samples is significantly affected by

the variable describing the kind of district the unemployed live in (REGION). Inhabitants of the

agricultural and industrial areas with outdated structure, the modern areas and the well-balanced,

industrial and agricultural developed areas, are less likely to be the long-term unemployed. On the

contrary, individuals living in the industrial, old-structured areas (for the GS and FS) as well as in the

industrial areas and suburbs (for the FS only) are more likely threatened with the long-term

unemployment.

In all samples tested in the study the risk of long-term unemployment was also associated with certain

variants of the variable pointing an occupation the unemployed practiced for the longest time (JOB).

The following relationships were identified. Work experience as a manager, craftsman or trade worker

reduces the risk of long-term unemployment in the GS and MS, when as a plant and machine operator

or an assembler – in the MS only. In contrast, a greater likelihood of remaining in the unemployed

registers for a long time associates with the individuals, who worked as a technician and associate

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professional and as a clerical support worker (in the GS and FS) or as a skilled agricultural, forestry and

fishery worker (significant in the GS only).

The risk of long-term unemployment was dependent on the level of education only in the MS. Those

with the lowest level of education were less likely, while those with tertiary education – most likely, to

become the long-term unemployed.

The individuals in the GS and FS were less likely to stay unemployed for a long time compared to those

with at least one child (variable: NO_CHILD).

Only in the GS and MS the individuals with knowledge of a foreign language were less likely to become

the long-term unemployed (NO_LANGUAGE). Moreover, in these two samples the unemployed

without disability (HEALTH) and unemployed living in rural areas (LIVING_PLACE) were significantly less

likely to experience twelve-month stay in the unemployed registers.

Comparison of the general results is presented below in Table 1 and the details of estimations in

Appendix, Tables C-E.

Table 1. General results comparison

Variable Model on

General Sample Model on

Female Sample Model on

Male Sample

GENDER positive *** X X

AGE_50_54 negative *** negative *** negative ***

AGE_55_59 negative ** . negative *

AGE_60_64 . X .

EDU_LOW ns. ns. .

EDU_MID ns. ns. positive **

EDU_HIGH ns. ns. positive **

MARIT negative *** negative * negative ***

NO_CHILD negative * negative ** ns.

NO_LANGUAGE positive * ns. positive *

HEALTH negative *** ns. negative ***

FIRST_REG positive *** positive *** positive ***

FLEXIB ns. ns. ns.

PROF_NUMB_0 positive *** positive *** positive ***

PROF_NUMB_12 positive *** positive *** positive ***

PROF_NUMB_34 positive *** positive *** positive **

PROF_NUMB_5 . . .

YEARS_EXP_1 positive *** positive *** positive ***




YEARS_EXP_5 . . .

LIVING_PLACE_1 ns. ns. ns.

LIVING_PLACE_2 negative ** ns. negative **

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LIVING_PLACE_3 ns. ns. ns.

REGION_1 negative *** ns. negative ***

REGION_2 positive ** positive *** ns.

REGION_3 ns. positive ** ns.

REGION_4 negative ** ns. negative ***

REGION_5 negative *** ns. negative ***

REGION_6 ns. ns. ns.

JOB_0 ns. ns. ns.

JOB_1 negative * ns. negative **

JOB_2 ns. ns. ns.

JOB_3 positive ** positive ** ns.

JOB_4 positive *** positive *** ns.

JOB_5 ns. ns. ns.

JOB_6 positive ** ns. ns.

JOB_7 negative ** ns. negative ***

JOB_8 ns. ns. negative **

JOB_9 ns. ns. ns.

PROFESS_0 ns. ns. ns.

PROFESS _1 ns. ns. ns.

PROFESS _2 ns. ns. negative *








*** p ≤ 0.01; ** p ≤ 0.05; * p ≤ 0.1; ns. – non-significant; . – reference category; X – variable not included to the model

6. Conclusions and recommendations

The pension system reform that came into effect in 2013 raising gradually the retirement ages up to

67 is strongly embedded in the present disadvantageous demographic phenomena and the care about

the future of pension system and the budget. On the other hand, however, it is possible that this

change would cause some opposite results, shifting the responsibility and burdens on the social

security system and labour market policy. In order not to allow it, decision-makers should be aware of

the risk of social exclusion of the older individuals in a consequence of the long-term unemployment

and take a set of actions regarding areas of employment policy, labour market policy and education.

The main conclusion that can be drawn from our research is a need for broad protective measures

focused mainly on women in the labour market. It is shown that they are a group threatened by the

higher long-term unemployment risk, even when the retirement age is yet at the level of 60. The social

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exclusion risk is likely to be considerably higher while the labour market activity of women would be

extended to the age of 67.

We also recommend to reduce gradually the 4-year protection period before retirement, during which

an employer cannot fire their older employees. Our study indicates that in spite of (or rather: because

of) existence of the protection period, the risk of long-term unemployment among women and men is

the highest in the last 5-year pre-retirement groups (i.e. 60-64 for men and 55-59 for women).

Additionally, among unemployed men the risk of long-term unemployment grows also in the 5-year

age group (i.e. 55-59) directly preceding this protection period.

Next, it is clear that the work experience is an important factor limiting the risk of long-term

unemployment. Longer period of human capital accumulation reduces the likelihood of long-term

unemployment among both sexes. But our research shows also that here the attention should be paid

primarily on professional activation of women. Among men no work experience, or the experience

shorter than one year, raise the risk of long-term unemployment only little more than experience of 1

to 20-year long, compared to the reference group of individuals who worked for 20 years or more. In

case of women the situation is different: having no or very limited professional experience makes up

the five-time increase in the risk of long-term unemployment, while the period of work lasting between

one year and 20 years associates with the likelihood only two times higher, compared with the most

experienced group of women.

Investment in the life-long learning is essential. Multi-skilling and knowledge of foreign languages

significantly reduce the risk of long-term unemployment. However, again, it should be stressed that

measures aimed at women, are the most desirable. Our research indicates that the greater number of

occupations ever practiced or learned reduces likelihood of long-term unemployment among men to

a lesser extent than it does among women.

One can also emphasize a need for raising the spatial mobility of people aged 50+, due to the significant

regional differences in the risk of long-term unemployment. Flexibility of the older could be enhanced

not only by life-long learning, but also through the housing policy (more investments in the real estate

for rental) as well as the family-friendly policies that helps the "sandwich generation" often burdened

simultaneously with care responsibilities towards their parents and grandchildren.

Finally, it is worth mentioning that there are some long-term cultural and social changes in Poland,

which would limit to some extent the long-time risk among the older generations in future. In the 50+

sample in the research one can assume that “other than married” means “living alone” (very low share

of informal relationships). Our outcome shows that the long-time unemployment is less likely among

married than among the other, regarding both sexes. However, living in a relationship reduces the

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long-term unemployment likelihood by more than 30% (at p-value = 0.000) in the case of men, whereas

among women – only by 13% (but at p-value = 0.1). This indicates that living in a relationship puts the

earning pressure (which implies also a decision on remaining active in the labour market for longer

time) mainly on men. In the future, when social acceptance for living with a new partner at the age

50+ would be higher than now (and demographic statistics show that it is very likely in a prospect of

10-15 years), one can assume its positive impact on reducing the long-term unemployment risk as well.

Appendix

Table A. Structure of General Sample, Female Sample and Male Sample (%)

Variant of the variable General sample Female sample Male sample

Share of the positive variant („1”) of the variable

Unemployment duration 365 days or more 52.6 57.2 49.2

50 to 54 year old 57.8 69.6 49.2

55 to 59 year old 35.5 30.4 39.2

60 and more year old 6.7 X 11.5

Married 63.4 67.6 60.4

Have no children 71.2 56.4 82.1

Tertiary education 3.8 3.4 4.1

Upper secondary education 27.3 36.5 20.5

Lower secondary, primary & no education 68.9 60.1 75.4

Disabled 17.4 16.9 17.8

Lack of foreign language knowledge 88.0 87.6 88.3

Not willing to take any job 14.8 13.3 15.8

First registration not later than 3 years before the survey 91.1 90.6 91.5

No profession 5.6 6.2 5.2

1 or 2 professions or occupations 49.6 53.8 46.5

3 or 4 professions or occupations 33.4 31.6 34.7

5 and more professions or occupations 11.4 8.4 13.6

No work experience 11.2 12.7 10.2

Shorter than 1 year work experience period 4.7 3.7 5.4

1 to 5 year work experience period 9.5 8.5 10.2

6 to 20 year work experience period 35.9 41.2 32.0

Longer than 20 year work experience period 38.7 33.9 42.2

Living in urban district 83.9 84.5 83.5

Living in rural district 13.3 11.9 14.4

Living in mixed (urban-rural) district 2.8 3.6 2.2

Living in agricultural and industrial area with an old

structure (region 1 – Sierpc)

7.1 6.2 7.8

Living in industrial area with an old structure (region 2 –

Przemysł)

10.6 12.0 9.5

Living in industrial area and suburbs (region 3 – Włoclawek) 24.2 27.2 22.0

Living in modern, post-industrial area (region 4 – Białystok) 39.1 34.6 42.5

Living in well balanced, industrial and agricultural developed

area (region 5 – Działdowo)

11.6 14.3 9.7

Living in a traditional, agricultural area without well-

developed service sector (region 6 – Krasnystaw)

7.3 5.7 8.4

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PROFESSION

PROFESS_0 - No profession or job 10.5 10.7 10.3

PROFESS_1 – Managers 0.9 0.6 1.1

PROFESS_2 – Professionals 3.1 4.1 2.4

PROFESS_3 – Technicians and associate professionals 8.2 11.6 5.7

PROFESS_4 – Clerical support workers 7.3 11.8 4.0

PROFESS_5 – Service and sales workers 14.1 21.9 8.3

PROFESS_6 – Skilled agricultural, forestry and fishery workers 1.1 1.4 0.8

PROFESS_7 – Craft and related trades workers 27.6 13.3 38.1

PROFESS_8 – Plant and machine operators, and assemblers 10.0 3.5 14.8

PROFESS_9 – Elementary occupations 17.4 21.1 14.6

JOB

JOB_0 – No profession or job 44.9 48.8 42.4

JOB_1 – Managers - - -

JOB_2 – Professionals 4.7 5.4 4.2

JOB_3 – Technicians and associate professionals 16.1 18.7 14.1

JOB_4 – Clerical support workers 0.9 1.8 0.3

JOB_5 – Service and sales workers 4.3 9.4 0.5

JOB_6 – Skilled agricultural, forestry and fishery workers 1.9 3.2 0.9

JOB_7 – Craft and related trades workers 24.5 10.9 34.5

JOB_8 – Plant and machine operators, and assemblers 2.2 1.7 2.5

JOB_9 – Elementary occupations 0.5 0.4 0.6

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Table B. List of the independent variables for model on General Sample, Male Sample and Female

Sample

Variable Definition

GENDER (only in the GS model)

Dummy variable (female=1, male=0)

AGE_50_54 Dummy variable (=1 for person aged 50 to 54)

AGE_55_59 Dummy variable (=1 for person 55 to 59)

AGE_60_64 (only in the GS and MS model)

Dummy variable (=1 for persons aged 60 to 64)

EDU_LOW Dummy variable (=1 no education, primarily and lower secondary education level, otherwise=0)

EDU_MID Dummy variable (=1 upper secondary education level, otherwise=0)

EDU_HIGH Dummy variable (=1 tertiary education level, otherwise=0)

MARIT Dummy variable (=1 for married, otherwise=0)

NO_CHILD Dummy variable (=1 for having no children, otherwise=0)

NO_LANGUAGE Dummy variable (=1 for unemployed who do not know any foreign language otherwise=0)

HEALTH Dummy variable (=1 for the unemployed who are not disabled, otherwise=0)

FIRST_REG Dummy variable (=1 if the first registration in employment office had been 3 or more years before the checking moment - numbers of registrations independently, otherwise=0)

FLEXIB Dummy variable (=1 willingness to take any job, otherwise=0)

PROF_NUMB_0 Dummy variable (=1 the unemployed has no profession or occupation, otherwise=0)

PROF_NUMB_12 Dummy variable (=1 the unemployed has one or two professions or occupations, otherwise=0)

PROF_NUMB_34 Dummy variable (=1 the unemployed has three or four professions or occupations, otherwise=0)

PROF_NUMB_5 Dummy variable (=1 the unemployed has at least 5 professions or occupations, otherwise=0)

YEARS_EXP_1 Dummy variable (=1 work experience shorter than one year, otherwise=0)

YEARS_EXP_2 Dummy variable (=1 no work experience, otherwise=0)

YEARS_EXP_3 Dummy variable (=1 work experience longer than 1 year but shorter than 6, otherwise=0)

YEARS_EXP_4 Dummy variable (=1 work experience longer than 5 year but shorter than 21, otherwise=0)

YEARS_EXP_5 Dummy variable (=1 work experience longer than 20 years, otherwise=0)

LIVING_PLACE_1 Dummy variable (=1 living in urban area, otherwise=0)

LIVING_PLACE_2 Dummy variable (=1 living in rural area, otherwise=0)

LIVING_PLACE_3 Dummy variable (=1 living in mixed rural-urban area, otherwise=0)

REGION_1 Dummy variable (=1 for unemployed living in agricultural and industrial area with an

old structure (Sierpc), otherwise=0)

REGION_2 Dummy variable (=1 for unemployed living in industrial area with an old structure

(Przemyśl), otherwise=0)

REGION_3 Dummy variable (=1 for unemployed living in industrial area and suburbs (Włocławek), otherwise=0)

REGION_4 Dummy variable (=1 for unemployed living in modern, post-industrial area (Białystok), otherwise=0)

REGION_5 Dummy variable (=1 for unemployed living in well-balanced, industrial and agricultural

developed area (Działdowo), otherwise=0)

REGION_6 Dummy variable (=1 for unemployed living in a traditional, agricultural area without

well-developed service sector (Krasnystaw), otherwise=0)

JOB_0 Dummy variable (=1 for the unemployed with no occupation ever practiced, otherwise=0)

15

JOB_1 Dummy variable (=1 for the unemployed with the longest experience in occupation practiced as Managers, otherwise=0)

JOB_2 Dummy variable (=1 for the unemployed with the longest experience in occupation practiced as Professionals, otherwise=0)

JOB_3 Dummy variable (=1 for the unemployed with the longest experience in occupation practiced as Technicians and associate professionals, otherwise=0)

JOB_4 Dummy variable (=1 for the unemployed with the longest experience in occupation practiced as Clerical support workers, otherwise=0)

JOB_5 Dummy variable (=1 for the unemployed with the longest experience in occupation practiced as Service and sales workers, otherwise=0)

JOB_6 Dummy variable (=1 for the unemployed with the longest experience in occupation practiced as Skilled agricultural, forestry and fishery workers, otherwise=0)

JOB_7 Dummy variable (=1 for the unemployed with the longest experience in occupation practiced as Craft and related trades workers, otherwise=0)

JOB_8 Dummy variable (=1 for the unemployed with the longest experience in occupation practiced as Plant and machine operators, and assemblers, otherwise=0)

JOB_9 Dummy variable (=1 for the unemployed with the longest experience in occupation practiced as Elementary occupations, otherwise=0)

PROFESS_0 Dummy variable (=1 for the unemployed without any studied profession, otherwise=0)

PROFESS _1 Dummy variable (=1 for the unemployed with the highest studied profession in major group: Managers, otherwise=0)

PROFESS _2 Dummy variable (=1 for the unemployed with the highest studied profession in major group: Professionals, otherwise=0)

PROFESS _3 Dummy variable (=1 for the unemployed with the highest studied profession in major group: Technicians and associate professionals, otherwise=0)

PROFESS _4 Dummy variable (=1 for the unemployed with the highest studied profession in major group: Clerical support workers, otherwise=0)

PROFESS _5 Dummy variable (=1 for the unemployed with the highest studied profession in major group: Service and sales workers, otherwise=0)

PROFESS _6 Dummy variable (=1 for the unemployed with the highest studied profession in major group: Skilled agricultural, forestry and fishery workers, otherwise=0)

PROFESS _7 Dummy variable (=1 for the unemployed with the highest studied profession in major group: Craft and related trades workers, otherwise=0)

PROFESS _8 Dummy variable (=1 for the unemployed with the highest studied profession in major group: Plant and machine operators, and assemblers, otherwise=0)

PROFESS _9 Dummy variable (=1 for the unemployed with the highest studied profession in major group: Elementary occupations, otherwise=0)

16

Table C. Estimation results for logit model on General Sample

Parameter Estimates

UNEMPL_DUR_OVER365a B

Std.

Error Wald df Sig. Exp(B)

95% Confidence Interval

for Exp(B)

Lower

Bound

Upper

Bound

1 Intercept -2.475 .201 151.552 1 .000

NO_CHILD -.097 .055 3.164 1 .075 .908 .816 1.010

NO_LANGUAGE .131 .076 2.994 1 .084 1.140 .983 1.322

AGE_50_54 -.579 .102 32.350 1 .000 .561 .459 .684

AGE_55_59 -.208 .102 4.121 1 .042 .812 .665 .993

AGE_60_64 0b . . 0 . . . .

JOB_1 -.472 .278 2.887 1 .089 .624 .362 1.075

JOB_3 .265 .091 8.505 1 .004 1.303 1.091 1.558

JOB_4 .370 .097 14.732 1 .000 1.448 1.199 1.750

JOB_6 .469 .236 3.946 1 .047 1.599 1.006 2.540

JOB_7 -.149 .058 6.621 1 .010 .862 .769 .965

REGION_2 .208 .087 5.684 1 .017 1.231 1.038 1.461

REGION_1 -.472 .103 20.816 1 .000 .624 .509 .764

REGION_4 -.157 .061 6.575 1 .010 .854 .757 .964

REGION_5 -.294 .087 11.459 1 .001 .745 .628 .883

YEARS_EXP_2 1.418 .103 190.358 1 .000 4.130 3.377 5.052

YEARS_EXP_1 1.291 .125 107.492 1 .000 3.636 2.849 4.641

YEARS_EXP_3 .896 .086 107.692 1 .000 2.449 2.068 2.901

YEARS_EXP_4 .944 .055 291.257 1 .000 2.570 2.306 2.865

YEARS_EXP_5 0b . . 0 . . . .

FIRST_REG 2.181 .112 380.662 1 .000 8.856 7.113 11.025

GENDER .235 .054 18.606 1 .000 1.265 1.137 1.407

MARIT -.263 .051 27.059 1 .000 .768 .696 .849

HEALTH -.207 .063 10.834 1 .001 .813 .718 .920

LIVING_PLACE_2 -.194 .084 5.357 1 .021 .823 .699 .971

PROF_NUMB_0 .923 .149 38.244 1 .000 2.517 1.879 3.372

PROF_NUMB_12 .929 .082 127.792 1 .000 2.532 2.155 2.974

PROF_NUMB_34 .417 .082 26.151 1 .000 1.517 1.293 1.780

PROF_NUMB_5 0b . . 0 . . . .

a. The reference category is: 0.

b. This parameter is set to zero because it is redundant.

Model

Model Fitting

Criteria Likelihood Ratio Tests

-2 Log Likelihood Chi-Square df Sig.

Intercept Only 8290.186

Final 6563.226 1726.960 25 .000

Cox and Snell

.178

Nagelkerke .237

McFadden .141

Classification

Observed

Predicted

0 1 Percent Correct

0 2602 1584 62.2%

1 1204 3441 74.1%

Overall Percentage 43.1% 56.9% 68.4%

Hit quotient = (n00*n11)/(n01*n10);

Hit quotient in GS =4.7

17

Table D. Estimation results for logit model on Female Sample

Parameter Estimates

UNEMPL_DUR_OVER365a B Std. Error Wald df Sig. Exp(B)

95% Confidence Interval for Exp(B)

Lower Bound

Upper Bound

1 Intercept -3.008 .226 176.823 1 .000

PROF_NUMB_0 1.105 .230 23.098 1 .000 3.020 1.924 4.739

PROF_NUMB_12 1.176 .138 72.709 1 .000 3.242 2.474 4.249

PROF_NUMB_34 .668 .140 22.795 1 .000 1.950 1.482 2.564

PROF_NUMB_5 0b . . 0 . . . .

NO_CHILD -.152 .076 3.998 1 .046 .859 .740 .997

FIRST_REG 2.145 .157 185.612 1 .000 8.539 6.272 11.626

MARIT -.136 .080 2.917 1 .088 .872 .746 1.020

REGION_3 .208 .088 5.615 1 .018 1.231 1.037 1.461

REGION_2 .438 .117 13.900 1 .000 1.550 1.231 1.951

YEARS_EXP_2 1.571 .156 101.562 1 .000 4.810 3.544 6.529

YEARS_EXP_1 1.667 .235 50.085 1 .000 5.294 3.337 8.399

YEARS_EXP_3 .735 .137 28.965 1 .000 2.086 1.596 2.727

YEARS_EXP_4 .863 .083 107.308 1 .000 2.370 2.013 2.790

YEARS_EXP_5 0b . . 0 . . . .

JOB_3 .325 .117 7.764 1 .005 1.384 1.101 1.739

JOB_4 .407 .118 11.950 1 .001 1.502 1.193 1.891

AGE_50_54 -.349 .080 18.825 1 .000 .706 .603 .826

AGE_55_59 0b . . 0 . . . .

a. The reference category is: 0.

b. This parameter is set to zero because it is redundant.

Model

Model Fitting Criteria Likelihood Ratio Tests


Intercept Only 2181.466 Final 1499.062 682.404 15 .000

Cox and Snell .167 Nagelkerke .224 McFadden .134

Classification

Observed

Predicted

0 1 Percent Correct

0 778 822 48.6%

1 383 1754 82.1%



Hit quotient in FS = 4.3

18

Table E. Estimation results for logit model on Male Sample

Parameter Estimates

UNEMPL_DUR_OVER365a B Std. Error Wald df Sig.

Exp(B)

95% Confidence Interval for Exp(B)

Lower Bound

Upper Bound

1 Intercept -2.139 .253 71.396 1 .000

NO_LANGUAGE .189 .104 3.327 1 .068 1.209 .986 1.481

PROF_NUMB_0 .786 .200 15.526 1 .000 2.195 1.485 3.246

PROF_NUMB_12 .801 .104 59.858 1 .000 2.229 1.819 2.731

PROF_NUMB_34 .303 .101 8.934 1 .003 1.354 1.110 1.651

PROF_NUMB_5 0b . . 0 . . . .

FIRST_REG 2.247 .161 194.771 1 .000 9.460 6.900 12.971

MARIT -.371 .067 30.921 1 .000 .690 .605 .786

HEALTH -.412 .083 24.682 1 .000 .662 .563 .779

EDU_HIGH .858 .433 3.924 1 .048 2.359 1.009 5.515

EDU_MID .187 .085 4.816 1 .028 1.206 1.020 1.425

EDU_LOW 0b . . 0 . . . .

REGION_1 -.670 .132 25.792 1 .000 .512 .395 .663

REGION_4 -.241 .073 10.991 1 .001 .786 .681 .906

REGION_5 -.510 .122 17.344 1 .000 .601 .473 .764

YEARS_EXP_2 1.158 .138 69.976 1 .000 3.184 2.427 4.176

YEARS_EXP_1 1.036 .152 46.193 1 .000 2.819 2.091 3.800

YEARS_EXP_3 .942 .113 70.001 1 .000 2.566 2.058 3.200

YEARS_EXP_4 .983 .075 172.103 1 .000 2.673 2.308 3.096

YEARS_EXP_5 0b . . 0 . . . .

JOB_1 -.799 .344 5.402 1 .020 .450 .229 .882

JOB_7 -.297 .074 16.110 1 .000 .743 .642 .859

JOB_8 -.222 .098 5.097 1 .024 .801 .660 .971

PROFESS_2 -.754 .421 3.209 1 .073 .470 .206 1.074

AGE_50_54 -.572 .104 30.131 1 .000 .564 .460 .692

AGE_55_59 -.174 .106 2.707 1 .100 .841 .683 1.034

AGE_60_64 0b . . 0 . . . .

LIVING_PLACE_2 -.278 .109 6.472 1 .011 .757 .611 .938

Model

Model Fitting Criteria Likelihood Ratio Tests


Intercept Only 4653.780 Final 3599.584 1054.196 23 .000

Cox and Snell

.187

Nagelkerke .249 McFadden .149

Classification

Observed

Predicted

0 1 Percent Correct

0 1754 831 67.9%

1 745 1763 70.3%



Hit quotient in MS = 5.0

19

Acknowledgements

In calculations we used data on the registered unemployed collected during research work on the

project „Analiza czynników wpływających na zwiększenie ryzyka długookresowego bezrobocia –

opracowanie metodologii profilowania bezrobotnych na lokalnym rynku pracy do stosowania przez PSZ”,

carried out by Nicolas Copernicus University in Toruń for Centrum Rozwoju Zasobów Ludzkich

(agreement no: U/44/B2.3/1.1/2011). We would like herby to express our gratitude to Dr. Barbara

Jaskolska (Nicolas Copernicus University in Torun) for designing and preparing the dataset that we used

in estimations of long-term unemployment risk.

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