Riscul Angajarii - O Abordare Prin Prisma Regresiei Logistice

8/18/2019 Riscul Angajarii - O Abordare Prin Prisma Regresiei Logistice

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Analele Universităţ ii “Constantin Brâncuşi” din Târgu Jiu, Seria Economie, Nr. 1/2008

Annals of the „Constantin Brâncuşi” University of Târgu Jiu, Economy Series, No. 1/2008

123

RISCUL ANGAJĂRII - O

ABORDARE PRIN PRISMA

REGRESIEI LOGISTICE

Daniela-Emanuela Dănăcică,

Lect.univ.drd.

Ana-Gabriela Babucea, Prof. univ.dr.

Facultatea de Ştiinţe Economice

Universitatea Constantin Brâncuşi din

Tîrgu-Jiu

Abstract

The aim of this paper is to determine

in which way gender, age and educationallevel influence the risk of a person to find a

job or to be re-employed. The empirical

investigation was made only for a Romanian

county, because the great volume of data

made it impossible to achieve them for the

entire country.

RISK OF EMPLOYMENT – A

LOGISTIC REGRESSION

APPROACH

Daniela-Emanuela Dănăcică,

Lect.univ.drd.

Ana-Gabriela Babucea, Prof. univ.dr.

Faculty of Economics

Constantin Brâncuşi University ofTîrgu-Jiu

Abstract

The aim of this paper is to determine

in which way gender, age and educational

level influence the risk of a person to find a

job or to be re-employed. The empirical

investigation was made only for a Romanian

county, because the great volume of data

made it impossible to achieve them for the

entire country.


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1. Introducere

Scopul acestui studiu este acela de

a determina in ce masura genul, varsta si

nivelul educational influenteaza riscul

unei persoane de a isi gasi un loc demunca sau de a se reangaja. Ca

metodologie am folosit regresia

logistica. Genul, varsta si nivelul

educational sunt variabile independente

(factoriale) in model, iar variabila

dependenta a fost denumita in studiu

„statut” , variabila calitativa binara ce ia

valorile 1 pentru o persoana devenita

angajat si 0 pentru o persoana ramasa

neangajata la sfarsitul perioadei

analizate. Esantionul contine 80961inregistrari cu informatii referitoare la

data de intrare in somaj, data de iesire

din somaj, sex, varsta, nivel educational

si motivul iesirii din somaj pentru fiecare

persoana inregistrata la Agentia

Nationala pentru Ocuparea Fortei de

Munca Bucuresti, in perioada 1 Ianuarie

2002 – 31 August 2006. Richard

Berthoud (2003) in capitolul

“Disadvantaging characteristics” al

monografiei sale intitulate “Multipledisadvantage in employment” analizeaza

influenta variabilelor independente

varsta, status familial, nivel educational,

stare de sanatate, apartenenta etnica

asupra probabilitatii de a isi gasi un loc

de munca sau de a se reangaja al

subiectilor din Marea Britanie. Alba

Ramirez (1998) in lucrarea sa “Re-

Employment Probabilities of Young

Workers in Spain” investigheaza

influenta genului, mediului urban/rural sia nivelului educational asupra

proabilitatii de a se reangaja a tinerilor

lucratori din Spania. Studii similare au

fost efectuate de catre Meghir C. si

Ioannides Y. (1989) pentru Grecia,

Tansel Aysit (2001) pentru Turcia sau

Tunali I si R. Asaad (1992) pentru Egipt.

Aspectele legate de somaj, angajare, si

alte probleme generate de dinamismul

pietei muncii in Romania au fost

1. Introduction

The purpose of this survey was to

determine the risk of the persons from the

database studied to be employed or unemployed

at the end of the period subject to analysis, riskestimated according to independent variables

gender, age and educational level. As a

methodology I used the logistic regression.

Gender, age and educational level are

independent (factorial) variables in the model,

and the dependent variable has been called

„status” in the survey, the binary qualitative

variable that takes values 1 for a person become

employed and 0 for a person unemployed at the

end of the analysed period. The sample has

80961 records with information concerning theentrance date into unemployment, the date of

unemployment end, sex, age, educational level

and the reason for unemployment leaving for

each person registered at the National Agency

for Employment Bucharest during January 1,

2002 - August 31, 2006.

Richard Berthoud (2003) in the chapter

“Disadvantaging characteristics” of his

monograph called “Multiple disadvantage in

employment” analyzes the influence ofindependent variables age, family status,

educational level, health and ethnic background

on the probability of subjects from Great Britain

to find a job or to be re-employed. Alba

Ramirez (1998) in his paper “Re-Employment

Probabilities of Young Workers in Spain”

inquires into the influence of gender,

urban/rural area and educational level on the

probability of young workers from Spain to

become employed. Similar surveys were made

by Meghir C. and Ioannides Y. (1989) forGreece, Tansel Aysit (2004) for Turkey or

Tunali I and R. Asaad (1992) for Egypt.

Aspects related to unemployment, employment

and other issues generated by the dynamism of

the labour market in Romania were

insufficiently investigated and there are no

surveys of this kind for our country.


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insuficient investigate, neexistand studii

de acest tip pentru tara noastra.

2. Descrierea bazei de date

Din 80961 persoane

inregistrate in baza de date a judetului

Gorj ca someri, in perioada 1.01.2002-

31.08.2006, 19369 persoane si-au gasit

loc de munca, pana la data de 31 august

2006; pentru acestia, in baza de date, la

motivul iesirii din somaj figureaza

“angajat”. Durata medie a somajului pana la gasirea unui loc de munca este de

6 luni, mediana de 2 luni, valoarea

maxima 57 luni si valoarea minima 0

luni. Dintre acestia 6390 persoane (33%)

sunt femei si 12979 (67%) sunt barbati.

Daca diferenta procentuala dintre

numarul somerilor barbati si numarul

somerilor femei inregistrati in baza de

date este de 17.8%, diferenta procentuala

dintre numarul barbatilor care s-au

angajat si numarul femeilor in aceeasi

situatie este de 34%, ceea ce arata ca

desi sunt mai multi barbati someri, si

stau in somaj in medie mai mult cu

aproximativ o luna decat femeile, totusi

acestia sunt preferati de catre angajatori.

In ceea ce priveste distributia pe grupe

de varsta, cei mai multi dintre acestia

(30.1%) apartin grupei de varsta 25-34

ani, urmata de grupa de varsta 15-24 ani

cu 25.7%, grupa 35-44 cu 25.4%, grupa45-54 cu 17.5% si grupa 55-64 ani cu

1.3%. 0.4% dintre cei angajati sunt fara

studii, 44.7% dintre cei angajati au un

nivel educational de pana la 10 clase,

37.4% sunt absolventi de liceu, 8.6%

dintre cei angajati sunt absolventi de

scoala profesionala sau de maistrii si

8.9% dintre cei angajati sunt absolventi

de invatamant superior.

2.

Database description

Of 80961 persons registered in thedatabase of Gorj County as unemployed, during

1.01.2002-31.08.2006, 19369 persons found a

job, until August 31, 2006; the reason for their

unemployment leaving was filled in the

database with “employed”. The average

duration of unemployment until finding a job is

of 6 months, the median of 2 months, the

maximum value of 57 months and the

minimum value of 0 months. Of these 6390

persons (33%) are women and 12979 (67%) are

men. If the percentage difference between themale unemployment and the female

unemployment registered in the database is of

17.8%, the percentage difference between the

number of men who become employed and the

number of women in the same situation is of

34%, which shows that although there are more

unemployed men, and on the average they stay

unemployed about one more week than women,

however they are preferred by employers. As

concerns the distribution on age groups, most of

them (30.1%) belong to the 25-34 year-old age

group, followed by the 15-24 year-old age

group with 25.7%, 35-44 group with 25.4%, 45-

54 group with 17.5% and 55-64 year-old age

group with 1.3%. 0.4% of the employed are

without education, 44.7% of the employed have

an educational level of up to 10 grades, 37.4%

are high school graduates, 8.6% of the

employed graduated from vocational or

foremen schools and 8.9% of the employed are

university education graduates.

3. Methodology

As methodology, in order to determine

the risk of the persons from the studied database

to be employed or unemployed at the end of the


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3.

Metodologie

Ca metodologie, pentru

determinarea riscului persoanelor din

baza de date studiata de a fi angajate sau

neangajate la sfarsitul perioadei supuse

analizei, risc estimat in functie de

variabilele independente gen, varsta si

nivel educational, am folosit regresia

logistica .

Spre deosebire de regresia liniara

multipla, unde se poate prezice, pe baza

mai multor variabile independente, o

variabila dependenta numerica, regresia

logistica ofera posibilitatea prezicerii

unei variabile nominale dihotomice.

Metoda regresiei liniare presupune ca

atat variabilele factoriale cat si variabila

rezultativa sa fie de tip continuu; prin

contrast, regresia logistica permite lucrul

cu alte tipuri de variabile.

Modelul de regresie logistica

descrie relatia dintre o variabilanominala dihotomica Y , ce ia valorile 1

(succes) si 0 (esec), si k variabile

factoriale k x x x x .......,, 321 . Variabilele

factoriale pot fi cantitative (numerice)

sau categoriale. Deoarece Y este o

variabila binara, prezinta o distributie de

tip Bernoulli, cu parametrul )1( == Y P p ,

unde p este probabilitatea de succes

pentru valorile date k x x x x .......,, 321 ale

variabilelor factoriale. Media uneivariabile de tip Bernoulli este data de:

pY P y E === )1(][ .

(1)

Modelul de regresie logistica se

defineste in felul urmator: presupunem

ca nY Y ........1 sunt variabile independente

Bernoulli, si fie i p media valorilor iY ,

period subject to analysis, risk estimated

according to the independent variables gender,

age and educational level, I used the logistic

regression .

Unlike the multiple linear regressionwhere, based on several independent variables,

a numeric dependent variable can be predicted,

the logistic regression gives the possibility to

predict a dichotomic nominal variable. The

method of linear regression implies that both the

factorial variables and the resultative variable

should be of a continuous type; by contrast, the

logistic regression allows working with other

types of variables.

The logistic regression model describesthe relation between a dichotomic nominal

variable Y , that takes the values 1 (success) and

0 (failure), and k factorial

variables k x x x x .......,, 321 . Factorial variables

can be quantitative (numeric) or categorical.

Since Y is a binary variable, it has a Bernoulli

type distribution, with the

parameter )1( == Y P p , where p is the

probability of success for the given values

k x x x x .......,, 321 of factorial variables. The

average of Bernoulli type variables is given by:

pY P y E === )1(][ .

(1)

The logistic regression model is defined

as follows: assuming that nY Y ........1 areindependent Bernoulli variables, and be i p the

average of values iY ,

then )1(][ === iii Y P Y E p . The value i p can

be expressed according to the factorial variables

ik iii x x x x .......,, 321 thus:


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atunci )1(][ === iii Y P Y E p . Valoarea i p

poate fi exprimata in functie de

variabilele factoriale ik iii x x x x .......,, 321

astfel:

∑=

−−+

=k

j

ij j

i

x

p

1

0 )exp(1

1

β β

(2)

Daca aplicam transformarea logit

relatiei (2) vom obtine o legatura de tip

linear intre i p si variabilele factoriale,

dupa cum urmeaza:

∑=

+=−

=k

j

ji j

i

ii x

p p pit

1

,0)1

log()(log β β

(3)

Ecuatia 3 este cunoscuta si sub

numele de forma logit a modelului. Logit

( i p ) este logaritmul „odds” pentru

succes, pentru valori date ale variabilelor

factoriale ik iii x x x x .......,, 321 .

Rezultatul regresiei logistice estetot o ecuatie, care prezice cel mai bine o

variabila efect binara (statut,

angajat/neangajat), pe baza uneia sau

mai multor variabile ce pot fi cantitative

(varsta, nivel educational), sau binare

(gen). In loc de a lucra cu probabilitati,

(care se pot afla intre 0 si 1), regresia

logistica lucreaza cu logaritmul natural

al cotei (odds), care poate lua orice

valoare, pozitiva sau negativa. Ecuatia

regresiei logistice poate fi exprimataastfel:

nn x x xoddsY β β β β ++++= .......)ln( 22110

(4)

O prezentare detaliata a

metodologiei regresiei logistice si a

problemelor ridicate de folosirea acesteia

a fost realizata de catre Amemiya, T.

(1985), Balakrishnan, N. (1991),

∑=

−−+

=k

j

ij j

i

x

p

1

0 )exp(1

1

β β

(2)

If we apply the logit transformation to

the relation (2) we obtain a linear type

connection between i p and the factorial

variables, as follows:

∑=+=−=

k

j ji j

i

i

i x p

p

pit 1,0)1log()(log β β

(3)

The 3rd equation is also known as a

logit shape of the model. Logit ( i p ) is the

“odds” logarithm for success, for given values

of the factorial variables ik iii x x x x .......,, 321 .

The result of the logistic regression isalso an equation, that predicts the best a binary

effect variable (status, employed/unemployed),

based on one or several variables that can be

quantitative (age, educational level), or binary

(gender). Instead of working with probabilities,

(that may range between 0 and 1), the logistic

regression works with the natural logarithm of

the quota (odds), that can take any value,

positive or negative. The equation of the logistic

regression can be expressed as follows:

nn x x xoddsY β β β β ++++= .......)ln( 22110

(4)

A detailed presentation of logistic

regression methodology and of the issues raised

by its use was performed by Amemiya, T.

(1985), Balakrishnan, N. (1991), Hosmer,


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Hosmer, David W.; Stanley Lemeshow

(2000), Agresti, Alan. (2002) si Green,

William H. (2003).

In studiul meu, variabilele

factoriale sunt: gen, variabila calitativaalternativa (masculin/feminin),

codificata in analiza cu 1 (masculin),

respectiv 0 (feminin); varsta, variabila

cantitativa, divizata in cinci intervale,

15-24, 25-34, 35-44, 45-54, 55-64, in

conformitate cu Anuarul Statistic al

Romaniei si nivel educational , variabila

calitativa in baza de date primita de la

ANOFM Bucuresti, dar transformata in

variabila numerica, in conformitate cu

reglementarile Ministerului Roman alEducatiei. Variabila dependenta statut

(statutul persoanei la sfarsitul perioadei

analizate), este o variabila nominala

dihotomica, ce ia valorile:

⎩⎨⎧ −

=neangajataincaeste persoanadaca

muncadelocun gasit a si persoanadaca statut

,0

,1

Ecuatia de regresie este:

l educationanivel sta gen statut odds 3210 var )ln( β β β β +++=

(4)

unde sunt si 21 β β coeficientii de regresie

calculati cu ajutorul programului statistic

SPSS 10.0, si care sunt in realitate

logaritmii naturali ai „odd ratio” ai fiecarei

variabile, iar 0 β este constanta,

reprezentand logaritmul natural al odds

statut pentru subiectii care prezinta valori

nule ale tuturor variabilelor factoriale (gen,

varsta, nivel educational in cazul de fata).

Metoda selectata pentru regresialogistica binara a fost metoda Enter,

variabilele factoriale fiind analizate

simultan.

In tabelul 1 sunt prezentate

rezultatele testului Omnibus pentru

coeficientii modelului. Rezultatele

testului 2 χ si ale ratei de verosimilitate -

2LL inregistrate la pasul 1 comparativ cu

pasul initial 0 ne permit respingerea

David W.; Stanley Lemeshow (2000), Agresti,

Alan (2002) and Green, William H. (2003).

In my survey, the factorial variables are:

gender , alternative qualitative variable

(male/female), encoded in the analysis with 1(male) and 0 (female); age, quantitative

variable, divided in five intervals, 15-24, 25-34,

35-44, 45-54, 55-64, in accordance with the

Statistical Yearbook of Romania and

educational level , qualitative variable in the

database received from NAE Bucharest, but

changed into numeric variable, in compliance

with the regulations of Romanian Ministry of

Education. The dependent variable status

(person’s status at the end of the analysed

period), is a dichotomic nominal variable thattakes the values:

⎩⎨⎧

=unemployedstillis persontheis,0

jobafound persontheif ,1 status

The regression equation is:

l educationnivel sta gen statut odds 3210 var )ln( β β β β +++=

(4)

where 21 β β and are regression coefficients

calculated using the statistics program SPSS

10.0, and which in reality are natural logarithm

of „odd ratio” and of each variable and 0 β is

the constant, representing the natural logarithmof odds status for subjects who have zero values

of all factorial variables (gender, age,

educational level in this case). The method

selected for the binary logistic regression was

the Enter method, analysing simultaneously the

factorial variables.

Table 1 shows the results of the

Omnibus test for the model coefficients. The

results of the test 2 χ and of the likelihood rate -


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ipotezei nule ( 0:0 =i H β ) si acceptarea

ipotezei alternative.

2LL recorded in step 1 compared to the initial

step 0 allow us to reject the null hypothesis

( 0:0 =i H β ) and to accept the alternative

hypothesis.

Tabelul 1: Rezultatele testului omnibus pentru coeficientii de regresie / Table 1: Results of the

omnibus test for regression coefficients

Chi-square df Sig.

Step 1 Step 1367.716 3 .000

Block 1367.716 3 .000

Model 1367.716 3 .000

In tabelul 2 este prezentat

resultatul testului Hosmer &Lemenshow. Testul Hosmer &

Lemenshow divide subiectii la nivel de

decile, pe baza probabilitatilor estímate,la pasul urmator aplicand testul

2 χ asupra frecventelor observate.

Valorile p=0.0000 sunt calculate pe baza

distributiei2

χ cu 3 grade de libertate si

indica faptul ca modelul logistic estevalid din punct de vedere statistic, deciipoteza nula poate fii respinsa.

Tabelul 2 : Testul Hosmer si Lemeshow

Step Chi-square

df Sig.

1 427.73

2

8 .000

Tabelul 3 prezinta valorileestimate ale coeficientilor de regresie alemodelului de regresie logistica binara.

Valorile Sig. egale cu zero ne arata catoate cele trei variabile factoriale ale

modelului de regresie sunt semnificativedin punct de vedere statistic, si

influenteaza variabila dependenta statut.

De asemenea, valorile testului Wald ne

arata ca parametrii de regresie i β sunt

diferiti de zero. Ipoteza nula este astfel

Table 2 shows the results of Hosmer &

Lemenshow test. The Hosmer &

Lemenshow test divides subjects at

deciles level, based on the estimated

probabilities, applying in the next step

the test2

χ on the frequencies noticed.

The values p=0.0000 are calculated

according to the distribution 2 χ with 3

degrees of freedom and indicate that thelogistic model is valid from a statistical

point of view, therefore the null

hypothesis can be rejected.

Table 2: Hosmer and Lemeshow test

Step Chi-square

df Sig.

1 427.73

2

8 .000

Table 3 shows the estimated values

of regression coefficients of the model of

binary logistic regression. Sig. values,equal to zero, show us that all the three

factorial variables of the regressionmodel are significant from statistical

point of view and they influence the


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respinsa.

Nivelurile estimate ale

coeficientilor de regresie i β sunt notate

cu B, iar Exp (B) reprezinta „odds ratio”

(OR) pentru fiecare variabila factoriala,adica ie

β . Odds ratio este estimarea

riscul unui subiect de a se ramaneneangajat (0), la o modificare cu o

unitate a variabilelor factoriale (candaceasta este numerica, ca de exemplu

varsta in cazul nostru).

Vom avea deci ecuatia de regresie

logistica:

(v66.0)(472.0986.1)ln( gen statut odds ++−= (5)

dependent variable of status. Similarly,

the Wald test values show us that the

regression parameters i β are different

from zero. Therefore the null hypothesisis rejected.

The estimated levels of the

regression coefficients i β are marked

with B and Exp (B) represents „oddsratio” (OR) for each factorial variable,

which is ie β

. Odds ratio is the estimation

of a risk of a subject to remain

unemployed (0), at a change with one unitof factorial variables (when it is numeric,

such as age in our case).

Therefore we shall have the

logistic regression equation:

(va66.0)(472.0986.1)ln( gen statut odds ++−=

(5)

Tabelul 3: Variabile in ecuatia de regresie logistica / Variables in the logistic regression

equation

B S.E. Wald df Sig. Exp(B) 95.0%

C.I.forEXP(B)

Lower Upper

Step 1 GEN .472 .017 734.116 1 .000 1.603 1.549 1.659

NVARSTA .066 .007 83.356 1 .000 1.068 1.053 1.083

NIVEL_ED

.223 .009 589.486 1 .000 1.250 1.227 1.272

Constant -1.986 .027 5233.793 1 .000 .137

a Variable(s) entered on step 1: GEN, NVARSTA, NIVEL_ED.

Aceste valori estimate ale

coeficientilor de regresie ne arata

legatura dintre variabilele factoriale sivariabila dependenta „statut”, cu cat

creste (sau descreste, daca semnulcoeficientului este negativ) valoarea

determinata log odds a variabilei statut=1 la modificarea cu o unitate a

uneia dintre variabilele factoriale,

influenta celorlalte factoriale fiind

These estimated values of the

regression coefficients show us the

relation between factorial variables andthe dependent variable “ status”, the more

it increases (or decreases, if thecoefficient sign is negative) the

determined value log odds of the variableof status=1 at a change with one unit of

one of the factorial variables; the

influence of the other factorial is


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considerata constanta.

Din tabelul 3 abservam ca riscul

angajarii creste cu 1.6 pentru subiectii barbati inregistrati in baza de date supusa

analizei. In perioada analizata in baza dedate au fost inregistrati 33270 someri

femei si 47691 someri barbati. Dintreacestia, la sfarsiul perioadei si-au gasit

loc de munca 6390 femei (19.21%), si

12979 barbati (27.21%). Desi exista unnumar mult mai mare de barbati

inregistrati ca someri in baza de dateanalizata, totusi numarul celor care

reusesc sa isi gaseasca un loc de muncaeste mai mare comparativ cu femeile,

ceea ce arata ca desi sunt mai multi barbati someri totusi acestia sunt preferati de catre angajatori.

Observam de asemenea ca risculangajarii creste cu 1.06 la modificarea cu

o unitate (an) a variabilei varsta. Din 25776 subiecti cu varsta cuprinsa intre 15-

24 ani si-au gasit loc de munca in perioada analizata 4 982 subiecti,

reprezentand 19,33%, din 21 138 someri

cu varsta cuprinsa intre 25-34 ani s-auangajat 5832, reprezentand 27,59%, din18225 someri cu varsta cuprinsa intre 35-

44 ani s-au angajat 4920, reprezentand27%, din 14452 subiecti someri cu varsta

cuprinsa intre 45-54 ani s-au angajat23,39 % iar din 1370 someri cu varsta

cuprinsa intre 55-64 ani s-au angajat

18,61%. Observam ca sansa cea mai bunade a isi gasi un loc de munca o au

persoanele cu varsta cuprinsa intre 24-54

ani. Acestia sunt fie absolventi de studiisuperioare ce dupa o perioada scurta desomaj isi gasesc locul de munca dorit fie

persoane cu experienta, ce inta in somaj

voluntar, generat de insatisfactii lavechiu loc de munca, sau sunt

disponibilizati, dar isi gasesc un loc demunca avantajati fiind de experienta

acumulata.

In ceea ce priveste variabila nivel

considered to be constant.

From table 3 one can notice that

the employment risk increases with 1.6for the male subjects registered in the

database subject to analysis. In the periodanalyzed in the database there were

registered 33270 female unemployed and

47691 male unemployed persons. Amongthem, at the end of the spell 6390 women

(19.21%) and 12979 men (27.21%) founda job. Although there are a much greater

number of men registered as unemployedin the analysed database, the number of

those who manage to find a job is highercompared to women, which indicates that

although there are more unemployedmen, however they are preferred byemployers.

We can also notice that theemployment risk increases with 1.06 at

the change with one unit (year) of the agevariable. Of 25 776 subjects aged

between 15-24 years, 4 982 subjectsfound a job in the analysed period,

representing 19,33%, of 21 138

unemployed aged between 25-34 years,5832 became employed, representing27,59%, of 18225 unemployed aged

between 35-44 years, 4920 becameemployed, representing 27%, of 14452

unemployed subjects aged between 45-54

years, 23,39 % became employed and of1370 unemployed aged between 55-64

years, 18,61% became employed. We cannotice that persons aged between 24-54

years have the best chance to find a job.

These are either university graduates thatafter a short duration of unemploymentfind the desired job, or experienced

persons that are voluntarily unemployed

because of dissatisfactions at the old jobor are laid off, but they can find a job

since they are favoured because of theirexperience.

As for the educational levelvariable, the employment risk increases


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educational, riscul angajarii creste cu

1.25 la modificarea cu o unitate aacesteia. Intr-adevar, cu cat nivelul

educational este mai ridicat, cu atat probabilitatea angajarii este mai mare

pentru un subiect.

with 1.25 at its change with one unit.

Indeed, the higher the educational level,the higher the subject’s employment

probability.


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4. Concluzii

Scopul studiului este acela de adetermina riscul persoanelor din baza de

date studiata de a fi angajate sauneangajate la sfarsitul perioadei supuse

analizei, risc estimat in functie devariabilele independente gen, varsta si

nivel educational. Datele statistice au

fost obtinute de la Agentia Nationala aFortelor de Munca Bucuresti, si ofera

informatii privind subiectii inregistrati casomeri in perioada 1 ianuarie 2002-31

august 2006. Esantionul cuprinde 80961

inregistrari, cu informatii referitoare ladata de intrare in somaj, data de iesiredin somaj, sex, varsta, nivel educational

si motivul iesirii din somaj pentru fiecare

persoana inregistrata. Ca metodologie amfolosit regresia logistica binara.

Rezultatele analizei arata ca risculangajarii creste cu 1.6 pentru barbatii

inregistrati ca someri comparativ cufemeile cu acelasi statut. In perioada

analizata in baza de date au fost

inregistrati 33270 someri femei si 47691someri barbati. Dintre acestia, la sfarsiul perioadei si-au gasit loc de munca 6390

femei (19.21%), si 12979 barbati(27.21%). Desi exista un numar mult mai

mare de barbati inregistrati ca someri in baza de date analizata, totusi numarul

celor care reusesc sa isi gaseasca un loc

de munca este mai mare comparativ cufemeile, ceea ce arata ca desi sunt mai

multi barbati someri totusi acestia sunt

preferati de catre angajatori. Pentruvariabila varsta, riscul angajarii creste cu1.06 la o modificare cu o unitate (an)

acesteia. Pe grupe de varsta, riscul

angajarii creste cu 1.3 pentru grupa devarsta 15-24 ani comparativ cu grupa 55-

64 ani, cu 2.107 pentru grupa 25-34 ani,comparativ cu grupa mentionata, cu

2.125 pentru grupa 35-44 ani comparativcu ultima grupa de varsta, si cu 1.664

pentru grupa 45-54, comparativ cu grupa

4. Conclusions

The purpose of this survey is to

determine the risk of the persons from

the analyzed database to becomeemployed or unemployed at the end ofthe period subject to analysis, risk

estimated according to the independentvariables gender, age and educational

level. The statistical data were obtainedfrom the National Agency for

Employment Bucharest, and they provide

information on the subjects registered asunemployed during January 1, 2002-

August 31, 2006. The sample includes80961 records, with information

concerning the date of unemployment beginning and end, sex, age, educational

level and the reason of unemployment

leaving for each registered person. Asmethodology I used the binary logistic

regression. The results of the surveyshow that the employment risk increases

with 1.6 for the men registered asunemployed compared to women with the

same status. In the period analyzed in thedatabase there were registered 33270

female unemployed and 47691 male

unemployed persons. Among them, at theend of the spell 6390 women (19.21%)

and 12979 men (27.21%) found a job.Although there are a much greater

number of men registered as unemployedin the analysed database, the number of

those who manage to find a job is higher

compared to women, which indicates that

although there are more unemployedmen, however they are preferred byemployers. For the age variable, the

employment risk increases with 1.06 atits change with one unit (year). By age

groups, the employment risk increases

with 1.3 for the age group of 15-24 years,compared to the group of 55-64 years,with 2.107 for the group of 25-34 years,

compared to the specified group, with2.125 for the group of 35-44 years,


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55-64 ani. In ceea ce priveste variabila

nivel educational, riscul angajarii crestecu 1.25 la modificarea cu o unitate a

acesteia. Intr-adevar, cu cat niveluleducational este mai ridicat, cu atat

probabilitatea angajarii este mai mare pentru un subiect. Cele mai dezavantajate

grupe educationale s-au dovedit a fi

persoanele fara studii, persoanele cuscoala generala incompleta, scoala profesionala si invatamant complementar

de ucenici si invatamant special si persoanele absolvente de liceu teoretic.

compared to the last age group, and with

1.664 for the group of 45-54, comparedto the group of 55-64 years. As for the

educational level variable, theemployment risk increases with 1.25 at

its change with one unit. Indeed, thehigher the educational level, the higher

the subject’s employment probability.

The most disadvantaged educationalgroups proved to be the persons withouteducation, the persons with unfinished

secondary school, vocational school andapprenticeship complementary education

and special education and the theoretical

high school graduates.


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Bibliografie:

[1] Agresti, A. (2002), Categorical Data Analysis. New York: Wiley-Interscience

[2] Aysit T., (2004), Determinants of

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http://www.tek.org.tr/dosyalar/A-

TANSEL-UNEMP.pdf[3] Amemiya, T. (1985), Advanced

Econometrics. Harvard University Press[4] Balakrishnan, N. (1991), Handbook of

the Logistic Distribution. Marcel Dekker,Inc

[5] Berthoud R. (2003), Multiple

disadvantage in employment , Published

by Joseph Rowntree Foundation

[6] Green, William H. (2003), Econometric Analysis, fifth edition.

Prentice Hall[7] Ham, J. C. and Rea, A. S. (1987),

Unemployment Insurance and MaleUnemployment Duration in Canada,

Journal of Labor Economics , 5, 325-353.[8] Hosmer, David W., Stanley

Lemeshow (2000). Applied Logistic Regression, 2nd ed . New York

Chichester, Wiley[9] Meghir C., Ioannides Y, Pissarides,

C. (1989), Female participation and male

unemployment duration in Greece: Evidence from the labour force survey,

European Economic Review, Elsevier,vol. 33(2-3), pages 395-406.

[10] Ramirez A (1998), Re-employment probabilities of young workers in Spain,

Investigaciones Economicas, Vol 22,Issue 2, pages 201-224.

[11] Tunali I and R. Asaad (1992),

Market Structure and Spells of

Employment and Unemployment:

References:

Agresti, A. (2002), Categorical Data Analysis. New York: Wiley-Interscience

Aysit T., (2004), Determinants of

unemployment duration for men andwomen in Turkey, Discussion Paper2004/6,

http://www.tek.org.tr/dosyalar/A-

TANSEL-UNEMP.pdfAmemiya, T. (1985), Advanced

Econometrics. Harvard University PressBalakrishnan, N. (1991), Handbook of

the Logistic Distribution. Marcel Dekker,Inc

Berthoud R. (2003), Multiple

disadvantage in employment , Published

by Joseph Rowntree Foundation

Green, William H. (2003), Econometric Analysis, fifth edition. Prentice Hall

Ham, J. C. and Rea, A. S. (1987),Unemployment Insurance and MaleUnemployment Duration in Canada,Journal of Labor Economics , 5, 325-353.

Hosmer, David W., Stanley Lemeshow(2000). Applied Logistic Regression, 2nd

ed . New YorkChichester, Wiley

Meghir C., Ioannides Y, Pissarides, C.(1989), Female participation and male

unemployment duration in Greece:

Evidence from the labour force survey,European Economic Review, Elsevier,

vol. 33(2-3), pages 395-406.Ramirez A (1998), Re-employment

probabilities of young workers in Spain,Investigaciones Economicas, Vol 22,

Issue 2, pages 201-224.Tunali I and R. Asaad (1992), Market

Structure and Spells of Employment and

Unemployment: Evidence from theConstruction Sector in Egypt , Journal of


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Evidence from the Construction Sector in Egypt , Journal of Applied Econometrics,7, 339-367

Applied Econometrics, 7, 339-367

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