(2 rânduri libere, 11p)

Studies and Scientific Researches. Economics Edition, No 25, 2017 http://sceco.ub.ro

THE STATISTICAL ANALYSIS OF LABOR MARKET AND

FEMALE LABOR FORCE CHARACTERISTICS IN CENTRAL

AND EASTERN EUROPEAN COUNTRIES

Elisabeta Jaba
Department of Accounting, Business Information Systems and Statistics, Faculty of Economics

and Business Administration, Alexandru Ioan Cuza University of Iași, Romania

ejaba@uaic.ro

Christiana Brigitte Sandu
Department of Accounting, Business Information Systems and Statistics, Faculty of Economics

and Business Administration, Alexandru Ioan Cuza University of Iași, Romania

christiana.balan@uaic.ro

Aurelian-Petruș Plopeanu
Alexandru Ioan Cuza University of Iași, Department of Interdisciplinary Research –

Humanities and Social Sciences, Romania

aplopeanu@gmail.com

Ioan-Bogdan Robu
Department of Economics, Faculty of Economics and Business Administration, Alexandru Ioan

Cuza University of Iași, Romania

bogdan.robu@feaa.uaic.ro

Marinela Istrate
Department of Geography, Faculty of Geography and Geology, Alexandru Ioan Cuza

University of Iași, Romania

Abstract
In this paper we analyze the characteristics of labor markets and female labor force in the

countries from Central and Eastern Europe in order to verify the existence of significant

differences in female labor participation rate among the countries that experienced the same

political and economic system before 1990. The paper seeks to address the following specific

objectives: 1) to identify the variables which are strongly correlated with female labor force

participation, objective met using correlation analysis; 2) to define clusters of countries based

on the determinants of female participation on the labor market, identified previously, using

hierarchical cluster method; 3) to verify if the female labor force participation rate differs

significantly among the identified clusters by applying the analysis of variance. The results of

this study highlight that, in Central and Eastern Europe, we can identify four clusters of countries

that share common characteristics of female labor market and female labor force. The analysis

of the variation in female labor force participation rate emphasizes different patterns according

to identified groups of countries.

Keywords
female employment; labor market; education; religion; hierarchical cluster analysis

JEL Classification
C38; E23; I23; Z12

Introduction
We aim to assess female labor force characteristics in relation to labor market

characteristics for Central and Eastern European countries. These countries

experienced, in the second half of the 20th century, similar political and social regimes

Jaba, Sandu, Plopeanu, Robu, Istrate

defined by planned economies that played an important role in shaping the female

employment and influenced the characteristics of female labor force. The high

participation rate of women on the labor market in these countries mirrors the two-

bread winner family scheme that was promoted by the socialist policies (Pastore and

Verashchagina, 2008; Jaba, 1979). This pattern evolved differently during the transition

to the market economy with defining clusters of countries according to female labor

market and female labor force characteristics. The process of privatization of state-

owned enterprises and the process of liberalization of wage setting mechanisms implied

hard budget constraints for companies in Central and Eastern European countries with

negative consequences on women employment (Paci and Reilly, 2004; Pastore and

Verashchagina, 2008).

Nowadays, in Europe, the female participation on the labor market is smaller than male

participation, and in addition, there are significant differences among countries. Recent

studies (OECD, 2009; Diaz Garcia and Welter, 2011) have shown the persistent gender

gap in employment in the European countries. In many Northern European countries,

the female activity rate is high, while the female unemployment rate is low (European

Commission, 2011). On the contrary, in many Southern and Eastern European

countries, the female activity rates are very low, while the female unemployment is

very high, in comparison to the average level for the European Union. However, the

female labor force participation has grown substantially in the last decades (Gutierrez-

Domenech and Bell, 2004; DiCecio, Engemann, Owyang and Wheeler, 2008; Cloin,

Keuzenkamp and Plantenga, 2011). In the Netherlands, United Kingdom, Denmark and

Germany there was an important increase in female labor market participation, but there

are important differences under the influence of various factors (Noback, Broersma and

Van Dijk, 2013). The gender gap in employment is explained either by the labor force

characteristics such as education or by labor market characteristics particular to each

country such as the level of development, the revenue and the unemployment rate. For

15 European Union countries over the last 20 years, Cipollone, Patacchini and Vallanti

(2013) found that female labor market participation is influenced, firstly, by the

differences in the labor market institutional settings and, secondly, in social policy.

Also, for the transition countries, tax rates, tax administration, and policy uncertainty,

as well as corruption, play an important role in explaining the results on the labor

market (Cazes, 2002).

Furthermore, religion is considered to be a causal factor in explaining many cross-

country differences in female participation on the labor market (Norton and Tomal,

2009; Pastore and Tenaglia, 2013). Nowadays, the emergent economies in Europe

experience both political and religious diversity with important effects over the

individuals and society (Brăilean, 2014). Commenting on the complex influence of the

religious ideas on the human action, Inglehart and Baker (2000: 49) consider that “the

fact that a society was historically shaped by Protestantism or Confucianism or Islam

leaves a cultural heritage with enduring effects that influence subsequent

developments”. Religion can explain more than one-third of the cross-country variation

in female labor force participation rates (Norton and Tomal, 2009).

The present paper focuses on identifying the female labor force and labor market

characteristics that are common to Central and Eastern European countries in order to

highlight the existence of similarities among them. There are three main objectives

subordinated to the aim of the paper: 1) to identify the variables that are correlated to

female labor force participation; 2) to define clusters of homogenous countries

according to female labor force and labor market characteristics; 3) to verify the

existence of significant differences among clusters of countries defined according to

female labor force participation.

The results of the study show that Central and Eastern European countries vary

according to the characteristics of labor market and female labor force. Based on these

THE STATISTICAL ANALYSIS OF LABOR MARKET AND FEMALE LABOR FORCE CHARACTERISTICS IN CENTRAL
AND EASTERN EUROPEAN COUNTRIES

variables the observed countries have been classified in four clusters that show different

patterns regarding the female labor force participation.

The remaining part of the paper proceeds as follows: Section Data and Method presents

the sample of countries used in the analysis as well as the definition of the labor market

indicators. This section also describes the statistical methods applied with the aim of

accomplishing the objectives set for this study; Section Results presents the main

findings related to the clusters of countries that share common characteristics of female

labor market and female labor force. The differences among clusters of countries

regarding female labor force participation rate are also explained by the religious

affiliation of the population; Conclusion section give a brief summary of the findings

and discuss the implications for labor markets’ policies in the region.

Data and Method

Data
For 15 Central and Eastern European countries (Bulgaria, Croatia, Czech Republic,

Estonia, Hungary, Latvia, Lithuania, Macedonia, Montenegro, Poland, Romania,

Russia, Serbia, Slovak Republic, and Slovenia), we have recorded a series of

macroeconomic indicators, which characterize both the labor market and the female

labor force. These variables which refer to the main characteristics of the labor markets

and female participation on the labor market are presented in Table 1.

Data on these variables have been gathered from various sources, mainly from the

World Bank, IMF, and OECD databases, for year 2012. Data on labor market and

business regulations index have been collected from the Economic Freedom of the

World: 2014 Annual Report published by Fraser Institute.

Data management and analysis were performed using SPSS 21.0.

Table 1 Variables describing labor market and female labor force characteristics

Variables Explanation Variables Explanation

X1–Labor

mar

ket

regulation

s index

Sub-index of the

Economic Freedom of

the World (EFW). The

rating scale ranges from 0

to 10 (higher values

represent more flexible

regulation)

X9–Prevalence of

foreign ownership

The rating scale

ranges from 1 to 7 (1

– very rare; 7 –

highly prevalent)

X2–

Business

regulation

s index

Sub-index of the

Economic Freedom of

the World (EFW). The

rating scale ranges from 0

to 10 (higher values

represent more flexible

regulation)

X10–Domestic

market size index

Sum of gross

domestic product

plus value of imports

of goods and

services, minus

value of exports of

goods and services,

normalized on a

1(poor) – 7 (best)

scale

X3–Labor

market

efficiency

The rating scale ranges

from 1 to 7 (higher values

represent more efficient

labor market)

X11–Flexibility of

wage

determination

The rating scale

ranges from 1 to 7 (1

– by a centralized

bargaining process;

Jaba, Sandu, Plopeanu, Robu, Istrate

7 – up to each

individual company)

X4–Trade

union

density

Share of employees in

trade unions (%)

X12–Firms with

female

participation

ownership (%)

The percentage of

firms with a woman

among the principal

owners (%)

X5–GDP

per capita

GDP per capita expressed

in current US dollars

X13–Female

tertiary education

enrollment (%)

Percentage of

students enrolled in

tertiary education

who are female ,

regardless of age, as

a share of the

population of the age

group that officially

corresponds to this

level of education

X6–

Intensity

of local

competitio

The rating scale ranges

from 1 to 7 (1 – limited

competition in most

industries; 7 – intense

competition in most

industries)

X14–Share of

women employed

in the non-

agricultural sector

(%)

The share of female

workers in the non-

agricultural sector

(industry and

services), expressed

as a percentage of

total employment in

the non-agricultural

sector

X7–Extent

of market

dominanc

The characterization of

corporate activity on a

scale from 1 to 7 (1 –

dominated by a few

business groups; 7 –

spread among many

firms)

X15–Female labor

force participation

rate (%)

The proportion of

the female

population ages 15

and older that is

economically active.

Female labor force

participation

consists in all female

supply labor used for

the production of

goods and services

during a specified

period of time.

X8–Total

tax rate

A combination of profit

tax (% of profits), labor

tax and contribution (%

of profits), and other

taxes (% of profits)

X16–X19–Share of

persons of a

specific religion

(%)

The share of

Christians / Muslims

/ Agnostics /

Atheists in the total

population of each

country
Source: World Bank; IMF; OECD; Fraser Institute

Methods
We propose a methodological approach to identify the differences in female labor force

participation rate among the clusters of similar countries according to a set of variables

regarding female labor force and labor market characteristics. It consists in the

following phases:

1) In the first phase, we identify the variables that are correlated with female
labor force participation, using the correlation analysis, namely Pearson

THE STATISTICAL ANALYSIS OF LABOR MARKET AND FEMALE LABOR FORCE CHARACTERISTICS IN CENTRAL
AND EASTERN EUROPEAN COUNTRIES

correlation coefficient. From the initial set of variables, we have selected

those variables that have a strong correlation with female labor force

participation. These variables are considered for the identification of

clusters of countries and, therefore, for the explanation of the differences

and the similarities among countries from Central and Eastern Europe.

2) Considering the labor market and female labor force characteristics,
identified previously through the correlation analysis, we cluster the

observed European countries into subgroups that have similar patterns.

In the literature, there are various algorithms used for data clustering, such as:

hierarchical cluster analysis, k-means cluster, and two-step cluster (Norusis, 2011). The

clustering methods are divided into hard methods (data subsets are mutually exclusive)

and fuzzy methods (units are allowed to belong to several clusters simultaneously, with

different degrees of membership) (Cattinelli, Valentini, Paulesu and Borghese, 2013;

Palașcă, Enea, Jaba and Roman, 2014).

In this study, we apply a hard method, using the agglomerative hierarchical clustering

technique. It starts with the countries as individual clusters, and at each step, it merges

the closest pair of clusters until only one cluster left. Clusters are defined using the

squared Euclidean distance, which is the sum of the squared differences over all of the

variables (
1 2
, ,...,

p
X X X ). The formula of the distance between two entities

(countries) i and j is the following:
2 2 2 2

1 1 2 2
( ) ( ) ... ( )

ij i j i j pi pj
D x x x x x x      

(1)

where

•
ij

D is the distance between two countries, i and j.

•
1 2

, ,...,
i i pi

x x x are the observed data for country i on the
1 2
, ,...,

p
X X X

variables;

•
1 2

, ,...,
j j pj

x x x are the observed data for country j on the same set of variables;

The distance
ij

D depends on the units in which the variables
1 2
, ,...,

p
X X X are

measured and is influenced by whichever variable takes numerically larger values.

Therefore, before applying cluster analysis, the variables are standardized, so that they

have mean 0 and variance 1 (Tryfos, 1998).

The visual representation of the distance at which clusters are combined is displayed

through the dendrogram (Tan, Steinbach and Kumar, 2006). The vertical lines of this

diagram show joined clusters and the distance at which these clusters are merged.

3) We identify significant differences according to female labor force
participation among the identified clusters of countries using the ANOVA

method (the analysis of variance).

This method allows comparing the clusters’ means, based on independent random

samples. We test the null hypothesis that k populations’ means are all equal against the

alternative hypothesis that at least two means differ:

0 1 2

: ...

: at least two of the group means are not equal

k
H

    

The ANOVA method allows quantifying two types of variation: the within groups’

variability, by the sum of squares of the differences between each value and its group

mean (SSwithin); and between groups’ variation, by the sum of the squares of the

differences between the groups’ means and the overall sample mean (SSbetween).

In order to test if the difference between the means is attributable to sampling error (the

means are approximately equal) or the population means differ, the Fisher statistic is

used. The F statistic is the ratio of the mean squares of the differences.

http://www.sciencedirect.com/science/article/pii/S2212567114004948

Jaba, Sandu, Plopeanu, Robu, Istrate

(k 1)

(n k)

between between between between

within within within within

MS SS df SS
F

MS SS df SS


  


(2)

where:

• MSbetween is the variance estimate explained by the different groups;

• MSwithin is the variance estimate due to chance (unexplained);

• dfbetween are the degrees of freedom for k groups;

• dfwithin are the degrees of freedom for errors within groups.
The test of the difference between the means of any pair of populations is conducted

using the multiple comparisons tests, namely Student-Newman-Keuls test for pairwise

comparisons. When there are three means, this test holds the familywise error rate at

0.05. The test of the difference between the means of two groups, e and f, is the

following:

e f

error

x x
q


 (3)

The variation within each cluster of countries is graphically presented using the box-

plots. The box-plot diagram shows the range of values of a variable, the range between

the middle 50% of the values fall, the median value and it shows whether a distribution

is symmetrical or skewed.

The most important results of the statistical methods applied in SPSS are presented in

the following section.

Results and Discussions

Correlations between female labor force participation and the

characteristics of female labor force and labor market
The analysis starts with a first set of variables proposed by the literature for the

description of the characteristics of both labor market and female labor force (see Table

1). From the initial set of variables we kept only the variables that are correlated

significantly with the female labor force participation rate. In 2012, the Eastern

European countries are diverse with respect to female labor force participation. It varies

among the 15 countries, as it is the highest in Russia (57%) and in the Baltic Countries

(55% and 56%), and has the lowest values in Macedonia and Montenegro (43%) and

in Serbia (44%). Compared to the Eastern Europe countries, it can be seen that, in

Europe, Northern European countries have higher values (Norway – 62%, Sweden –

60%, and Denmark – 59%), and, in the world, China has even more important female

participation rate (64%). In United States of America, the female labor force

participation is 57%.

There are six variables that are correlated with female labor force participation rate, and

all correlations have positive signs. The first two variables are related to female labor

force characteristics and the last four variables are related to the labor market

characteristics. These variables are:

- Share of women employed in the non-agricultural sector (r = 0.792 with a p-value
smaller than 1%). It is considered that female labor-force participation in non-

agricultural sector in the developing countries is a key factor for the structural

adjustment and to the international competitiveness (Karshenas, 1997). Moreover,

Heinegg, Melzig and Sprout (2007) underline that in the transition countries, the

female labor force in services are in as high as in the developed economies. As

regarding the share of women employed in the non-agricultural sector in Central

and Eastern Europe, Macedonia has the smallest percentage (42%), while the

Baltic countries have the maximum values (54% in Latvia and Lithuania).

THE STATISTICAL ANALYSIS OF LABOR MARKET AND FEMALE LABOR FORCE CHARACTERISTICS IN CENTRAL
AND EASTERN EUROPEAN COUNTRIES

- Female tertiary education enrolment (r = 0.731 with a p-value smaller than 1%).
There are many studies that emphasize the impact of female education on the

structure of labor market. According to McDaniel (2014), if females continue to

outpace men in tertiary enrolment, the effect would be a general decrease of gender

gaps in the level of wages and an increase participation in the labor market.

However, Aboohamidi and Chimdi (2013) found out that in Egypt, Morocco,

Turkey and Pakistan, female tertiary education had a negative effect on female

labor participation rate due to activities that did not require complicated knowledge

and skills. There is important variation among the observed countries according to

female school enrolment in tertiary education. Slovenia has the highest percentage

of female tertiary education enrollment, comparable to the levels of USA or

Finland; while in Macedonia and Romania this indicator show the lowest values

among the 15 Eastern European countries.

- Gross domestic product per capita (r = 0.690 correlation with a p-value smaller
than 1%), measures the total value per capita, in market prices, for the goods and

services produced within a country during one year by the national economic and

non-residents under alternative exchange rate between the currencies of two

countries. Several studies demonstrated the macroeconomic impact of the gender

divided labor market. It has conclusively been shown that a country may benefit

by the fully insertion of women in the labor market (Dollar and Gatti, 1999). While

Cuberes and Teignier (2012) demonstrated that gender gaps in the labor market

may generate GDP per capita losses up to 27% in some regions, other researchers,

however, stressed that, in the case of several countries like United States of

America, Japan, United Arab Emirates, and Egypt, raising the female labor force

participation rate to male levels could raise the level of GDP (DeAnne, Hoteit,

Rupp, Sabbagh, 2012). Other studies highlighted that female labor force

participation rate varies pointing to a U-shaped relationship with per capita income

(Elborgh-Woytek, Newiak, Kochhar, Fabrizio, Kpodar, Wingender, Clements,

Schwartz, 2013). Nevertheless, Aboohamidi and Chimdi (2013) noted that in

Egypt, Morocco, Turkey and Pakistan, when GDP per capita raised, the female

labor force participation rate declined. GDP per capita in Central and Eastern

European countries is below the level reached by the developed countries in

Europe. Among the 15 observed countries, Slovenia is the country with the highest

levels of GDP per capita (22,756 USD per capita). However, at European level,

GDP per capita is more important, reaching 60,000 USD per capita in Denmark.

- Total tax rate (r = 0.620 with a p-value smaller than 5%). The tax system influences
the work incentives and the female participation by affecting differently married

and single women (Jaumotte, 2003).

- Labor market efficiency (r = 0.584 with a p-value smaller than 5%). It is considered
that low female employment rate has a negative effect on labor market efficiency

because important human capital is diminished and, consequently, low levels of

incomes are generated (Anker, 1998).

- Intensity of local competition (r = 0.533 with a p-value smaller than 5%). Global
and local competition is an important factor of employment rate as it has impact

on the quality of working life (Wilton, 2013). The increasing competition on the

labor market, especially for flexible work opportunities in the private sector,

requires government strategies for growth in order to reach more women (Sands,

2013).

The variables defining the structure of the population by religion are poorly

correlated with female labor force participation rate. However, the sign of the

correlations differs for the four religions considered in the study: the share of Christian

population and the share of Muslim population are both negatively correlated with

Jaba, Sandu, Plopeanu, Robu, Istrate

female labor force participation, while the other two religions show a positive effect on

the female participation in the labor market. The correlations are:

- Share of Christian population (r = - 0.030 with a p-value higher than 10%). Other
studies validated that Christian Orthodox and Muslim women presented a higher

risk of non-employment than the agnostics and, in a much greater degree, a

probability of employment of 30% to 40% lower than the average level.

- Share of Muslim population(r = - 0.481 with a p-value lower than 10%). In a cross-
section of countries in the period 1985 - 2005, [12] found that female labor force

participation varies depending on the religion practice: it is lower in Muslim

countries and higher in the Protestant ones or where no religion is practiced.

- Share of Agnostic population (r = 0.367 with a p-value higher than 10%).
- Share of Atheist population (r = 0.233 with a p-value higher than 10%).

Clusters of countries according to the characteristics of labor

market and female labor force
Since some countries have similar patterns to other countries on some of the

characteristics regarding female labor force and labor market, and different patterns on

other characteristics, we aim to identify similarities in our data. Therefore, we apply the

cluster analysis on ten variables identified at the previous step, including four other

variables regarding the structure of the population by religion.

Based on the distance between the pairs of clusters merged at each step, we obtained

four groups of countries, as shown in the dendrogram below (Figure 1).

Figure 1 Clusters of countries according to determinant factors
Source: Authors’ tabulation in SPSS 20.0

The solution with four clusters underlines the following groups of countries:

- Cluster 1 (5 countries): Bulgaria, Croatia, Montenegro, Romania, and
Serbia;

THE STATISTICAL ANALYSIS OF LABOR MARKET AND FEMALE LABOR FORCE CHARACTERISTICS IN CENTRAL
AND EASTERN EUROPEAN COUNTRIES

- Cluster 2 (3 countries): Czech Republic, Estonia, and Latvia;
- Cluster 3 (6 countries): Hungary, Lithuania, Poland, Russia, Slovakia,

and Slovenia;

- Cluster 4 (1 country): Macedonia.
The profile of the four clusters is derived using the descriptive statistics of the variables

on female labor force and labor market characteristics, by clusters (Table 2).

Table 2 The clusters’ profile according to the labor market and female labor

force characteristics

Cluster Mean Std. Deviation Min. Max.

Labor Market

Efficiency

1 4.14 0.227 4.00 4.54

2 4.73 0.396 4.32 5.11

3 4.29 0.128 4.15 4.48

4 4.13 - 4.13 4.13

GDP per capita (current

US dollars)

1 8546.60 3002.124 5907.00 13562.00

2 17698.33 2161.041 15205.00 19032.00

3 16347.16 3542.622 13394.00 22756.00

4 4944.00 - 4944.00 4944.00

Female tertiary

education enrollment

rate

1 56.76 5.146 51.60 62.70

2 68.66 6.971 64.20 76.70

3 70.65 11.369 55.10 86.00

4 38.50 - 38.50 38.50

Intensity of local

competition

1 4.48 0.465 3.90 5.00

2 5.60 0.100 5.50 5.70

3 5.30 0.228 5.00 5.60

4 5.40 - 5.40 5.40

Total tax rate 1 29.62 10.050 19.80 42.90

2 44.46 7.447 35.90 49.40

3 44.13 6.728 32.50 50.70

4 8.20 - 8.20 8.20

Share of women

employed in

nonagricultural sector

1 47.00 1.870 45.00 50.00

2 51.00 4.358 46.00 54.00

3 49.50 2.588 47.00 54.00

4 42.00 - 42.00 42.00

Christians (%) 1 86.35 8.502 77.67 98.49

2 65.23 8.111 55.93 70.84

3 88.05 4.973 81.25 96.15

4 64.56 - 64.56 64.56

Muslims (%) 1 7.65 6.685 0.42 16.45

2 0.18 0.147 0.01 0.28

3 2.22 4.103 0.01 10.39

4 28.97 - 28.97 28.97

Agnostics (%) 1 4.70 3.238 0.88 9.66

2 29.27 8.340 23.97 38.89

3 7.25 2.955 3.41 11.12

4 5.38 - 5.38 5.38

Atheists (%) 1 1.25 0.978 0.16 2.79

2 4.99 0.376 4.66 5.40

3 2.02 1.613 0.27 4.41

4 1.40 - 1.40 1.40
Source: Authors’ tabulation in SPSS 20.0

Jaba, Sandu, Plopeanu, Robu, Istrate

It can be noticed that, among the four clusters, clusters 2 and 3 have the highest average

values for all the characteristics regarding labor market and female labor force.

The profile of cluster 2 (Czech Republic, Estonia, and Latvia) shows that these

countries have a good performance of the indicators that influence female labor force

participation. These indicators refer to both labor market characteristics (Labor market

efficiency, GDP per capita, Intensity of local competition, and Total tax rate) and to

female labor force characteristics (Share of women employed in the non-agricultural

sector). Cluster 2 is also characterized by a greater within cluster heterogeneity

according to Share of women employed in the non-agricultural sector compared to

other clusters.

Cluster 3 (Hungary, Lithuania, Poland, Russia, Slovakia, and Slovenia) is characterized

by the highest level of Female tertiary education enrollment rate (70.65%), and also

by a higher within cluster heterogeneity according to this characteristic, comparing to

the others cluster.

Countries that belong to cluster 1 (Bulgaria, Croatia, Montenegro, Romania, and

Serbia) lag behind the countries grouped in clusters 2 and 3 with respect to the

determinant factors of female labor force participation. Regarding the average value of

GDP per capita for cluster 1, it can be seen that it is almost half the value for clusters 2

or 3. Most of the countries in cluster 1 (Romania, Bulgaria, Montenegro, and Serbia)

have values of GDP per capita inferior to 10,000 USD (Serbia has the lowest level equal

to 5,907 USD), the only exception being Croatia with GDP per capita around 13,500

USD). Within this cluster, Bulgaria has the highest value for the variable Share of

women employed in the non-agricultural sector.

Comparing to the first three clusters, Macedonia (cluster 4) has the lowest levels for

almost all the variables, excepting Intensity of local competition.

Among the four clusters, clusters 1 and 3 have the highest percentages of Christian

population (86.35% in cluster 1 and 88.05% in cluster 3), while cluster 4 (Macedonia)

has the highest percentage of Muslims (28.97%).Moreover, cluster 2 has the highest

percentage of Agnostics (29.27%) as compared to the other clusters. Actually, Czech

Republic registers the highest percentage of Agnostic population (38.89%) among the

15 countries in the study.

The variation of the determinant factors of labor force participation among countries

may explain the differences in female participation by clusters.

Differences in female labor force participation by clusters of

countries in Central and Eastern Europe
We have tested the differences in the means of each variable among the four clusters

using ANOVA method and multiple comparisons tests. The differences among the four

clusters are tested with F test, while for pairwise comparisons we used the Student-

Newman-Keuls statistic.

The profile of clusters according to Female labor force participation rate highlights the

highest average female labor force participation rate (53.66%) for cluster 2 (Czech

Republic, Estonia, and Latvia). The highest within cluster variation according to this

variable is obtained for cluster 3. In this cluster, Russia and Lithuania have the highest

participation rates of women on the labor market (57% and 56%, respectively) while

Hungary has a much lower value (45%), (Table 3). In cluster 1, female labor

participation rate varies from 49% in Romania to 43% in Montenegro.

THE STATISTICAL ANALYSIS OF LABOR MARKET AND FEMALE LABOR FORCE CHARACTERISTICS IN CENTRAL
AND EASTERN EUROPEAN COUNTRIES

Table 3 The clusters’ profile according to female labor force participation rate

Cluster Mean Std. Deviation Minimum Maximum

1 45.80 2.588 43.00 49.00

2 53.66 3.214 50.00 56.00

3 51.66 4.457 45.00 57.00

4 43.00 – 43.00 43.00
Source: Authors’ tabulation in SPSS 20.0

The assumption regarding the homogeneity of variances was met (the Levene’s test has

a significance level higher than 0.05) and therefore the ANOVA results are presented

in Table 4.

Table 4 The ANOVA results for female labor force participation rate (%)

Sum of

Squares

Df Mean Square F Sig.

Between Groups 190.933 3 63.644 4.769 .023

Within Groups 146.800 11 13.345

Total 337.733 14
Source: Authors’ tabulation in SPSS 20.0

The differences in female labor force participation rate among the four clusters are

significant at a level associated to ANOVA F test lower than 0.01.

In order to identify the differences for all possible pairs of clusters we apply the post-

hoc multiple comparisons procedure. Because cluster 4 is composed of only one

country, this cluster is excluded from the multiple comparisons procedure.

Based on the results obtained for the Student-Newman Keulstest (Table 5) and on the

box-plot (Figure 2), we notice that there are significant differences between the clusters

according to female labor force participation rate. Moreover, we can see that these

differences are more evident between cluster 1 and the other two clusters.

Table 5 The results of Student-Newman-Keuls pairwise comparisons test

Female labor force participation rate

Cluster N Subset for alpha = 0.05

1 2

1 5 45.8000

3 6 51.6667

2 3 53.6667

Sig. 1.000 .440

Means for groups in homogeneous subsets are displayed.

a. Uses Harmonic Mean Sample Size = 4.286.

b. The group sizes are unequal. The harmonic mean of the

group sizes is used. Type I error levels are not guaranteed.
Source: Authors’ tabulation in SPSS 20.0

Jaba, Sandu, Plopeanu, Robu, Istrate

Clusters 2 and 3 that are characterized by a good performance of the indicators

concerning labor market and female labor force characteristics show also the highest

level of female labor force participation.

Furthermore, the association of religion to the clusters shows that religion has shaped

differently the female labor force participation. It can be seen that cluster 2 (formed of

countries with a high percentage of Agnostics and Atheists) has important participation

rates of female population. These results are consistent with the ones published by

Pastore and Tenaglia (2013).

Figure 2 The distribution of female labor force participation rate by clusters of

countries
Source: Authors’ tabulation in SPSS 20.0

The positive relationship between female labor force participation and GDP per capita

is represented graphically by clusters of countries in the scatter plot (Figure 3). The

clusters 1 and 4 are characterized by lower GDP per capita and female participation

rate levels compared to clusters 2 and 3.

Figure 3 The distribution of Central and Eastern European countries, by

clusters, according to GDP per capita and female labor force participation rate
Source: Authors’ tabulation in SPSS 20.0

THE STATISTICAL ANALYSIS OF LABOR MARKET AND FEMALE LABOR FORCE CHARACTERISTICS IN CENTRAL
AND EASTERN EUROPEAN COUNTRIES

Macedonia (cluster 4) is characterized by low values on both variables, GDP per capita

and female force participation rate. On the opposite side, cluster 2 has the highest mean

values (centroids) both for female participation rate and GDP per capita. In cluster 3,

Slovenia, the country with the highest level of GDP per capita (22,756 USD) is opposed

to Hungary, the country with the lowest level of GDP per capita in cluster 2 (13,405

USD).

Conclusions
In this paper we aimed to analyze the characteristics of labor markets and female labor

force in countries from Central and Eastern Europe in order to identify differences in

female labor force participation rate.

To this purpose, we have first selected the characteristics of female labor force and

labor market that are highly correlated with female labor force participation rate. In the

second step, considering the variables that characterize both female labor force and the

labor market, we have identified four groups of countries. Finally, we emphasized the

disparities among the four clusters according to female labor force participation.

The results show, despite the fact that Central and Eastern European countries had a

common body-the socialist policy regarding female employment, the transition period

outlined a different female labor market, with specific characteristics of female labor

force.

We identified clusters of countries with a similar profile of female labor force

characteristics that explain the variation in female labor force participation.

Czech Republic, Estonia, and Latvia belong to the same cluster that is defined by the

highest average level of female participation rate and of the indicators describing labor

market and female labor force characteristics. In addition, the high participation rate of

women on labor market in these countries is associated with high percentage of

Agnostic population.

Macedonia and other countries, such as Hungary, Lithuania, Poland, Russia, Slovakia,

and Slovenia, have lower participation rates of female population due to the reduced

performance of their labor markets.

The results of this study would motivate the necessity of creating similar policies that

would allow the efficient utilization of a potential important resource and would

increase the individuals’ quality of life.

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